essay on reaction time

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• J Phys Ther Sci
• v.31(3); 2019 Mar

The effect of different visual stimuli on reaction times: a performance comparison of young and middle-aged people

Makoto otaki.

1) Faculty of Rehabilitation, Kobegakuin University, Japan

2) Division of Health Science, Graduate School of Medical Science, Kanazawa University: 518 Arise Ikawadani-cho, Nishi-ku, Kobe, Hyogo 651-2180, Japan

Katsuyuki Shibata,

3) Faculty of Health Science, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Japan

[Purpose] Here, we evaluated the reaction times of young and middle-aged people in different tasks. [Participants and Methods] The study included 23 young and 28 middle-aged volunteers. Their reaction times were measured in three tasks featuring different symbols (arrow and figure symbols) and spatial attributes (left, right, and ipsilateral choices). [Results] No significant inter-group differences in the reaction times were found for the simple reaction time task. In the choice reaction time and go/no-go reaction time tasks, the middle-aged participants demonstrated significantly slower reaction times. When the correct response was congruous with the direction of an arrow stimulus, the reaction times were shortened significantly among the middle-aged participants. In the go/no-go reaction time task, the reactions were delayed due to an inhibition of responses to upcoming stimuli. [Conclusion] The slower reaction time of the middle-aged participants in the choice reaction time task suggested that their responses were guided by the arrow stimulus to a greater extent compared to that of the younger participants. In the go/no-go reaction time task, the reaction times may have been slower in middle-aged participants because of a non-response possibility, which meant that participants had to first check the stimulus before deciding whether to respond.

INTRODUCTION

When driving a vehicle, we make split-second decisions based on instantaneous visual information. To perform appropriate actions in everyday life, we sometimes focus more on a particular stimulus, or we might delay our response to a stimulus to check whether the response is appropriate. According to Lewis et al., older people have diminished neuromotor function 1 ) . Reaction time has been used to measure age-related response quality 2 ) . There are three types of reaction time tasks: simple reaction time (simple RT), choice reaction time (choice RT), and go/no-go reaction time (go/no-go RT) 3 ) . MacDonald et al. found that older age groups exhibited longer reaction times and greater intra-individual variability in reaction time 4 ) . Many studies have compared the reaction times of young and old individuals 5 ) , but little is known about the reaction times of middle-aged people. Rehabilitative interventions cover a broad age range, so it is essential that such interventions are tailored to the response characteristics corresponding to the person’s age.

This study aimed to ascertain the characteristics of young and middle-aged people’s reaction times in different reaction time tasks from multiple perspectives. The ultimate goal was to provide findings that can guide rehabilitative interventions. To this end, we conducted two experiments. The first consisted of simple RT and choice RT tasks; the second consisted of a go/no-go RT task in which responses were inhibited.

PARTICIPANTS AND METHODS

We recruited 51 healthy volunteers, 23 of whom were young (9 males and 14 females; average age: 20.5 ± 0.6) and 28 of whom were middle-aged (16 men and 12 women; average age: 47.7 ± 8.0). We confirmed that each participant was right-handed using the Edinburgh Handedness Inventory 6 ) . Each of these individuals provided their informed consent to participate after receiving a briefing about the study’s purpose. The study was approved by Kobe Gakuin University’s ethics committee for human experimentation (No. HEB 17-15).

In the experiments, we used a personal computer, E-Prime 2.0, and Chronos (Psychology Software Tools, Inc.). When performing the tasks, the participants sat approximately 80 cm from the PC screen with their fingers poised on the response buttons ( Fig. 1 left). The participants were presented with a fixation point for 3,000 ms. After this, the target stimulus appeared and the participants had to execute a response ( Fig. 1 right). Each task consisted of a brief instruction, 10 practice rounds, and then 20 rounds in which we measured reaction times.

Experimental setting with an overhead view of response buttons (left), and visual stimulus samples (right).

The left figure shows the experimental setting, including the position of the desk, chair, and screen, with an overhead enlarged view of the response buttons. The right figure is a sample of the visual stimulus in the choice reaction task, indicates the direction of the passage of time. ←: Arrow symbol, ●: Figure symbol.

We only analyzed the reaction times of correct responses. We excluded reaction times of <100 ms deemed as “too early” and reaction times of >2,000 ms deemed as “too late.” The stimuli were randomized for each participant.

The statistical software we used was SPSS (version 24). We performed a multiple comparison (Bonferroni correction) on the three choice variables in the choice RT and go/no-go RT tasks (left/right, ipsilateral, contralateral). We used a paired t-test to compare reaction times between the two symbolic variables (arrow symbol, figure symbol) in the choice RT task. We used an unpaired t-test to compare the reaction times of the young and middle-aged participants. We set the statistical significance threshold at >5%. For the path analysis, we used SPSS Amos (version 24).

In the simple RT task, participants had to press a response button as quickly as possible when a stimulus appeared in the middle of the screen. In the choice RT task, each stimulus corresponded to one of three choice variables: left and right choice (LR choice), ipsilateral choice (IL choice), or contralateral choice (CL choice). For LR choice, the stimulus appeared in the middle of the screen and participants had to press either the left or right-hand button. For IL choice, participants had to press the left-hand button if the stimulus appeared on the left of the screen, and the right-hand button if it appeared on the right. For CL choice, participants had to press the right-hand button if the stimulus appeared on the left of the screen and vice-versa. The target stimuli consisted of two types of symbols: 1. an arrow (arrow symbol) that pointed in the direction of the button to press; and 2. a dot or cross (figure symbol) that did point in a direction. The arrow and figure symbols appeared at a ratio of 1:1, as did the two responses (left and right). The go/no-go RT task included the same three choice variables as the choice RT task—LR choice, IL choice, and CL choice. However, it also included a no-response condition (“no-go”). Two types of stimuli were used in this task: a black dot, which indicated that the response should be executed (“go”); and a white circle, which indicated that no response should be executed (“no-go”). The black dot and white circle appeared at a ratio of 1:1. The ipsilateral and contralateral choices appeared at a ratio of 3:2.

Table 1 shows the reaction times for the simple RT and choice RT tasks. In each task, around 1% of the responses were erroneous. None of the responses were too early or too late. To start with the young people, when the stimulus was an arrow, reaction times for the CL choice were significantly slower compared to that for the LR and IL choices (p<0.01). With a figure symbol, reaction times were slowest for the CL choice followed by the LR and IL choices. Reaction times were quicker with an arrow than with a figure symbol. Of the choice variables, the LR choice was associated with the quickest reaction times, and the CL choice with the slowest.

n=51 (for each task).

All data are presented as the mean ± SD. Simple: Simple RT task; Choice: Choice RT task; Go/no-go: Go/no-go RT task; Arrow: Arrow symbol; Figure: Figure symbol; Response: Spatial attribute of button to press; Choice comparison: Comparison of left/right ipsilateral, and contralateral choice variables; Symbolic comparison: Comparison of the two symbol variables; Age comparison: Comparison of young and middle-aged participant responses; LR choice: Left and right choice; IL choice: Ipshilateral choice; CL choice: Contralateral choice. a: Left/right vs. Ipsilateral, b: Ipsilateral vs. Contralateral, c: Left/right vs. Contralateral, d: Left/right with arrow symbol vs. Left/right with figure symbol, e: Contralateral with arrow symbol vs. Contralateral with figure symbol.

