new research on working memory

Adaptive forgetting speed in working memory

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• Published: 08 May 2024

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• Joost de Jong   ORCID: orcid.org/0000-0001-8841-5646 1 ,
• Sophia Wilhelm 1 &
• Elkan G. Akyürek 1  

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Working memory is known to be capacity-limited and is therefore selective not only for what it encodes but also what it forgets. Explicit forgetting cues can be used effectively to free up capacity, but it is not clear how working memory adaptively forgets in the absence of explicit cues. An important implicit cue that may tune forgetting in working memory is the passage of time. When information becomes irrelevant more quickly, working memory should also forget information more quickly. In three delayed-estimation experiments, we systematically manipulated how probing probability changed as time passed on after encoding an item (i.e., the “probing hazard”). In some blocks, probing hazard decreased after encoding an item, requiring participants to only briefly retain the memory item. In other blocks, the probing hazard increased or stayed flat, as the retention interval was lengthened. In line with our hypothesis, we found that participants adapted their forgetting rate to the probing dynamics of the working memory task. When the memory item quickly became irrelevant (“decreasing” probing hazard), forgetting rate was higher than in blocks where probing hazard increased or stayed flat. The time course of these adaptations in forgetting implies a fast and flexible mechanism. Interestingly, participants could not explicitly report the order of conditions, suggesting forgetting is implicitly sped up. These findings suggest that implicit adaptations to the temporal structure of our environment tune forgetting speed in working memory, possibly contributing to the flexible allocation of limited working memory resources.

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Introduction

Working memory has clear capacity limits (Cowan, 2001 ). As a consequence, working memory is not only selective about what it encodes, but also selective about what it forgets (Souza & Oberauer, 2016 ). Humans and non-human animals can use explicit cues to drop irrelevant information in order to “free” capacity (Dames & Oberauer, 2022 ; Williams et al., 2013 ; Williams & Woodman, 2012 ). Information that has been cued to forget is remembered less well, or even completely lost, and subsequent information is encoded better as a result of this cleared capacity (Dames & Oberauer, 2022 ; Williams et al., 2013 ), provided that forgetting cues are highly reliable (Williams & Woodman, 2012 ).

Reliable, explicit cues to forget information, however, might be rare in real-life scenarios. Yet, there are numerous implicit cues that inform us about the relevance of information in our environment. For instance, an important implicit cue to forget is the passage of time. As time passes on, information typically becomes less relevant to our current goals. Crucially, in some situations, information becomes irrelevant more quickly than in others. Consider reading an article. When trying to understand the full scope of an article, individual pieces of information stay relevant throughout our read. Conversely, browsing that same article for a specific quote does not require us to maintain each sentence we checked. Hence, an important question is whether forgetting mechanisms in working memory can adapt to these implicit temporal regularities, as has also been found when encoding information (de Jong et al., 2023 ). That is, can humans detect implicit cues that information becomes irrelevant more quickly, and speed up their forgetting accordingly?

There is some evidence to suggest that humans can adapt their rate of forgetting to how quickly information becomes irrelevant. Anderson and Schooler ( 1991 ) demonstrated that, for a variety of real-life scenarios, the probability that a piece of information re-occurs in the future declines over time. The authors hypothesize that forgetting in memory systems adapts to these temporal features of the environment. In a series of experiments, Anderson et al. ( 1997 ) empirically confirmed that working memory adapts to the rate at which information becomes irrelevant. When the probability that a verbal item would be probed declined over time (i.e., when the hazard rate of the probe declined), participants forgot that item more quickly. However, it is not clear whether these results reflect adaptations in working memory per se or a gradual reallocation of attention to the secondary articulatory suppression task that Anderson and colleagues (Anderson et al., 1997 ) employed simultaneously. It is also not clear whether their results also apply to visual working memory.

In three delayed-estimation experiments without a concurrent task, we tested whether forgetting in visual working memory adapts to how quickly information becomes irrelevant. In our experiment, we manipulated the time course of the probing hazard by sometimes not probing the item currently in memory but presenting a new item that had to be remembered instead (Fig. 1 ). In some blocks, probing hazard increased or stayed constant, while in others it decreased as time passed on after encoding an item. That is, in “decreasing” blocks, it was very likely that a color wheel probe would appear one second after encoding an item, but as time passed on, this probability dropped, making it more likely that a new item needed to be encoded (and vice versa in “increasing” blocks). We hypothesized that if forgetting in working memory is adaptive, then the steeper the drop in probing hazard, the faster the forgetting. To preview our results, we found that participants implicitly sped up forgetting when information became irrelevant more quickly.

Overview of the experimental set-up. Participants were presented with a single memory item (colored circle), which was followed by a mask to disrupt the afterimage. After a retention interval (1,000 or 3,000 ms) participants were either (1) probed by a color wheel, where they reproduced the memory item, or (2) presented with a new memory item, after which the trial proceeded. Importantly, the hazard that an item would be probed was either increasing, constant or decreasing as time passed on after encoding an item, and this was varied between consecutive sets of blocks (mini-blocks)

Participants

Participants were first-year psychology students (mean age = 20.1 years; 73% female) at the University of Groningen who participated in exchange for course credit. On the basis of a checklist developed by the EC-BSS at the University of Groningen, the study was exempt from full ethical review. The study was conducted in accordance with the ethical standards of the 1964 Helsinki Declaration. Some participants did not perform the task well. Therefore, participants whose mean absolute error was higher than Q3 (third quartile) + 3 x IQR were excluded from further analysis (criterion was determined per experiment). No participants were excluded in Experiment 1 , one in Experiment 2 , and two in Experiment 3 .

Apparatus and stimuli

The experiment was programmed using OpenSesame, a freely available software tool for designing experiments (Mathôt et al., 2012 ). Stimuli were presented on a 19-in. CRT screen with a resolution of 1,280 x 1,024 pixels, refreshing at 100 Hz. The testing room was sound-attenuated with dimmed lights and participants were seated approximately 60 cm away from the screen. A gray background was maintained throughout the experiment. Memory items were colors presented centrally on the screen. Colors were presented in CIElab color space (L* = 70, a = 0, b = 0, radius = 38). Memory items were followed by a mask to disrupt the retinal or cortical afterimage. The mask consisted of a square containing 25 individual squares in a 5 x 5 grid each filled with a different color randomly sampled from the CIElab color space.

Participants signed written informed consent and were given verbal instructions. They then performed several practice trials before moving on to the experimental trials. On any given trial, participants were presented with a fixation dot for 500–1,000 ms (uniformly sampled), after which the memory item was presented for 100 ms. Immediately following this, a mask was presented for 100 ms to eliminate any retinal or cortical after-image. The reason for using such a brief presentation time was to create conditions where preemptive forgetting was beneficial. If participants get ample time to encode the new item, there would be no need to forget the previous item (e.g., at longer encoding times, participants might have time to actively disregard information before encoding the next item). Once the mask was removed, the delay period started, during which the participants had to keep the memory item in mind. The duration of the retention interval was either 1,000 or 3,000 ms. After the retention interval, one of two things happened: (1) a randomly rotated color wheel was presented, prompting participants to report the color with a mouse click, or (2) a new color would be presented that they needed to store in memory and the trial proceeded (i.e., after 1,000 or 3,000 ms the new color would either be probed, or yet another item would be presented, etc.). This allowed us to systematically vary how probing hazard evolved after item presentation. In some blocks, probing hazard decreased (“decreasing” blocks), and in some it increased (“increasing” blocks), or alternatively, stayed flat (“flat” blocks).

Once participants indicated their response, feedback was provided by assigning points as a function of absolute error. The score was 0 when |error| > 45°. Between 45 and 0°, the score scaled linearly with absolute error from 0 to 100 points. At the end of each block, participants were presented with their cumulative score of points acquired during the block, and their block-wise high score so far. Participants were able to set new high scores, as a means to motivate them to keep up their performance throughout the task. Block order was counterbalanced across participants, such that half of the participants started with a mini-block with “decreasing” probing hazard, and the other half with a mini-block with “increasing” (Experiments 1 and 3 ) or “flat” (Experiment 2 ) probing hazard (see Fig. 1 ).

Experiment 1

In this initial experiment (N = 50), we tested whether forgetting was faster in blocks where probing hazard decreased, as opposed to blocks where probing hazard increased. For our first experiment, we did not know the effect size a priori, but we would have a power of 93% assuming a medium effect size (Cohen’s d = 0.5) for a paired-sample t-test. The time course of probing hazard within trials varied between blocks. In “decreasing” blocks, the overall probing probability was 60%, but the probing hazard decreased over time. That is, after 1 s of retention time, the probability that the item would be probed was .5, but after 3 s, the probability that the item would be probed was .25 (see Fig. 1 ). In “increasing” blocks, the probing hazard increased from 0.2 to 0.4. Note, however, that both retention intervals occurred equally often. Participants would switch between conditions every three blocks, each containing 40 trials. The order with which condition participants started was counterbalanced. In total participants completed 18 blocks, totaling 720 trials. Participants were not made aware of the manipulation of probing hazard.

Experiment 2

The second experiment (N = 45) was performed to replicate the findings of the first experiment, while equating probing hazard after one second of retention time. Also, we wanted to enhance the effect by inducing a sharper drop in probing hazard for the “decreasing” blocks. Assuming at least the same effect size as in Experiment 1 ( d = 0.35), the power was 76%. In blocks with “decreasing” probing hazard (overall probing probability = 58%), the probing hazard after one second was .5, which decreased to .167 at 3 s. In blocks with “flat” probing hazard, the overall probing probability was now 75%, but stayed flat at .5. As in Experiment 1 , both retention intervals occurred equally often. Furthermore, we also slightly increased the duration of each block, so that each block now consisted of 48 trials. Also, participants completed four blocks before switching block type. The number of blocks stayed the same, but in total participants now completed 864 trials. As in the previous experiment, participants were not made aware of the manipulation of probing hazard.

Experiment 3

The third experiment (N = 47) was aimed at checking whether forgetting rate adapted throughout an experiment, and whether those adaptations were implicit or explicit. Experiment 3 was the same as Experiment 1 , except for two things. First, the order of “increasing” and “decreasing” blocks was such that participants started with nine consecutive blocks of the same condition (increasing/decreasing), and then switched to the other condition. Second, at the end of the experiment, we asked three questions to probe explicit knowledge of the switch between conditions. The first question was intended to elicit explicit knowledge spontaneously: “Did you notice that something had changed halfway through the experiment? If yes, please state (in either Dutch or English) what you think changed. If not, you can leave this question empty.” The second question gave away what had changed and asked participants whether they had indeed noticed this change: “In one half of the experiment, you needed to remember the color for a brief period of time before it was probed. In the other half of the experiment, you needed to remember the color for a longer time before it was probed. Did you notice this (Yes / No)?” The third question was aimed at checking whether participants had accurate knowledge about the order of conditions: “Can you indicate which order you experienced in the experiment? If you don’t know, please make a guess (First half: remember for a SHORT time. Second half: remember for a LONG time / First half: remember for a LONG time. Second half: remember for a SHORT time).”

All analyses were performed in R (R Core Team, 2018 ; version 4.2.2). The data of the three experiments were analyzed using the same approach. Trials where participants took longer than 10 s to respond were excluded from any analyses (0.1 % of all trials).

Absolute error angle was calculated as the absolute difference between the reproduced angle and the actual angle of the memory item (thus, |error| spans from 0 to 180°). Forgetting rate (in degrees per second) was calculated for each participant and each block type (i.e., “decreasing”, “flat”, “increasing”), by fitting a linear regression that predicted mean absolute error with retention interval as a predictor. The resulting coefficient for retention interval represents the forgetting rate in degrees per second, which was then used in subsequent analyses. The rationale for this approach was twofold. Firstly, we want to have an approximately normally distributed dependent variable to perform inference on. Absolute error angle is heavily skewed but forgetting rate less so. Secondly, we wanted to perform a “paired” analysis that takes into account that trials are grouped within participants. By calculating forgetting rate per participant, per condition, we take that grouping into account.

To assess whether forgetting rates differed between block types, we computed forgetting rates pooled across experimental blocks and performed a one-sided paired t-test. Comparisons between Experiments 1 and 2 were done with a linear regression, predicting forgetting rate with “hazard drop” (i.e., how quickly the hazard increased or decreased after encoding). For the linear regression we report t-values and p-values associated with the absolute t-value. In order to study how adaptation evolved over the course of the experiment, we computed the forgetting rate for each individual experimental block and performed between and within-subject comparisons using one-sided t-tests. In Experiment 3 , we wanted to consider the entire time course of adaptation in the first and second half of the experiment. To this end, we fitted a linear regression model for even and odd participants, with experimental block as a predictor and forgetting rate as the dependent variable. The resulting coefficient represented how quickly forgetting rate increased. We also added the interaction with block type to assess whether the change in forgetting rate was different when switching to an increasing versus decreasing probing hazard. We report t-values and p-values associated with the absolute t-value. We also computed Bayes factors (BF 10 ) for t-tests using the BayesFactor package (Morey & Rouder, 2022 ; version 0.9 12-4.4). We used the default Cauchy prior ( r = 1/√2). One-sided tests were performed as described in Wetzels et al. ( 2009 ) by limiting the prior mass to the region (0, ∞). The reported BF 10 reflects the ratio between the posterior probability for the alternative hypothesis that the standardized effect is bigger than zero (i.e., forgetting rate is larger in “decreasing” than “increasing/flat” blocks) and the null hypothesis that the standardized effect size is exactly zero. For linear regressions, we also compute BF 10 , where we compare the null model (i.e., “intercepts-only” model or “main effects only” models) with models including the main effect or interaction effect. The reported BF 10 reflects the ratio between the posterior probability for the alternative and the null model.

Information that outdates quickly is more quickly forgotten

In Experiment 1 , we found that forgetting was faster in “decreasing” blocks ( M = 2.05 °/s, SD = 2.42), where information became outdated quickly, than “increasing” blocks ( M = 1.13 °/s, SD = 1.38; Fig. 2 ; t (49) = 2.40, p = 0.010, BF 10 = 4.06). Consistent with our hypothesis, this suggests that when information quickly became irrelevant (decreasing hazard), participants forgot that information more quickly compared to when information stayed relevant for a longer time (increasing hazard). In Experiment 2 , forgetting rate in “decreasing” blocks ( M = 2.19 °/s, SD = 3.25) was not significantly different from forgetting rate in “flat” blocks ( M = 1.61, SD = 2.27; Fig. 2 B; t (44) = 1.43, p = 0.079, BF 10 = 0.77), although no strong evidence for the null hypothesis was obtained.

Results of Experiments 1 and 2 . Mean |error| is plotted over retention interval for “decreasing” (red) and “increasing/flat” (blue) blocks. Note that all measures on the y-axis reflect performance, where higher is better (the axis for mean absolute error is flipped)

Adaptive forgetting is driven by the rate at which information gets outdated

Why were the effects in Experiment 2 less pronounced than in Experiment 1 ? There were some small procedural differences between experiments (e.g., number of trials in an experimental block, number of trials per block type), but the main difference was how probing hazard evolved over a trial. In Experiment 1 , probing hazard increased in some blocks, while in Experiment 2 probing hazard remained constant for some blocks (Fig. 1 ). Also, for “decreasing” blocks, the probing hazard dropped more steeply in Experiment 2 . If forgetting rate adapts to the rate at which information becomes outdated, the slope of the hazard function should be the main driver of adaptive forgetting. More specifically, forgetting rate in the “flat” blocks should lie somewhere between the “decreasing” and “increasing” blocks, while the “decreasing” blocks of Experiment 2 should be at least as high as the “decreasing” blocks of Experiment 1 . To this end, we computed the “hazard drop” (i.e., how quickly the hazard increased or decreased per second) for each condition in each experiment and tested its effect on forgetting rate

We found that forgetting rate increased with hazard drop (Fig. 3 ; β = 4.13, SE = 1.57, t = 2.64, p = 0.009, BF 10 = 3.87). This suggests that discrepancies between Experiment 1 and 2 can be partly explained by differences in how probing hazard evolves over time. In sum, this again demonstrates a link between the temporal dynamics of the working memory task and the temporal dynamics of forgetting.

Analysis across experiments. For Experiments 1 (circles) and 2 (squares), the forgetting rate per block type is plotted for each hazard drop (in hazard rate per second)

Adaptations in forgetting rate are fast and flexible

How is adaptive forgetting learned over the course of the experiment? In order to study the time course of adaptations in forgetting, we computed forgetting rates (based on |error|) for each experimental block, for each participant. This allows us to do a comparison of the forgetting rate between participants who started with “decreasing” blocks and participants who started with “increasing” or “flat” blocks. In Experiment 1 , forgetting was significantly higher in the “decreasing” blocks compared to the “increasing” blocks at the third experimental block of the experiment ( t = 3.41, df = 39.494, p < 0.001, BF 10 = 49.80), just before switching block type. These results suggest that participants learned to forget relatively quickly (also see, Anderson et al., 1997 ). In Experiment 2 , participants in the “decreasing” short block did not show more forgetting statistically than participants in the “flat” block in the fourth experimental block ( t = 1.39, df = 34.409, p = 0.09, BF 10 = 1.00), just before switching block type.

We also tested for adaptation within participants. Using paired t-tests, we checked (1) whether achieved levels of adaptation could be unlearned, and (2) whether forgetting rate could still be adapted after having experienced several blocks with “increasing” or “flat” hazard rates. In Experiment 1 , we found that participants who started with three “decreasing” blocks (odd participants) showed significant adaptation (block 1 vs 3; t (24) = 2.73, p = 0.005, BF 10 = 8.36; see Fig. 4 ). Moreover, those same participants significantly unlearned their achieved levels of adaptation over the course of three “increasing” blocks (block 3 vs. block 6; t (24) = -2.19, p = 0.019, BF 10 = 3.10). Conversely, participants who started with three “increasing” blocks (even participants), did not show statistically significant adaptation after three “increasing” blocks (block 1 vs. 3; t (24) = -0.41, p = 0.687, BF 10 = 0.23). However, they were still able to adapt their forgetting rate after three “decreasing” blocks (block 3 vs. 6; t (24) = 2.75, p = 0.006, BF 10 = 8.67). These findings demonstrate that adaptation of forgetting rate in working memory can be relatively fast and flexible.

Time course of forgetting rates in Experiment 1 , divided into even and odd participants, who started with different block types. Brackets represent the significance of paired-samples t-tests, representing (absence of) adaptation between block 1 and block 3, and re-adaptation between block 3 and block 6

The results of Experiment 2 were less convincing. We found that participants who started with four “decreasing” blocks (odd participants) did not show significant adaptation (block 1 vs. 4; t (22) = 1.13, p = 0.13, BF 10 = 0.66; see Fig. 5 ). However, those same participants significantly unlearned their achieved levels of adaptation over the course of four “flat” blocks (block 4 vs. block 8; t (22) = -2.12, p = 0.023, BF 10 = 2.75). Participants who started with four “flat” blocks (even participants), did not show statistically significant adaptation after four “flat” blocks (block 1 vs. 4; t (21) = 0.73, p = 0.47, BF 10 = 0.28), but they also did not increase their forgetting rate after four “decreasing” blocks (block 4 vs. 8; t (21) = 0.55, p = 0.29, BF 10 = 0.36).

Time course of forgetting rates in Experiment 2 , divided into even and odd participants. Brackets represent the significance of paired-samples t-tests, representing (absence of) adaptation between block 1 and block 4, and re-adaptation between block 4 and block 8

Forgetting rate adapts throughout the experiment

As we have seen in Experiments 1 and 2 , adaptations in forgetting rate are relatively fast and flexible. However, as the experiment progressed, differences in conditions became less and less apparent, possibly due to lingering effects of previous blocks. In Experiment 3 , we set out to study whether forgetting rate adapts throughout the experiment (see Fig. 6 ). Participants were exposed to nine consecutive blocks of decreasing or increasing probing hazard, and then switched halfway through the experiment. Contrary to our expectations, forgetting rate did not differ between block types in the first half of the experiment ( t( 354.85) = 0.92, p = 0.35, BF 10 = 0.27). However, after switching to a decreasing probing hazard, participants steadily increased their forgetting rate (β = 0.47 (forgetting rate per block), t = 3.01, p = 0.003, BF 10 = 9.90), while participants switching to an increasing probing hazard maintained a stable forgetting rate (β = -0.04 (forgetting rate per block), t = -0.43, p = 0.738, BF 10 = 0.16). This change in forgetting rate was statistically different between conditions ( t = 2.55, p = 0.011, BF 10 = 3.48). Notably, while forgetting rate increased for odd participants, performance at the 1-s retention interval remained stable. This suggests that not encoding, but specifically forgetting was adapted to the new task demands. Odd participants started with nominally higher forgetting rates than even participants. However, unlike Experiments 1 and 2 , they were unable to slow down their forgetting after switching to an increasing probing hazard. These findings suggest that it may be easier to “learn to forget” than to “forget to forget”.

Forgetting over the course of Experiment 3 . Absolute error is plotted as a function of retention interval. Left panel: Odd participants start out with nine consecutive blocks with an increasing probing hazard (shades of blue; left). Then, they switch to a decreasing probing hazard (shades of red; right). Right panel: Even participants start out with nine consecutive blocks with a decreasing probing hazard (shades of red; left). Then, they switch to an increasing probing hazard (shades of blue; right). Note that all measures on the y-axis reflect performance, where higher is better (the axis for mean absolute error is flipped)

Adaptations in forgetting rate are largely implicit

We did not inform participants about manipulations of probing hazard in any of the experiments, suggesting that participants did not need explicit instructions to adapt their forgetting rate. However, it is not clear whether adaptations in forgetting rate were due to an explicit awareness of changes in probing hazard, or whether adaptations in forgetting rate were driven by implicit knowledge, like in statistical learning (Sherman et al., 2020 ). Fortunately, Experiment 3 was ideally suited to answer this question. At the end of the experiment, participants were first asked whether they noticed anything had changed halfway through the experiment, and if so, to write a short description of what had changed. In total, 57% of participants did not notice anything had changed, and those who indicated that they did notice something were all unable to identify what had changed. Then, participants were asked whether they had noticed that they needed to remember the color for longer/shorter in one half of the experiment, and shorter/longer in the other half. Most participants (62%) indicated that they did not notice the change in overall retention interval. Finally, participants were prompted to guess the order of probing hazard conditions with a forced two-alternative question. Overall, participants could not correctly indicate the order of conditions, regardless of whether they said they had noticed the change (50% correct) or not (57% correct). Notably, participants were biased towards indicating that retention intervals became longer in the second half of the experiment (72%), possibly due to time-on-task effects. In sum, participants did not spontaneously report the switch in probing hazard, (mostly) did not say that they had noticed the change, and could not accurately report the order of conditions when prompted. In sum, it seems that the adaptations in forgetting rate that we observed were not mediated by explicit awareness of how quickly information became outdated.

Humans can use explicit cues to forget information (Dames & Oberauer, 2022 ; Williams et al., 2013 ; Williams & Woodman, 2012 ), freeing up working memory capacity, but these cues might be rare in real-life situations. We hypothesized that humans implicitly tune their rate of forgetting to the temporal contingencies of whether they need an item. Indeed, we found that information was forgotten more quickly when information became irrelevant more quickly.

Our findings are in stark contrast to accounts that regard forgetting in working memory as an unfortunate limitation of cognitive or neural processes (Jonides et al., 2008 ). Instead of treating working memory forgetting as “decay” (Ricker et al., 2016 ), “sudden death” (Zhang & Luck, 2009 ) or “interference” (Souza & Oberauer, 2015 ), our findings suggest that forgetting in working memory is more adaptive than those labels signify. To be clear, some forms of forgetting are likely the result of systematic limitations of neural or cognitive processes. However, those incidental forms of forgetting might have little to do with the adaptive forgetting mechanisms we demonstrated here, and exist alongside the mechanisms demonstrated with the current set of experiments. For instance, forgetting in typical working memory experiments may have been systematically underestimated because of an increasing probing hazard (also see, e.g., Muter, 1980 ). Instead, we show substantial forgetting for a single item in working memory with decreasing probing hazard, comparable to forgetting at higher set sizes (e.g., Pertzov et al., 2017 ; Souza & Oberauer, 2015 ; Zhang & Luck, 2009 ). This also speaks against accounts that claim forgetting results exclusively from competing items held in working memory (Pertzov et al., 2017 ), or from interference from concurrent tasks (Barrouillet et al., 2011 ). In our experiments, only a single item was held in working memory, and there was no concurrent task.

One source of interference that was still possible in our task was proactive interference. Items previously held in working memory may interfere with currently maintained items (Keppel & Underwood, 1962 ). In “decreasing” blocks, items were probed in more rapid succession, which might lead to more proactive interference (Souza & Oberauer, 2015 ). However, some patterns in our data suggest that proactive interference cannot fully explain our findings. For instance, previous studies have found that proactive interference impacts visual working memory performance at all retention intervals (Shoval & Makovski, 2021 ; Souza & Oberauer, 2015 ). In contrast, we did not observe such a cost for the shortest retention interval. If anything, participants performed better in “decreasing” blocks at the briefest retention interval than in “increasing” or “flat” blocks. While the effect of proactive interference was likely minimal in our experiments, future studies should attempt to experimentally (e.g., by having non-overlapping item presentations; Makovski, 2016 ) or statistically control for proactive interference (Bays et al., 2009 ; Taylor et al., 2023 ).

We believe that the main “rationale” of adaptive forgetting is to optimize the use of limited working memory capacity. In light of this rationale, adaptive forgetting should be systematically related to set size and capacity. For instance, if a participant’s capacity is three items, we expect quite drastic adaptive forgetting if they are shown two items at a time. The participant would need to drop at least one of the current items in order to optimally prepare encoding for the next two items. Therefore, we might expect individuals with low capacity to have a larger need to forget. Alternatively, individual differences in working memory capacity may stem from suboptimal forgetting mechanisms. Individuals with low capacity may be less able to suppress obsolete items, overloading their working memory in the process (cf. Engle, 2002 ). Future research should aim at dissociating these competing hypotheses.