††p<0.01, *p<0.05, **p<0.01.

As for the middle-aged participants, their reaction times were similar to the young participants overall. Although their reaction times in the simple RT task were not significantly different from that of the young participants, those in choice RT were significantly slower in the choice task in all conditions.

Table 1 also shows the reaction times for the go/no-go RT task. Among the young participants, reaction times were quickest for the LR choice (p<0.01) followed by the IL and CL choices. We observed a similar pattern in the middle-aged participants’ reaction times, but these reaction times were significantly slower than those of the young participants in all conditions.

To determine the difficulty level of each task variant, we compared the responses in all tasks on a Brinley plot 7 ) . The results revealed a linear relationship with an incline of 1.15, indicating that the reaction time of the middle-aged group increased as the difficulty level rose in each task. The most difficult task variants were go/no-go RT task with CL choice, arrow symbol with CL choice, and figure symbol with CL choice. We performed a covariance structure analysis (maximum likelihood estimation) to model the relationships of the task variants. The analysis of all measured variables yielded facilitating and inhibiting variables (χ 2 =33.6, p<0.01, RMSEA=0.023; Fig. 2 ). We extracted the variables based on the standardized coefficients. There was one facilitating variable: arrow symbol with LR choice. There were two inhibiting variables: 1. figure symbol with CL choice, and 2. go/no-go RT task.

Path analysis of facilitating and inhibiting factors.

The figures shown on the paths indicate standardized coefficients.

Simple RT: Simple reaction time; Choice RT: Choice reaction time; Go/no-go RT: Go/no-go reaction time; Arrow: Arrow symbol; Figure: Figure symbol; LR choice: Left and Right choice; IL choice: Ipsilateral choice; CL choice: Contralateral choice.

**p<0.01.

Age did not appear to influence reaction times in the simple RT task. In the choice RT and go/no-go RT tasks, however, reaction times were slower among middle-aged participants, who had more response choices associated with slower reaction times. Since response choices were more easily guided by an arrow symbol indicating a direction, more time was needed to determine selection of responses to left and right and whether to press the response button in tasks involving figure symbols.

Compared to figure symbols, the arrow symbol was associated with significantly quicker reaction times. According to affordance theory, instantaneous visual information elicits specific actions immediately without the mediation of complex cognitions or decisions 8 ) . Arrows are stimuli that contain information known by the perceiver such that attention is attracted in the direction of the arrow. As such, arrows can facilitate choice responses, which explains why the path analysis revealed the arrow symbol to be a facilitating factor for reaction times. Reaction times were significantly quicker in the IL choice than they were in the CL choice. DeJong et al. describes the response processes as follows; after the stimulus is presented, the stimulus is identified, the response is selected, and then the response is executed 9 ) . Responses are selected for stimuli during the response-selection step 10 ) . In the case of the IL choice, the response and stimulus are congruous (both are on the same side), so the response is selected automatically. In the CL choice, however, the response and stimulus are incongruous (they are on opposite sides), so the automatically selected response must first be inhibited, and then the response opposite to the automatic response must be selected. Therefore, in the CL choice, stimulus identification conflicts with stimulus response 11 ) . These procedural differences in response selection explain why the IL choice was associated with quicker reactions times while the CL choice were associated with slower reaction times. At higher difficulty levels, the go/no-go RT task was associated with slower reaction times than the choice RT task. In the choice RT task, every stimulus required a response. In the go/no-go RT task, however, only a “go” stimulus required a response (“no-go” required a non-response). According to Miller et al., a “go” decision activates a prepared response, but when “no-go” is a possibility, this prepared response must be inhibited until the person has checked whether it should be executed 12 ) . Accordingly, the go/no-go RT task increased the impetus to check the response against the stimulus. The presence of this checking process would explain why reaction times were slower in this task, as well as why the path analysis indicated go/no-go RT task as an inhibiting variable.

Reaction times were slower in general among both young and middle-aged participants; we noted an age effect. Given the tendency for an arrow symbol to elicit a response toward the direction it is facing, the presence of this symbol as the stimulus may have generated conflict between the spatial attribute of stimulus side and that of the response, resulting in delayed reaction times. The go/no-go RT task was more difficult because of the possibility of a no-go condition. The presence of the no-go condition may have delayed reaction times because the stimuli needed to be checked (as to whether it is “go” or “no-go”) before the response could be executed.

We only analyzed correct responses. Future studies should additionally analyze incorrect responses, including “go” responses to “no-go” stimuli. There is a trade-off between response time and response accuracy, and we did not control for this relationship in our study. Additionally, male may generally respond faster than females; such a gender bias shall be considered in the future studies which need to examine the effect in a more gender-balanced population.

Conflicts of interest

We have no conflicts of interest to declare.

Acknowledgments

We wish to thank all the students and faculty staff in the Faculty of Rehabilitation, Kobegakuin University who cooperated as participants.

ORIGINAL RESEARCH article

Relationships between reaction time, selective attention, physical activity, and physical fitness in children.

• 1 University of Málaga, Málaga, Spain
• 2 Department of Methodology for Behavioral Science, University of Granada, Granada, Spain
• 3 Department of Social Psychology, Social Work, Anthropology and East Asian Studies, University of Málaga, Málaga, Spain
• 4 Department of Evolutionary Psychology and Education, University of Málaga, Málaga, Spain

The objective of this study was to analyze the relationships between simple and complex reaction times (RTs) with the physical activity performed weekly, the physical fitness and selective attention in children of the third cycle of primary education. Participants were 119 children aged between 10 and 12 years ( M = 10.71; SD = 0.77). The instruments used for data collection were the D2 attention test to analyze selective attention, various tests of the Eurofit and ALPHA-Fitness Battery to evaluate the physical condition, a bioimpedanciometer Tanita TBF 300 to evaluate the body composition, and the FITLIGHT Trainer to measure the simple and complex RTs. The group that carried out more weekly physical activity used less time in simple ( p < 0.05, d = −0.68, 95% CI [−1.19, −0.17]) and complex RT tests ( p < 0.05, d = −0.63, 95% CI [−1.14, −0.12]). Also, the results showed that the simple RT was related in a significant way with physical fitness, while the complex RT was related significantly to attentional capacity and physical fitness.

Introduction

Reaction time (RT) is a relevant variable in areas such as sports, academics, and other tasks of daily life ( Metin et al., 2016 ; Sant’Ana et al., 2016 ). It can be defined as the time that elapses from when a stimulus appears until a response is given and is considered a good measure to assess the capacity of the cognitive system to process information ( Jensen, 2006 ; Kuang, 2017 ). From a physiological point of view, RT is a complex phenomenon whose functioning has been studied by numerous researchers ( Kuang, 2017 ). The RT depends on the speed of the sensorimotor cycle, composed by the detection of the initial stimulus, transfer of the information through the afferent nerves, generation of the response from the central nervous system, and final response ( Adleman et al., 2016 ; Greenhouse et al., 2017 ).