In terms of underlying mechanisms, two opposing accounts might explain how information is adaptively forgotten. First, by the time an item is likely obsolete, participants might terminate active maintenance of that memory item, exposing it to decay (Barrouillet et al., 2011 ) or interference (Souza & Oberauer, 2015 ). According to this hypothesis, it is the disengagement of active maintenance processes that implements adaptive forgetting. For instance, Dames and Oberauer ( 2022 ) concluded that directed forgetting in working memory is implemented by boosting the representations of to-be-remembered items, while leaving to-be-forgotten items untouched. Another possibility is that by the time an item is likely obsolete, participants actively inhibit the to-be-forgotten item. Accordingly, participants employ an active mechanism to remove to-be-forgotten items from working memory (Lewis-Peacock et al., 2018 ). Several findings suggest that active inhibitory mechanisms operate to remove information from working memory after explicit forgetting cues (Festini, 2020 ), to resolve proactive interference in working memory (Jonides et al., 1998 ), and to settle competition between items during working memory retrieval (Kang & Choi, 2015 ). Furthermore, it has been suggested that active forgetting from working memory is accomplished by “unbinding” an item from its context (e.g., spatial position, temporal order; Lewis-Peacock et al., 2018 ). More work is needed to uncover which of these mechanisms underlies adaptive forgetting rate in working memory.

We observed that the adaptations in forgetting rate in our experiments can be subtle. Experiment 2 did not show a significant effect of probing hazard, and Experiment 3 did not find a difference in the first half of the experiment. There are several factors that may have limited our power to detect adaptive forgetting rates. For instance, the number of trials that could be used to compute forgetting rates was limited as a result of the manipulation of hazard drop. Future studies should attempt more powerful replications. We recommend including more participants for between-participant comparisons, using more sensitive measures of working memory performance (e.g., Honig et al., 2020 ), and boosting the effect, for instance, by increasing the hazard drop or possibly the set size.

In conclusion, we found evidence that humans can implicitly tune their rate of forgetting in working memory to the rate at which information becomes obsolete. Specifically, when the probability that a piece of information would be probed decreased over time, participants forgot that information more quickly. This may serve the purpose of freeing up capacity in environments where the relevance of information is ever-changing. Notably, participants were unaware of changes in the rate at which information became obsolete, suggesting that adaptations in forgetting are largely implicit. Our findings also raise some questions for future research, such as the relationship between an individual’s working memory capacity on adaptive forgetting, the fate of adaptively forgotten memories, and the active or passive mechanisms by which information is adaptively cleared from working memory.

Availability of data and materials

Data and materials for all experiments are available on the Open Science Framework website ( https://osf.io/chxp3/ )

Code availability

Experiment and analysis code are available on the Open Science Framework website ( https://osf.io/chxp3/ )

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Scientists Pinpoint the Uncertainty of Our Working Memory

The human brain regions responsible for working memory content are also used to gauge the quality, or uncertainty, of memories, a team of scientists has found, uncovering how these neural responses allow us to act and make decisions based on how sure we are about our memories.

New Study Shows the Extent We Trust Our Memory in Decision-Making

The human brain regions responsible for working memory content are also used to gauge the quality, or uncertainty, of memories, a team of scientists has found. Its study uncovers how these neural responses allow us to act and make decisions based on how sure we are about our memories.

“Access to the uncertainty in our working memory enables us to determine how much to ‘trust’ our memory in making decisions,” explains Hsin-Hung Li, a postdoctoral fellow in New York University’s Department of Psychology and Center for Neural Science and the lead author of the paper , which appears in the journal Neuron . “Our research is the first to reveal that the neural populations that encode the content of working memory also represent the uncertainty of memory.”

Working memory, which enables us to maintain information in our minds, is an essential cognitive system that is involved in almost every aspect of human behavior—notably decision-making and learning. 

For example, when reading, working memory allows us to store the content we just read a few seconds ago while our eyes keep scanning through the new sentences. Similarly, when shopping online, we may compare, “in our mind,” the item in front of us on the screen with previous items already viewed and still remembered. 

“It is not only crucial for the brain to remember things, but also to weigh how good the memory is: How certain are we that a specific memory is accurate?” explains Li. “If we feel that our memory for the previously viewed online item is poor, or uncertain, we would scroll back and check that item again in order to ensure an accurate comparison.”

While studies on human behaviors have shown that people are able to evaluate the quality of their memory, less clear is how the brain achieves this. 

More specifically, it had previously been unknown whether the brain regions that hold the memorized item also register the quality of that memory.

In uncovering this, the researchers conducted a pair of experiments to better understand how the brain stores working memory information and how, simultaneously, the brain represents the uncertainty—or, how good the memory is—of remembered items. 

In the first experiment, human participants performed a spatial visual working memory task while a functional magnetic resonance imaging (fMRI) scanner recorded their brain activity. For each task, or trial, the participant had to remember the location of a target—a white dot shown briefly on a computer screen—presented at a random location on the screen and later report the remembered location through eye movement by looking in the direction of the remembered target location.

Here, fMRI signals allowed the researchers to decode the location of the memory target—what the subjects were asked to remember—in each trial. By analyzing brain signals corresponding to the time during which participants held their memory, they could determine the location of the target the subjects were asked to memorize. In addition, through this method, the scientists could accurately predict memory errors made by the participants; by decoding their brain signals, the team could determine what the subjects were remembering and therefore spot errors in their recollections.  

In the second experiment, the participants reported not only the remembered location, but also how uncertain they felt about their memory in each trial. The resulting fMRI signals recorded from the same brain regions allowed the scientists to decode the uncertainty reported by the participants about their memory. 

Taken together, the results yielded the first evidence that the human brain registers both the content and the uncertainty of working memory in the same cortical regions.

“The knowledge of uncertainty of memory also guides people to seek more information when we are unsure of our own memory,” Li says in noting the utility of the findings.

The study’s other researchers included Wei Ji Ma and Clayton Curtis, professors in NYU’s Department of Psychology; Thomas Sprague, an NYU postdoctoral researcher at the time of the study and now an assistant professor at the University of California, Santa Barbara; and Aspen Yoo, an NYU doctoral student at the time of the study and now a postdoctoral fellow at the University of California, Berkeley.

The research was supported by grants from the National Eye Institute (NEI) (R01 EY-016407, R01 EY-027925, F32 EY-028438) and the NEI Visual Neuroscience Training Program (T32-EY007136).

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REVIEW article

Working memory from the psychological and neurosciences perspectives: a review.

• 1 Department of Neurosciences, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia
• 2 Center for Neuroscience Services and Research, Universiti Sains Malaysia, Kubang Kerian, Malaysia

Since the concept of working memory was introduced over 50 years ago, different schools of thought have offered different definitions for working memory based on the various cognitive domains that it encompasses. The general consensus regarding working memory supports the idea that working memory is extensively involved in goal-directed behaviors in which information must be retained and manipulated to ensure successful task execution. Before the emergence of other competing models, the concept of working memory was described by the multicomponent working memory model proposed by Baddeley and Hitch. In the present article, the authors provide an overview of several working memory-relevant studies in order to harmonize the findings of working memory from the neurosciences and psychological standpoints, especially after citing evidence from past studies of healthy, aging, diseased, and/or lesioned brains. In particular, the theoretical framework behind working memory, in which the related domains that are considered to play a part in different frameworks (such as memory’s capacity limit and temporary storage) are presented and discussed. From the neuroscience perspective, it has been established that working memory activates the fronto-parietal brain regions, including the prefrontal, cingulate, and parietal cortices. Recent studies have subsequently implicated the roles of subcortical regions (such as the midbrain and cerebellum) in working memory. Aging also appears to have modulatory effects on working memory; age interactions with emotion, caffeine and hormones appear to affect working memory performances at the neurobiological level. Moreover, working memory deficits are apparent in older individuals, who are susceptible to cognitive deterioration. Another younger population with working memory impairment consists of those with mental, developmental, and/or neurological disorders such as major depressive disorder and others. A less coherent and organized neural pattern has been consistently reported in these disadvantaged groups. Working memory of patients with traumatic brain injury was similarly affected and shown to have unusual neural activity (hyper- or hypoactivation) as a general observation. Decoding the underlying neural mechanisms of working memory helps support the current theoretical understandings concerning working memory, and at the same time provides insights into rehabilitation programs that target working memory impairments from neurophysiological or psychological aspects.

Introduction

Working memory has fascinated scholars since its inception in the 1960’s ( Baddeley, 2010 ; D’Esposito and Postle, 2015 ). Indeed, more than a century of scientific studies revolving around memory in the fields of psychology, biology, or neuroscience have not completely agreed upon a unified categorization of memory, especially in terms of its functions and mechanisms ( Cowan, 2005 , 2008 ; Baddeley, 2010 ). From the coining of the term “memory” in the 1880’s by Hermann Ebbinghaus, to the distinction made between primary and secondary memory by William James in 1890, and to the now widely accepted and used categorizations of memory that include: short-term, long-term, and working memories, studies that have tried to decode and understand this abstract concept called memory have been extensive ( Cowan, 2005 , 2008 ). Short and long-term memory suggest that the difference between the two lies in the period that the encoded information is retained. Other than that, long-term memory has been unanimously understood as a huge reserve of knowledge about past events, and its existence in a functioning human being is without dispute ( Cowan, 2008 ). Further categorizations of long-term memory include several categories: (1) episodic; (2) semantic; (3) Pavlovian; and (4) procedural memory ( Humphreys et al., 1989 ). For example, understanding and using language in reading and writing demonstrates long-term storage of semantics. Meanwhile, short-term memory was defined as temporarily accessible information that has a limited storage time ( Cowan, 2008 ). Holding a string of meaningless numbers in the mind for brief delays reflects this short-term component of memory. Thus, the concept of working memory that shares similarities with short-term memory but attempts to address the oversimplification of short-term memory by introducing the role of information manipulation has emerged ( Baddeley, 2012 ). This article seeks to present an up-to-date introductory overview of the realm of working memory by outlining several working memory studies from the psychological and neurosciences perspectives in an effort to refine and unite the scientific knowledge concerning working memory.

The Multicomponent Working Memory Model

When one describes working memory, the multicomponent working memory model is undeniably one of the most prominent working memory models that is widely cited in literatures ( Baars and Franklin, 2003 ; Cowan, 2005 ; Chein et al., 2011 ; Ashkenazi et al., 2013 ; D’Esposito and Postle, 2015 ; Kim et al., 2015 ). Baddeley and Hitch (1974) proposed a working memory model that revolutionized the rigid and dichotomous view of memory as being short or long-term, although the term “working memory” was first introduced by Miller et al. (1960) . The working memory model posited that as opposed to the simplistic functions of short-term memory in providing short-term storage of information, working memory is a multicomponent system that manipulates information storage for greater and more complex cognitive utility ( Baddeley and Hitch, 1974 ; Baddeley, 1996 , 2000b ). The three subcomponents involved are phonological loop (or the verbal working memory), visuospatial sketchpad (the visual-spatial working memory), and the central executive which involves the attentional control system ( Baddeley and Hitch, 1974 ; Baddeley, 2000b ). It was not until 2000 that another component termed “episodic buffer” was introduced into this working memory model ( Baddeley, 2000a ). Episodic buffer was regarded as a temporary storage system that modulates and integrates different sensory information ( Baddeley, 2000a ). In short, the central executive functions as the “control center” that oversees manipulation, recall, and processing of information (non-verbal or verbal) for meaningful functions such as decision-making, problem-solving or even manuscript writing. In Baddeley and Hitch (1974) ’s well-cited paper, information received during the engagement of working memory can also be transferred to long-term storage. Instead of seeing working memory as merely an extension and a useful version of short-term memory, it appears to be more closely related to activated long-term memory, as suggested by Cowan (2005 , 2008 ), who emphasized the role of attention in working memory; his conjectures were later supported by Baddeley (2010) . Following this, the current development of the multicomponent working memory model could be retrieved from Baddeley’s article titled “Working Memory” published in Current Biology , in Figure 2 ( Baddeley, 2010 ).

An Embedded-Processes Model of Working Memory

Notwithstanding the widespread use of the multicomponent working memory model, Cowan (1999 , 2005 ) proposed the embedded-processes model that highlights the roles of long-term memory and attention in facilitating working memory functioning. Arguing that the Baddeley and Hitch (1974) model simplified perceptual processing of information presentation to the working memory store without considering the focus of attention to the stimuli presented, Cowan (2005 , 2010 ) stressed the pivotal and central roles of working memory capacity for understanding the working memory concept. According to Cowan (2008) , working memory can be conceptualized as a short-term storage component with a capacity limit that is heavily dependent on attention and other central executive processes that make use of stored information or that interact with long-term memory. The relationships between short-term, long-term, and working memory could be presented in a hierarchical manner whereby in the domain of long-term memory, there exists an intermediate subset of activated long-term memory (also the short-term storage component) and working memory belongs to the subset of activated long-term memory that is being attended to ( Cowan, 1999 , 2008 ). An illustration of Cowan’s theoretical framework on working memory can be traced back to Figure 1 in his paper titled “What are the differences between long-term, short-term, and working memory?” published in Progress in Brain Research ( Cowan, 2008 ).

Alternative Models

Cowan’s theoretical framework toward working memory is consistent with Engle (2002) ’s view, in which it was posited that working memory capacity is comparable to directed or held attention information inhibition. Indeed, in their classic study on reading span and reading comprehension, Daneman and Carpenter (1980) demonstrated that working memory capacity, which was believed to be reflected by the reading span task, strongly correlated with various comprehension tests. Surely, recent and continual growth in the memory field has also demonstrated the development of other models such as the time-based resource-sharing model proposed by several researchers ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This model similarly demonstrated that cognitive load and working memory capacity that were so often discussed by working memory researchers were mainly a product of attention that one receives to allocate to tasks at hand ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). In fact, the allocated cognitive resources for a task (such as provided attention) and the duration of such allocation dictated the likelihood of success in performing the tasks ( Barrouillet et al., 2004 , 2009 ; Barrouillet and Camos, 2007 ). This further highlighted the significance of working memory in comparison with short-term memory in that, although information retained during working memory is not as long-lasting as long-term memory, it is not the same and deviates from short-term memory for it involves higher-order processing and executive cognitive controls that are not observed in short-term memory. A more detailed presentation of other relevant working memory models that shared similar foundations with Cowan’s and emphasized the roles of long-term memory can be found in the review article by ( D’Esposito and Postle, 2015 ).

In addition, in order to understand and compare similarities and disparities in different proposed models, about 20 years ago, Miyake and Shah (1999) suggested theoretical questions to authors of different models in their book on working memory models. The answers to these questions and presentations of models by these authors gave rise to a comprehensive definition of working memory proposed by Miyake and Shah (1999 , p. 450), “working memory is those mechanisms or processes that are involved in the control, regulation, and active maintenance of task-relevant information in the service of complex cognition, including novel as well as familiar, skilled tasks. It consists of a set of processes and mechanisms and is not a fixed ‘place’ or ‘box’ in the cognitive architecture. It is not a completely unitary system in the sense that it involves multiple representational codes and/or different subsystems. Its capacity limits reflect multiple factors and may even be an emergent property of the multiple processes and mechanisms involved. Working memory is closely linked to LTM, and its contents consist primarily of currently activated LTM representations, but can also extend to LTM representations that are closely linked to activated retrieval cues and, hence, can be quickly activated.” That said, in spite of the variability and differences that have been observed following the rapid expansion of working memory understanding and its range of models since the inception of the multicomponent working memory model, it is worth highlighting that the roles of executive processes involved in working memory are indisputable, irrespective of whether different components exist. Such notion is well-supported as Miyake and Shah, at the time of documenting the volume back in the 1990’s, similarly noted that the mechanisms of executive control were being heavily investigated and emphasized ( Miyake and Shah, 1999 ). In particular, several domains of working memory such as the focus of attention ( Cowan, 1999 , 2008 ), inhibitory controls ( Engle and Kane, 2004 ), maintenance, manipulation, and updating of information ( Baddeley, 2000a , 2010 ), capacity limits ( Cowan, 2005 ), and episodic buffer ( Baddeley, 2000a ) were executive processes that relied on executive control efficacy (see also Miyake and Shah, 1999 ; Barrouillet et al., 2004 ; D’Esposito and Postle, 2015 ).

The Neuroscience Perspective

Following such cognitive conceptualization of working memory developed more than four decades ago, numerous studies have intended to tackle this fascinating working memory using various means such as decoding its existence at the neuronal level and/or proposing different theoretical models in terms of neuronal activity or brain activation patterns. Table 1 offers the summarized findings of these literatures. From the cognitive neuroscientific standpoint, for example, the verbal and visual-spatial working memories were examined separately, and the distinction between the two forms was documented through studies of patients with overt impairment in short-term storage for different verbal or visual tasks ( Baddeley, 2000b ). Based on these findings, associations or dissociations with the different systems of working memory (such as phonological loops and visuospatial sketchpad) were then made ( Baddeley, 2000b ). It has been established that verbal and acoustic information activates Broca’s and Wernicke’s areas while visuospatial information is represented in the right hemisphere ( Baddeley, 2000b ). Not surprisingly, many supporting research studies have pointed to the fronto-parietal network involving the dorsolateral prefrontal cortex (DLPFC), the anterior cingulate cortex (ACC), and the parietal cortex (PAR) as the working memory neural network ( Osaka et al., 2003 ; Owen et al., 2005 ; Chein et al., 2011 ; Kim et al., 2015 ). More precisely, the DLPFC has been largely implicated in tasks demanding executive control such as those requiring integration of information for decision-making ( Kim et al., 2015 ; Jimura et al., 2017 ), maintenance and manipulation/retrieval of stored information or relating to taxing loads (such as capacity limit) ( Osaka et al., 2003 ; Moore et al., 2013 ; Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ), and information updating ( Murty et al., 2011 ). Meanwhile, the ACC has been shown to act as an “attention controller” that evaluates the needs for adjustment and adaptation of received information based on task demands ( Osaka et al., 2003 ), and the PAR has been regarded as the “workspace” for sensory or perceptual processing ( Owen et al., 2005 ; Andersen and Cui, 2009 ). Figure 1 attempted to translate the theoretical formulation of the multicomponent working memory model ( Baddeley, 2010 ) to specific regions in the human brain. It is, however, to be acknowledged that the current neuroscientific understanding on working memory adopted that working memory, like other cognitive systems, involves the functional integration of the brain as a whole; and to clearly delineate its roles into multiple components with only a few regions serving as specific buffers was deemed impractical ( D’Esposito and Postle, 2015 ). Nonetheless, depicting the multicomponent working memory model in the brain offers a glimpse into the functional segregation of working memory.

TABLE 1. Working memory (WM) studies in the healthy brain.

FIGURE 1. A simplified depiction (adapted from the multicomponent working memory model by Baddeley, 2010 ) as implicated in the brain, in which the central executive assumes the role to exert control and oversee the manipulation of incoming information for intended execution. ACC, Anterior cingulate cortex.

Further investigation has recently revealed that other than the generally informed cortical structures involved in verbal working memory, basal ganglia, which lies in the subcortical layer, plays a role too ( Moore et al., 2013 ). Particularly, the caudate and thalamus were activated during task encoding, and the medial thalamus during the maintenance phase, while recorded activity in the fronto-parietal network, which includes the DLPFC and the parietal lobules, was observed only during retrieval ( Moore et al., 2013 ). These findings support the notion that the basal ganglia functions to enhance focusing on a target while at the same time suppressing irrelevant distractors during verbal working memory tasks, which is especially crucial at the encoding phase ( Moore et al., 2013 ). Besides, a study conducted on mice yielded a similar conclusion in which the mediodorsal thalamus aided the medial prefrontal cortex in the maintenance of working memory ( Bolkan et al., 2017 ). In another study by Murty et al. (2011) in which information updating, which is one of the important aspects of working memory, was investigated, the midbrain including the substantia nigra/ventral tegmental area and caudate was activated together with DLPFC and other parietal regions. Taken together, these studies indicated that brain activation of working memory are not only limited to the cortical layer ( Murty et al., 2011 ; Moore et al., 2013 ). In fact, studies on cerebellar lesions subsequently discovered that patients suffered from impairments in attention-related working memory or executive functions, suggesting that in spite of the motor functions widely attributed to the cerebellum, the cerebellum is also involved in higher-order cognitive functions including working memory ( Gottwald et al., 2004 ; Ziemus et al., 2007 ).

Shifting the attention to the neuronal network involved in working memory, effective connectivity analysis during engagement of a working memory task reinforced the idea that the DLPFC, PAR and ACC belong to the working memory circuitry, and bidirectional endogenous connections between all these regions were observed in which the left and right PAR were the modeled input regions ( Dima et al., 2014 ) (refer to Supplementary Figure 1 in Dima et al., 2014 ). Effective connectivity describes the attempt to model causal influence of neuronal connections in order to better understand the hidden neuronal states underlying detected neuronal responses ( Friston et al., 2013 ). Another similar study of working memory using an effective connectivity analysis that involved more brain regions, including the bilateral middle frontal gyrus (MFG), ACC, inferior frontal cortex (IFC), and posterior parietal cortex (PPC) established the modulatory effect of working memory load in this fronto-parietal network with memory delay as the driving input to the bilateral PPC ( Ma et al., 2012 ) (refer to Figure 1 in Ma et al., 2012 ).

Moving away from brain regions activated but toward the in-depth neurobiological side of working memory, it has long been understood that the limited capacity of working memory and its transient nature, which are considered two of the defining characteristics of working memory, indicate the role of persistent neuronal firing (see Review Article by D’Esposito and Postle, 2015 ; Zylberberg and Strowbridge, 2017 ; see also Silvanto, 2017 ), that is, continuous action potentials are generated in neurons along the neural network. However, this view was challenged when activity-silent synaptic mechanisms were found to also be involved ( Mongillo et al., 2008 ; Rose et al., 2016 ; see also Silvanto, 2017 ). Instead of holding relevant information through heightened and persistent neuronal firing, residual calcium at the presynaptic terminals was suggested to have mediated the working memory process ( Mongillo et al., 2008 ). This synaptic theory was further supported when TMS application produced a reactivation effect of past information that was not needed or attended at the conscious level, hence the TMS application facilitated working memory efficacy ( Rose et al., 2016 ). As it happens, this provided evidence from the neurobiological viewpoint to support Cowan’s theorized idea of “activated long-term memory” being a feature of working memory as non-cued past items in working memory that were assumed to be no longer accessible were actually stored in a latent state and could be brought back into consciousness. However, the researchers cautioned the use of the term “activated long-term memory” and opted for “prioritized long-term memory” because these unattended items maintained in working memory seemed to employ a different mechanism than items that were dropped from working memory ( Rose et al., 2016 ). Other than the synaptic theory, the spiking working memory model proposed by Fiebig and Lansner (2017) that borrowed the concept from fast Hebbian plasticity similarly disagreed with persistent neuronal activity and demonstrated that working memory processes were instead manifested in discrete oscillatory bursts.

Age and Working Memory

Nevertheless, having established a clear working memory circuitry in the brain, differences in brain activations, neural patterns or working memory performances are still apparent in different study groups, especially in those with diseased or aging brains. For a start, it is well understood that working memory declines with age ( Hedden and Gabrieli, 2004 ; Ziaei et al., 2017 ). Hence, older participants are expected to perform poorer on a working memory task when making comparison with relatively younger task takers. In fact, it was reported that decreases in cortical surface area in the frontal lobe of the right hemisphere was associated with poorer performers ( Nissim et al., 2017 ). In their study, healthy (those without mild cognitive impairments [MCI] or neurodegenerative diseases such as dementia or Alzheimer’s) elderly people with an average age of 70 took the n-back working memory task while magnetic resonance imaging (MRI) scans were obtained from them ( Nissim et al., 2017 ). The outcomes exhibited that a decrease in cortical surface areas in the superior frontal gyrus, pars opercularis of the inferior frontal gyrus, and medial orbital frontal gyrus that was lateralized to the right hemisphere, was significantly detected among low performers, implying an association between loss of brain structural integrity and working memory performance ( Nissim et al., 2017 ). There was no observed significant decline in cortical thickness of the studied brains, which is assumed to implicate neurodegenerative tissue loss ( Nissim et al., 2017 ).

Moreover, another extensive study that examined cognitive functions of participants across the lifespan using functional magnetic resonance imaging (fMRI) reported that the right lateralized fronto-parietal regions in addition to the ventromedial prefrontal cortex (VMPFC), posterior cingulate cortex, and left angular and middle frontal gyri (the default mode regions) in older adults showed reduced modulation of task difficulty, which was reflective of poorer task performance ( Rieck et al., 2017 ). In particular, older-age adults (55–69 years) exhibited diminished brain activations (positive modulation) as compared to middle-age adults (35–54 years) with increasing task difficulty, whereas lesser deactivation (negative modulation) was observed between the transition from younger adults (20–34 years) to middle-age adults ( Rieck et al., 2017 ). This provided insights on cognitive function differences during an individual’s lifespan at the neurobiological level, which hinted at the reduced ability or efficacy of the brain to modulate functional regions to increased difficulty as one grows old ( Rieck et al., 2017 ). As a matter of fact, such an opinion was in line with the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH) proposed by Reuter-Lorenz and Cappell (2008) . The CRUNCH likewise agreed upon reduced neural efficiency in older adults and contended that age-associated cognitive decline brought over-activation as a compensatory mechanism; yet, a shift would occur as task loads increase and under-activation would then be expected because older adults with relatively lesser cognitive resources would max out their ‘cognitive reserve’ sooner than younger adults ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ).

In addition to those findings, emotional distractors presented during a working memory task were shown to alter or affect task performance in older adults ( Oren et al., 2017 ; Ziaei et al., 2017 ). Based on the study by Oren et al. (2017) who utilized the n-back task paired with emotional distractors with neutral or negative valence in the background, negative distractors with low load (such as 1-back) resulted in shorter response time (RT) in the older participants ( M age = 71.8), although their responses were not significantly more accurate when neutral distractors were shown. Also, lesser activations in the bilateral MFG, VMPFC, and left PAR were reported in the old-age group during negative low load condition. This finding subsequently demonstrated the results of emotional effects on working memory performance in older adults ( Oren et al., 2017 ). Further functional connectivity analyses revealed that the amygdala, the region well-known to be involved in emotional processing, was deactivated and displayed similar strength in functional connectivity regardless of emotional or load conditions in the old-age group ( Oren et al., 2017 ). This finding went in the opposite direction of that observed in the younger group in which the amygdala was strongly activated with less functional connections to the bilateral MFG and left PAR ( Oren et al., 2017 ). This might explain the shorter reported RT, which was an indication of improved working memory performance, during the emotional working memory task in the older adults as their amygdala activation was suppressed as compared to the younger adults ( Oren et al., 2017 ).