There are differences between simple and complex RTs. The first is defined as the interval time between when a stimulus appears, its detection, and the given response ( Jayaswal, 2016 ). The second involves the identification and selection of a response to various stimuli ( Boisgontier et al., 2014 ). The simple RT is significantly shorter than the complex RT ( Vences de Brito et al., 2011 ). The factors that influence the RT are numerous, being able to differentiate between those dependent on the own person and those related to the stimulus ( Baayen and Milin, 2010 ). Among the first can be included the fatigue, physical condition, experience, motivation, gender, age, or dominance of the body member with which one responds. Second, the physical characteristics of the stimulus, its intensity, or duration ( Der and Deary, 2006 ; Woods et al., 2015 ; Jayaswal, 2016 ).

In the set of internal factors, the influence of cognitive processes has been described as elements that determine the RT ( Deary and Der, 2005 ; Leckie et al., 2014 ). Among them, attention would be a variable involved in the RT manifested by a person, which has been suggested in previous research ( Prinzmetal et al., 2005 ; Vaportzis et al., 2013 ; Jehu et al., 2015 ). Attention is a cognitive function involved in the activation and selection processes, distribution, and maintenance of psychological activity ( Chun et al., 2011 ; Greimel et al., 2011 ). It is a process of great anatomical and functional complexity, being able to differentiate manifestations as arousal, focal, selective, divided, alternating, or sustained attention ( Petersen and Posner, 2012 ; Tamm et al., 2013 ). Specifically, selective attention would allude to the ability to attend to some specific stimuli and ignore others ( Giuliano et al., 2014 ; Gomez-Ramirez et al., 2016 ).

It has been highlighted that physical activity and sports would be related to improvement in RT ( Jain et al., 2015 ; Okubo et al., 2017 ; van de Water et al., 2017 ; Walton et al., 2018 ). The RT can be deliberately trained ( Rabiner et al., 2010 ; Kirk et al., 2017 ), and physical activity and sports allow development of a wide variety of actions that would influence its development ( Lynall et al., 2018 ; Walton et al., 2018 ). It is relevant in individual sports such as swimming or athletics, because it is necessary to respond quickly to start a movement ( Nuri et al., 2013 ; Tønnessen et al., 2013 ). In other adversary or collective sports, such as badminton, karate, football, or basketball, RT is essential in multiple game situations, because athletes need to make quick decisions to be successful in their actions ( Ruschel et al., 2011 ; Mudric et al., 2015 ; van de Water et al., 2017 ). Some studies had also shown that more fit people would be associated with less RT in a set of tasks ( Luque-Casado et al., 2016 ; Westfall et al., 2018 ).

Previous research had also shown that physical activity and exercise and the improvement of physical fitness could support the development of cognitive functioning and specifically different aspects of attention ( Hillman et al., 2009 ; Kao et al., 2017 ; Reloba-Martínez et al., 2017 ). For this reason, it could be considered that the practice of physical exercise and the development of physical condition could have an impact on RT, directly by the training of the capacity to respond to a given stimulus and indirectly by the impact it would have on cognitive functioning ( Gentier et al., 2013 ; Syväoja et al., 2014 ).

To evaluate the RT, there are instruments, such as the FITLIGHT Trainer TM (FITLIGHT Sports Corp., Canada) or the Dynavision TM D2 Visuomotor Training Device (Dynavision International LLC, West Chester, OH), that have been implemented in different studies ( Appelbaum and Erickson, 2018 ). Specifically, with the FITLIGHT Trainer, several investigations have been carried out. For example, Zwierko et al. (2014) conducted research with the FITLIGHT Trainer system, which showed that non-athletes had longer RTs than athletes. Likewise, Fischer et al. (2015) used this instrument for the training and analysis of the RT in the United States Air Force. On the other hand, Zurek et al. (2015) investigated the simple and complex RTs of 10 football players who had undergone knee surgery and a rehabilitation program to assess their recovery.

The literature consulted has highlighted the relationships between RT and variables such as selective attention and physical condition, although there are no previous studies that analyze them together in the preadolescent population. Therefore, the objective of the present study was to analyze the relationships between RT, selective attention, concentration, and physical condition in a sample aged from 10 to 12 years.

Materials and Methods

Participants.

One hundred nineteen students (65 boys and 54 girls) participated in the study, aged between 10 and 12 years old ( M ± DT : age = 10.71 ± 0.77 years; height = 1.45 ± 8.21 cm; weight = 42.58 ± 9.87 kg; body mass index = 19.96 ± 3.27 kg/m 2 ; fat mass = 22.73 ± 8.37%) from Alcalá la Real (Jaén, Spain). All of them were in the fifth and sixth years of primary school and did not present any physical or psychological difficulties that could affect the study.

Measures and Instruments

Reaction time.

The FITLIGHT Trainer (FITLIGHT Sports Corp., Ontario, Canada) was used to measure the RT. This is a wireless system consisting of eight sensors, which were placed on a table 1 m high and drawing a semicircle. They had a separation between them of 20 cm, with 40 cm from the central point. To perform the task, the student stood in front of it with his hand in contact with the table. Two tests were performed (simple RT and complex RT). The simple reaction test (SRT) was performed with the dominant hand and consisted of 60 luminous stimuli. The complex reaction test (CRT) was performed with both hands and also consisted of 60 stimuli. In this last one, the visual stimuli were of two colors, blue or green; to the first, one had to react with the left hand, while to the second, one had to react with the right hand. Two sequences of random numbers were programed for the creation of the tests, one for the SRT and the other for the CRT.

Physical Condition

Physical fitness tests were estimated with ALPHA-Fitness Battery ( Ruiz et al., 2011 ) and Eurofit (1993) . The following tests were carried out: (a) manual dynamometry, to evaluate the state of the isometric force in the upper train, in both dominant and non-dominant members (the digital dynamometer model TKK-5401 Grip D, Takei, Tokyo, Japan, was used); (b) horizontal jump test, to estimate the force of the lower train; (c) Course–Navette test to analyze the aerobic capacity of the participants, from which the VO2max was indirectly estimated ( Léger et al., 1988 ). For the specific calculation of oxygen consumption, the formula VO2max = 31.025 + 3.238 V − 3.248 E + 0.1536 VE was applied ( V = the speed reached in the last completed stage; E = the age of each participant); (d) speed test 5 × 10 m to analyze travel speed, agility, and general coordination.

Selective Attention and Concentration

The D2 attention test was used ( Brickenkamp, 2002 ). Participants had to selectively attend to certain relevant aspects of the task while ignoring irrelevant ones. The test, in this investigation, was administered collectively and lasted between 8 and 10 min. There are 14 lines with 47 elements each (total = 658 items). The elements were letters “d” or “p,” which are accompanied by small lines at the top or bottom of each letter; these small lines could be in pairs or individually. The work that the participant had to do was to check from left to right each line and to mark every letter “d” that is accompanied by two lines (two above, two below, or one above and one below). The participant had 20 s to complete each line. The scores that can be obtained are as follows: TA (total number of attempts), TH (total number of hits), O (omissions or number of relevant stimuli not crossed out), C (omissions or errors), TET [total effectiveness in the test = TP − (O + C)], CON (concentration = TS − C), and VAR [index of variation between the last stimulus analyzed between different rows = (TP+) – (TP−)]. TP+ is the last stimulus analyzed in the row with the most attempted elements, and TP− is the last stimulus analyzed in the row with the fewest attempted elements. This test possesses a test–retest reliability in the original study superior to 0.90.