Interestingly, a contrasting neural connection outcome was reported in the study by Ziaei et al. (2017) in which differential functional networks relating to emotional working memory task were employed by the two studied groups: (1) younger ( M age = 22.6) and (2) older ( M age = 68.2) adults. In the study, emotional distractors with positive, neutral, and negative valence were presented during a visual working memory task and older adults were reported to adopt two distinct networks involving the VMPFC to encode and process positive and negative distractors while younger adults engaged only one neural pathway ( Ziaei et al., 2017 ). The role of amygdala engagement in processing only negative items in the younger adults, but both negative and positive distractors in the older adults, could be reflective of the older adults’ better ability at regulating negative emotions which might subsequently provide a better platform for monitoring working memory performance and efficacy as compared to their younger counterparts ( Ziaei et al., 2017 ). This study’s findings contradict those by Oren et al. (2017) in which the amygdala was found to play a bigger role in emotional working memory tasks among older participants as opposed to being suppressed as reported by Oren et al. (2017) . Nonetheless, after overlooking the underlying neural mechanism relating to emotional distractors, it was still agreed that effective emotional processing sustained working memory performance among older/elderly people ( Oren et al., 2017 ; Ziaei et al., 2017 ).

Aside from the interaction effect between emotion and aging on working memory, the impact of caffeine was also investigated among elders susceptible to age-related cognitive decline; and those reporting subtle cognitive deterioration 18-months after baseline measurement showed less marked effects of caffeine in the right hemisphere, unlike those with either intact cognitive ability or MCI ( Haller et al., 2017 ). It was concluded that while caffeine’s effects were more pronounced in MCI participants, elders in the early stages of cognitive decline displayed diminished sensitivity to caffeine after being tested with the n-back task during fMRI acquisition ( Haller et al., 2017 ). It is, however, to be noted that the working memory performance of those displaying minimal cognitive deterioration was maintained even though their brain imaging uncovered weaker brain activation in a more restricted area ( Haller et al., 2017 ). Of great interest, such results might present a useful brain-based marker that can be used to identify possible age-related cognitive decline.

Similar findings that demonstrated more pronounced effects of caffeine on elderly participants were reported in an older study, whereas older participants in the age range of 50–65 years old exhibited better working memory performance that offset the cognitive decline observed in those with no caffeine consumption, in addition to displaying shorter reaction times and better motor speeds than observed in those without caffeine ( Rees et al., 1999 ). Animal studies using mice showed replication of these results in mutated mice models of Alzheimer’s disease or older albino mice, both possibly due to the reported results of reduced amyloid production or brain-derived neurotrophic factor and tyrosine-kinase receptor. These mice performed significantly better after caffeine treatment in tasks that supposedly tapped into working memory or cognitive functions ( Arendash et al., 2006 ). Such direct effects of caffeine on working memory in relation to age was further supported by neuroimaging studies ( Haller et al., 2013 ; Klaassen et al., 2013 ). fMRI uncovered increased brain activation in regions or networks of working memory, including the fronto-parietal network or the prefrontal cortex in old-aged ( Haller et al., 2013 ) or middle-aged adults ( Klaassen et al., 2013 ), even though the behavioral measures of working memory did not differ. Taken together, these outcomes offered insight at the neurobiological level in which caffeine acts as a psychoactive agent that introduces changes and alters the aging brain’s biological environment that explicit behavioral testing might fail to capture due to performance maintenance ( Haller et al., 2013 , 2017 ; Klaassen et al., 2013 ).

With respect to physiological effects on cognitive functions (such as effects of caffeine on brain physiology), estradiol, the primary female sex hormone that regulates menstrual cycles, was found to also modulate working memory by engaging different brain activity patterns during different phases of the menstrual cycle ( Joseph et al., 2012 ). The late follicular (LF) phase of the menstrual cycle, characterized by high estradiol levels, was shown to recruit more of the right hemisphere that was associated with improved working memory performance than did the early follicular (EF) phase, which has lower estradiol levels although overall, the direct association between estradiol levels and working memory was inconclusive ( Joseph et al., 2012 ). The finding that estradiol levels modified brain recruitment patterns at the neurobiological level, which could indirectly affect working memory performance, presents implications that working memory impairment reported in post-menopausal women (older aged women) could indicate a link with estradiol loss ( Joseph et al., 2012 ). In 2000, post-menopausal women undergoing hormone replacement therapy, specifically estrogen, were found to have better working memory performance in comparison with women who took estrogen and progestin or women who did not receive the therapy ( Duff and Hampson, 2000 ). Yet, interestingly, a study by Janowsky et al. (2000) showed that testosterone supplementation counteracted age-related working memory decline in older males, but a similar effect was not detected in older females who were supplemented with estrogen. A relatively recent paper might have provided the explanation to such contradicting outcomes ( Schöning et al., 2007 ). As demonstrated in the study using fMRI, the nature of the task (such as verbal or visual-spatial) might have played a role as a higher level of testosterone (in males) correlated with activations of the left inferior parietal cortex, which was deemed a key region in spatial processing that subsequently brought on better performance in a mental-rotation task. In contrast, significant correlation between estradiol and other cortical activations in females in the midluteal phase, who had higher estradiol levels, did not result in better performance of the task compared to women in the EF phase or men ( Schöning et al., 2007 ). Nonetheless, it remains premature to conclude that age-related cognitive decline was a result of hormonal (estradiol or testosterone) fluctuations although hormones might have modulated the effect of aging on working memory.

Other than the presented interaction effects of age and emotions, caffeine, and hormones, other studies looked at working memory training in the older population in order to investigate working memory malleability in the aging brain. Findings of improved performance for the same working memory task after training were consistent across studies ( Dahlin et al., 2008 ; Borella et al., 2017 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ). Such positive results demonstrated effective training gains regardless of age difference that could even be maintained until 18 months later ( Dahlin et al., 2008 ) even though the transfer effects of such training to other working memory tasks need to be further elucidated as strong evidence of transfer with medium to large effect size is lacking ( Dahlin et al., 2008 ; Guye and von Bastian, 2017 ; Heinzel et al., 2017 ; see also Karbach and Verhaeghen, 2014 ). The studies showcasing the effectiveness of working memory training presented a useful cognitive intervention that could partially stall or delay cognitive decline. Table 2 presents an overview of the age-related working memory studies.

TABLE 2. Working memory (WM) studies in relation to age.

The Diseased Brain and Working Memory

Age is not the only factor influencing working memory. In recent studies, working memory deficits in populations with mental or neurological disorders were also being investigated (see Table 3 ). Having identified that the working memory circuitry involves the fronto-parietal region, especially the prefrontal and parietal cortices, in a healthy functioning brain, targeting these areas in order to understand how working memory is affected in a diseased brain might provide an explanation for the underlying deficits observed at the behavioral level. For example, it was found that individuals with generalized or social anxiety disorder exhibited reduced DLPFC activation that translated to poorer n-back task performance in terms of accuracy and RT when compared with the controls ( Balderston et al., 2017 ). Also, VMPFC and ACC, representing the default mode network (DMN), were less inhibited in these individuals, indicating that cognitive resources might have been divided and resulted in working memory deficits due to the failure to disengage attention from persistent anxiety-related thoughts ( Balderston et al., 2017 ). Similar speculation can be made about individuals with schizophrenia. Observed working memory deficits might be traced back to impairments in the neural networks that govern attentional-control and information manipulation and maintenance ( Grot et al., 2017 ). The participants performed a working memory binding task, whereby they had to make sure that the word-ellipse pairs presented during the retrieval phase were identical to those in the encoding phase in terms of location and verbal information; results concluded that participants with schizophrenia had an overall poorer performance compared to healthy controls when they were asked to actively bind verbal and spatial information ( Grot et al., 2017 ). This was reflected in the diminished activation in the schizophrenia group’s ventrolateral prefrontal cortex and the PPC that were said to play a role in manipulation and reorganization of information during encoding and maintenance of information after encoding ( Grot et al., 2017 ).

TABLE 3. Working memory (WM) studies in the diseased brain.

In addition, patients with major depressive disorder (MDD) displayed weaker performance in the working memory updating domain in which information manipulation was needed when completing a visual working memory task ( Le et al., 2017 ). The working memory task employed in the study was a delayed recognition task that required participants to remember and recognize the faces or scenes as informed after stimuli presentation while undergoing fMRI scan ( Le et al., 2017 ). Subsequent functional connectivity analyses revealed that the fusiform face area (FFA), parahippocampal place area (PPA), and left MFG showed aberrant activity in the MDD group as compared to the control group ( Le et al., 2017 ). These brain regions are known to be the visual association area and the control center of working memory and have been implicated in visual working memory updating in healthy adults ( Le et al., 2017 ). Therefore, altered visual cortical functions and load-related activation in the prefrontal cortex in the MDD group implied that the cognitive control for visual information processing and updating might be impaired at the input or control level, which could have ultimately played a part in the depressive symptoms ( Le et al., 2017 ).

Similarly, during a verbal delayed match to sample task that asked participants to sub-articulatorly rehearse presented target letters for subsequent letter-matching, individuals with bipolar affective disorder displayed aberrant neural interactions between the right amygdala, which is part of the limbic system implicated in emotional processing as previously described, and ipsilateral cortical regions often concerned with verbal working memory, pointing out that the cortico-amygdalar connectivity was disrupted, which led to verbal working memory deficits ( Stegmayer et al., 2015 ). As an attempt to gather insights into previously reported hyperactivation in the amygdala in bipolar affective disorder during an articulatory working memory task, functional connectivity analyses revealed that negative functional interactions seen in healthy controls were not replicated in patients with bipolar affective disorder ( Stegmayer et al., 2015 ). Consistent with the previously described study about emotional processing effects on working memory in older adults, this reported outcome was suggestive of the brain’s failed attempts to suppress pathological amygdalar activation during a verbal working memory task ( Stegmayer et al., 2015 ).

Another affected group with working memory deficits that has been the subject of research interest was children with developmental disorders such as attention deficit/hyperactivity disorder (ADHD), developmental dyscalculia, and reading difficulties ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ; Wang and Gathercole, 2013 ; Maehler and Schuchardt, 2016 ). For instance, looking into the different working memory subsystems based on Baddeley’s multicomponent working memory model in children with dyslexia and/or ADHD and children with dyscalculia and/or ADHD through a series of tests, it was reported that distinctive working memory deficits by groups could be detected such that phonological loop (e.g., digit span) impairment was observed in the dyslexia group, visuospatial sketchpad (e.g., Corsi block tasks) deficits in the dyscalculia group, while central executive (e.g., complex counting span) deficits in children with ADHD ( Maehler and Schuchardt, 2016 ). Meanwhile, examination of working memory impairment in a delayed match-to-sample visual task that put emphasis on the maintenance phase of working memory by examining the brainwaves of adults with ADHD using electroencephalography (EEG) also revealed a marginally significantly lower alpha band power in the posterior regions as compared to healthy individuals, and such an observation was not significantly improved after working memory training (Cogmed working memory training, CWMT Program) ( Liu et al., 2016 ). The alpha power was considered important in the maintenance of working memory items; and lower working memory accuracy paired with lower alpha band power was indeed observed in the ADHD group ( Liu et al., 2016 ).

Not dismissing the above compiled results, children encountering disabilities in mathematical operations likewise indicated deficits in the working memory domain that were traceable to unusual brain activities at the neurobiological level ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). It was speculated that visuospatial working memory plays a vital role when arithmetic problem-solving is involved in order to ensure intact mental representations of the numerical information ( Rotzer et al., 2009 ). Indeed, Ashkenazi et al. (2013) revealed that Block Recall, a variant of the Corsi Block Tapping test and a subtest of the Working Memory Test Battery for Children (WMTB-C) that explored visuospatial sketchpad ability, was significantly predictive of math abilities. In relation to this, studies investigating brain activation patterns and performance of visuospatial working memory task in children with mathematical disabilities identified the intraparietal sulcus (IPS), in conjunction with other regions in the prefrontal and parietal cortices, to have less activation when visuospatial working memory was deemed involved (during an adapted form of Corsi Block Tapping test made suitable for fMRI [ Rotzer et al., 2009 ]); in contrast the control group demonstrated correlations of the IPS in addition to the fronto-parietal cortical activation with the task ( Rotzer et al., 2009 ; Ashkenazi et al., 2013 ). These brain activity variations that translated to differences in overt performances between healthily developing individuals and those with atypical development highlighted the need for intervention and attention for the disadvantaged groups.

Traumatic Brain Injury and Working Memory

Physical injuries impacting the frontal or parietal lobes would reasonably be damaging to one’s working memory. This is supported in studies employing neuropsychological testing to assess cognitive impairments in patients with traumatic brain injury; and poorer cognitive performances especially involving the working memory domains were reported (see Review Articles by Dikmen et al., 2009 ; Dunning et al., 2016 ; Phillips et al., 2017 ). Research on cognitive deficits in traumatic brain injury has been extensive due to the debilitating conditions brought upon an individual daily life after the injury. Traumatic brain injuries (TBI) refer to accidental damage to the brain after being hit by an object or following rapid acceleration or deceleration ( Farrer, 2017 ). These accidents include falls, assaults, or automobile accidents and patients with TBI can be then categorized into three groups; (1) mild TBI with GCS – Glasgow Coma Scale – score of 13–15; (2) moderate TBI with GCS score of 9–12; and (3) severe TBI with GCS score of 3–8 ( Farrer, 2017 ). In a recently published meta-analysis that specifically looked at working memory impairments in patients with moderate to severe TBI, patients displayed reduced cognitive functions in verbal short-term memory in addition to verbal and visuospatial working memory in comparison to control groups ( Dunning et al., 2016 ). It was also understood from the analysis that the time lapse since injury and age of injury were deciding factors that influenced these cognitive deficits in which longer time post-injury or older age during injury were associated with greater cognitive decline ( Dunning et al., 2016 ).

Nonetheless, it is to be noted that such findings relating to age of injury could not be generalized to the child population since results from the pediatric TBI cases showed that damage could negatively impact developmental skills that could indicate a greater lag in cognitive competency as the child’s frontal lobe had yet to mature ( Anderson and Catroppa, 2007 ; Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). These studies all reported working memory impairment of different domains such as attentional control, executive functions, or verbal and visuospatial working memory in the TBI group, especially for children with severe TBI ( Mandalis et al., 2007 ; Nadebaum et al., 2007 ; Gorman et al., 2012 ). Investigation of whether working memory deficits are domain-specific or -general or involve one or more mechanisms, has yielded inconsistent results. For example, Perlstein et al. (2004) found that working memory was impaired in the TBI group only when complex manipulation such as sequential coding of information is required and not accounted for by processing speed or maintenance of information, but two teams of researchers ( Perbal et al., 2003 ; Gorman et al., 2012 ) suggested otherwise. From their study on timing judgments, Perbal et al. (2003) concluded that deficits were not related to time estimation but more on generalized attentional control, working memory and processing speed problems; while Gorman et al. (2012) also attributed the lack of attentional focus to impairments observed during the working memory task. In fact, in a later study by Gorman et al. (2016) , it was shown that processing speed mediated TBI effects on working memory even though the mediation was partial. On the other hand, Vallat-Azouvi et al. (2007) reported impairments in the working memory updating domain that came with high executive demands for TBI patients. Also, Mandalis et al. (2007) similarly highlighted potential problems with attention and taxing cognitive demands in the TBI group.

From the neuroscientific perspective, hyper-activation or -connectivity in the working memory circuitry was reported in TBI patients in comparison with healthy controls when both groups engaged in working memory tasks, suggesting that the brain attempted to compensate for or re-establish lost connections upon the injury ( Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ). For a start, it was observed that participants with mild TBI displayed increased activation in the right prefrontal cortex during a working memory task when comparing to controls ( Wylie et al., 2015 ). Interestingly, this activation pattern only occurred in patients who did not experience a complete recovery 1 week after the injury ( Wylie et al., 2015 ). Besides, low activation in the DMN was observed in mild TBI patients without cognitive recovery, and such results seemed to be useful in predicting recovery in patients in which the patients did not recover when hypoactivation (low activation) was reported, and vice versa ( Wylie et al., 2015 ). This might be suggestive of the potential of cognitive recovery simply by looking at the intensity of brain activation of the DMN, for an increase in activation of the DMN seemed to be superseded before cognitive recovery was present ( Wylie et al., 2015 ).

In fact, several studies lent support to the speculation mentioned above as hyperactivation or hypoactivation in comparison with healthy participants was similarly identified. When sex differences were being examined in working memory functional activity in mild TBI patients, hyperactivation was reported in male patients when comparing to the male control group, suggesting that the hyperactivation pattern might be the brain’s attempt at recovering impaired functions; even though hypoactivation was shown in female patients as compared to the female control group ( Hsu et al., 2015 ). The researchers from the study further explained that such hyperactivation after the trauma acted as a neural compensatory mechanism so that task performance could be maintained while hypoactivation with a poorer performance could have been the result of a more severe injury ( Hsu et al., 2015 ). Therefore, the decrease in activation in female patients, in addition to the observed worse performance, was speculated to be due to a more serious injury sustained by the female patients group ( Hsu et al., 2015 ).

In addition, investigation of the effective connectivity of moderate and severe TBI participants during a working memory task revealed that the VMPFC influenced the ACC in these TBI participants when the opposite was observed in healthy subjects ( Dobryakova et al., 2015 ). Moreover, increased inter-hemispheric transfer due to an increased number of connections between the left and right hemispheres (hyper-connectivity) without clear directionality of information flow (redundant connectivity) was also reported in the TBI participants ( Dobryakova et al., 2015 ). This study was suggestive of location-specific changes in the neural network connectivity following TBI depending on the cognitive functions at work, other than providing another support to the neural compensatory hypothesis due to the observed hyper-connectivity ( Dobryakova et al., 2015 ).

Nevertheless, inconsistent findings should not be neglected. In a study that also focused on brain connectivity analysis among patients with mild TBI by Hillary et al. (2011) , elevated task-related connectivity in the right hemisphere, in particular the prefrontal cortex, was consistently demonstrated during a working memory task while the control group showed greater left hemispheric activation. This further supported the right lateralization of the brain to reallocate cognitive resources of TBI patients post-injury. Meanwhile, the study did not manage to obtain the expected outcome in terms of greater clustering of whole-brain connections in TBI participants as hypothesized ( Hillary et al., 2011 ). That said, no significant loss or gain of connections due to the injury could be concluded from the study, as opposed to the hyper- or hypoactivation or hyper-connectivity frequently highlighted in other similar researches ( Hillary et al., 2011 ). Furthermore, a study by Chen et al. (2012) also failed to establish the same results of increased brain activation. Instead, with every increase of the working memory load, increase in brain activation, as expected to occur and as demonstrated in the control group, was unable to be detected in the TBI group ( Chen et al., 2012 ).

Taken all the insightful studies together, another aspect not to be neglected is the neuroimaging techniques employed in contributing to the literature on TBI. Modalities other than fMRI, which focuses on localization of brain activities, show other sides of the story of working memory impairments in TBI to offer a more holistic understanding. Studies adopting electroencephalography (EEG) or diffusor tensor imaging (DTI) reported atypical brainwaves coherence or white matter integrity in patients with TBI ( Treble et al., 2013 ; Ellis et al., 2016 ; Bailey et al., 2017 ; Owens et al., 2017 ). Investigating the supero-lateral medial forebrain bundle (MFB) that innervates and consequently terminates at the prefrontal cortex, microstructural white matter damage at the said area was indicated in participants with moderate to severe TBI by comparing its integrity with the control group ( Owens et al., 2017 ). Such observation was backed up by evidence showing that the patients performed more poorly on attention-loaded cognitive tasks of factors relating to slow processing speed than the healthy participants, although a direct association between MFB and impaired attentional system was not found ( Owens et al., 2017 ).

Correspondingly, DTI study of the corpus callosum (CC), which described to hold a vital role in connecting and coordinating both hemispheres to ensure competent cognitive functions, also found compromised microstructure of the CC with low fractional anisotropy and high mean diffusivity, both of which are indications of reduced white matter integrity ( Treble et al., 2013 ). This reported observation was also found to be predictive of poorer verbal or visuospatial working memory performance in callosal subregions connecting the parietal and temporal cortices ( Treble et al., 2013 ). Adding on to these results, using EEG to examine the functional consequences of CC damage revealed that interhemispheric transfer time (IHTT) of the CC was slower in the TBI group than the control group, suggesting an inefficient communication between the two hemispheres ( Ellis et al., 2016 ). In addition, the TBI group with slow IHTT as well exhibited poorer neurocognitive functioning including working memory than the healthy controls ( Ellis et al., 2016 ).

Furthermore, comparing the working memory between TBI, MDD, TBI-MDD, and healthy participants discovered that groups with MDD and TBI-MDD performed poorer on the Sternberg working memory task but functional connectivity on the other hand, showed that increased inter-hemispheric working memory gamma connectivity was observed in the TBI and TBI-MDD groups ( Bailey et al., 2017 ). Speculation provided for the findings of such neuronal state that was not reflected in the explicit working memory performance was that the deficits might not be detected or tested by the utilized Sternberg task ( Bailey et al., 2017 ). Another explanation attempting to answer the increase in gamma connectivity in these groups was the involvement of the neural compensatory mechanism after TBI to improve performance ( Bailey et al., 2017 ). Nevertheless, such outcome implies that behavioral performances or neuropsychological outcomes might not always be reflective of the functional changes happening in the brain.

Yet, bearing in mind that TBI consequences can be vast and crippling, cognitive improvement or recovery, though complicated due to the injury severity-dependent nature, is not impossible (see Review Article by Anderson and Catroppa, 2007 ; Nadebaum et al., 2007 ; Dikmen et al., 2009 ; Chen et al., 2012 ). As reported by Wylie et al. (2015) , cognitive improvement together with functional changes in the brain could be detected in individuals with mild TBI. Increased activation in the brain during 6-week follow-up was also observed in the mild TBI participants, implicating the regaining of connections in the brain ( Chen et al., 2012 ). Administration of certain cognitively enhancing drugs such as methylphenidate was reported to be helpful in improving working memory performance too ( Manktelow et al., 2017 ). Methylphenidate as a dopamine reuptake inhibitor was found to have modulated the neural activity in the left cerebellum which subsequently correlated with improved working memory performance ( Manktelow et al., 2017 ). A simplified summary of recent studies on working memory and TBI is tabulated in Table 4 .

TABLE 4. Working memory (WM) studies in the TBI group.

General Discussion and Future Direction

In practice, all of the aforementioned studies contribute to the working memory puzzle by addressing the topic from different perspectives and employing various methodologies to study it. Several theoretical models of working memory that conceptualized different working memory mechanisms or domains (such as focus of attention, inhibitory controls, maintenance and manipulation of information, updating and integration of information, capacity limits, evaluative and executive controls, and episodic buffer) have been proposed. Coupled with the working memory tasks of various means that cover a broad range (such as Sternberg task, n-back task, Corsi block-tapping test, Wechsler’s Memory Scale [WMS], and working memory subtests in the Wechsler Adult Intelligence Scale [WAIS] – Digit Span, Letter Number Sequencing), it has been difficult, if not highly improbable, for working memory studies to reach an agreement upon a consistent study protocol that is acceptable for generalization of results due to the constraints bound by the nature of the study. Various data acquisition and neuroimaging techniques that come with inconsistent validity such as paper-and-pen neuropsychological measures, fMRI, EEG, DTI, and functional near-infrared spectroscopy (fNIRS), or even animal studies can also be added to the list. This poses further challenges to quantitatively measure working memory as only a single entity. For example, when studying the neural patterns of working memory based on Cowan’s processes-embedded model using fMRI, one has to ensure that the working memory task selected is fMRI-compatible, and demands executive control of attention directed at activated long-term memory (domain-specific). That said, on the one hand, there are tasks that rely heavily on the information maintenance such as the Sternberg task; on the other hand, there are also tasks that look into the information manipulation updating such as the n-back or arithmetic task. Meanwhile, the digit span task in WAIS investigates working memory capacity, although it can be argued that it also encompasses the domain on information maintenance and updating-. Another consideration involves the different natures (verbal/phonological and visuospatial) of the working memory tasks as verbal or visuospatial information is believed to engage differing sensory mechanisms that might influence comparison of working memory performance between tasks of different nature ( Baddeley and Hitch, 1974 ; Cowan, 1999 ). For instance, though both are n-back tasks that includes the same working memory domains, the auditory n-back differs than the visual n-back as the information is presented in different forms. This feature is especially crucial with regards to the study populations as it differentiates between verbal and visuospatial working memory competence within individuals, which are assumed to be domain-specific as demonstrated by vast studies (such as Nadler and Archibald, 2014 ; Pham and Hasson, 2014 ; Nakagawa et al., 2016 ). These test variations undeniably present further difficulties in selecting an appropriate task. Nevertheless, the adoption of different modalities yielded diverging outcomes and knowledge such as behavioral performances, functional segregation and integration in the brain, white matter integrity, brainwave coherence, and oxy- and deoxyhaemoglobin concentrations that are undeniably useful in application to different fields of study.