Anthropometry and Body Composition

Anthropometric data were measured with the Tanita body composition analyzer TBF 300 and the mechanical measuring rod kern MSF 200. Data obtained were height, weight, body mass index, and percentage of fat mass.

Level of Physical Activity and Manual Dominance

An interview was conducted with each subject whose objective was to collect the extracurricular sports activity of each participant in order to separate the participants according to their level of physical activity. The groups were classified into the following: (1) students who did not engage in any type of extracurricular physical activity; (2) students who engaged in 1–3 h of extracurricular physical activity per week; and (3) students who engaged in more than 3 h of extracurricular physical activity per week. Data were also collected from participants on their dominant hand, i.e., whether they were right-handed or left-handed.

In order to carry out the research, the participating schools were contacted, and permission was requested from the school management for their participation. In addition, informed and written consent was obtained from parents or legal guardians for students to participate. Throughout the research process, the principles established in the Declaration of Helsinki ( World Medical Association, 2013 ) were respected, and approval was obtained from the Ethics Committee of the University of Jaén, Spain (Ref. ABR.16/6).

The tests were performed at the school in 2 days. First, anthropometric data were collected from the participants, and then physical condition tests were performed. Anthropometric measurements were taken in the school gymnasium, with light clothing (shorts and t-shirt), without footwear, and without any metallic object on the body (earrings, chains, watches, etc.). Also, to improve the reliability of body composition measures, the following guidelines were indicated: avoid strenuous exercise the previous day, do not significantly alter the diet the day before the test, wear comfortable clothing, control the taking of medicines that may alter body water levels, and do not retain fluids. As for the physical condition tests, the order was as follows: manual dynamometry, horizontal jump, 5 × 10 m speed test, and 20 m round-trip test. Both were carried out in the school’s sports facilities (multisport courts and gymnasium). The dynamometry, horizontal jump, and speed tests were performed twice, and each participant’s best mark was scored while the Course–Navette test was performed only once.

On the second day, the attention tests and interview were conducted, and the RT was measured. The D2 test was performed collectively in the classroom of the participants. They were previously instructed according to the test manual, and doubts were clarified. The RT was measured in a classroom on an individual basis. First, the simple task was done, and second, the complex task was performed. At the end of the RT test, the student was interviewed to obtain data related to his or her weekly physical activity.

Participants were divided into three groups based on their physical activity habits and routines, not including the physical activity that took place at school during physical education classes. The three groups formed were (a) group 1 ( n = 57), children who did not carry out any type of physical activity outside school hours; (b) group 2 ( n = 41), children who carried out between 1 and 3 h a week of physical activity outside school hours; and (c) group 3 ( n = 21), children who carried out more than 3 h a week of physical activity and/or competed for being federated in some sport.

Statistical Analysis of Data

The RT measured with the FITLIGHT Trainer was studied. The reliability of the device was studied using the intraclass correlation index (IC), the standard error of measurement (SEM), and the minimal difference (MD). In addition to descriptive data, ANOVAs were performed with the RT as a dependent measure and to see its variation depending on the type of task in all cases, taking into account the position of light in the test, the 10 trials in which the subjects responded, and gender and age.

We analyzed the RT in the three physical activity groups by means of ANOVA of a factor, as well as the Bonferroni and Cohen d statistics. Also, correlation analysis (Pearson and Spearman) between physical condition, body composition, and attention measurements with RT values was performed. Linear regression analysis was performed by successive steps to predict the SRT and CRT from the rest of the variables. Data were analyzed with the SPSS statistical program (SPSS Inc., v.20.0, Chicago, IL, United States).

Reaction Time (FITLIGHT Trainer)

Table 1 shows the RTs for each of the eight sensor positions and the total mean.

Table 1. Descriptive statistics ( M and SD ) for the RT in the simple and complex tasks of the 119 subjects as a function of the eight positions of the sensor and the total (dominant hand).

A reliability analysis was performed by calculating the ICC, the SEM, and the MD ( Weir, 2005 ). The ICC 2 , 1 was calculated for two halves ( Wells et al., 2014 ) by calculating the mean RT for the lights at positions 1, 3, 5, 7 and 2, 4, 6, 8. The type of ICC(2, 1) used considered the effect of trials as a random factor while trials were considered as a sample of possible levels. The results showed ICC 2 , 1 = 0.92, SEM = 39.87, and MD = 110.51 ms for the simple task (SRT). For the complex task (CRT), the reliability indices were ICC 2 , 1 = 0.85, SEM = 63.50, and MD = 176.00 ms, which can be considered as adequate reliability indices.

ANOVA of repeated measurements was performed to evaluate the effect of the type of task and the position of light in the RT. The Mauchly sphericity assumption was previously analyzed, obtaining significance for the position ( W = 0.11; p < 0.001) and the interaction of both variables ( W = 0.48 ; p < 0.001), so the Greenhouse–Geisser statistic was used. The results showed that the variable type of task was significant ( F 1 , 118 = 982.98; p < 0.001; η 2 = 89; 1 − β = 1.00) with lower time in SRT, as well as the position of the light ( F 3 . 57 , 420 . 92 = 32.44; p < 0.001; η 2 = 0.22; 1 − β = 1.00) and the interaction between both ( F 5 . 65 , 668 . 53 = 23.15; p < 0.001; η 2 = 0.16; 1 − β = 1.00). Peer comparisons for light positions showed significant differences ( p < 0.05) in all cases except for 1–7, 2–8, 3–6, 3–8, 4–5, and 5–6. The lights located at the ends (1, 2, 7, and 8) obtained lower RT, positions 3 and 6 had intermediate RT, and those located in positions 4 and 5 had higher RT.

In the comparisons by type of task and position of light, it was observed that in the RT of the SRT, there were differences ( p < 0.05) between all the cases except between 1–2, 1–3, 2–8, 3–6, 4–5, and 7–8. In the CRT, all differences were significant ( p < 0.001 in all cases except between light 1 and all others, as well as couples 4–7 ( p = 0.003) and 7–8 ( p = 0.006). It can be seen that in the SRT, the RT increases for the most central positions, while in the CRT, the position with the least RT is position 1, followed by position 7, with the rest having more or less the same times, the greater being that relating to light in position 4.

An analysis of the RT was done according to the test in which the RT was measured with the aim of studying whether fatigue affects the RT differentially. We have considered the average RT for every 10 trials in each subject. Since each of us performed 60 trials, we compared six dozen trials ( Table 2 ).

Table 2. Descriptive statistics ( M and SD ) for the RT in the simple and complex tasks of the 119 subjects according to the six dozen trials.

An ANOVA of repeated measurements was performed to study the effect of the type of task and tests in RT, with the Mauchly sphericity test previously performed, being significant for the variable trial ( W = 0.74; p = 0.001) but not for the interaction type of task × trial ( W = 0.83; p = 0.079). Therefore, only Greenhouse–Geisser was applied in the first case. The variable type of task, as it happened before, was significant ( F 1 , 118 = 10,008.22; p < 0.001; η 2 = 0.89; 1 − β = 1.00), with time being higher in the complex task. In terms of the trial, significant differences were obtained between the six levels ( F 4 . 42 , 521 . 69 = 19.08; p < 0.001; η 2 = 0.14; 1 − β = 1.00) and also in the interaction of type of task × trial ( F 5 , 590 = 22.96; p < 0.001; η 2 = 0.16; 1 − β = 1.00). In the comparisons of Bonferroni by pairs of the trial variable, there was a significance ( p < 0.05) of the first and second tens with all the others, while there were no differences between the third, fourth, fifth, and sixth tens. That is to say, the RT was lower in the first ten, increased significantly in the second, increased significantly in the third, and did not increase until the end.