In theory, the neural efficiency hypothesis explains that increased efficiency of the neural processes recruit fewer cerebral resources in addition to displaying lower activation in the involved neural network ( Vartanian et al., 2013 ; Rodriguez Merzagora et al., 2014 ). This is in contrast with the neural compensatory hypothesis in which it attempted to understand diminished activation that is generally reported in participants with TBI ( Hillary et al., 2011 ; Dobryakova et al., 2015 ; Hsu et al., 2015 ; Wylie et al., 2015 ; Bailey et al., 2017 ). In the diseased brain, low activation has often been associated with impaired cognitive function ( Chen et al., 2012 ; Dobryakova et al., 2015 ; Wylie et al., 2015 ). Opportunely, the CRUNCH model proposed within the field of aging might be translated and integrated the two hypotheses here as it suitably resolved the disparity of cerebral hypo- and hyper-activation observed in weaker, less efficient brains as compared to healthy, adept brains ( Reuter-Lorenz and Park, 2010 ; Schneider-Garces et al., 2010 ). Moreover, other factors such as the relationship between fluid intelligence and working memory might complicate the current understanding of working memory as a single, isolated construct since working memory is often implied in measurements of the intelligence quotient ( Cowan, 2008 ; Vartanian et al., 2013 ). Indeed, the process overlap theory of intelligence proposed by Kovacs and Conway (2016) in which the constructs of intelligence were heavily scrutinized (such as general intelligence factors, g and its smaller counterparts, fluid intelligence or reasoning, crystallized intelligence, perceptual speed, and visual-spatial ability), and fittingly connected working memory capacity with fluid reasoning. Cognitive tests such as Raven’s Progressive Matrices or other similar intelligence tests that demand complex cognition and were reported in the paper had been found to correlate strongly with tests of working memory ( Kovacs and Conway, 2016 ). Furthermore, in accordance with such views, in the same paper, neuroimaging studies found intelligence tests also activated the same fronto-parietal network observed in working memory ( Kovacs and Conway, 2016 ).

On the other hand, even though the roles of the prefrontal cortex in working memory have been widely established, region specificity and localization in the prefrontal cortex in relation to the different working memory domains such as manipulation or delayed retention of information remain at the premature stage (see Review Article by D’Esposito and Postle, 2015 ). It has been postulated that the neural mechanisms involved in working memory are of high-dimensionality and could not always be directly captured and investigated using neurophysiological techniques such as fMRI, EEG, or patch clamp recordings even when comparing with lesion data ( D’Esposito and Postle, 2015 ). According to D’Esposito and Postle (2015) , human fMRI studies have demonstrated that a rostral-caudal functional gradient related to level of abstraction required of working memory along the frontal cortex (in which different regions in the prefrontal cortex [from rostral to caudal] might be associated with different abstraction levels) might exist. Other functional gradients relating to different aspects of working memory were similarly unraveled ( D’Esposito and Postle, 2015 ). These proposed mechanisms with different empirical evidence point to the fact that conclusive understanding regarding working memory could not yet be achieved before the inconsistent views are reconciled.

Not surprisingly, with so many aspects of working memory yet to be understood and its growing complexity, the cognitive neuroscience basis of working memory requires constant research before an exhaustive account can be gathered. From the psychological conceptualization of working memory as attempted in the multicomponent working memory model ( Baddeley and Hitch, 1974 ), to the neural representations of working memory in the brain, especially in the frontal regions ( D’Esposito and Postle, 2015 ), one important implication derives from the present review of the literatures is that working memory as a psychological construct or a neuroscientific mechanism cannot be investigated as an isolated event. The need for psychology and neuroscience to interact with each other in an active feedback cycle exists in which this cognitive system called working memory can be dissected at the biological level and refined both empirically, and theoretically.

In summary, the present article offers an account of working memory from the psychological and neuroscientific perspectives, in which theoretical models of working memory are presented, and neural patterns and brain regions engaging in working memory are discussed among healthy and diseased brains. It is believed that working memory lays the foundation for many other cognitive controls in humans, and decoding the working memory mechanisms would be the first step in facilitating understanding toward other aspects of human cognition such as perceptual or emotional processing. Subsequently, the interactions between working memory and other cognitive systems could reasonably be examined.

Author Contributions

WC wrote the manuscript with critical feedback and consultation from AAH. WC and AAH contributed to the final version of the manuscript. JA supervised the process and proofread the manuscript.

This work was supported by the Transdisciplinary Research Grant Scheme (TRGS) 203/CNEURO/6768003 and the USAINS Research Grant 2016.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer EB and handling Editor declared their shared affiliation.

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Keywords : working memory, neuroscience, psychology, cognition, brain, central executive, prefrontal cortex, review

Citation: Chai WJ, Abd Hamid AI and Abdullah JM (2018) Working Memory From the Psychological and Neurosciences Perspectives: A Review. Front. Psychol. 9:401. doi: 10.3389/fpsyg.2018.00401

Received: 24 November 2017; Accepted: 09 March 2018; Published: 27 March 2018.

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*Correspondence: Aini Ismafairus Abd Hamid, [email protected]

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Article contents

Working memory.

• Tom Hartley Tom Hartley University of York
•  and  Graham J. Hitch Graham J. Hitch University of York
• https://doi.org/10.1093/acrefore/9780190236557.013.768
• Published online: 19 October 2022

Working memory is an aspect of human memory that permits the maintenance and manipulation of temporary information in the service of goal-directed behavior. Its apparently inelastic capacity limits impose constraints on a huge range of activities from language learning to planning, problem-solving, and decision-making. A substantial body of empirical research has revealed reliable benchmark effects that extend to a wide range of different tasks and modalities. These effects support the view that working memory comprises distinct components responsible for attention-like control and for short-term storage. However, the nature of these components, their potential subdivision, and their interrelationships with long-term memory and other aspects of cognition, such as perception and action, remain controversial and are still under investigation. Although working memory has so far resisted theoretical consensus and even a clear-cut definition, research findings demonstrate its critical role in both enabling and limiting human cognition and behavior.

• short-term memory
• serial order
• intelligence

Introduction

The term working memory refers to human memory functions that serve to maintain and manipulate temporary information. There is believed to be a limited capacity to support these functions which combine to play a key role in cognitive processes such as thinking and reasoning, problem-solving, and planning. A common illustration is mental calculation which typically involves maintaining some initial numerical information whilst carrying out a series of arithmetical operations on parts and maintaining any interim results. However, the range of activities that depend on working memory is very much wider than that example might suggest. Thus, perception and action can also depend critically on maintaining and manipulating temporary information, as for instance when identifying a familiar constellation in the night sky, or when preparing a meal.

Information about a stimulus remains available for a few seconds after it is perceived (short-term memory) but without active maintenance it rapidly becomes inaccessible ( Peterson & Peterson, 1959 ; Posner & Konick, 1966 ). Conceptually, working memory extends short-term memory by adding the active, attentional processes required to hold information in mind and to manipulate that information in the service of goal-directed behavior.

The short-term storage required for working memory can be distinguished from long-term memory, which is concerned with more permanent information acquired through learning or experience and includes declarative memory (retention of factual information and events) and procedural memory (underpinning skilled behavior; see Cohen & Squire, 1980 ). Notably, and in contrast to short-term memory, these forms of long-term memory are passive in the sense that, once acquired, memory for facts, events, and well-learned skills can persist over very long periods without moment-to-moment awareness. For example, a vocabulary of many thousands of words, including the relationship between their spoken forms and meanings, can be retained effortlessly over a lifetime. Similarly, once acquired through practice, complex and initially challenging behaviors such as swimming or riding a bicycle can become almost automatic and can be carried out with relatively little conscious control.

In early models of the human memory system (e.g., Atkinson & Shiffrin, 1968 ; see Logie, 1996 ) short-term memory was seen as a staging post or gateway to long-term memory, and it was recognized that it could also support more complex operations, such as reasoning, thus acting as a working memory. Subsequent research has attempted to refine the concept of working memory, characterizing its functional role, limits, and substructure, and distinguishing the processes involved in maintenance and manipulation of information from the storage systems with which they interact.

It has proven difficult, however, to disentangle working memory function from other aspects of cognition with which it overlaps. First, as described in more detail in the section “ Substructure and Relationship to Other Aspects of Cognition ,” many current accounts view the mechanisms of working memory as contributing to other perhaps more fundamental functions such as attention, long-term memory, perception, action, and representation. It is also notable that many informal descriptions of working memory emphasize consciousness and awareness as key features. Intuitively, many working memory functions are accessible to consciousness, and concepts such as mental manipulation, rehearsal, and losing track of information through inattention are subjectively encountered as characteristics of the conscious mind. Of course, by definition, people cannot be subjectively aware of any unconscious contributions to working memory (although they can potentially be inferred from behavior). Some theorists have argued that working memory is central to conscious thought (e.g., Baars, 2005 ; Carruthers, 2017 ), while other empirical researchers have sought to demonstrate nonconscious processes operating in what would typically be considered working memory tasks (e.g., Hassin et al., 2009 ; Soto et al., 2011 ). It is not clear whether, how, or to what extent consciousness is essential for working memory functions, or whether indeed the definition of working memory ought to include, or avoid, aspects of conscious experience. This article steers away from the topic, but the current status of the debate is captured in reviews such as Persuh et al. (2018) . Overall, it is difficult to precisely delineate the boundaries of working memory, whether with other cognitive functions or with consciousness and awareness; in philosophical terms it may not constitute a “natural kind” ( Gomez-Lavin, 2021 ).

These challenges make it difficult to establish a clear-cut and uncontroversial definition of working memory itself, its function, and substructure. Yet it is clear that working memory describes a cluster of related abilities that play a critical role in everyday thinking, placing important constraints on what we can and cannot do. Research on the topic has proved fruitful and although there remain many theoretical controversies about how working memory should be defined and analyzed, these mainly relate to the way in which its operations and substrates can be usefully subdivided, and their interrelationships with other cognitive systems such as those responsible for long-term memory and attention (see Logie et al., 2021 for in-depth discussion).

The following sections begin by identifying relatively uncontroversial characteristics of working memory and its temporal and capacity limits before outlining the main theoretical perspectives on the structure of working memory and its relationship to other forms of cognition. This is followed by a summary of the main experimental tasks and key empirical observations which underpin current understanding. Finally, a brief discussion of the importance of working memory beyond the laboratory is provided.

Temporal Limits

It is broadly agreed that its temporary or labile character is a defining characteristic of working memory. In contrast with established declarative and procedural memories that can be retained indefinitely, recently presented novel information is typically lost after a few seconds unless actively maintained. This active maintenance of short-term memory in order to complete a task is one of the core functions of working memory. As discussed further (see “ Limiting Mechanisms ”), it is less clear how such information is lost over time, or whether forgetting is strictly linked to the passage of time (decay) or merely correlated with it (for example, through an accumulation of interfering information). Nonetheless the vulnerability of short-term memory to degradation over time constrains the uses to which it can be put. Active maintenance processes include rehearsal—covertly subvocalizing verbal material, and attentional refreshing—selectively attending to an item that has not yet become inactive (see e.g., Camos et al., 2009 ). These active processes are themselves limited by the modality and quantity of the stored material, so that for instance subvocal rehearsal is disrupted by speaking aloud at the same time (“articulatory suppression”; Murray, 1967 ), and attentional refreshing can only be directed at a limited number of items in a given period of time ( Camos et al., 2018 ). Even though such active maintenance processes extend the temporal limits of short-term memory, when they do so at the cost of limited attentional resources, this reduces the availability of those resources for other goals.

Capacity Limits

It is also agreed that the limited capacity of working memory is a defining characteristic; in subjective terms, only a limited number of items can be “held in mind” at once. For example, in the classic digit span test of short-term memory capacity, participants are asked to briefly store, and then recall in order, arbitrary sequences of digits of gradually increasing length. In this type of task, accurate performance is typically only possible for very short sequences of up to three or four items beyond which errors of ordering become ever more frequent. Memory span is defined as the sequence length at which recall is correct half the time and is found to be between six and seven for digits, and even less for items such as unrelated words ( Crannell & Parrish, 1957 ). Similar capacity constraints are evident in nonverbal tasks requiring the recall of spatial sequences or the locations or visual properties of objects in spatial arrays. For instance, in the Corsi Block task, participants follow an assessor in tapping out a sequence of blocks in a tabletop array or a sequence of highlighted squares on a computer display. In the standard task, nine blocks are used in a fixed configuration and healthy participants can only recall sequences of around six taps even when tested immediately after presentation ( Corsi, 1972 ; Milner, 1971 ). Such tasks are helpful in identifying the fundamental capacity constraints on short-term memory but working memory capacity is also constrained by the active processes that maintain and manipulate information. This is typically assessed using complex span tasks which measure how many items can be held in mind while carrying out an attention-demanding concurrent task, leading to far lower estimates than simple spans ( Daneman & Carpenter, 1980 ). Similarly, participants show greatly reduced performance on a backward digit span task where mental manipulation is required to reverse the original sequence at recall. (Interestingly the Corsi span is the same in both directions; Kessels et al., 2008 ). Notably, forward and backward digit span and Corsi Block tasks are all used in the clinical assessment of neuropsychological patients as well as in research studies, highlighting the importance of working memory capacity in characterizing healthy and impaired cognitive function.

Just as the temporal limits of short-term memory can be extended by active maintenance processes, its capacity limits can be mitigated through strategic processing. Although it is clear that the number of items that can be stored in working memory is limited, there is some flexibility about what constitutes an item. For example, the sequence “1-0-0” might constitute three digits or might be represented as a single item, “hundred.” The possibility of more efficient forms of coding depends on interactions with long-term memory and can be exploited strategically to extend working memory capacity through “chunking” ( Miller, 1956 ). Thus, for an IT professional, the sequence “CPUBIOSPC” is more easily maintained as the familiar acronyms “CPU,” “BIOS,” and “PC” than as an arbitrary sequence of 10 letters.

While the previous example exploits long-term knowledge, even arbitrary grouping can extend the capacity of working memory, for example, in the immediate serial recall of verbal sequences, performance is improved when items are presented in groups. A spoken sequence of digits like “352-168” (i.e., with a pause between the two groups of digits) is recalled more easily than the ungrouped sequence “352168” ( Ryan, 1969 ). Again, this effect can be deployed strategically, and there is evidence that participants spontaneously group verbal material in memory.

More generally, prior learning and experience can not only expand effective storage capacity but can also contribute to efficient active processing operations. For example, children may initially use a counting-on strategy to perform simple sums such as 2 + 3 = 5, but later typically learn arithmetic number facts that automate such operations, in turn permitting more demanding mental arithmetic to be carried out within working memory ( Raghubar et al., 2010 ). In the extreme, expert calculators may collect extraordinarily large “mental libraries” of number facts ( Pesenti et al., 1999 ). Another powerful strategy for extending working memory capacity is seen in expert abacus operators who in mental calculation are able to use visual imagery to internalize algorithms learned from using the physical device ( Stigler, 1984 ).

Limiting Mechanisms

Despite the clear consensus that limited capacity and duration are defining characteristics of working memory, distinguishing it from other forms of memory and learning, there is less agreement about the mechanisms through which information is limited and forgotten.

In one account, the ultimate capacity limits of the system are determined by its access to a limited number of discrete slots, each of which can be used to hold a chunk of information ( Cowan, 2001 ; Luck & Vogel, 1997 ). However, an alternative and increasingly influential view is that working memory has access to a continuous resource which can be flexibly deployed to support a greater number of chunks or items on the one hand, or greater fidelity and precision on the other ( Bays & Husain, 2008 ; see Ma et al., 2014 for discussion).

The loss of information from working memory over time can similarly be attributed to different mechanisms, although here they do not amount to mutually exclusive models of the same phenomenon. One potential mechanism is decay, assumed to be a fundamental property of the substrate of short-term memory, through which information is lost due to the passage of time alone. In this view the attentional/executive component of working memory is typically deployed to extend its capacity by strategically (but effortfully) refreshing or rehearsing the content of short-term memory before it decays irretrievably. A further potential mechanism is interference. In this account, memory traces are prone to be confused with, or gradually corrupt one another. Several current models incorporate a combination of decay and interference ( Baddeley et al., 2021 ; Barrouillet & Camos, 2021 ; Cowan et al., 2021 ; Vandierendonck, 2021 ), while Oberauer (2021) stands out in rejecting time-based forgetting and maintenance processes, proposing in their place loss due to interference, and requiring a process dedicated to the active removal of outdated information from working memory.

Substructure and Relationship to Other Aspects of Cognition

Because it is linked to such a wide range of cognitive capacities, it can be difficult to clearly distinguish mechanisms of working memory from those of its specialized subcomponents or of general-purpose cognitive mechanisms which contribute to nonmemory functions. There is a broad consensus that working memory involves the interaction of an active process (corresponding to “attention” or “executive control”) with a substrate that can represent the content of memory and thus act as a short-term store. Authors disagree, or are sometimes agnostic, as to the extent to which these components can be usefully subdivided and the degree to which they are uniquely involved in working memory or more generally in cognition. Authors also differ in the emphasis they put on different modalities and tasks. These different emphases may sometimes mask a deeper consensus in which models are complementary rather than incompatible ( Miyake & Shah, 1999 ).

Although the term working memory had already been applied to the use of short-term memory in goal-directed behavior ( Atkinson & Shiffrin, 1968 ), it was the influential work of Baddeley and Hitch ( Baddeley, 1986 ; Baddeley & Hitch, 1974 ), that introduced the separation of attentional control processes (governed by a “central executive”) and short-term storage systems (thought of as “buffers,” i.e., distinct and specialized systems). They further identified a distinction between verbal and visual buffers which were subject to different forms of disruption and appeared to use distinct codes. In particular, verbal information could be stored in a speech-based system (termed the “phonological loop”), in which similar sounding items were more likely to be confused and which was disrupted by concurrent articulation. This work led to the development of the multicomponent model, which subsequently incorporated a richer characterization of the visuo-spatial store (the “visuospatial sketchpad,” see e.g., Baddeley & Logie, 1999 ; Logie, 1995) and, later, an additional store—the “episodic buffer” which holds amodal information and interacts with episodic long-term memory ( Baddeley, 2000 ). The possibility of further substructure within these core components is also recognized (e.g., Logie, 1995 on distinguishing visual and spatial subcomponents; see also Logie et al., 2021 on the possibility of multiple substrates within a multicomponent perspective).

An alternative view, the embedded processes model put forward by Cowan (1999) , is that working memory can be seen as the controlled, temporary activation of long-term memory representations, with access to awareness being limited to three to four items or chunks. A key distinction with the multicomponent model hangs on whether working memory relies on a distinct substrate (as implied by the term “buffer”), or whether the substrate is shared with long-term memory. Oberauer (2002) similarly identifies working memory with activated representations in long-term memory. In this account, the activated region forms a concentric structure within which a subset of individual chunks inside a “region of direct access” compete to be selected as the focus of attention.

Other more recent theoretical accounts have also emphasized the role of attentional control in determining the limits of working memory. For example, Engle (2002) regarded capacity constraints as reflecting the limited ability to control domain-general executive attention in situations where there is the potential for interference among conflicting responses. The time-based resource sharing account ( Barrouillet & Camos, 2004 ) highlights the need to balance the active refreshing of short-term with concurrent processing demands. In this view, constraints arise from the necessary trade-off between maintenance and manipulation, both of which rely on common attentional resources.

Many theoretical approaches to working memory do not follow Baddeley and Hitch in identifying modality-specific substrates for the temporary storage of information and assume instead a unitary system in which many different types of feature can be represented (e.g., Cowan et al., 2021 ; Oberauer, 2021 ). In such accounts, modality-specific phenomena are attributed to differences in the extent to which such features overlap within and between modalities. On the other hand, some authors acknowledge the possibility that there may be many alternative substrates, and that even within a modality further subdivisions may be possible. So, for example substrates supporting memory of verbal/linguistic content might further distinguish auditory-verbal, lexical, and semantic levels of representation ( Barnard, 1985 ; Martin, 1993 ).

Neuroscientific investigations have tended to support the consensus idea of a broad separation between executive and attentional control processes on the one hand, and (often modality-specific) stores on the other, but if anything have highlighted even more extensive overlap of the neural substrate of working memory with other cognitive functions including sensory–perceptual and action–motor representation, and greater granularity and fractionation of function within both storage and control systems. This led Postle (2006) to argue that working memory should be seen as an emergent property of the mind and brain rather than a specialized system in its own right:

Working memory functions arise through the coordinated recruitment, via attention, of brain systems that have evolved to accomplish sensory-, representation-, and action-related functions. ( Postle, 2006 ), p. 23

Even in this view it is clear that the mechanisms of working memory (however they overlap with other cognitive functions) involve the interaction of distinct components (at minimum “attention” is distinguished from sensory/representation and action-related function, and these latter functions may also be further subdivided).

Empirical Investigation and Key Findings

A variety of tasks have been developed to investigate working memory in the laboratory. These tasks, of course, always require participants to briefly retain some novel information, often the identity of a set of items which might be visual (for example, colored shapes) or verbal (digits, words, letters). However, they vary quite considerably in the extent to which they require memory for the structure of the set (such as, for verbal stimuli, their order or the spatial layout of an array of items), the degree to which they place an ongoing or concurrent load on memory and attention, and the precision with which sensory and perceptual properties of the individual items must be represented. An excellent overview of these techniques and associated benchmark findings can be found in Oberauer et al. (2018) .

In an item recognition task, participants determine whether a specific item was in a set (a sequentially presented list or simultaneously displayed array) that they previously studied ( McElree & Dosher, 1989 ). In probed recall , they are provided with a cue that uniquely specifies a given item from a previously presented set, which they are then required to recall ( Fuchs, 1969 ). In free recall tasks, typically employed with verbal stimuli, participants are presented with an ordered list, but are allowed to recall the items in any order ( Postman & Phillips, 1965 ), whereas in serial recall ( Jahnke, 1963 ) they are required to retain the original order of presentation.

The preceding tasks place increasing demands on short-term memory for the structure as well as the content of the presented stimuli, but place relatively little requirement for attention or the manipulation of memory content. To address these aspects of working memory, a range of additional tasks have been developed. In complex span tasks the to-be-remembered items are interleaved with a processing task, placing a greater concurrent load on the attentional system ( Daneman & Carpenter, 1980 ). In the N-back task , items are presented rapidly and continuously, with the participant being required to decide whether each new item repeats one encountered exactly n-items earlier in the sequence; to do this they must not only maintain the order of the previous n-items, but also manage the capacity-limited short-term memory resource as every new item arrives. These demands become increasingly taxing as the value of n increases, again giving an indication of the effects of load on performance or, since it is particularly amenable to neuroimaging, brain activity (see Owen et al., 2005 for review). 1 As mentioned, the manipulation requirements of serial recall can be increased by reversing the order in which items are to be recalled. More involved forms of mental manipulation are explicitly tested in memory updating paradigms ( Morris & Jones, 1990 ), within which, after being presented with an array or description, participants are instructed to carry out a sequence of operations before retrieving the result.

To assess its fidelity over brief intervals, tasks that require memory for detailed properties of the items are useful. In change detection tasks (e.g., Luck & Vogel, 1997 ), participants are required to respond to alterations in the stimulus (typically a visually presented array) between presentation and testing. These alterations can be made arbitrarily small, thus testing the precision of the underlying memory representation. Going beyond recognition -like responses to change, in continuous reproduction or delayed estimation tasks , participants are asked to recall continuous features of the stimuli such as the precise color or orientation of a shape within a previously-studied array (e.g., Bays & Husain, 2008 ). These tasks allow researchers to go beyond the question of whether information is merely retained or lost; they can be used to characterize and quantify the quality of the underlying representation, which in turn can shed light on the potential trade-off between capacity and precision in working memory.

The preceding tasks provide a very useful set of tools for investigating working memory in the laboratory. To investigate the structure and operation of the system, experiments typically manipulate characteristics of the items to be stored, and often employ concurrent tasks devised to selectively disrupt putative components or processes. In their standard forms, the individual items are treated as equally valuable or important, but it is also possible to cue specific items, locations, or serial positions in order to encourage participants to prioritize specific content (e.g., Hitch et al., 2020 ; Myers et al., 2017 ). Improved recall for such prioritized items can then reveal the operation of strategic processes. Overall, such manipulations show a range of replicable effects, not just on overall performance and response times, but also on patterns of error. In turn these benchmark effects have provided the impetus for current theories and provide important constraints for emerging computational models of working memory ( Oberauer et al., 2018 ).

Set Size and Retention Interval Effects

The most important effects relate to capacity and temporal limits that have already been discussed, and these apply across all applicable experimental paradigms and modalities. Specifically, in terms of capacity limits, task accuracy is impaired as the number of items (set size) is increased (response times also generally increase with set size), and in terms of temporal limits, accuracy declines monotonically with the duration of a delay between presentation and testing. The latter effect is reliably seen for both verbal and spatial materials when the retention interval is filled with a distracting task. It does not apply to unfilled delays in tasks with verbal materials, and only sometimes occurs with spatial materials. The difference between filled and unfilled delays forms part of the evidence in favor of the core working memory concept of active executive/attentional processes in sustaining otherwise fleeting short-term memories.

Primacy and Recency Effects

Another signature of working memory is that items are retrieved with greater accuracy if they are presented at the beginning (primacy) or end (recency) of a sequence relative to other items. The operation of primacy and recency effects is seen in immediate serial recall and other tasks where the presentation order is well-defined, and for both verbal and visuo-spatial content. This leads to a serial position curve (in which accuracy is plotted for each serial position in a list) with a characteristic bowed shape. The effect suggests that a shared or general serial ordering mechanism privileges access to these serial positions in an ordered list and/or impairs access to other serial positions. It is important to note that primacy and recency effects are also observed in the immediate free recall of lists of words when the capacity of working memory is greatly exceeded and where they may have a very different explanation (see e.g., Baddeley & Hitch, 1993 ).

Errors and Effects of Similarity

Working memory errors frequently involve confusion between items in the memory set. This is evident in a wide range of tasks (including variants of recognition, change-detection, and continuous reproduction tasks), but is perhaps clearest in immediate serial recall, where the most common forms of error involve the misordering of items. These errors most frequently involve local transpositions in which an item moves to a nearby list position, often exchanging with the item in that position. For example, a sequence like “D, F, E, O, P, Q” might be recalled as “D, F, O, E, P, Q.” Items are most likely to transpose to immediately adjacent list positions, with the probability of a transposition decreasing monotonically as the distance within the sequence increases. Note that there are fewer opportunities for local transpositions at the beginning and end of a sequence so the locality constraint on transpositions likely plays at least some role in primacy and recency effects.

In a verbal working memory task, when items from the memory set are confused with one another, they are most likely to be confused with phonologically similar items making performance for lists of similar sounding items poorer than for phonologically distinct items. In serial recall, this effect manifests itself as an increased tendency for phonologically similar items to transpose with one another, so that in the preceding example, items “D,” “E” and “P” (because they rhyme) would be more likely to transpose with one another than items “F,” “O,” and “Q.” Although these similarity effects are largely reported in verbal paradigms, analogous findings are sometimes observed with visual materials (for example, a sequence of similar colored shapes is harder to reconstruct than a sequence of distinctively colored shapes; Jalbert et al., 2008 ).

The analysis of errors and confusion has been critical in understanding the nature of representation in verbal working memory (for example, demonstrating the importance of speech-based rather than semantic codes), in developing the concept of the phonological loop, and in developing computational models which account for these findings in terms of underpinning serial ordering mechanisms.

Individual Differences and Links With Other Facets of Cognition

Speaking to questions about the relationship between working memory and other aspects of cognition, another set of benchmark findings is concerned with correlations between performance on working memory tasks and other measures. In particular, working memory is correlated with measures of attention and fluid intelligence (the capacity to solve novel problems independent of prior learning; see e.g., Engle, 2002 ) suggesting that all three constructs involve common resources. There is consensus that aspects of attention contribute to working memory, but attention is also relevant to tasks that make minimal demands on memory. At the same time, working memory plays an important role in problem solving in the absence of relevant prior learning, but it can also be applied to tasks that do not involve complex problems. This suggests a hierarchical relationship in which limited cognitive resources (i.e., attention) are applied to maintain and manipulate information in memory (attention + short-term memory = working memory) in the context of demanding problems (working memory + problem solving = fluid intelligence).

This somewhat simplistic sketch of the relationship between constructs omits the contribution of long-term memory and learning to working memory. That contribution is evident in several empirical phenomena. For example, the beneficial effect of chunking on recall often depends on familiarity with the chunks, as in the examples given previously. It is easily overlooked that the familiarity of the materials themselves is also important. For example, familiar words are recalled much better than nonwords ( Hulme et al., 1991 ) suggesting that words act as specialized phonological/semantic “chunks.” Similarly, grammatical sentences are recalled better than arbitrarily ordered lists or jumbled sentences ( Brener, 1940 ). The word–nonword and sentence superiority effects show that well-learned constraints on serial order (whether through syntax or phonotactics) can benefit recall. A related phenomenon, the Hebb repetition effect ( Hebb, 1961 ), can be seen in the laboratory: immediate serial recall for a specific random list gradually improves over successive trials when it becomes more familiar through being repeatedly but covertly presented interleaved among other lists.

The Importance of Working Memory

The laboratory tasks and benchmark findings outlined in the section “ Empirical Investigation and Key Findings ” have established its key characteristics, but the practical significance of working memory extends well beyond these phenomena into everyday cognition and learning. Notably the limits of working memory constrain what we can think about on a moment-to-moment basis and hence how quickly we can learn and what we can ultimately understand. An appreciation of the impact of working memory and its limitations is thus vitally important in the context of education (see e.g., Alloway & Gathercole, 2006 for a review). For example, individual differences in the capacity of phonological storage in verbal working memory are reciprocally linked to vocabulary acquisition in early childhood; children’s ability to repeat nonwords at age four (i.e., unfamiliar phonological sequences) predicts their vocabulary a year later. In turn, the emergence of vocabulary (i.e., phonological chunks) is associated with later improvements in nonword repetition ( Gathercole et al., 1992 ). It is not hard to imagine that this process amplifies the initial effect of variation in capacity, affecting literacy and then more advanced learning (potentially well beyond language abilities) that depends on reading. Working memory can similarly exert an influence on the emergence of numeracy and through it more advanced skills in arithmetic and mathematics. For example, kindergartners’ performance on a backward digit span task predicts their scores on a mathematics test a year later ( Gersten et al., 2005 ). In addition to these effects on the acquisition of foundational skills such as literacy and numeracy, working memory is important in maintaining and manipulating the information needed to carry out complex tasks in the classroom. Thus, students with lower working memory capacity can have difficulty retaining and following instructions ( Gathercole et al., 2008 ) again potentially hampering their ability to build more advanced skills and knowledge. Because of its critical involvement in classroom learning, working memory plays a central role in Cognitive Load Theory” ( Sweller, 2011 ) an influential educational framework which aims to incorporate principles derived from the architecture of human cognition into teaching methods.

Many measures of short-term memory and working memory show marked year-on-year improvement in childhood, with developmental change likely reflecting the maturation of several components that underpin performance ( Gathercole, 1999 ; Gathercole et al., 2004 ). These include changes in processes such as verbal recoding, subvocal rehearsal, the activation of temporary information and executive attentional control ( Camos & Barrouillet, 2011 ; Cowan et al., 2002 ; Hitch & Halliday, 1983 ). As might be expected given the centrality of working memory in the acquisition of language and numeracy, developmental disorders are commonly associated with reduced short-term or working memory capacity. Prominent examples include dyslexia ( Berninger et al., 2008 ), developmental language disorder ( Archibald & Gathercole, 2006 ; Montgomery et al., 2010 ), and dyscalculia ( Fias et al., 2013 ; McLean & Hitch, 1999 ). However, the nature of any causal role for working memory in developmental disorders has been controversial (see e.g., Masoura, 2006 ).

In adulthood, working memory capacity continues to limit the bandwidth that is available for cognitive operations, for example affecting planning and decision-making ( Gilhooly, 2005 ; Hinson et al., 2003 ). As we grow older, working memory capacity tends to decline, and there are some indications that this is associated with failing attention and greater vulnerability to distraction ( Hasher & Zacks, 1988 ; McNab et al., 2015 ; Park & Payer, 2006 ) rather than a mere reversal of earlier developmental gains. Across the entire lifespan, as it waxes and wanes, working memory plays an important part in shaping our daily experience.

Given its central role in constraining human cognitive abilities, extensive efforts have been made to develop interventions that can improve working memory, for example through computerized training programs. However, these efforts have so far met with limited success. Some working memory tasks show improvements with practice, but these effects tend to reflect near or intermediate transfer , specific to the trained task or (often closely-related) direct measures of working memory, rather than far transfer extending to more general improvements in other tasks thought to depend on working memory, such as reading comprehension or arithmetic ( Melby-Lervåg et al., 2016 ; Owen et al., 2010 ; Sala & Gobet, 2017 ). It has been argued that near and intermediate transfer effects arise through improvements in task-specific efficiency via refinement of strategies and long-term memory support (e.g., chunking) whereas more general benefits and far transfer would be expected to depend on the underlying capacity of attentional and storage systems ( von Bastian & Oberauer, 2014 ). The absence of clear evidence for far transfer despite such extensive research thus suggests that working memory capacity limits are a fundamental and unalterable feature of the human cognitive system.

Although it is perhaps premature to rule out the possibility of interventions that achieve increased working memory capacity, it appears at present that it can only be extended in specific contexts through more specialized training with particular tasks and materials. Paradoxically, this resistance to more general training may be what makes working memory so important; to the extent that its capacity limits are unavoidable, working memory helps to determine the scope of human cognition and spurs us to find strategies, technologies and cultural tools that allow us to go beyond them.

In conclusion, through the development of a powerful toolkit of experimental methods and of replicable empirical phenomena, the study of working memory function has provided many useful insights into interactions between attention and short-term memory. On the one hand these interactions can be used strategically to enhance goal-directed behavior and long-term learning while on the other they provide fundamental limits on cognition across the lifespan. Ongoing controversy over the structure of working memory relates to the difficulty in isolating these interactions from other facets of cognition, but there is little doubt about their importance in governing what we can and cannot do.

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1. However, note that, in at least one study ( Kane et al., 2007 ) n-back performance correlated only weakly with a measure of span, suggesting that, despite face validity, it may tax distinct cognitive resources.

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New study reveals how brain waves control working memory

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MIT neuroscientists have found evidence that the brain’s ability to control what it’s thinking about relies on low-frequency brain waves known as beta rhythms.

In a memory task requiring information to be held in working memory for short periods of time, the MIT team found that the brain uses beta waves to consciously switch between different pieces of information. The findings support the researchers’ hypothesis that beta rhythms act as a gate that determines when information held in working memory is either read out or cleared out so we can think about something else.  

“The beta rhythm acts like a brake, controlling when to express information held in working memory and allow it to influence behavior,” says Mikael Lundqvist, a postdoc at MIT’s Picower Institute for Learning and Memory and the lead author of the study.

Earl Miller, the Picower Professor of Neuroscience at the Picower Institute and in the Department of Brain and Cognitive Sciences, is the senior author of the study, which appears in the Jan. 26 issue of Nature Communications .

Working in rhythm

There are millions of neurons in the brain, and each neuron produces its own electrical signals. These combined signals generate oscillations known as brain waves, which vary in frequency. In a 2016 study , Miller and Lundqvist found that gamma rhythms are associated with encoding and retrieving sensory information.

They also found that when gamma rhythms went up, beta rhythms went down, and vice versa. Previous work in their lab had shown that beta rhythms are associated with “top-down” information such as what the current goal is, how to achieve it, and what the rules of the task are.

All of this evidence led them to theorize that beta rhythms act as a control mechanism that determines what pieces of information are allowed to be read out from working memory — the brain function that allows control over conscious thought, Miller says.

“Working memory is the sketchpad of consciousness, and it is under our control. We choose what to think about,” he says. “You choose when to clear out working memory and choose when to forget about things. You can hold things in mind and wait to make a decision until you have more information.”

To test this hypothesis, the researchers recorded brain activity from the prefrontal cortex, which is the seat of working memory, in animals trained to perform a working memory task. The animals first saw one pair of objects, for example, A followed by B. Then they were shown a different pair and had to determine if it matched the first pair. A followed by B would be a match, but not B followed by A, or A followed by C. After this entire sequence, the animals released a bar if they determined that the two sequences matched.

The researchers found that brain activity varied depending on whether the two pairs matched or not. As an animal anticipated the beginning of the second sequence, it held the memory of object A, represented by gamma waves. If the next object seen was indeed A, beta waves then went up, which the researchers believe clears object A from working memory. Gamma waves then went up again, but this time the brain switched to holding information about object B, as this was now the relevant information to determine if the sequence matched.

However, if the first object shown was not a match for A, beta waves went way up, completely clearing out working memory, because the animal already knew that the sequence as a whole could not be a match.

“The interplay between beta and gamma acts exactly as you would expect a volitional control mechanism to act,” Miller says. “Beta is acting like a signal that gates access to working memory. It clears out working memory, and can act as a switch from one thought or item to another.”

A new model

Previous models of working memory proposed that information is held in mind by steady neuronal firing. The new study, in combination with their earlier work, supports the researchers’ new hypothesis that working memory is supported by brief episodes of spiking, which are controlled by beta rhythms.

“When we hold things in working memory (i.e. hold something ‘in mind’), we have the feeling that they are stable, like a light bulb that we’ve turned on to represent some thought. For a long time, neuroscientists have thought that this must mean that the way the brain represents these thoughts is through constant activity. This study shows that this isn’t the case — rather, our memories are blinking in and out of existence. Furthermore, each time a memory blinks on, it is riding on top of a wave of activity in the brain,” says Tim Buschman, an assistant professor of psychology at Princeton University who was not involved in the study.

Two other recent papers from Miller’s lab offer additional evidence for beta as a cognitive control mechanism.

In a study that recently appeared in the journal Neuron , they found similar patterns of interaction between beta and gamma rhythms in a different task involving assigning patterns of dots into categories. In cases where two patterns were easy to distinguish, gamma rhythms, carrying visual information, predominated during the identification. If the distinction task was more difficult, beta rhythms, carrying information about past experience with the categories, predominated.

In a recent paper published in the Proceedings of the National Academy of Sciences , Miller’s lab found that beta waves are produced by deep layers of the prefrontal cortex, and gamma rhythms are produced by superficial layers, which process sensory information. They also found that the beta waves were controlling the interaction of the two types of rhythms.

“When you find that kind of anatomical segregation and it’s in the infrastructure where you expect it to be, that adds a lot of weight to our hypothesis,” Miller says.

The researchers are now studying whether these types of rhythms control other brain functions such as attention. They also hope to study whether the interaction of beta and gamma rhythms explains why it is so difficult to hold more than a few pieces of information in mind at once.

“Eventually we’d like to see how these rhythms explain the limited capacity of working memory, why we can only hold a few thoughts in mind simultaneously, and what happens when you exceed capacity,” Miller says. “You have to have a mechanism that compensates for the fact that you overload your working memory and make decisions on which things are more important than others.”

The research was funded by the National Institute of Mental Health, the Office of Naval Research, and the Picower JFDP Fellowship.

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The effect of total sleep deprivation on working memory: evidence from diffusion model

Affiliations.

• 1 Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou 510631, China.
• 2 Center for Sleep Research, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, School of Psychology, South China Normal University, Guangzhou 510631, China.
• PMID: 38181126
• DOI: 10.1093/sleep/zsae006

Study objectives: Working memory is crucial in human daily life and is vulnerable to sleep loss. The current study investigated the impact of sleep deprivation on working memory from the information processing perspective, to explore whether sleep deprivation affects the working memory via impairing information manipulation.

Methods: Thirty-seven healthy adults attended two counterbalanced protocols: a normal sleep night and a total sleep deprivation (TSD). The N-back and the psychomotor vigilance task (PVT) assessed working memory and sustained attention. Response time distribution and drift-diffusion model analyses were applied to explore cognitive process alterations.

Results: TSD increased the loading effect of accuracy, but not the loading effect of response time in the N-back task. TSD reduced the speed of information accumulation, increased the variability of the speed of accumulation, and elevated the decision threshold only in 1-back task. Moreover, the slow responses of PVT and N-back were severely impaired after TSD, mainly due to increased information accumulation variability.

Conclusions: The present study provides a new perspective to investigate behavioral performance by using response time distribution and drift-diffusion models, revealing that sleep deprivation affected multicognitive processes underlying working memory, especially information accumulation processes.

Keywords: drift-diffusion models; sleep deprivation; state instability; sustained attention; working memory.

© The Author(s) 2024. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For permissions, please e-mail: [email protected].

• Attention / physiology
• Memory, Short-Term* / physiology
• Psychomotor Performance / physiology
• Reaction Time / physiology
• Sleep / physiology
• Sleep Deprivation* / complications
• Sleep Deprivation* / psychology

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• 2023A1515011873/Guangdong Basic and Applied Basic Research Foundation

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• Review Article
• Published: 25 January 2023

The roles of attention, executive function and knowledge in cognitive ageing of working memory

• Moshe Naveh-Benjamin 1 &
• Nelson Cowan   ORCID: orcid.org/0000-0003-3711-4338 1  

Nature Reviews Psychology volume  2 ,  pages 151–165 ( 2023 ) Cite this article

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• Human behaviour
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Working memory is an ensemble of components that temporarily hold information in a heightened state of availability for use in ongoing information processing. Working memory is crucial for everyday behaviours such as remembering names and faces, following recipes, remembering the gist of a conversation, and making decisions based on multiple factors. In this Review, we examine how working memory relates to other aspects of information processing to understand age-related decline in working memory. We first contrast several theoretical approaches to working memory. We then discuss benchmark behavioural findings on working memory during ageing and describe general underlying mechanisms that might explain age-related declines and stability. In particular, we emphasize how attention and executive function interact with knowledge. Finally, we assess the relevance of these findings for theories of working memory. Even as executive functions decrease in efficiency with age, some basic attention functions and preserved knowledge can help to blunt the effects of ageing on working memory.

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Naveh-Benjamin, M., Cowan, N. The roles of attention, executive function and knowledge in cognitive ageing of working memory. Nat Rev Psychol 2 , 151–165 (2023). https://doi.org/10.1038/s44159-023-00149-0

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Working Memory Model (Baddeley and Hitch)

Saul Mcleod, PhD

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The Working Memory Model, proposed by Baddeley and Hitch in 1974, describes short-term memory as a system with multiple components.

It comprises the central executive, which controls attention and coordinates the phonological loop (handling auditory information), and the visuospatial sketchpad (processing visual and spatial information).

Later, the episodic buffer was added to integrate information across these systems and link to long-term memory. This model suggests that short-term memory is dynamic and multifaceted.

Take-home Messages

• Working memory is a limited capacity store for retaining information for a brief period while performing mental operations on that information.
• Working memory is a multi-component system that includes the central executive, visuospatial sketchpad, phonological loop, and episodic buffer.
• Working memory is important for reasoning, learning, and comprehension.
• Working memory theories assume that complex reasoning and learning tasks require a mental workspace to hold and manipulate information.

Atkinson’s and Shiffrin’s (1968) multi-store model was extremely successful in terms of the amount of research it generated. However, as a result of this research, it became apparent that there were a number of problems with their ideas concerning the characteristics of short-term memory.

Fig 1 . The Working Memory Model (Baddeley and Hitch, 1974)

Baddeley and Hitch (1974) argue that the picture of short-term memory (STM) provided by the Multi-Store Model is far too simple.

According to the Multi-Store Model , STM holds limited amounts of information for short periods of time with relatively little processing.  It is a unitary system. This means it is a single system (or store) without any subsystems. Whereas working memory is a multi-component system (auditory and visual).

Therefore, whereas short-term memory can only hold information, working memory can both retain and process information.

Working memory is short-term memory . However, instead of all information going into one single store, there are different systems for different types of information.

Central Executive

Visuospatial sketchpad (inner eye), phonological loop.

• Phonological Store (inner ear) processes speech perception and stores spoken words we hear for 1-2 seconds.
• Articulatory control process (inner voice) processes speech production, and rehearses and stores verbal information from the phonological store.

Fig 2 . The Working Memory Model Components (Baddeley and Hitch, 1974)

The labels given to the components (see Fig 2) of the working memory reflect their function and the type of information they process and manipulate.

The phonological loop is assumed to be responsible for the manipulation of speech-based information, whereas the visuospatial sketchpad is assumed to be responsible for manipulating visual images.

The model proposes that every component of working memory has a limited capacity, and also that the components are relatively independent of each other.

The Central Executive

The central executive is the most important component of the model, although little is known about how it functions.  It is responsible for monitoring and coordinating the operation of the slave systems (i.e., visuospatial sketchpad and phonological loop) and relates them to long-term  memory (LTM).

The central executive decides which information is attended to and which parts of the working memory to send that information to be dealt with. For example, two activities sometimes come into conflict, such as driving a car and talking.

Rather than hitting a cyclist who is wobbling all over the road, it is preferable to stop talking and concentrate on driving. The central executive directs attention and gives priority to particular activities.

p> The central executive is the most versatile and important component of the working memory system. However, despite its importance in the working-memory model, we know considerably less about this component than the two subsystems it controls.

Baddeley suggests that the central executive acts more like a system which controls attentional processes rather than as a memory store.  This is unlike the phonological loop and the visuospatial sketchpad, which are specialized storage systems. The central executive enables the working memory system to selectively attend to some stimuli and ignore others.

Baddeley (1986) uses the metaphor of a company boss to describe the way in which the central executive operates.  The company boss makes decisions about which issues deserve attention and which should be ignored.

They also select strategies for dealing with problems, but like any person in the company, the boss can only do a limited number of things at the same time. The boss of a company will collect information from a number of different sources.

If we continue applying this metaphor, then we can see the central executive in working memory integrating (i.e., combining) information from two assistants (the phonological loop and the visuospatial sketchpad) and also drawing on information held in a large database (long-term memory).

The Phonological Loop

The phonological loop is the part of working memory that deals with spoken and written material. It consists of two parts (see Figure 3).

The phonological store (linked to speech perception) acts as an inner ear and holds information in a speech-based form (i.e., spoken words) for 1-2 seconds. Spoken words enter the store directly. Written words must first be converted into an articulatory (spoken) code before they can enter the phonological store.

Fig 3 . The phonological loop

The articulatory control process (linked to speech production) acts like an inner voice rehearsing information from the phonological store. It circulates information round and round like a tape loop. This is how we remember a telephone number we have just heard. As long as we keep repeating it, we can retain the information in working memory.

The articulatory control process also converts written material into an articulatory code and transfers it to the phonological store.

The Visuospatial Sketchpad

The visuospatial sketchpad ( inner eye ) deals with visual and spatial information. Visual information refers to what things look like. It is likely that the visuospatial sketchpad plays an important role in helping us keep track of where we are in relation to other objects as we move through our environment (Baddeley, 1997).

As we move around, our position in relation to objects is constantly changing and it is important that we can update this information.  For example, being aware of where we are in relation to desks, chairs and tables when we are walking around a classroom means that we don”t bump into things too often!

The sketchpad also displays and manipulates visual and spatial information held in long-term memory. For example, the spatial layout of your house is held in LTM. Try answering this question: How many windows are there in the front of your house?

You probably find yourself picturing the front of your house and counting the windows. An image has been retrieved from LTM and pictured on the sketchpad.

Evidence suggests that working memory uses two different systems for dealing with visual and verbal information. A visual processing task and a verbal processing task can be performed at the same time.

It is more difficult to perform two visual tasks at the same time because they interfere with each other and performance is reduced. The same applies to performing two verbal tasks at the same time. This supports the view that the phonological loop and the sketchpad are separate systems within working memory.

The Episodic Buffer

The original model was updated by Baddeley (2000) after the model failed to explain the results of various experiments. An additional component was added called the episodic buffer.

The episodic buffer acts as a “backup” store which communicates with both long-term memory and the components of working memory.

Fig 3 . Updated Model to include the Episodic Buffer

Critical Evaluation

Researchers today generally agree that short-term memory is made up of a number of components or subsystems. The working memory model has replaced the idea of a unitary (one part) STM as suggested by the multistore model.

The working memory model explains a lot more than the multistore model. It makes sense of a range of tasks – verbal reasoning, comprehension, reading, problem-solving and visual and spatial processing. The model is supported by considerable experimental evidence.

The working memory applies to real-life tasks:

• reading (phonological loop)
• problem-solving (central executive)
• navigation (visual and spatial processing)

The KF Case Study supports the Working Memory Model. KF suffered brain damage from a motorcycle accident that damaged his short-term memory.

KF’s impairment was mainly for verbal information – his memory for visual information was largely unaffected. This shows that there are separate STM components for visual information (VSS) and verbal information (phonological loop).

The working memory model does not over-emphasize the importance of rehearsal for STM retention, in contrast to the multi-store model.

Empirical Evidence for Working Memory

What evidence is there that working memory exists, that it comprises several parts, that perform different tasks? Working memory is supported by dual-task studies (Baddeley and Hitch, 1976).

The working memory model makes the following two predictions:

1 . If two tasks make use of the same component (of working memory), they cannot be performed successfully together. 2 . If two tasks make use of different components, it should be possible to perform them as well as together as separately.

Key Study: Baddeley and Hitch (1976)

Aim : To investigate if participants can use different parts of working memory at the same time.

Method : Conducted an experiment in which participants were asked to perform two tasks at the same time (dual task technique) – a digit span task which required them to repeat a list of numbers, and a verbal reasoning task which required them to answer true or false to various questions (e.g., B is followed by A?).

Results : As the number of digits increased in the digit span tasks, participants took longer to answer the reasoning questions, but not much longer – only fractions of a second.  And, they didn”t make any more errors in the verbal reasoning tasks as the number of digits increased.

Conclusion : The verbal reasoning task made use of the central executive and the digit span task made use of the phonological loop.

Brain Imaging Studies

Several neuroimaging studies have attempted to identify distinct neural correlates for the phonological loop and visuospatial sketchpad posited by the multi-component model.

For example, some studies have found that tasks tapping phonological storage tend to activate more left-hemisphere perisylvian language areas, whereas visuospatial tasks activate more right posterior regions like the parietal cortex (Smith & Jonides, 1997).

However, the overall pattern of results remains complex and controversial. Meta-analyses often fail to show consistent localization of verbal and visuospatial working memory (Baddeley, 2012).

There is significant overlap in activation, which may reflect binding processes through the episodic buffer, as well as common executive demands.

Differences in paradigms and limitations of neuroimaging methodology further complicate mapping the components of working memory onto distinct brain regions or circuits (Henson, 2001).

While neuroscience offers insight into working memory, Baddeley (2012) argues that clear anatomical localization is unlikely given the distributed and interactive nature of working memory. Specifically, he suggests that each component likely comprises a complex neural circuit rather than a circumscribed brain area.

Additionally, working memory processes are closely interrelated with other systems for attention, perception and long-term memory . Thus, neuroimaging provides clues but has not yet offered definitive evidence to validate the separable storage components posited in the multi-component framework.

Further research using techniques with higher spatial and temporal resolution may help better delineate the neural basis of verbal and visuo-spatial working memory.

Lieberman (1980) criticizes the working memory model as the visuospatial sketchpad (VSS) implies that all spatial information was first visual (they are linked).

However, Lieberman points out that blind people have excellent spatial awareness, although they have never had any visual information. Lieberman argues that the VSS should be separated into two different components: one for visual information and one for spatial.

There is little direct evidence for how the central executive works and what it does. The capacity of the central executive has never been measured.

Working memory only involves STM, so it is not a comprehensive model of memory (as it does not include SM or LTM).

The working memory model does not explain changes in processing ability that occur as the result of practice or time.

State-based models of WM

Early models of working memory proposed specialized storage systems, such as the phonological loop and visuospatial sketchpad, in Baddeley and Hitch’s (1974) influential multi-component model.

However, newer “state-based” models suggest working memory arises from temporarily activating representations that already exist in your brain’s long-term memory or perceptual systems.

For example, you activate your memory of number concepts to remember a phone number. Or, to remember where your keys are, you activate your mental map of the room.

According to state-based models, you hold information in mind by directing your attention to these internal representations. This gives them a temporary “boost” of activity.

More recent state-based models argue against dedicated buffers, and propose that working memory relies on temporarily activating long-term memory representations through attention (Cowan, 1995; Oberauer, 2002) or recruiting perceptual and motor systems (Postle, 2006; D’Esposito, 2007).

Evidence from multivariate pattern analysis (MVPA) of fMRI data largely supports state-based models, rather than dedicated storage buffers.

For example, Lewis-Peacock and Postle (2008) showed MVPA classifiers could decode information being held in working memory from patterns of activity associated with long-term memory for that content.

Other studies have shown stimulus-specific patterns of activity in sensory cortices support the retention of perceptual information (Harrison & Tong, 2009; Serences et al., 2009).

Atkinson, R. C., & Shiffrin, R. M. (1968). Chapter: Human memory: A proposed system and its control processes. In Spence, K. W., & Spence, J. T. The psychology of learning and motivation (Volume 2). New York: Academic Press. pp. 89–195.