As for the interaction in the simple type, differences were found between tens 1–3, 2–3, 2–5, 3–4, and 4–5 ( p < 0.01) although there is no tendency to increase or decrease as the tens increase, but rather a sawtooth trend was produced, the RT being greater in the odd tens than in the pairs. However, in the complex task, there was an increase in the RT as the tens increased except between the fifth and sixth tens that had practically equal RT although only these increases are significant between the first ten compared with all the others ( p < 0.01) and the second ten compared with the fifth ( p < 0.01) and sixth ( p = 0.04) tens.

Finally, with regard to the FITLIGHT system, the aim was to study whether there were differences between the RTs according to the two tasks in terms of gender and age ( Table 3 ).

Table 3. Descriptive statistics ( M and SD ) for the RT in the simple and complex tasks according to gender and age.

ANOVA showed significant differences for the variable type of task ( F 1 , 113 = 822.97; p < 0.001; η 2 = 0.88; 1 − β = 1.00) and age ( F 2 , 113 = 5.86; p = 0.004 ; η 2 = 0.09; 1 − β = 0.87) but not gender ( F 1 , 113 = 3.56; p = 0.06; η 2 = 0.03; 1 − β = 0.46). There was also no significance in the interaction type of task × age ( F 2 , 113 = 0.38; p = 0.68; η 2 = 0.01; 1 − β = 0.11), type of task × gender ( F 1 , 113 = 2.23; p = 0.14; η 2 = 0.02; 1 − β = 0.32), age × gender ( F 2 , 112 = 0.63; p = 0.54; η 2 = 0.01; 1 − β = 0.15), or type of task × age × gender ( F 2 , 113 = 0.27; p = 0.76; η 2 = 0.01; 1 − β = 0.09).

Reaction Time and Physical Activity

Table 4 shows descriptive and normal analyses (Kolmogorov–Smirnov, n > 50; Shapiro–Wilk, n < 50) of reaction time (simple and complex) for each physical activity group.

Table 4. Descriptive statistics ( M and SD ) for the RT in the simple and complex tasks according to physical activity.

ANOVA was performed for each RT measure, with differences observed between groups for SRT ( F 2 , 116 = 4.43; p < 0.05) and CRT ( F 2 , 116 = 5.04; p < 0.01). Bonferroni’s statistic was applied to analyze the differences between the groups, observing differences between group 3 and group 1 in SRT ( p < 0.05, d = −0.68, 95% CI [−1.19, −0.17]) and CRT ( p < 0.05, d = −0.63, 95% CI [−1.14, −0.12]), as well as differences between group 3 and group 2 in CRT ( p < 0.01, d = −1.01, 95% CI [−1.56, −0.45]) ( Figure 1 ).

Figure 1. Differences between groups for SRT and CRT. SRT, simple reaction time; CRT, complex reaction time. ∗ p < 0.05; ∗∗ p < 0.01.

Reaction Time, Physical Condition, and Attention

Table 5 shows the descriptive statistics for the variables of physical condition, selective attention, and RT, as well as the existing correlation with the RT in the two tasks.

Table 5. Descriptive statistics for the variables of physical condition, selective attention, and anthropometry and their correlation with the two RT tasks.

Two regression analyses (successive steps) were performed, one for SRT and another for CRT, using as predictive variables the physical condition and measurements of the D2 attention test. The linearity assumptions were met in the relationship between predictor variables and criteria, homoscedasticity, and normal waste distribution. Durbin–Watson values were 2.05 and 1.93, so it can be assumed that the waste is independent, and the assumption of independence of the independent variables with respect to the dependent one is fulfilled ( Pardo and Ruiz, 2005 ).

In the case of SRT, the regression model included two variables, velocity test (β = 0.30) and dynamometry (non-dominant) (β = −0.23). The following values were obtained for this model: R = 0.43; R 2 = 0.19; R 2 adjusted = 0.17; F = 11.09; p < 0.001. The tolerance (0.90) and variance inflation factor (1.11) values of the model were adequate.

In the case of CRT, the regression model included dynamometry (dominant) (β = −0.40), concentration (D2-CON) (β = −0.40), and VO2max (β = −0.40). The following values were obtained for this model: R = 0.44; R 2 = 0.20; R 2 adjusted = 0.17; F = 7.92; p < 0.001. The tolerance (0.93–0.97) and variance inflation factor (1.04–1.08) values of the model were adequate.

The objective of the present study was to analyze the relationships between RT with selective attention and concentration and also with weekly physical activity volume and physical fitness in a sample of children with ages from 10 to 12 years. Likewise, this investigation evaluated whether cognitive functioning and physical condition were adequate predictors of RT, both simple and complex. The results showed the relationship between RT and weekly physical activity volume, physical fitness, selective attention, and concentration. In general, physical fitness predicted RT scores. However, only cognitive functioning was a predictor of complex RT.

First, the amount of weekly physical activity has been related to simple and complex RTs. Those who did more hours of physical activity a week showed less RT on both tasks. These results are congruent with previous research that had pointed out these associations ( Zwierko et al., 2014 ; Jain et al., 2015 ; Okubo et al., 2017 ; van de Water et al., 2017 ). Although the groups have not been divided according to the type of physical activity or sports performed, these results would support the idea that physical practice could be a useful activity to develop RT. When doing physical exercise, it is necessary to act effectively in a series of events, so this type of practice could have favored an increase in the capacity to act with greater speed and effectiveness in similar tasks ( Nuri et al., 2013 ; van de Water et al., 2017 ; Lynall et al., 2018 ; Walton et al., 2018 ), transferring this ability to others such as those evaluated in this work ( Rabiner et al., 2010 ; Kirk et al., 2017 ).

Second, relations have been observed between the level of physical fitness, attentional capacity, and concentration with RT, which would approximate studies that had previously pointed out this phenomenon ( Vaportzis et al., 2013 ; Jehu et al., 2015 ; Luque-Casado et al., 2016 ; Westfall et al., 2018 ). Being a correlational study, it is not possible to determine causal effects, but according to the findings found in various investigations, there could be multiple links between the variables studied. Reloba-Martínez et al. (2017) highlighted that a high-intensity exercise program had positive effects on selective attention and fitness. Therefore, the combination of physical exercise and the development of cognitive functioning could be an appropriate formula to improve RT in people.

Specifically, linear regression analyses have shown that the simple RT has been predicted solely by physical condition measurements. However, the model generated for the complex RT has combined physical condition and attentional measures. Specifically, the dominant manual dynamometry, concentration, and maximum oxygen consumption have been included variables. This is consistent with previous studies that had highlighted a greater relationship between these measures in situations requiring greater cognitive control ( Westfall et al., 2018 ). The complex RT requires selecting a response to different possibilities ( Boisgontier et al., 2014 ), so the demands to respond effectively are greater. In this work, the RT has been evaluated by means of an oculo-manual coordination task using the FITLIGHT Trainer system, which suggests that a better physical condition and a greater capacity to concentrate could have influenced the developed behavior, as indicated by the data obtained.