Baddeley, A. D. (1986). Working memory . Oxford: Oxford University Press.

Baddeley, A. (1996). Exploring the central executive.  The Quarterly Journal of Experimental Psychology Section A ,  49 (1), 5-28.

Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences , 4, (11): 417-423.

Baddeley, A. (2010). Working memory.  Current biology ,  20 (4), R136-R140.

Baddeley, A. (2012). Working memory: Theories, models, and controversies.  Annual review of psychology ,  63 , 1-29.

Baddeley, A. D., & Hitch, G. (1974). Working memory. In G.H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory (Vol. 8, pp. 47–89). New York: Academic Press.

Baddeley, A. D., & Lieberman, K. (1980). Spatial working memory. ln R. Nickerson. Attention and Performance, VIII . Hillsdale, N): Erlbaum.

Borella, E., Carretti, B., Sciore, R., Capotosto, E., Taconnat, L., Cornoldi, C., & De Beni, R. (2017). Training working memory in older adults: Is there an advantage of using strategies?.  Psychology and Aging ,  32 (2), 178.

Chai, W. J., Abd Hamid, A. I., & Abdullah, J. M. (2018). Working memory from the psychological and neurosciences perspectives: a review.  Frontiers in Psychology ,  9 , 401.

Cowan, N. (1995). Attention and memory: An integrated framework . Oxford Psychology Series, No. 26. New York: Oxford University Press.

Cowan, N. (2005). Working memory capacity.  Exp. Psychology,  54, 245–246.

Cowan, N. (2008). What are the differences between long-term, short-term, and working memory?.  Progress in brain research ,  169 , 323-338.

Curtis, C.E., & D’Esposito, M. (2003). Persistent activity in the prefrontal cortex during working memory. Trends in Cognitive Sciences, 7 (9), 415-423.

D’Esposito, M. (2007). From cognitive to neural models of working memory. Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1481), 761-772.

D’Esposito, M., & Postle, B. R. (2015). The cognitive neuroscience of working memory.  Annual review of psychology ,  66 , 115-142.

Fell, J., & Axmacher, N. (2011). The role of phase synchronization in memory processes. Nature Reviews Neuroscience, 12 (2), 105-118.

Harrison, S. A., & Tong, F. (2009). Decoding reveals the contents of visual working memory in early visual areas. Nature, 458 (7238), 632-635.

Henson, R. (2001). Neural working memory. In J. Andrade (Ed.), Working memory in perspective (pp. 151-174). Psychology Press.

Lewis-Peacock, J. A., & Postle, B. R. (2008). Temporary activation of long-term memory supports working memory. Journal of Neuroscience, 28 (35), 8765-8771.

Lieberman, M. D. (2000). Introversion and working memory: Central executive differences.  Personality and Individual Differences ,  28 (3), 479-486.

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Serences, J.T., Ester, E.F., Vogel, E.K., & Awh, E. (2009). Stimulus-specific delay activity in human primary visual cortex. Psychological Science, 20( 2), 207-214.

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Why writing by hand beats typing for thinking and learning

Jonathan Lambert

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

Is this some kind of joke? A school facing shortages starts teaching standup comedy

In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

Shots - Health News

Feeling artsy here's how making art helps your brain.

"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

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THE COGNITIVE NEUROSCIENCE OF WORKING MEMORY

Mark d’esposito.

1 Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, Berkeley, CA 94720

Bradley R. Postle

2 Department of Psychology and Psychiatry, University of Wisconsin-Madison, Madison, WI 53719

For over 50 years, psychologists and neuroscientists have recognized the importance of a “working memory” to coordinate processing when multiple goals are active, and to guide behavior with information that is not present in the immediate environment. In recent years, psychological theory and cognitive neuroscience data have converged on the idea that information is encoded into working memory via the allocation of attention to internal representations – be they semantic long-term memory (e.g., letters, digits, words), sensory, or motoric. Thus, information-based multivariate analyses of human functional MRI data typically find evidence for the temporary representation of stimuli in regions that also process this information in nonworking-memory contexts. The prefrontal cortex, on the other hand, exerts control over behavior by biasing the salience of mnemonic representations, and adjudicating among competing, context-dependent rules. The “control of the controller” emerges from a complex interplay between PFC and striatal circuits, and ascending dopaminergic neuromodulatory signals.

INTRODUCTION

The introduction of the term “working memory” into the behavioral literature can be traced back to a passage in Miller, Galanter and Pribram’s book “Plans and the Structure of Behavior” ( 1960 ). In it, the authors state:

“Without committing ourselves to any specific machinery, therefore, we should like to speak of the memory we use for the execution of our Plans as a kind of quick access, “working memory”. There may be several Plans, or several parts of a single Plan, all stored in working memory at the same time. In particular, when one Plan is interrupted by the requirements of some other Plan, we must be able to remember the interrupted Plan in order to resume its execution when the opportunity arises. When a Plan has been transferred into the working memory we recognize the special status of its incompleted parts by calling them “intentions.” (p. 65)

Soon thereafter, Pribham and colleagues (1964) posited that the neural machinery supporting working memory might include the prefrontal cortex (PFC). They did so based on the deficits that PFC lesions were known to produce on various tests that imposed a delay between the target stimulus and the subsequent target-related response. (Or, in the case of delayed alternation, between the execution of one action and the execution of a subsequent action that depended on the former.)

The most enduring conceptualization of working memory, however, has been that of the multicomponent model, introduced in 1974 by experimental psychologists Alan Baddeley and Graham Hitch (1974) . The model was developed to address two factors in the literature of the time. One was Baddeley and Hitch’s assessment that contemporary models of short-term memory (STM) did not capture the fact that mental operations performed on information in conscious awareness can be carried out independent of interaction with, or influence on, long-term memory (LTM); for example, “maintenance rehearsal” had recently been shown not to enhance encoding into LTM.) A second factor was that their own work indicated that performance on each of two tasks under dual-task conditions could approach levels of performance under single-task conditions if the two engaged different domains of information, specifically verbal and visuo-spatial. Thus, the original version of their model called for two STM buffers (dubbed the “phonological loop” and the “visuospatial sketchpad”, respectively) that could operate independently of each other and independently of LTM, although both under control of a separate system that they dubbed the “Central Executive”.

From a functional perspective, the multicomponent model of working memory accomplished the buffering and coordinating operations that Miller et al. (1960) had identified as critical if one is to be able to simultaneously maintain, and successfully carry out, multiple behavioral goals. In 1986, Baddeley summarized it as “a system for the temporary holding and manipulation of information during the performance of a range of cognitive tasks such as [language] comprehension, learning and reasoning”. The following year, Patricia Goldman-Rakic (1987) echoed these ideas in an influential synthesis of cognitive and neurobiological perspectives, stating that “the evolution of a capacity to guide behavior by [mnemonic] representation of stimuli rather than by the stimuli themselves introduces the possibility that concepts and plans can govern behavior”. Thus, “the ability to guide behavior by representations of discriminative stimuli rather than by the discriminative stimuli themselves is a major achievement of evolution”. What is captured in each of these seminal writings is that working memory underlies the successful execution of complex behavior, regardless of the cognitive domain or domains that are being engaged. When working memory fails, so too does the ability to carry out many activities of daily living. Viewed from this perspective, it is not surprising that working memory can be shown to be impaired in many neurological and psychiatric syndromes that are characterized by disordered behavior ( Devinsky & D’Esposito, 2003 ). The centrality of working memory to understanding normal, as well as pathological, behavior is presumably reflected in the intensity with which it has been studied: In mid-2014, a search of the term “working memory” in PubMed retrieved 17,597 citations and in Google Scholar 1,580,000 results were returned. Although we cannot hope to do justice to such a vast literature in just one review, what we hope to accomplish here is to highlight what we consider to be important developments in working memory research from a cognitive neuroscience perspective.

COGNITIVE MODELS OF WORKING MEMORY

As we write this review the multicomponent model of working memory is marking its 40 th anniversary, and from roughly 1985 through 2005 – what one might consider the first 20 years of the cognitive neuroscience study of working memory – this was the dominant theoretical framework. More recently, however, what might be called state-based models have taken on increased prominence. As a class, these models assume that the allocation of attention to internal representations – be they semantic long-term memory (LTM; e.g., letters, digits, words), sensory, or motoric – underlies the short-term retention of information in working memory. These models conceptualize information being held “in” working memory as existing in one of several states of activation established by the allocation of attention.

Our brief review of state-based models will organize these into two categories: “activated LTM” models and “sensorimotor recruitment” models. Although these two types of models have arisen within different literatures, the principal difference between them seems to be simply the class of stimuli for which each has been proposed. That is, activated LTM models have by-and-large been articulated for, and tested with, symbolic stimuli typically considered to be “semantic” (e.g., letters, words, digits). Sensorimotor recruitment models, on the other hand, have typically been invoked for classes of stimuli considered to be “perceptual” (e.g., visual colors and orientations, auditory pitches, tactile vibrational frequencies). Despite these surface-level differences, however, both of these classes of state-dependent models of working memory are grounded in the idea that the attentional selection of mental representations brings them into working memory, and that the consequences of attentional prioritization explains such properties as capacity limitations, proactive interference from no-longer-relevant items, and so on.

The temporary activation of LTM representations

The subset of state-based models that has been formalized to the highest degree are those pertaining to working memory for information for which there exists a semantic representation in LTM. In perhaps the most well-known of the state-based models, Cowan (1995) describes two distinct states in STM: a small, capacity-limited state referred to as the focus of attention (FoA) and a more expansive state referred to as the activated portion of LTM (“activated LTM”). In this model, the FoA corresponds to approximately four chunks of information that one can hold in working memory at any moment in time using top-down attentional control. When attention subsequently shifts to other information, the items that were previously in the FoA transition into activated LTM. Activated LTM has no capacity limit per se, but is susceptible to temporal decay and interference effects. A variant on this two-level model has been proposed by Oberauer (2002 , 2009 ) in a three-embedded-components theory. In it, the four-item FoA from Cowan’s model is recast as a region of direct access from which a narrower FoA can efficiently select information.

Capacity limits, per se, do not exist for either of these two hypothesized states in working memory. Rather, the amount of information that can be retained in the region of direct access and the FoA is limited only by interference from bindings between object features being retained in working memory ( Oberauer, 2013 ). A third model, advocated by McElree (1998 , 2006 ), posits two states of memory: a FoA with a strict capacity limit of one item, and LTM, in which all items exist along a graded continuum of “memory strength”, with memory strength (which we construe as level of activation) of an item falling off as a function of how recently it was in the FoA, and from which all items are equally accessible.

Setting aside some differences in terminology, these models all posit the following: When we are presented with symbolic information that is to-be-remembered (e.g., a list of names or a telephone number), the LTM representations of this information are accessed during the process of perceptual recognition, and they are subsequently maintained in an elevated state of activation, via attention, until this information is no longer needed to achieve some proximal goal. (For our purposes, we will gloss over whether there may exist one or more distinct states of attentional prioritization, and summarize all as a FoA.) These models account for extensive behavioral findings suggesting the existence of different states of representation of information being held in working memory. For example, Oberauer and colleagues (2001 , 2002 , 2005 ) have made clever use of the Sternberg effect, whereby reaction time for a recognition judgment about a memory probe increases linearly with the number of items concurrently held in working memory. The Oberauer studies have modified the basic Sternberg memory paradigm by introducing a retrocue during the memory delay that informs the subject that only a subset of the initially presented memory items will be relevant for an upcoming probe. Given sufficient time to process this retrocue, subjects respond more quickly to memory probes of the cued items (i.e., as would be predicted if they were now holding a smaller memory set). The uncued items are not fully forgotten, however, and continue to influence ongoing processing in the form of intrusion costs on response times when they are presented as negative (to-be-rejected) memory probes. This intrusion effect persists for 5 seconds, long after the uncued items cease to affect response times for the cued items. The uncued items are therefore hypothesized to have been removed from the FoA, but to persist in activated LTM ( Oberauer, 2001 ). By varying the retrocue-to-memory probe asynchrony, it has been estimated that it takes approximately 1 second to remove uncued items from the FoA ( Oberauer, 2005 ). Questions about whether there exists one or more qualitatively discrete states of activation outside of the FoA remain a topic of active research.

Sensorimotor recruitment

The basic premise of sensorimotor recruitment models of working memory is that the same systems and representations that are engaged in the perception of information can also contribute to the short-term retention of that information. An early, paradigmatic example of such models is that of attention-based rehearsal, whereby a location in space can be held in working memory via the covert allocation of attention to that location (e.g. Awh & Jonides, 2001 ). For other domains of sensory information, such as visually perceived spatial frequency, contrast, orientation, or motion, behavioral evidence indicates that each is retained in a highly stimulus-specific manner ( Magnussen, 2000 , Magnussen & Greenlee, 1999 , Zaksas et al. 2001 ) that is most parsimoniously explained as the persistent activation of the sensory representations themselves. We shall see in the next section that a growing body of neural evidence supports this contention.

In the literature, the label “sensory recruitment” is much more common than is “sensorimotor recruitment”. We prefer the latter, however, to accommodate the intimate, often inextricable, coupling between sensory attention and motor intention. This is particularly important in the context of spatial working memory, which is not only disrupted by drawing attention to a distracting location (e.g., Awh et al. 1998 ), but also by concurrent performance of task-irrelevant motor sequences, such as eye movements, tapping, and so on (reviewed in Postle et al. 2006 ). Conversely, motoric activity, such as the trajectory of a saccade, can be altered when one is concurrently holding a location in working memory ( Theeuwes et al. 2005 ). These results support the idea that the coordinates of a to-be-remembered location are immediately incorporated into a salience map that simultaneously holds them in brain systems that represent them as a percept and as a target for action by any of various motor effectors ( Postle 2011 ).

Capacity limits of visual working memory

A focus of intensive investigation for sensorimotor recruitment models has been the factors that explain capacity limitations. Much of this work has followed from Luck and Vogel’s (1997) experiments with a “change detection” task in which a target array of colored squares (varying across trials from a single square to 10 or more) is presented for a few hundred milliseconds, followed by a brief (roughly 1-second) blank delay, followed by a probe array containing the same number of items, but with one item appearing in a different color on half the trials (a “Yes/No recognition” procedure). By applying a simple algebraic formula to the results, they estimated that subjects had a visual STM capacity of between 3 and 4 items. Importantly, they found that an individual’s capacity did not change with number of features used to individuate objects, up through objects defined by conjunctions of 4 features. This led them to hypothesize that the capacity of visual STM is constrained by a finite number of hypothetical “slots”, each one capable of storing an object representation, regardless of the complexity of any single object ( Vogel et al. 2001 ).

This “slots model” has been challenged from at least two perspectives and, at the time of this writing, the nature of visual STM capacity limits remains a topic of vigorous debate. One open question is that of the influence of object complexity – contrary to the findings of Vogel et al. (2001) , others have found that visual short-term memory capacity declines with increasing object complexity (e.g., Alvarez & Cavanagh, 2004 ). A second challenge arises from the perspective that visual STM capacity may not depend on a finite number of slots but, instead, on a single attentional resource. Evidence for this latter view is marshaled when the procedure for testing visual STM is changed from recognition to recall. This procedural change allows for the estimation of the precision of a mnemonic representation, by measuring the error in the recall response. With STM for the orientation of one or more line segments, for example, the average error in recalling the orientation of the probed stimulus is larger when subjects are remembering several stimuli simultaneously, in comparison to when they are remembering just one ( Bays and Hussain, 2008 ). That is, mnemonic precision (the inverse of recall error) declines monotonically as a function of memory set size, an outcome that one would expect if STM were supported by a limited resource that must be apportioned ever more thinly as the number of items in the memory set increases. Slots models have been modified to allow for variable representational precision within a slot, but one contentious question that remains is how to best explain capacity limitations: As an individual approaches the limit of the amount of information that she can retain in STM, is it because she has run out of slots (in which case an absolute ceiling in performance is predicted), or because her attentional resources have been spread so thin that any one item’s mnemonic fidelity is too poor to be retrievable? For excellent reviews on these issues see Ma et al. 2014 and Luck & Vogel 2013 .

NEURAL MECHANISMS UNDERLYING WORKING MEMORY

There are many grains of detail at which a “mechanism” – “the process by which something takes place” – can be considered. Here we will first consider evidence for the general ideas of activated LTM and of sensorimotor recruitment, at a relatively abstract level. Subsequently, we will consider specific systems-level neural mechanisms that may underlie these phenomena. To anticipate one conclusion, it is likely that there are numerous neural mechanisms that can support the short-term retention of information in working memory, and many likely operate in parallel.

The “neural plausibility” of state-based accounts of working memory

One reason that state-based models of working memory have gained prominence in recent years is that cognitive neuroscience research has shown them to do a good job of accommodating neural data. This is particularly true since the advent of applying multivariate pattern analysis (MVPA) techniques to human functional neuroimaging data. (These techniques have been summarized in many places, one of them being Lewis-Peacock and Postle, 2012 ). With regard to the idea of the temporary activation of LTM, for example, Lewis-Peacock and Postle (2008) have done the following. First, they scanned subjects with functional magnetic resonance imaging (fMRI) while the subjects made judgments that required accessing information from LTM: the likability of famous individuals; the desirability of visiting famous locations; the recency with which they had used a familiar object. Next, outside the scanner, they taught subjects arbitrary paired associations among items in the stimulus set. Finally, they scanned subjects a second time, but this time while performing delayed recognition of paired associates (i.e., see one item from the LTM memory set at the beginning of the trial, and indicate whether or not the trial-ending probe is that item’s associate). The finding was that multivariate pattern classifiers trained on data from the first scanning session, when subjects were accessing and thinking about information from LTM, were successful at decoding the category of information that subjects were holding in working memory in the second scanning session. Such an outcome could only be possible if the working memory task and the LTM task drew on the same neural representations.

MVPA has also been used to generate compelling evidence for sensorimotor recruitment models of working memory. Thus, for example, two studies have demonstrated that primary visual cortex (V1) supports the delay period-spanning representation of the color or orientation of target stimuli on tests of delayed recognition ( Harrison & Tong 2009 , Serences et al. 2009 ). This pattern of results has been replicated with other classes of stimuli: the short-term retention of motion can be decoded from lateral extrastriate cortex, including area MT+, as well as from medial calcarine and extracalcarine cortex ( Riggall & Postle, 2012 , Emrich et al. 2013 ); the short-term retention of complex visusospatial patterns can be decoded from occipital and parietal cortex ( Christophel et al. 2012 ); and the short-term retention of familiar objects, faces, houses, scenes, and body stimuli can be decoded from ventral occipitotemporal cortex (Han et al. 2013, Lee et al. 2013 , Nelissen et al. 2013, Sreenivasan et al. 2014 ). Finally, in relation to the frontoparietal salience map, Jerde and colleagues (2012) have demonstrated that a classifier trained only on performance of a test of attention to location, or only on performance of oculomotor delayed response, or only on performance of spatial delayed recognition, can recover trial-specific target direction from any of the three trial types. That is, for example, a classifier trained to discriminate leftward from rightward sustained attention can also correctly discriminate leftward from rightward motor preparation, and leftward from rightward spatial STM, even though it has never been trained on the latter two. Thus, the functions that we label attention , intention , and retention may be treated identically by the brain.

Perhaps the most compelling evidence in support of sensorimotor recruitment models of working memory derive from two studies using multivariate approaches that have linked the precision of the delay-period neural representation of target stimuli in sensory cortex with behavioral estimates of mnemonic precision, showing that “the relative ‘quality’ of … patterns [of activity in sensory cortex] determine the clarity of an individual’s memory” ( Ester et al. 2013 ). In one, Ester and colleagues (2013) showed that the precision of population tuning curves in V1 and V2v estimated from the delay-period signal from these regions predicted the fidelity with which a subject was able to reconstruct the target orientation at the end of the delay period. In another, Emrich et al. (2013) varied from trial to trial the number of directions of motion that had to be remembered, and found a reliable within-subject correlation between the load-related decline in delayed-recall precision, and a load-related decline in MVPA decoding performance.

Complementing these fMRI studies are results of studies using transcranial magnetic stimulation (TMS) to alter activity in sensorimotor regions during the delay period of tests of working memory for visually presented stimuli. Hamidi and colleagues (2008 ; 2009) , for example, have shown that delay-period repetitive TMS of parietal cortex and frontal eye fields selectively affects spatial working memory performance. A more nuanced approach, taken for working memory for visual motion, has leveraged the fact that TMS of visual area MT can produce the percept of a “moving phosphene” – a flash of light that contains coherent motion within the area of the flash. The perceived direction of motion is reproducibly toward the periphery, away from the fovea, in the visual field contralateral to the side of stimulation. Silvanto and Cattaneo (2010) demonstrated that this percept is systematically influenced when TMS is delivered while the subject is engaged in STM for the direction of motion of a target stimulus. When the target motion is in the same direction as the expected motion of the phosphene, the perception of the moving phosphine is enhanced. However, when the target motion is in the opposite direction, perception of the moving phosphene is reduced. These results indicate that the physiological state of MT varies systematically as a function of the direction of motion being remembered, just as it does, when a stimulus is present, as a function of the direction of motion being perceived.

Working memory at the systems level

Working memory does not derive from a discrete system, as do vision and motor control. Rather, working memory is a property of the brain that supports successful attainment of behavioral goals that are being carried out by any of several systems, including sensory systems, those that underlie semantic and episodic memory, and motor systems. We next review five neural mechanisms that likely underlie working memory function.

Persistent Neural Activity

The study of the neural underpinnings of working memory took a significant leap forward in 1971 with the publication of two studies featuring extracellular recordings from the PFC in monkeys performing working memory tasks. In one, Fuster and Alexander (1971) reported that PFC neurons exhibited persistent activity during the stage of a delayed-response task in which the monkey had to actively maintain information that was no longer present, yet relevant for successfully completing the task. In the second, Kubota and Niki (1971) reported a comparable finding during the delay period of a delayed alternation task. The ability of neurons to generate persistent activity in the absence of external stimuli is likely of fundamental importance to the neural basis of working memory. Following on these landmark discoveries, many other labs have found such “working memory-related” neurons within the PFC (e.g. Funahashi et al. 1989 ; Miller et al., 1996 ). With the advent of fMRI in the early 1990s, it was subsequently demonstrated that human PFC also exhibits persistent neural activity that appeared to be coding task-relevant information during working memory tasks (see Courtney et al., 1997 and Zarahn et al., 1997 for the first of such studies). Many characteristics of this activity seems consistent with the notion that it reflects the maintenance of representations critical for guiding behavior. First, it endures throughout the entire length of the delay-period until it can be presumably used to guide a response ( Fuster & Alexander, 1971 Funahashi et al. 1989 ). Second, it directly relates to behavior. For example, during the performance of an oculomotor delayed-response task, the magnitude of fMRI signal in frontal cortex reflects the fidelity of the maintained representation ( Curtis et al., 2004 ). An open question that cannot be answered with fMRI is the mechanism that underlies persistent neural activity. Specifically, the relative importance of cortico-cortical loops (long-range recurrent interactions), thalamo-cortical loops, or local cortical mechanisms (such as excitatory reverberation), for the generation of persistent neural activity has yet to be determined ( Wang 2001 ; Pesaran et al. 2002 ).

In addition to the circuit-level questions summarized above, recent research applying MVPA to fMRI and electroencephalography (EEG) data has raised intriguing questions about the functions supported by persistent neural activity. These questions fall into two categories, one relating to the nature of the persistent delay-period activity that supports the short-term retention of information, the second relating to the very necessity of this activity. The first question arises from dissociations between the elevated activity that is classically observed in frontoparietal regions, and subthreshold patterns of activity that MVPA detects in sensory processing-related regions. As first described by Tong (2009) and by Serences (2009) , the delay-period retention of visual stimulus information can be decoded from primary visual cortex, despite the absence of sustained, elevated signal levels of signal in this region. Subsequent studies by Riggall & Postle (2012) , and by Emrich et al. (2013) replicated these findings, and also explicitly failed to find evidence for stimulus information in the elevated delay-period activity that was present in frontal and parietal regions. Further, Sreenivasan et al. (2014) showed that the magnitude of above-threshold delay-period activity does not correlate positively with the feature weightings that underlie MVPA classification. One implication of all these studies is that above-threshold delay-period activity may support functions other than information storage per se. What these functions may be is the topic of the final section of this review. A second implication is that the neuronal processes that drive the MVPA-decodable activity in sensory areas are operating at a level that is subthreshold from the perspective of traditional univariate statistics. Thus, an important focus of future research will be to understand the nature of this “subthreshold” delay-period activity. One candidate explanation is that it simply reflects reduced spiking at the population level, as would be expected for a sensory area in the absence of a bottom-up drive. A second possibility, mutually compatible with the first, is that MVPA may be detecting regional heterogeneity in oscillations of local field potentials (LFPs). That is, delay-period stimulus representations may be encoded in LFPs that persist in the same networks that exhibit elevated firing when the stimulus is present.

A second question highlighted by MVPA of fMRI and EEG data is whether persistent activity is even necessary for the retention of information in working memory. This was raised when Lewis-Peacock and colleagues (2012) scanned subjects performing a multistep delayed-recognition task that first presented two sample stimuli, then a retro-cue indicating which of the two would be relevant for the first memory probe, then, following this first probe, a second retro-cue indicating which stimulus would be relevant for the trial-ending second memory probe. With this procedure, an item could be irrelevant for the first memory probe (an “unattended memory item”), but then relevant for the second memory probe. Although the authors initially predicted that MVPA evidence for unattended memory items would take on an intermediate level between the item that was in the FoA and baseline, this is not what they found. Instead, in response to the retro-cue, MVPA evidence for the unattended memory item dropped to baseline levels. This item nonetheless remained “in” working memory, as evidenced by its successful retrieval if cued by the second retro-cue. This finding has been replicated in an EEG study, thereby discounting the possibility that the unattended memory item may be transferred to an oscillatory code to which fMRI is insensitive ( LaRocque et al. 2013 ). These findings, therefore, highlight the intriguing possibility that persistent neural activity may not be necessary for maintaining representations held in working memory. Indeed, this possibility has also been explored by researchers working at other levels of investigation, including computational modeling, in vitro electrophysiology, and extracellular recordings in the behaving monkey. Computationally, it has been suggested that information can be sustained over brief intervals via rapid shifts in synaptic weights. In such a scenario, the encoding of the sample stimulus would be accomplished via a transient reconfiguration of synaptic weights in the networks engaged in its initial processing. The contents of working memory could then be read out when the network was activated by a subsequent sweep of activation through this network ( Itskov et al. 2011 , Mongillo et al. 2008 , Sugase-Miyamoto et al. 2008 ). Empirical evidence that is consistent with such a mechanism has been recorded from ventral temporal cortex ( Sugase-Miyamoto et al. 2008 ) and PFC ( Stokes et al. 2013 ) in monkeys. What mechanism could support the short-term synaptic facilitation that would be needed to implement such a scheme? Theoretically, “residual” presynaptic calcium levels have been proposed ( Mongillo et al. 2008 ). Empirically, an associative short-term potentiation has been demonstrated to be gluR1-dependent in an in vitro preparation ( Erickson et al. 2010 ) Clearly, the relative contribution of persistent neural activity versus other mechanisms that do not rely on above-baseline activity to sustain working memory representations should be a high priority for future research.

Whether or not WM representations are maintained via persistent neural activity, synaptic mechanisms, or some combination of both, these storage mechanisms are consistent with state-based models of working memory, which eliminate the need for currently relevant representations to be transferred to a limited number of dedicated, specialized buffers ( Postle 2006 , D’Esposito 2007 ). In neural terms, any population of neurons can serve as a buffer. Moreover, the ability to exhibit persistent neural activity, or a shift in synaptic weights, is likely a property of all neurons, from primary cortex to multimodal association cortex. In other words, networks of neurons located anywhere in the brain can potentially store information that can be activated in the service of goal-directed behavior.

Hierarchical representations in prefrontal cortex

What is the nature of the neural code within PFC? Some have put forth the idea that persistent activity in PFC represents sensory features of information maintained in working memory (Goldman-Rakic 2005). Indeed, in the systems and cognitive neuroscience literatures, one can see that the waxing and waning of popularity of “stimulus representation” models of the PFC tracked very closely the multicomponent model of working memory. More recently, there has been greater emphasis on the fact that PFC actually exhibits at best, coarse selectivity for items and features maintained in working memory ( Constantinidis et al. 2001 ). Further, PFC delay-period activity can represent a broad range of task variables that are not directly related to the to-be-remembered stimuli. For example, lateral PFC neurons recorded from monkeys exhibit differential preferences for task rules ( Warden & Miller 2010 ), contingent motor responses ( Romo et al. 1999 ), and stimulus–response mappings ( Wallis et al. 2001 ). Studies examining population coding of lateral PFC delay activity have similarly found information about stimuli ( Stokes et al. 2013 ), rules ( Riggall & Postle 2012 ), and object categories ( Meyers et al. 2008 ) throughout the delay period of working memory tasks. In fact, Rigotti and colleagues have recently demonstrated that neuronal activity within PFC is tuned to mixtures of multiple task-related variables, suggesting that PFC representations exhibit high-dimensionality ( Rigotti et al. 2013 ). That is, a high number of dimensions is needed to characterize the distinct (multivariate) patterns that can be taken on by the sampled population of neurons across a variety of experimental conditions. Moreover, it was shown that this dimensionality is predictive of the animal’s behavior, in that the population of PFC neurons exhibited a decrease in dimensionality on error trials. Interestingly, the authors of the very first reports of persistent activity within the PFC offered interpretations that are in line with many current models. For example, Fuster and Alexander [1971] wrote that:

The temporal pattern of firing frequency observed in prefrontal and thalamic units during cue and delay periods suggest the participation of these units in the acquisition and temporary storage of sensory information which are implicated in delay response performance. Their function, however, does not seem to be the neural coding of information contained in the test cues, at least according to a frequency code, for we have not found any unit showing differential reactions to the two positions of the reward.It is during the transition from cue to delay that apparently the greatest number of prefrontal units discharge at firing levels higher than the intertrial baseline… We believe that the excitatory reactions of neurons in MD and granular frontal cortex during delayed response trials are specifically related to the focusing of attention by the animal on information that is being or has been placed in temporary memory storage for prospective utilization.” (p. 654)

Several human fMRI studies have directly investigated the nature of representations being maintained in PFC as compared to posterior cortical regions. In one study, subjects viewed a sample display of dot motion, then, halfway through the delay period, were cued as to whether they would be probed on memory for the speed or the direction of the sample motion. Delay-period MVPA decoding of stimulus direction was only successful at lateral and medial regions of occipital cortex that are associated with visual perception. PFC, however, was seen to represent a more abstract level of task performance: whether a trial was a “speed” trial or a “direction” trial ( Riggall & Postle, 2012 ). A different study using different stimuli but a similar procedure, found analogous results. In it, subjects first viewed a common object, and were then cued as to whether the memory probe would require a fine-grained perceptual judgment or a category-membership judgment. On perceptual trials, MVPA decoded stimulus identity in ventral occipitotemporal cortex, but not PFC. On category trials, MVPA decoded stimulus category from PFC, but not occipitotemporal cortex ( Lee et al. 2013 ). These two findings are consistent with prior studies demonstrating that the lateral PFC preferentially encodes and maintains arbitrary and abstract representations of object category over representations of visual similarity ( Meyers et al. 2008 , Freedman et al. 2001 , 2003 , Chen et al. 2012 ). Further support for the distinction between stimulus-selective lateral PFC representations and sensory representations comes from a second fMRI study that required subjects to remember over a short interval either faces, scenes or both categories of information ( Sreenivasan et al. 2014 ). It was reasoned that if a region supports a sensory representation of working memory stimuli, then the “remember faces” trials should be incorrectly classified as “remember both” trials more often than they should be misclassified as “remember scenes” trials, because the sensory representation of faces is more similar to the representation of remembering faces and scenes together than it is to remembering only scenes. Similarly, “remember scenes” trials should also be disproportionately misclassified as “remember both” trials if activity patterns encode sensory representations. The findings from this fMRI study suggested that what is represented by PFC is higher-order information, such as task rules, goals, or abstract representations of the categories, as compared to what is represented by extrastriate cortex, which may be more stimulus-specific (e.g., the identity of specific faces).

These empirical findings fit nicely with the original theoretical notions put forth by Fuster (1990) , and Miller and Cohen (2001) , that integrated representations of task contingencies and rules are maintained in the PFC, which is critical for the mediation of events separated in time but contingent on one another. This formulation of PFC function places less emphasis on a storage role and instead emphasizes its role in providing top-down control over all other brain regions where information is actually stored ( Smith & Jonides, 1999 ; D’Esposito et al. 2000 , Petrides 2000 ). Thus, the sustained activity in the PFC does not reflect the storage of representations, per se; it reflects the maintenance of high-level representations that provide top-down signals that can guide the flow of activity across brain networks. This idea will be expanded upon in the next section. However, first, we must consider the nature of the information represented within PFC with respect to the functional organization of PFC as a whole.

The PFC is a heterogeneous region covering a significant amount of territory in the brain. In this review we are focusing on lateral PFC, and not medial or orbital PFC regions, which likely have distinct yet complementary functions ( Cummings 1993 ). Any understanding of the nature of the representations stored and maintained in PFC necessary for goal-directed behavior must consider sub-regional differences in both cellular makeup and connectivity. Numerous neuropsychological, physiological, imaging studies support the general idea that as one moves rostral (anteriorly) in the frontal cortex, from premotor cortex to frontopolar cortex, the processing requirement of these regions necessary for planning and selection of action are of higher-order ( Burgess et al. 2007 , Christoff et al. 2003 , Ramnani & Owen, Koechlin and colleagues (2003) have put forth a hypothesis that frontal cortex may be organized from rostral to caudal in a hierarchical fashion en route to action (also see Fuster (2004) for earlier formulation of a similar idea). Specifically, a ‘cascade model’ is proposed ( Koechlin & Summerfield, 2007 ) that predicts that competition among alternative action representations is resolved based on mutual information with various contextual information, termed control signals. Using fMRI in healthy subjects, Koechlin and colleagues (2003) found support for their predictions by demonstrating that as contextual information required to select a response was more abstract and relevant over a longer temporal interval, fMRI activation progressed from caudal to more rostral regions of frontal cortex.

In an fMRI study ( Badre & D’Esposito, 2007 ), we aimed to replicate and extend Koechlin’s findings regarding the proposed rostral-caudal functional gradient along frontal cortex. We specifically tested an alternative idea that this gradient derives from a hierarchy ranked by the abstractness of the representation to be selected. In this study healthy subjects performed a response selection task that required more abstract action decisions to be made across behavioral conditions. The lowest level of the task performed was called the Response task where subjects learned that a colored square corresponded to a particular finger response. At the next level called the Feature task, each colored square corresponded to a particular shape, and then subjects chose their motor response if the colored square matched the shape. Thus, at this level, there is not enough information in color alone to determine the correct response. The object shape had to be considered in conjunction with the color to make a response. The only difference from the Response task was that the colors now mapped to relevant shapes that cued a correct response, rather than mapping directly to the correct response. In other words, an action decision must be based on a more abstract action representation. At the next level called the Dimension task, subjects learned that a particular color corresponded to a particular dimension of an object (shape or orientation), and they were required to compare the two objects along a particular dimension and indicate with a motor response whether the objects matched or mismatched along only the relevant dimension. The subject knew which dimension was relevant based on the color of the square bounding the objects. Hence, the design for the mappings was identical to the Feature and Response task, except that now color mapped to dimension rather than feature or response. Again, the action decision must be based on more abstract representation. The final and highest level was called the Context task where subjects performed the Dimension task, however, conflict was manipulated by varying the frequency of the sets of color to dimension mappings. In this case, the temporal context was required to select the appropriate context (the color cue) for determining the dimension. Thus, selection of the relevant context was more abstract.

During the lowest level Response task, activation was found in posterior frontal cortex within premotor cortex (PMd, area 6). At the next higher level Feature task, activation was found anterior to premotor cortex within pre-premotor cortex (pre-PMd; area 8). On the next higher level Dimension task, activation was noted anterior to this location within the inferior frontal sulcus (IFS) on the border of areas 45 and 9/46. Finally, activation on the highest level Context task was found in the most anterior location within frontopolar cortex or area 10. Thus, as action representations became more abstract, activation within frontal cortex moved anteriorly (or rostrally). Importantly, this progression of activation from posterior to anterior portions of frontal cortex was not simply due to the task becoming more complex or difficult, because we also varied the difficulty within each individual task (e.g. Response , Feature , Dimension or Context ), and found that activation within that particular region engaged by each task increased in magnitude with difficulty but did not change it’s location within frontal cortex. In contrast to the emphasis of Koechlin et al. (2003) on temporal and contextual factors in differentiating regions of frontal cortex, these results suggest that regions of PFC may be differentiated by the level of abstraction at which the action representations must be selected over competition.

Thus, human fMRI studies support the notion that there is a functional gradient along the anterior-to-posterior axis of the frontal lateral cortex. A similar functional gradient relating to motivational aspects of cognitive control has been identified along the medial PFC axis ( Kouneiher at al., 2009 , Venkatraman et al., 2009 ) and functional connectivity between medial and lateral PFC has been observed ( Blumenfeld et al. 2013 ). It is important to point out that consensus has not been reached regarding the specific details of functional gradient observed in PFC (see Badre 2008 for review). Nevertheless, an important component of emerging models of the organization of lateral PFC is that a hierarchy exists. A processing hierarchy within the frontal cortex requires that anterior regions influence the processing in posterior regions more than posterior regions influence anterior regions. How can one obtain direct evidence to support this claim? Essential clues (albeit indirect ones) regarding a hierarchical rostro-caudal organization of the frontal lobe can be derived from its anatomical organization. If there were a hierarchical arrangement, anatomical connectivity among PFC subregions would likely display a pattern where area 10, at the highest level would have projections back down to area 6 at the lowest level. However, area 6 would not necessarily project back up to area 10. Such a pattern does appear to exist, at least in rhesus monkeys ( Badre & D’Esposito, 2009 ). Barbas and Pandya (1991) have also noted that different frontal regions have different degrees of differentiation at the columnar level. More differentiated regions are more laminated (e.g. aggregation of cells into cortical layers). Caudal areas with well-developed laminar differentiation (such as area 8 or caudal 46) have restricted connections mostly to neighboring regions. In contrast, rostral areas that have less laminar differentiation (such as area 10) have widespread connections to other areas. In this scheme, less differentiated areas such as those in rostral PFC (areas 10, 9, 46), which have more diffuse projections, are well situated to be the top of a hierarchy. In contrast, more differentiated areas such as those in caudal PFC (area 9/46, 8) have more intrinsic connections and are well situated to be lower in a hierarchy.

Further indirect evidence for a hierarchical organization within lateral PFC derives from functional neuroimaging studies examining effective connectivity, or the causal influence that one brain region may have on another. For example, in the Koechlin et al. (2003) study previously mentioned, structural equation modeling of the imaging data showed that activation in rostral frontal regions accounted for variance in activation in caudal frontal regions but not vice versa. Direct evidence for a hierarchical organization within lateral PFC requires lesion data. That is, a rostral to caudal flow of control processing within the frontal lobes predicts that performance on tasks requiring higher order control should be impaired by disruptions to lower order processors, even when the higher order processors are intact. However, when a higher order control processor is disrupted, performance should be unaffected on tasks that require only lower order control. This hypothesized asymmetric pattern of deficit cannot be directly tested with neurophysiological methods such as fMRI, EEG, and single-unit recording. Rather, it requires a lesion method that leads to isolated disruption of specific processors along the proposed hierarchical gradient.

Additionally, using the cognitive tasks we implemented in the fMRI study, we have carried out a behavioral study of patients with focal frontal lesions to test the hypothesis that there is a hierarchical organization in frontal cortex ( Badre et al. 2009 ). Specifically, we tested whether a lesion to the pre-PMd region of frontal cortex (area 8), assumed to damage a 2nd level processor, would impair performance on the Feature task as well as the Dimension and Context tasks, but would not affect performance on the Response task. The reasoning was that disruption of the 2 nd level of a hierarchy should interfere with processing at higher levels ( Feature , Dimension , and Context tasks at the 3rd and 4th levels), but not at lower levels ( Response task, 1st level). By contrast, a more anterior IFS lesion (areas 45; 9/46), which would damage a 3rd level processor, should impair performance on the Dimension task (3 rd level) as well as the Context task (4th level), but not on the Feature (2 nd level) or Response (1 st level) tasks. Such a pattern of behavioral results in patients with focal frontal lesions would be direct evidence for a hierarchical organization of frontal lobe function. We predicted that because of the asymmetric dependencies predicted by a hierarchy, deficits in higher level tasks will be more likely across patients, regardless of the site of their lesion, than deficits in lower level tasks. Thus, the presence of an impairment at any level should increase the likelihood of an impairment at all higher levels, but should not increase the odds of an impairment at a lower level. We observed that the probability of a deficit on any task was 62% across the patients. Critically, however, the probability of a deficit at any level given a deficit at a lower level was 91% across patients, a significant change over the probability of a deficit on any task. By contrast, the probability of a deficit at any level given a deficit at a higher level was only 76%, a weak change over the prior probability of a deficit on any task. This asymmetry provides initial support for the hierarchical dependencies among behavioral deficits at the different levels of the task and the aggregation account of the group data. Recently, this pattern of findings supporting a frontal hierarchy has been replicated in another group of patients with focal frontal lesions ( Azuar et al. 2014 ).

Hierarchical organization of rules and goals has many advantages. For example, increasingly abstract representations of rules and goals could serve as different top-down signals that could bias particular but different action pathways over competitors allowing for flexible goal-directed behavior. Take the example of the seemingly simple act of hitting a golf ball. Hitting the ball in the proper direction requires temporary maintenance of the location of the flag on the green – a relatively concrete representation. If the golf ball is in a fairway bunker, it also requires the temporary maintenance of more abstract representation of the golf rule stating that the golf club cannot touch the sand before hitting the ball, or a penalty will be assessed. Finally, throughout this act of hitting the ball it might also be beneficial to maintain an even more abstract representation of the knowledge that golf provides exercise and is a healthy behavior. In this way, simultaneous maintenance of hierarchically organized representations within PFC can provide independent, yet likely interactive top-down bias signals that may (or may not) lead to a successful goal-directed behavior!

Top-down signaling

The PFC has long been implicated as a source of top-down signals that can influence processing in other cortical and subcortical brain regions ( Duncan 2001 , Fuster 2008 , Shallice 1982 , Braver et al. 2008 ). One type of PFC top-down signal likely provides direct feedback to posterior cortical regions that process incoming sensory input from a particular modality (e.g., visual or auditory). For example, when a person is looking into a crowd of people, the visual scene presented to the retina may include a vast array of visual information. However, if someone is searching for a friend, some top-down mechanism must exist that allows for suppressing irrelevant visual information while enhancing task-relevant information allowing for an efficient yet effective search. In this way, the maintenance and representation of the goal (e.g. find your friend) by PFC serves as a bias signal. As Miller and Cohen (2001) have stated, “cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task”. As described earlier in this review, given that the PFC represents rules and goals at multiple levels of abstraction, it is in an ideal position to influence processing in downstream brain regions that receive its anatomical projections.

We have used fMRI and evoked-related potentials (ERP) in humans to investigate such top-down mechanisms ( Gazzaley et al. 2005 ). In this study, during each trial of a working memory task participants observed sequences of two faces and two natural scenes presented in a randomized order. In separate blocks of trials subjects were required to Remember Faces and Ignore Scenes , Remember Scenes and Ignore Faces , or Passively View faces and scenes without attempting to remember them. Since each trial had equivalent bottom-up visual information (e.g. faces and scenes), we could directly determine if top-down signals were engaged. Moreover, the inclusion of a passive baseline allowed for the dissociation of possible enhancement and suppression mechanisms. With both fMRI and ERP we obtained activity measures from areas of visual association cortex specialized in face and scene processing. For fMRI, we used an independent functional localizer to identify both stimulus-selective face regions (within the fusiform face area or FFA; Kanwisher et al. 1997 ) and scene regions (within the parahippocampal place area or PPA; Epstein & Kanwisher, 1998 ). For ERP, we utilized a face-selective ERP, the N 170 , a component localized to posterior occipital electrodes reflecting visual association cortex activity with face specificity ( Bentin et al. 1996 ). Our fMRI and ERP data revealed top-down modulation of both activity magnitude and processing speed that occurred above and below the perceptual baseline depending on task instruction. That is, during the encoding period of the delay task, FFA activity was enhanced, and the N 170 occurred earlier, when faces had to be remembered as compared to a condition where they were passively viewed. Likewise, FFA activity was suppressed, and the N 170 occurred later, when faces had to be ignored compared to a condition where they were passively viewed. These results suggest that there are least two types of top-down signals, one that serves to enhance task-relevant information, and the other that serves to suppress task-relevant information. By generating contrast via enhancing and suppressing activity magnitude and processing speed, top-down signals can bias the likelihood of successful representation of relevant information in a competitive system ( Hillyard et al. 1973 , Moran & Desimone 1985 , Corbetta et al. 1990 ).

With fMRI or any type of neurophysiological method applied to animals or humans, there is no direct way to determine the source of top down signals. Thus, to obtain evidence that the PFC is the source of top-down signals that modulate visual association cortex, the physiological responses of visual association cortex must be examined after disruption of PFC function ( Miller & D’Esposito, 2005 ). The first attempt at such an approach was performed by Joaquin Fuster and colleagues (1985) in monkeys where the effect of PFC inactivation by cooling on spiking activity in inferotemporal cortex neurons during a delayed-match-to-sample color task was investigated. During the delay interval in this task – when persistent stimulus-specific activity in inferotemporal cortical neurons is observed – PFC inactivation caused attenuated spiking activity and a loss of stimulus-specificity of inferotemporal cortical neurons. These two alterations of inferotemporal cortex activity strongly implicated the PFC as a source of top-down signals necessary for maintaining robust sensory representations in the absence of bottom-up sensory activity.

Many years passed before any other attempt was made with animals or humans to follow-up this landmark finding by Fuster. In fact, the combined lesion/electrophysiological approach continues to be rarely implemented. Translating this approach to humans, Chao and Knight (1998) investigated patients with lateral PFC lesions during delayed match-to-sample tasks. It was found that when distracting stimuli are presented during the delay period the amplitude of the ERP recorded from posterior electrodes was markedly increased in patients with frontal lesions compared to controls. These results were interpreted as demonstrating disinhibition of sensory processing supporting a role of the PFC in suppressing the representation of task-irrelevant stimuli. Recently, we investigated the causal role of the PFC in the modulation of evoked-activity in human extrastriate cortex during the encoding of faces and scenes ( Miller, et al. 2011 ). We employed two experimental approaches to disrupt PFC function: TMS of PFC in healthy subjects and focal PFC lesions in stroke patients. We then investigated the effect of disrupted PFC function on the selectivity of category representations (faces or scenes) in temporal cortex. Different object categories, like faces and scenes are represented by spatially distributed, yet overlapping, assemblies in extrastriate visual cortex ( Haxby, et al. 2001 ). Thus, we reasoned that disruption of PFC function would lead to higher spatial correlations between scene- and face-evoked activity in extrastriate cortex, suggesting a decrease in category selectivity. Consistent with our predictions, following disruption of PFC function (i.e., TMS session vs. baseline, or lesion vs. intact hemisphere in stroke patients), stimulus-evoked activity in extrastriate cortex exhibited less distinct category selectivity to faces and scenes (more spatial overlap). In a follow-up study ( Lee & D’Esposito, 2012 ), we were able to further demonstrate that the decreased tuning of extrastriate cortex response coincided with decrements in working memory performance. This work extended the findings of Fuster and colleagues (1985) in monkeys to humans and suggests that the PFC may sharpen the representations of different object categories in extrastriate cortex by increasing the distinctiveness of their distributed neural representations. These findings are also consistent with other recent combined TMS/fMRI and TMS/EEG studies demonstrating decreased attentional modulation of stimulus-selective visual regions following PFC disruption ( Feredoes et al. 2011 , Higo et al. 2011 , Zanto et al. 2011 ). Together, such causal evidence clearly supports the notion that the PFC is the source of top-down signals that act via both gain and selectivity mechanisms.

A key to understanding the role of the PFC in cognition likely rests in its connectivity with other regions ( Yeterian et al., 2012 ). Any top-down signal from a particular PFC region, representing a particular goal, could have a different influence and behavioral consequence depending on what brain regions are recipients of these signals. For example, PFC top-down signals could enhance internal representations of relevant sensory stimuli in extrastriate cortex or anticipated motor plans in premotor cortex. It is likely that multiple top-down signals are engaged in a parallel fashion during the evolution of any goal-directed behavior. Moreover, other cortical regions, such as the parietal cortex and hippocampus have also been proposed to provide top-down signals during cognition ( Eichenbaum , Ruff 2013 ). Consideration of the mechanisms by which multiple higher-order brain regions can influence lower-order brain regions highlights the enormous complexity of the human brain, and how much further we must travel to understand it.

Long-range connectivity

Another mechanism critical for working memory is the synchronization of activity among distributed brain regions. Owing to the limitations in available methodology in both animals and humans, only a limited number of studies to date have been able to assess if and how neurons and brain regions communicate and interact to support working memory. We developed a multivariate method designed specifically to characterize functional connectivity in event-related fMRI data that can measure inter-regional correlations during the individual stages of a cognitive task ( Rissman et al. 2004 ). Using this method, we specifically sought to characterize the network of brain regions associated with the maintenance of a representation of face stimuli over a short delay interval. With this approach ( Gazzaley et al., 2004 ), we found significant functional connectivity between the FFA and the PFC and parietal cortex during the delay period of the task, which supports the notion that higher order association cortices interact with posterior sensory regions to facilitate the active maintenance of a sensory percept. Similarly, we have also found that posterior language-related areas involved in the maintenance of words in the absence of visual input also exhibit increased functional connectivity with the PFC ( Fiebach et al. 2006 ).

Distributed synchronized activity could occur via synaptic reverberations in recurrent circuits ( Wang 1999 , Durstewitz et al. 2000 ), or synchronous oscillations between neuronal populations ( Buzsaki & Draguhn, 2004 , Fries, 2005 , Singer 2009 ). In humans, electroencephalographic (EEG) magnetoencephalographic (MEG) and electrocorticographic (ECoG) recordings have been utilized to investigate which particular frequencies of oscillations may be related to working memory. Activity in low and high frequencies in the theta (4–7 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–200 Hz) ranges have all been found to be modulated during working memory tasks (for a comprehension review of 26 studies see Roux & Uhlhaas, 2014 ). Roux and Uhlhaas (2014) have proposed a different functional role for each of these frequency bands. Specifically, they propose that gamma-band oscillations are involved in the active maintenance of working memory information, theta-band oscillations are specifically involved in the temporal organization of working memory items and alpha-band oscillations are involved in the inhibition of task-irrelevant information. These notions are based on studies that have demonstrated amplitude modulation of neural oscillations presumably emanating from particular brain regions involved in working memory. For example, during a delayed match to sample task while recording human EEG it was observed that occipital gamma and frontal beta oscillations were sustained across the retention interval. Moreover, as this delay interval lengthened, these oscillations decreased in parallel with decreased performance on the task ( Tallon-Baudry et al., 1999 ). In a recent study ( Anderson et al., 2014 ), it was shown the spatial distribution of power in the alpha frequency band (8 –12 Hz) tracked both the content and quality of the representations stored in visual WM. These empirical findings support the notion that neural oscillations are critical for working memory maintenance processes.