It is interesting to note that the physical fitness measurements that predicted the values in complex RT were manual dynamometry and maximum oxygen consumption. Cardiorespiratory fitness has been widely documented as an ability linked to improved cognitive ability and improved performance on tasks requiring cognitive control ( Kao et al., 2017 ; Westfall et al., 2018 ). However, the dominant manual dynamometry has been the strongest factor in the regression equation. This could have happened because of the type of task analyzed, which required a quick and efficient motor action of the upper limbs in the face of the visual stimuli of the FITLIGHT Trainer test. Probably, the neuromuscular requirements intrinsic to the task itself could have conditioned the results found. This could indicate that it is important, when carrying out this type of studies, to take into account the type of activity analyzed, given that the nature of the activity could modulate the conclusions derived from it.

This paper presents a number of limitations. On the one hand, the analysis of oxygen consumption has been carried out indirectly, which is data with a certain margin of error. In future works, it would be interesting to use a type of direct gas analysis test in an incremental stress test to obtain more reliable data. On the other hand, the type of design used does not allow establishing causal relationships between the variables analyzed. It would be interesting to carry out longitudinal or quasi-experimental work to observe how the data evolve as a function of changes in the physical condition or in the cognitive functioning of the study sample. In any case, this research carries out an interesting analysis in which it has linked variables of cognitive functioning, physical practice, and physical condition with RT, providing data that allow us to delve deeper into this phenomenon and that increase empirical evidence of the internal factors that could condition RT in preadolescents.

The findings found in this study suggest that better development of attention and concentration, as well as physical condition, could help improve RT at these ages. This could contribute to improving efficiency in tasks that are important for the personal and social growth of children and adolescents. Therefore, it would be interesting to contribute to its improvement when considering psychomotor development programs in this population.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by Ethics Committee of the University of Jaén, Spain (Ref. ABR.16/6). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author Contributions

SB, IM, AH-M, VM-S, RR, and RJ-R participated in the study design and data collection, performed the statistical analyses, contributed to the interpretation of the results, wrote the manuscript, and approved the final manuscript. RR, AH-M, SB, and IM conceived the study and participated in its design and coordination. AH-M, VM-S, RR, RJ-R, SB, and IM contributed to the interpretation of the results, and reviewed and provided feedback to the manuscript. All authors made substantial contributions to the final manuscript.

Conflict of Interest

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.

Adleman, N. E., Chen, G., Reynolds, R. C., Frackman, A., Razdan, V., Weissman, D. H., et al. (2016). Age-related differences in the neural correlates of trial-to-trial variations of reaction time. Dev. Cogn. Neurosci. 19, 248–257. doi: 10.1016/j.dcn.2016.05.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Appelbaum, L. G., and Erickson, G. (2018). Sports vision training: a review of the state-of-the-art in digital training techniques. Int. Rev. Sport Exerc. Psychol. 11, 160–189. doi: 10.1080/1750984X.2016.1266376

CrossRef Full Text | Google Scholar

Baayen, R. H., and Milin, P. (2010). Analyzing reaction times. Int. J. Psychol. Res. 3, 12–28. doi: 10.21500/20112084.807

Boisgontier, M. P., Wittenberg, G. F., Fujiyama, H., Levin, O., and Swinnen, S. P. (2014). Complexity of central processing in simple and choice multilimb reaction-time tasks. PLoS One 9:e90457. doi: 10.1371/journal.pone.0090457

Brickenkamp, R. (2002). D2, Test de Atención. Madrid: TEA Ediciones.

Google Scholar

Chun, M. M., Golomb, J. D., and Turk-Browne, N. B. (2011). A taxonomy of external and internal attention. Annu. Rev. Psychol. 62, 73–101. doi: 10.1146/annurev.psych.093008.100427

Deary, I. J., and Der, G. (2005). Reaction time, age, and cognitive ability: longitudinal findings from age 16 to 63 years in representative population samples. Aging Neuropsychol. Cogn. 12, 187–215. doi: 10.1080/13825580590969235

Der, G., and Deary, I. J. (2006). Age and sex differences in reaction time in adulthood: results from the United Kingdom health lifestyle survey. Psychol. Aging 21, 62–73. doi: 10.1037/a0015515

Eurofit, (1993). Eurofit Tests of Physical Fitness , 2nd Edn, Strasbourg: Committee of Experts on Sports Research.

Fischer, M. V., Stone, J., Hawkes, T. D., Eveland, E., and Strang, A. J. (2015). Integrative physical and cognitive training development to better meet airmen mission requirements. Procedia Manuf. 3, 1580–1586. doi: 10.1016/j.promfg.2015.07.445

Gentier, I., Augustijn, M., Deforche, B., Tanghe, A., De Bourdeaudhuij, I., Lenoir, M., et al. (2013). A comparative study of performance in simple and choice reaction time tasks between obese and healthy-weight children. Res. Dev. Disabil. 34, 2635–2641. doi: 10.1016/j.ridd.2013.04.016

Giuliano, R. J., Karns, C. M., Neville, H. J., and Hillyard, S. A. (2014). Early auditory evoked potential is modulated by selective attention and related to individual differences in visual working memory capacity. J. Cogn. Neurosci. 26, 2682–2690. doi: 10.1162/jocn-a-00684

Gomez-Ramirez, M., Hysaj, K., and Niebur, E. (2016). Neural mechanisms of selective attention in the somatosensory system. J. Neurophysiol. 116, 1218–1231. doi: 10.1152/jn.00637.2015

Greenhouse, I., King, M., Noah, S., Maddock, R. J., and Ivry, R. B. (2017). Individual differences in resting corticospinal excitability are correlated with reaction time and GABA content in motor cortex. J. Neurosci. 37, 2686–2696. doi: 10.1523/JNEUROSCI.3129-16.2017

Greimel, E., Wanderer, S., Rothenberger, A., Herpertz-Dahlmann, B., Konrad, K., and Roessner, V. (2011). Attentional performance in children and adolescents with tic disorder and co-occurring attention-deficit/hyperactivity disorder: new insights from a 2x2 factorial design study. J. Abnorm. Child Psych. 39, 819–828. doi: 10.1007/s10802-011-9493-7

Hillman, C. H., Pontifex, M. B., Raine, L. B., Castelli, D. M., Hall, E. E., and Kramer, A. F. (2009). The effect of acute treadmill walking on cognitive control and academic achievement in preadolescent children. Neuroscience 159, 1044–1054. doi: 10.1016/j.neuroscience.2009.01.057

Jain, A., Bansal, R., Kumar, A., and Singh, K. D. (2015). A comparative study of visual and auditory reaction times on the basis of gender and physical activity levels of medical first year students. Int. J. Appl. Basic Med. Res. 5, 124–127. doi: 10.4103/2229-516X.157168

Jayaswal, A. A. (2016). Comparison between auditory and visual simple reaction times and its relationship with gender in 1st year MBBS students of jawaharlal nehru medical college, Bhagalpur, Bihar. Int. J. Med. Res. Rev. 4, 1228–1232. doi: 10.17511/ijmrr.2016.i07.26

Jehu, D. A., Desponts, A., Paquet, N., and Lajoie, Y. (2015). Prioritizing attention on a reaction time task improves postural control and reaction time. Int. J. Neurosci. 125, 100–106. doi: 10.3109/00207454.2014.907573

Jensen, A. (2006). Cloking the Mind: Mental Chronometry and Individual Differences. Amsterdam: Elsevier.

Kao, S. C., Westfall, D. R., Soneson, J., Gurd, B., and Hillman, C. H. (2017). Comparison of the acute effects of high-intensity interval training and continuous aerobic walking on inhibitory control. Psychophysiology 54, 1335–1345. doi: 10.1111/psyp.12889

Kirk, H., Gray, K., Ellis, K., Taffe, J., and Cornish, K. (2017). Impact of attention training on academic achievement, executive functioning, and behavior: a Randomized controlled trial. Am. J. Intellect. 122, 97–117. doi: 10.1352/1944-7558-122.2.97

Kuang, S. (2017). Is reaction time an index of white matter connectivity during training? Cogn. Neurosci. 8, 126–128. doi: 10.1080/17588928.2016.1205575

Leckie, R. L., Oberlin, L. E., Voss, M. W., Prakash, R. S., Szabo-Reed, A., Chaddock-Heyman, L., et al. (2014). BDNF mediates improvements in executive function following a 1-year exercise intervention. Front. Hum. Neurosci. 8:985. doi: 10.3389/fnhum.2014.00985

Léger, L. A., Mercier, D., Gadoury, C., and Lambert, J. (1988). The multistage 20 metre shuttle run test for aerobic fitness. J. Sport Sci. 6, 93–101. doi: 10.1080/02640418808729800

Luque-Casado, A., Perakakis, P., Hillman, C. H., Kao, S. C., Llorens, F., Guerra, P., et al. (2016). Differences in sustained attention capacity as a function of aerobic fitness. Med. Sci. Sports Exerc. 48, 887–895. doi: 10.1249/MSS.0000000000000857

Lynall, R. C., Blackburn, J. T., Guskiewicz, K. M., Marshall, S. W., Plummer, P., and Mihalik, J. P. (2018). Reaction time and joint kinematics during functional movement in recently concussed individuals. Arch. Phys. Med. Rehabil. 99, 880–886. doi: 10.1016/j.apmr.2017.12.011

Metin, B., Wiersema, J. R., Verguts, T., Gasthuys, R., van Der Meere, J. J., Roeyers, H., et al. (2016). Event rate and reaction time performance in ADHD: testing predictions from the state regulation deficit hypothesis using an ex-Gaussian model. Child Neuropsychol. 22, 99–109. doi: 10.1080/09297049.2014.986082

Mudric, M., Cuk, I., Nedeljkovic, A., Jovanovic, S., and Jaric, S. (2015). Evaluation of Video-based method for the measurement of reaction time in specific sport situation. Int. J. Perf. Anal. Sports 15, 1077–1089. doi: 10.1080/24748668.2015.11868852

Nuri, L., Shadmehr, A., Ghotbi, N., and Attarbashi Moghadam, B. (2013). Reaction time and anticipatory skill of athletes in open and closed skill-dominated sport. Eur. J. Sport Sci. 13, 431–436. doi: 10.1080/17461391.2012.738712

Okubo, Y., Schoene, D., and Lord, S. R. (2017). Step training improves reaction time, gait and balance and reduces falls in older people: a systematic review and meta-analysis. Br. J. Sports Med. 51, 586–593. doi: 10.1136/bjsports-2015-095452

Pardo, A., and Ruiz, M. A. (2005). Data Analysis with SPSS 13 Base. Madrid: McGraw Hill.

Petersen, S. E., and Posner, M. I. (2012). The attention system of the human brain: 20 years after. Annu. Rev. Neurosci. 35, 73–89. doi: 10.1146/annurev-neuro-062111-150525

Prinzmetal, W., McCool, C., and Park, S. (2005). Attention: reaction time and accuracy reveal different mechanisms. J. Exp. Psychol. Gen. 134, 73–92. doi: 10.1037/0096-3445.134.1.73

Rabiner, D. L., Murray, D. W., Skinner, A. T. Y., and Malone, P. S. (2010). A randomized trial of two promising computer-based interventions for students with attention difficulties. J. Abnorm. Child Psych. 38, 131–142. doi: 10.1007/s10802-009-9353-x

Reloba-Martínez, S., Reigal, R. E., Hernández-Mendo, A., Martínez-López, E. J., Martín-Tamayo, I., and Chirosa-Ríos, L. J. (2017). Effects of vigorous extracurricular physical exercise on the attention of schoolchildren. Rev. Psicol. Deporte. 26, 29–36.

Ruiz, J. R., Castro-Piñero, J., España-Romero, V., Artero, E. G., Ortega, F. B., Cuenca, M. M., et al. (2011). Field-based fitness assessment in young people: the ALPHA health-related fitness test battery for children and adolescents. Br. J. Sport Med. 45, 518–524. doi: 10.1136/bjsm.2010.075341

Ruschel, C., Haupenthal, A., Hubert, M., Fontana, H. B., Pereira, S. M., and Roesler, H. (2011). Simple reaction time in soccer players from differing categories and field positions. Motricidad 7, 73–82.

Sant’Ana, J., Franchini, E., da Silva, V., and Diefenthaeler, F. (2016). Effect of fatigue on reaction time, response time, performance time, and kick impact in taekwondo roundhouse kick. Sport Biomech. 16, 201–209. doi: 10.1080/14763141.2016.1217347

Syväoja, H. J., Tammelin, T. H., Ahonen, T., Kankaanpää, A., and Kantomaa, M. T. (2014). The associations of objectively measured physical activity and sedentary time with cognitive functions in school-aged children. PLoS One 9:e103559. doi: 10.1371/journal.pone.0103559

Tamm, L., Epstein, J. N., Peugh, J. L., Nakonezny, P. A., and Hughes, C. W. (2013). Preliminary data suggesting the efficacy of attention training for school-aged children with ADHD. Dev. Cogn. Neurosci. 4, 16–28. doi: 10.1016/j.dcn.2012.11.004

Tønnessen, E., Haugen, T., and Shalfawi, S. A. (2013). Reaction time aspects of elite sprinters in athletic world championships. J. Strength Cond. Res. 27, 885–892. doi: 10.1519/JSC.0b013e31826520c3

van de Water, T., Huijgen, B., Faber, I., and Elferink-Gemser, M. (2017). Assessing cognitive performance in badminton players: a reproducibility and validity study. J. Hum. Kinet. 55, 149–159. doi: 10.1515/hukin-2017-0014

Vaportzis, E., Georgiou-Karistianis, N., and Stout, J. C. (2013). Dual task performance in normal aging: a comparison of choice reaction time tasks. PLoS One 8:e60265. doi: 10.1371/journal.pone.0060265

Vences de Brito, A., Salva, C., Cid, L., Ferreira, R., and Marques, A. (2011). Attention and reaction time in shotokan karate practitioners. J. Asian Martial Arts 1, 141–156.