It is likely that long-range synchronization of these oscillations between brain regions also plays an important role in working memory function (Sauseng et al. 2010, Crespo-Garcia et al., 2013 ). For example, in a human MEG study, synchronized oscillations in the alpha, beta and gamma bands was observed between frontoparietal and visual areas during the retention interval of a delayed match-to-sample visual working memory task. Moreover, these observed synchronized oscillations were sustained and stable throughout the delay period of the task, memory load dependent, and correlated with an individuals working memory capacity ( Palva et al., 2010 ). Monkey physiology data have also provided considerable insight into the possible mechanisms underlying communication between brain regions during working memory. For example, in one study (Liebe et al. 2011), neural interactions between visual area V4 and lateral PFC were investigated during the performance of a visual delayed match to sample task. During the retention interval of the task, these two areas exhibited synchronization of local field potentials in the theta frequencies. Moreover, there was phase-locking of neuronal spiking activity in these two regions to these observed theta oscillations. Most importantly, the strength of this inter-cortical locking was predictive of the animal’s performance, that is, higher for subsequently correctly remembered stimuli and session-to-session variability in memory performance. The authors concluded that these findings reflect a mechanism for effective communication between brain regions involved in the temporary maintenance of relevant visual information, an idea also put forth by others ( Fries 2005 , Fell & Axmacher 2011 ). An intriguing recent finding suggests a critical role for the thalamus in regulating information transmission across cortical regions, at least at the local level ( Saalmann et al. 2012 ).

Brainstem Neuromodulators

In many models of cognition, neuromodulators such as dopamine, serotonin, norepinephrine or acetylcholine, play a limited role, if any role at all. Yet, given that brainstem neuromodulatory neurons send projections to all areas of brain, their influence on cognitive function is without question. For working memory, there is abundant evidence from both animal and human studies that dopaminergic modulation of fronto-striatal circuitry in particular, is critical for its function ( Cools & D’Esposito 2009 ).

Dopaminergic neurons in the human brain are organized into several major subsystems (mesocortical, mesolimbic and nigrostriatal). The mesocortical and mesolimbic dopaminergic systems originate in the ventral tegmental area of the midbrain and project to the frontal cortex, anterior cingulate, nucleus accumbens, and anterior temporal structures such as the amygdala, hippocampus and entorhinal cortex ( Bannon & Roth, 1983 ). Across the cerebral cortex, the concentration of dopamine is highest within the frontal cortex ( Brown et al. 1979 , Williams & Goldman-Rakic 1993 ). However, there is also a strong dopaminergic input into the hippocampus ( Samson et al 1990 ), and there is abundant evidence from both animal and humans studies that dopamine is involved in hippocampal-dependent long-term memory (for a review of this topic see Shohamy & Adcock, 2010 ).

The functional importance of dopamine to working memory and PFC function has been demonstrated in several ways. First, in monkeys depletion of PFC dopamine or pharmacological blockade of dopamine receptors induces working memory deficits ( Brozoski et al. 1979 ; Sawaguchi & Goldman-Rakic 1991 ). These deficits are as severe as in monkeys with PFC lesions, and are not observed in monkeys in which other neurotransmitters, such as serotonin are depleted. Furthermore, dopaminergic agonists administered to monkeys with dopamine depletion reverses their working memory deficits ( Brozoski et al. 1979 , Arnsten et al. 1994 ). Likewise, numerous studies have shown that administration of dopamine receptor agonists to healthy young human subjects improves working memory performance (Luciana & Collins, 1992, Kimberg, et al. 1997 , Muller et al. 1998 , Kimberg & D’Esposito, 2003 ). An important feature of the dopaminergic system is that it exhibits a U-shaped dose-response curve which leads to specific dosages of dopaminergic drugs producing optimal performance on working memory tasks ( Arnsten et al. 1997 , Kimberg et al. 1997 , reviewed in detail in Cools, 2011 ). These observations illustrate that “more” is not “better” but rather an optimal brain dopamine concentration is necessary for optimal working memory function.

Different classes of dopamine receptors exist in varying concentrations throughout the brain. D-2 dopamine receptors are present in much lower concentrations in the cortex than D-1 receptors, and are mostly within the striatum ( Camps et al. 1989 ). However, D-2 receptors are at their highest concentrations in the PFC ( Goldman-Rakic et al. 1990 ). Moreover, dopamine release in the brain can be either transient (phasic) or sustained (tonic). Grace (2000) has proposed that these two mechanisms of action of dopamine are functionally distinct and antagonistic. Specifically, it is proposed that tonic dopamine release is mediated by D1 receptors whereas D2 receptor mediated effects are phasic. In support of this notion, during performance of a working memory task in monkeys, a dopamine D2 receptor agonist selectively modulated the phasic component of the task yet had little effect on the persistent mnemonic-related activity, which was instead modulated by a D1 receptor agonist ( Sawaguchi et al 2001 , Wang et al. 2004 ). Thus, these two dopamine receptors likely have complementary functions, which serve to modulate active memory representations stored within PFC ( Cohen et al. 2002 ). The dual-state theory of PFC dopamine function put forward by Durstewitz and Seamans (2008) states that a D1-dominated state favors robust online maintenance of information, while a D2-dominated state is beneficial for flexible and fast switching among representational states.

Regarding working memory function, it is proposed that tonic dopamine effects may increase the stability of maintained representations whereas phasic dopamine effects may serve as a gating signal, indicating when new inputs should be encoded and maintained, or when currently maintained representations should be updated ( Braver & Cohen 1999 ). In this way, two separate mechanisms underlie cognitive flexibility and stability that nevertheless must work together: dopamine would promote stability or flexibility of maintained representations depending on the neural site of modulation ( Cools & Robbins, 2004 ). Specifically, dopamine receptor stimulation in the PFC would promote stability by increasing distractor-resistance ( Durstewitz et al., 2000 ). Conversely, dopamine receptor stimulation in the striatum would promote flexibility by allowing the updating of newly relevant representations ( Frank et al. 2001 , Bilder et al., 2004 ). In the context of real world situations, demands for cognitive flexibility and stability are reciprocal: if we are too flexible, we are likely to become distracted; if we are too stable, we become inflexible and unresponsive to new information.

We have tested this dopaminergic model of working memory with a human pharmacological fMRI study ( Cools et al. 2007 ). Healthy young subjects underwent fMRI scanning on two occasions, once after intake of the dopaminergic agonist bromocriptine and once after placebo (in a double-blind, cross-over design). During scanning, subjects performed a working memory task that allowed the separate investigation of working memory updating and maintenance processes. Specifically, subjects had to encode, maintain and retrieve visual stimuli over a short delay. Two faces and two scenes were always presented during the encoding period and subjects were instructed to remember either the face or scenes. During the retention period another stimulus was presented, which subjects were instructed to ignore. This distractor was either a scrambled image or a novel face or scene. The critical measure of working memory updating was the behavioral switch-cost, which was calculated by subtracting performance (error rates and reaction times measured at probe) on trials where they switched to a new instruction as compared to remaining with the same instruction. The critical measure of working memory maintenance was the behavioral distractor-cost, which was calculated by subtracting performance (measured at probe) after scrambled as compared to non-scrambled distractors. We predicted that bromocriptine would modulate PFC activity during the epoch of the task following distraction, but the striatum would be modulated during the instruction epoch. This is exactly what we observed which is is consistent with the hypothesis that working memory maintenance and updating processes are modulated by differential dopaminergic stimulation of the PFC and striatum, respectively. This finding suggests that high levels of dopamine within the PFC (and lower levels in the striatum) optimizes the maintenance of task-relevant representations, whereas high levels of dopamine within the striatum (and low levels in the PFC) optimizes the flexible updating of information (for a more detailed review of dopaminergic functions, see Cools & D’Esposito, 2009 ). The functional opponency between stability and flexibility of working memory representations maps well onto the neurochemical reciprocity between DA in the PFC and the striatum: increases and decreases in PFC dopamine leads to decreases and increases in striatal dopamine respectively ( Pycock et al. 1980 , Meyer-Lindenberg et al, 2005 , Akil et al, 2003 ).

A working memory “gate” provides a computationally efficient mechanism for allowing information necessary for goal-directed behavior to be updated when it is open, but preventing current information to be sustained and irrelevant information to be kept out when it is “closed” ( O’Reilly & Frank 2006 , Badre 2012 ). Using high-resolution MRI of the midbrain, D’Ardenne (2008) and colleagues demonstrated activation in a region likely comprising the substantia nigra and ventral tegmental area during trials on a task that required working memory updating. Midbrain activity also correlated with PFC activity as well as with behavior. These findings support the idea that dopamine acts as a gating signal to the PFC when updating of maintained representations are required. Recently, Badre and Frank have provided computational and empirical evidence for the possible mechanisms underlying working memory gating ( Badre & Frank, 2012 , Frank and Badre 2012 , Chatham et al., 2013 ). Specifically, as a refinement of the original O’Reilly and Frank model that proposed that the striatum can deliver selective gating inputs into the PFC, Frank and Badre propose two types of striatal gating signals. The first type provides gating of inputs to be maintained by frontal cortex (input gating) and the second type of gating signal determines which of these maintained representations will have an influence on particular actions that are selected (output gating). Selective gating (rather than a global mechanism arising from midbrain dopaminergic input that would update everything) allows for some information to be maintained by PFC while other information is updated. The idea of selective striatal gating also allows for a hierarchy within fronto-striatal circuitry such that contextual representations in rostral frontal cortex can influence striatal gating of contextual representations in caudal frontal cortex. An MRI study using diffusion tractography has demonstrated that the proper wiring is in place for such a mechanism in that there is a rostral-caudal correspondence in the connectivity pattern between frontal and striatal regions ( Verstynen et al. 2012 ).

CONCLUSIONS

Working memory is a construct that has motivated research in many domains – cognitive, neuroscientific, clinical – for the past 50 years. The results from this half-century of research, cumulatively, have reinforced the centrality, articulated in seminal writings from the 1960s, 70s, and 80s, of working memory in the control of behavior. The past decade has witnessed many exciting advances in our understanding of the mechanisms that underlie working memory, and these have necessarily prompted the near-continuous updating of our models of how working memory works. At a broader level, however, one could make the case that our current neural systems-level models were foreshadowed by a core feature of the Baddeley and Hitch (1974) multiple component model, and that is the important distinction between stimulus representation, on the one hand, and the control of behavior with those representations, on the other. Baddeley has always been clear that his construal of the Central Executive of the multiple-component model was of something akin to Shallice’s Supervisory Attentional System. That is, a control system that was not in any sense “specialized for” or “dedicated to” working memory operations, but one that could use and/or manipulate the contents of working memory storage to more effectively guide behavior. The prefrontal, basal ganglia, thalamic, and brainstem systems reviewed here can be construed as a neural substrate for this Central Executive. We believe that a conceptual error at the root of some of the systems- and cognitive-neuroscience research from the 1980s–2000s was misattribution of PFC activity to the functioning of one of the storage buffers from the multicomponent model, rather than to the Central Executive. The research that we have reviewed here makes it clear that the functions of PFC (and related systems) are too flexible, and operate on too abstract a level, to be construed as simply performing a buffering role.

The past ten years have also witnessed considerable progress in our understanding of how the function of buffering is accomplished in the primate brain. In digital computers, this function is carried out by random access memory (RAM), circuitry that is physically distinct from “hard drive” storage, and that is specialized for and dedicated to this role. The analogy to computer architecture may have, at least implicitly, influenced previous thinking about biological working memory. What recent research has established, however, is that there are no dedicated “RAM” circuits in the primate brain. Rather, the operation of holding information “in” working memory occurs within the same circuits that process that information in non-mnenomic contexts. For symbolic information, this has been captured by models of activated semantic LTM. For sensorimotor information, by sensorimotor recruitment models.

In this review, we have emphasized the fundamental importance of working memory for cognitive control. It is our belief that any understanding of the basic mechanisms of working memory leads directly to a further understanding of the most complex aspects of human cognition. The frontal cortex continues to be a primary area of focus in attempts to uncover the neural mechanisms that support component processes that are necessary for cognitive control. The frontal cortex is hierarchically organized and provides critical bias signals that sculpt goal-directed behavior. Much work is still needed regarding the nature of these signals, and the mechanisms by which the frontal cortex maintains relevant information and communicates with other brain regions. Moreover, ascending brainstem neuromodulatory systems, such as the dopaminergic system, likely influence most of the cognitive processes mentioned in this review. A consideration of all of these mechanisms together, rather than in isolation, should provide a clearer picture of the neural bases of cognitive control.

SUMMARY POINTS

• An enduring principle of the multiple-component model of working memory ( Baddeley and Hitch, 1974 ) is that the short-term retention of information (a.k.a. “working memory storage”) and the control of how that information is used to guide behavior are subserved by distinct processes. With regard to the former, however, earlier ideas of specialized buffers have been largely superceded by state-based models.
• Although state-based models of working-memory storage are often categorized as “activated LTM” models or “sensorimotor recruitment” models, all are grounded in the idea that the attentional selection of mental representations brings them into working memory, and that the consequences of attentional prioritization explain such properties as capacity limitations, proactive interference from no-longer-relevant items, and so on.
• Recent research applying multivariate pattern analysis (MVPA) to fMRI and EEG data has provided compelling neural evidence for state-based models of working memory storage.
• Some recent findings from computational modeling, extracellular electrophysiology, fMRI, and EEG, suggest that working memory storage may depend on the transient reorganization of synaptic weights, rather than on sustained, elevated activity.
• The PFC likely represents higher-order information, such as task rules, goals, or abstract representations of categories, as compared to feature- and stimulus-specific representations in posterior cortex. Moreover, a critical mechanism for working memory function is the synchronization of PFC activity with activity in other brain regions.
• One dimension of functional organization of PFC is a hierarchical caudal-to-rostral gradient of the level of abstraction of the rules and goals that guide behavior.
• Top-down control signals emanating from PFC likely take at least two forms: signals that modulate gain by either enhancing task-relevant information or suppressing task-irrelevant information, and signals that can modulate the selectivity of information represented in posterior cortical regions.
• Dopamine plays a critical role in working memory function. The complex interplay of midbrain dopamine in prefrontal and striatal circuits underlies “tonic maintenance” and “phasic gating” functions that govern the balance between cognitive flexibility and stability.

FUTURE ISSUES

• How is the focus of attention organized? Does it have a strict capacity limit of one item or can it contain multiple items? Are there multiple distinct levels within the focus of attention (or levels of activation within working memory), or is everything outside a unitary focus of attention in the same state of long-term memory?
• What class of models better account for capacity limitations in visual STM – slots models, single-resource models, a hybrid of the two, or some as-yet-to-be-described alternative?
• Because recent MVPA studies have dissociated working-memory storage from sustained, elevated delay-period activity, what functions does the latter subserve?
• Is it possible, as suggested by recent experiments, that all delay-period activity that is decodable with MVPA, even activity that is below univariate statistical thresholds, corresponds to the focus of attention, rather than the storage of information per se? If so, is that the latter accomplished via the transient reorganization of synaptic weights?
• Is the high dimensionality that has been ascribed to ensembles of PFC neurons a property that is unique to that region, or is the property also characteristic of other brain regions?
• What are the different functional roles of particular frequencies of oscillations (e.g. theta (4–7 Hz), alpha (8–13 Hz), and gamma (30–200 Hz)) for working memory?
• Does dopamine play a similar role in both “input” and “output” working memory gating signals?
• What is the role of other neurotransmitters and hormones, in addition dopamine, in working memory function?

Acknowledgments

In this chapter we have drawn from the original ideas and empirical work of many of our trainees whom we wish to sincerely acknowledge; including but not limited to David Badre, Brad Buchsbaum, Roshan Cools, Clay Curtis, Adam Gazzaley, Joshua LaRocque, Jarrod Lewis-Peacock, Jesse Rissman, Kartik Sreenivasan, Charan Ranganath and Bart Rypma. We also wish to acknowledge the generous funding we have received over the years from the National Institutes of Health.

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ANNOTATED REFERENCES

Professor Barry Dickson elected Fellow of the Royal Society

The University of Queensland’s Professor Barry Dickson has been elected as a Fellow of the Royal Society in recognition of his contribution to science over 30 years.

Professor Dickson, a neurobiologist with the Queensland Brain Institute (UQ), joins the ranks of the world’s most eminent scientists, elected by the UK’s national academy of sciences.

He is particularly known for his studies of fruit fly mating behaviour, which are helping to uncover how the brain processes information and makes decisions.

“ An enduring feature of our work has been the neurobiology of instinctive behaviours,” Professor Dickson said.

“Male and female fruit flies have essentially the same genome and very similar brains, yet they instinctively perform very different roles in mating. We wanted to find out why.”

His lab was the first to manipulate a gene that can induce profound changes in the sexual behaviour of fruit flies, causing females to behave like males and vice versa. This genetic insight led them on to the specific neurons and circuits in the fly brain that underpin the male and female mating instincts.

Professor Dickson’s current research focusses on the question of how females choose one male over another to mate.

“This is a cognitively challenging problem,” he said.

“The female has to assess how good each male is – which she does by evaluating his courtship song – and then compare the quality of any suitor against others she might reasonably expect in the future.

“Those future expectations are based on her prior experience – encounters with other males she has heard but not mated with.

“It’s just like we often have to choose the best of the options available to us, often foregoing one good choice in anticipation of a better one in the future.

“A neuroscientist today is a little like an engineer who knows how computer chips work and knows a good algorithm for playing chess, but can’t build a chess playing machine.

“We know how neurons work, and we know some of the strategies brains use to solve problems, but we can’t explain how those strategies are actually implemented in neural circuits of the brain.

“In the fly, we know the complete wiring diagram of its brain and we have tools to measure the activities of individual neurons and to turn them on or off at will.

“This is exactly what you need to figure out how this particular computer works – how the fly’s brain solves difficult problems like choosing a mate or navigating its environment. Human brains are vastly more complex but will surely use the same principles.”

QBI Executive Director, Professor Pankaj Sah , congratulated Professor Dickson for his contribution to the field of neuroscience which has pushed the boundaries of human knowledge about the brain.

“Barry is an outstanding researcher and widely respected for his research on understanding how animal instincts are programmed into their brain during development,” Professor Sah said.

“His research o pens new avenues for understanding the intricate interplay between genes, environment, and behaviour, with potential implications for broader fields ranging from evolutionary biology to neurobiology.”

Professor Dickson is one of 90 Fellows announced today and joins five UQ academics previously elected to the Royal Society – Emeritus Professor Gerard Milburn , Professor Peter Visscher, Emeritus Professor Ian Frazer , Emeritus Professor Mandyam (Srini) Srinivasan and Professor David Craik .

Media: QBI Communications, [email protected] u , Merrett Pye +61 422 096 049

• Study shows heightened sensitivity to PTSD in autism

Dynamic DNA structures and formation of memory

Reality’s how we relate to it: Distinguished professor speaks at prestigious philosophy of mind lecture series

Lisa Feldman Barrett , university distinguished professor of psychology and co-lead of the Interdisciplinary Affective Science Laboratory , says that she began her career “trying to answer what I thought was a modest question about the nature of emotion.”

This turned into a more sizable question than she’d expected, and led to her pursuit for “a general theoretical framework for understanding how a brain — in conversation with the body and the world — creates your mind.” 

Barrett calls her approach relational realism, and, at some point along this journey, she realized that this “framework actually has relevance as a philosophy of mind and as a philosophy of science.”

Now, as a psychologist, neuroscientist and philosopher, Barrett has received multiple invitations to present at named lectures around the world, including the annual Rudolf Carnap Lectures at Ruhr University Bochum , Germany.

Named for the philosopher Rudolf Carnap, the lectures “provide a platform for distinguished scholars to present their work in the form of several talks on their preferred topic,” focusing on the philosophy of mind, language or science, according to the lecture series’ website.

Named lectures, Barrett says, don’t come along very often in her primary fields of psychology and neuroscience. “But in philosophy, it’s a very big deal. And I’m giving two.” In addition to the Carnap lectures, which occurred in March, Barrett will give the annual Pufendorf Lectures at Lund University in May.

“To be asked to speak about the philosophical writings that I’m working on, and how they came from my scientific work, is a real privilege.”

Barrett says that the way many scientists have approached the question of mind has been suboptimal, working backward from experience rather than toward it.

“Instead of starting with brain evolution, brain structure, brain function, brain development,” she says, “scientists [and] philosophers, they start with their own experiences, and they go looking for the physical basis of those experiences in the brain.” 

She uses the feeling of anger as an example. “Probably all neurotypical scientists experience anger if they have lived in a Western culture or are familiar with one,” she says. Given this shared personal experience, “they then  go looking for the biological basis of anger in the brain somewhere.”

But using this kind of personal experience “as a guideline for what’s universal is a bad way to do science,” she says. “It assumes that objectivity is that which is shared by neurotypical scientists from the West.”

“It’s not like there’s a place in your brain that’s dedicated to anger and another place that’s dedicated to rationality and another place that’s dedicated to the self,” she continues. Rather, “your brain is having constant conversation with your body and with other brains-in-bodies in the world around you.”

Under what she calls traditional or conventional realism, “Most of us walk around in the world believing that the world is out there, that there’s a reality that’s separate from us, that we might never know that reality objectively, but we believe it’s there.”

But traditional realism misses a big part of the picture, she says.

“When you experience a solid object, you are not experiencing something in an objective reality that is separate from you,” Barrett says. But neither are experiences “only in your head. Your experiences emerge as the relations between the signals in your brain, the signals in your body and the signals in the world.”

Take an apple. While traditional realists might say “the apple is red,” her research suggests that “red” emerges through a relationship, “the relation between the [light] signals coming off the apple — which hit your retina and make their way to your brain — and the signals in your brain, assuming you have a neurotypical retina with three cones, and a neurotypical brain,” she notes.

“Red is a property of the relationship between the signals in the world and the signals in your brain.”

Barrett calls this model relational realism, “the idea that everything that is real — everything that exists in reality — exists only in relation to something else. And much of the time, that ‘something else’ is you,” she wrote in a follow up note.

Barrett gave a series of four talks for the Rudolf Carnap Lectures, discussing her research on the nature of emotion, what that research taught her about the structure and function of the human brain and her developing philosophy of relational realism. An awards ceremony preceded the second lecture, where she received the first Ruhr Award for Philosophy and the Mind Sciences.

For someone with a career that began in clinical psychology, then moved into neuroscience, evolutionary biology, engineering and now philosophy, Barrett’s work is deeply interdisciplinary.   

She’s quick to note that she has been fortunate to work with brilliant students and generous colleagues. Barrett points to “a wonderful saying by the well-known social psychologist Hazel Markus . She said, ‘You can’t be a self by yourself.’” 

“You have to have great colleagues who are in other disciplines who want to work with you, and who want to teach you and who are patient and generous.”

The Rudolf Carnap Lectures were Barrett’s first philosophy-specific lectures, she says, and she noted the generosity displayed by the attendees there. “They had a hundred suggestions for things I should read,” she says. “There’s a certain generosity of spirit that is required for interdisciplinary work.”

Noah Lloyd is a Senior Writer for NGN Research. Email him at [email protected] . Follow him on X/Twitter at @noahghola .

‘Associations Between Cognitive Resources and Emotion Regulation Tactics in an Adult Lifespan Sample’

‘effect of emotion naming on emotion regulation in younger and older adults’, ‘physical activity-related individual differences in functional human connectome are linked to fluid intelligence in older adults’, ‘early life adversity accelerates hypothalamic drive of pubertal timing in female rats with associated enhanced acoustic startle’, ‘addressing climate change with behavioral science: a global intervention tournament in 63 countries’, ‘adaptive thresholding increases sensitivity to detect … rate of skin conductance responses to psychologically arousing stimuli’, ‘emotion regulation convoys: individual and age differences in the hierarchical configuration of emotion regulation behaviors in everyday life’, ‘the expressive function of public policy: renewable energy mandates signal social norms’, jaeggi and seitz receive dod grant to develop attention-measuring tools in high performers.

• Inside eLife

Ben Barres Spotlight Awards: Applications open for 2024

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Through the Ben Barres Spotlight Awards, eLife has supported over 50 pioneering researchers from backgrounds historically underrepresented in biology and medicine, including many working in countries where research funding is limited. As we now enter the scheme’s sixth year, we are pleased to welcome eligible authors of publicly reviewed preprints or eLife articles to once again apply for awards of up to $5,000 to support their research, careers or communities.

Successful applicants have made diverse use of their awarded funds over the past five years to remove barriers and seize new opportunities.

Noelia Weisstaub used her 2019 award to overcome a funding shortfall caused by the devaluation of the Argentine peso. She says, “My Ben Barres Spotlight Award came at an absolutely critical time and helped me begin new lines of research in my lab.”

Md Noor Akhtar was one of the winners of the 2023 awards and is based at the Indian Institute of Science. He says, “My award granted me the privilege to attend the Targeted Protein Degradation conference in the United States, allowing me to establish valuable connections with fellow researchers and leaders in my field.”

A runner-up in last year’s awards , Sol Fittipaldi plans to use part of her funds to support a survey into barriers faced by women scientists in Latin America working on dementia. “I feel proud that my award will make women’s contributions to the field more visible and hopefully offer unique insights that will narrow the gender gap in my research community,” she adds.

Alagie Jassey’s 2023 award was used to buy two microscopes needed for diagnostic and research purposes. He says, "Winning the Ben Barres Spotlight Award means a lot to me. The publicity has been invaluable, especially as a postdoc aspiring to become an independent investigator. But it also enabled me to fulfil a long-standing personal goal of giving back to Edward Francis Small Teaching Hospital in The Gambia, where my career began."

Ready to apply?

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Celebrating the winners

We're also pleased to invite you to join us at the online award ceremony on July 3, where we'll announce the results of the 2024 awards. Join us for an inspiring event as we hear from past recipients about the impact of their awards and celebrate this year's winners.

• REGISTER HERE

In memory of a remarkable colleague

The Ben Barres Spotlight Awards pay homage to our esteemed late colleague, Ben Barres . As a Reviewing Editor, Ben brought invaluable scientific expertise, boundless energy, and passionate advocacy to eLife's communities. He was a vocal champion for trans and women’s rights, shining a light on injustices within academia. Through the Ben Barres Spotlight Awards, we are proud to help uphold Ben's legacy and strive towards his vision of a more inclusive and equitable scientific community.

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Applicants must be:

• An active researcher in the life or biomedical sciences at a university or other non-commercial research institute AND
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• A member of one or more of the following groups based on their background or identity:
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Awards will be made for research in any area of the life sciences and biomedicine covered by the eLife journal.

eLife does not specify the purpose for the awards. We simply invite candidates to explain their requirements and describe how the money, if granted, will support a major improvement in their research, career or community. Applicants also need to provide a high-level budget for their proposed expenses under the award. ( A PDF copy of the application form can be viewed here , to help applicants prepare their applications.)

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