Walton, C. C., Keegan, R. J., Martin, M., and Hallock, H. (2018). The potential role for cognitive training in sport: more research needed. Front. Psychol. 9:1121. doi: 10.3389/fpsyg.2018.01121

Weir, J. P. (2005). Quantifying test-retest reliability using the intraclass correlation coefficient and the SEM. J. Strength Cond. Res. 19, 231–240. doi: 10.1519/15184.1

Wells, A. J., Hoffman, J. R., Beyer, K. S., Jajtner, A. R., González, A. M., Townsend, J. R., et al. (2014). Reliability of the dynavision TM d2 for assessing reaction time performance. J. Sport Sci. Med. 13, 145–150.

PubMed Abstract | Google Scholar

Westfall, D. R., Gejl, A. K., Tarp, J., Wedderkopp, N., Kramer, A. F., Hillman, C. H., et al. (2018). Associations between aerobic fitness and cognitive control in adolescents. Front. Psychol. 9:1298. doi: 10.3389/fpsyg.2018.01298

Woods, D. L., Wyma, J. M., Yund, E. W., Herron, T. J., and Reed, B. (2015). Factors influencing the latency of simple reaction time. Front. Hum. Neurosci. 9:131. doi: 10.3389/fnhum.2015.00131

World Medical Association (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310, 2191–2194. doi: 10.1001/jama.2013.281053

Zurek, M., Cosmi, S., Cicchela, A., and Roi, G. S. (2015). “Simple and complex reaction time at visual stimulation, before and after a rehabilitation after knee surgery in football players,” in Poster Presented in XXIV International Conference on Sports Rehabilitation and Traumatology , London.

Zwierko, T., Florkiewicz, B., Slawomir, F., and Kszak-Krzyzanowska, A. (2014). The ability to maintain atention during visuomotor task performance in handball players and non athletes. Cent. Eur. J. Sport Sci. Med. 7, 99–106.

Keywords : reaction time, selective attention, FITLIGHT trainer, physical activity, physical fitness

Citation: Reigal RE, Barrero S, Martín I, Morales-Sánchez V, Juárez-Ruiz de Mier R and Hernández-Mendo A (2019) Relationships Between Reaction Time, Selective Attention, Physical Activity, and Physical Fitness in Children. Front. Psychol. 10:2278. doi: 10.3389/fpsyg.2019.02278

Received: 28 June 2019; Accepted: 23 September 2019; Published: 15 October 2019.

Reviewed by:

Copyright © 2019 Reigal, Barrero, Martín, Morales-Sánchez, Juárez-Ruiz de Mier and Hernández-Mendo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Antonio Hernández-Mendo, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Research and Analysis on the Influence Factors of Visual Reaction Time

• Conference paper
• First Online: 28 June 2020
• Cite this conference paper

• Minxia Liu 16 ,
• Jiping Lu 16 ,
• Chunxiang Gao 17 &
• Weiwei Lv 16  

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1201))

Included in the following conference series:

• International Conference on Applied Human Factors and Ergonomics

1172 Accesses

The research goal is to find out factors, which affected the visual reaction time in our daily life, and to find out that how to improve our reaction time by changing these factors in some way. The research method: we collected 100 students’ visual reaction experiment’s data, and we use the statistic software SPSS to analyze the 100 students’ experiment data. The research results: we find out that dominant hand, color, stimulus probability and arrangement mode are the main factors affecting the visual reaction time. The research conclusions: on the one hand, if we require short visual reaction time, we should use longer wavelength like red, yellow color, and we should arrange a graph or text in a neat as far as possible; on the other hand, we use high probability stimulus and dominant hand will decrease the visual reaction time in our daily life.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Ye, G.: Modern Children and Adolescents Hygiene, pp. 625–628. People’s Health Press, Beijing (1999)

Google Scholar  

Li, B.: Research on the effect of visual reaction time on table tennis performance [J]. Liaoning Sports Science and technology 34 (5), 59–60 (2012)

Xue, Q., Pang, Y.: Human Factors Comprehensive Training Course, pp. 98–101. Beijing University of Technology Press, Beijing (2007)

Download references

Author information

Authors and affiliations.

Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China

Minxia Liu, Jiping Lu & Weiwei Lv

Beijing Railway Signal Company Limited, No.456 Sicun Langfa Huangcun, Daxing District, Beijing, 102613, China

Yu Gu & Chunxiang Gao

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Minxia Liu .

Editor information

Editors and affiliations.

Drexel University, Philadelphia, PA, USA

National University of Sciences and Tech (NUST), School of Mech and Manufacturing Eng (SMME), Islamabad, Pakistan

Umer Asgher

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Cite this paper.

Liu, M., Lu, J., Gu, Y., Gao, C., Lv, W. (2021). Research and Analysis on the Influence Factors of Visual Reaction Time. In: Ayaz, H., Asgher, U. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1201. Springer, Cham. https://doi.org/10.1007/978-3-030-51041-1_20

Download citation

DOI : https://doi.org/10.1007/978-3-030-51041-1_20

Published : 28 June 2020

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-51040-4

Online ISBN : 978-3-030-51041-1

eBook Packages : Intelligent Technologies and Robotics Intelligent Technologies and Robotics (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 26 April 2022

Reaction time and working memory in gamers and non-gamers

• Gal Ziv 1 ,
• Ronnie Lidor 1 &
• Oron Levin 2  

Scientific Reports volume  12 , Article number:  6798 ( 2022 ) Cite this article

24k Accesses

5 Citations

14 Altmetric

Metrics details

• Human behaviour

The purpose of this pre-registered study was to examine whether asking gamers and non-gamers about their video game playing habits before or after they performed computerized cognitive-motor tasks affects their performance of those tasks. We recruited 187 participants from an online participants’ recruitment platform. Out of those participants, 131 matched our criteria as gamers or non-gamers. They were then divided to two subgroups, and performed a choice-RT task, a Simon task, an alternate task-switching task, and a digit span memory task either before or after answering a video-game playing habits questionnaire. The results showed that gamers who completed a video-games questionnaire before performing the tasks had faster reaction times (RTs) in the Simon task compared with gamers who answered the questionnaire after performing the tasks. In contrast, non-gamers who answered the questionnaire before the task had slower RTs in the Simon task and the alternate task-switching task compared with non-gamers who answered the questionnaire after performing the tasks. The results suggest that answering a video-games questionnaire before the start of a study can lead to a response expectancy effect—positive for gamers and negative for non-gamers. This may bias findings of studies examining video games and the performance of cognitive-motor tasks.

Similar content being viewed by others

Novel 3-D action video game mechanics reveal differentiable cognitive constructs in young players, but not in old

Tomihiro Ono, Takeshi Sakurai, … Toshiya Murai

Neurocognitive analyses reveal that video game players exhibit enhanced implicit temporal processing

Francois R. Foerster, Matthieu Chidharom, … Anne Giersch

The use of commercial computerised cognitive games in older adults: a meta-analysis

Bruno Bonnechère, Christelle Langley & Barbara Jacquelyn Sahakian

Introduction

With over 2.7 billion gamers worldwide 1 , playing video games can be considered as one of today's favorite pastimes. As the popularity of video games grows, research interest in the effects of playing video games on human behavior and psychology increases as well. In the past few decades, researchers have examined the relationship between video games and aggression (e.g., 2 , 3 ), depression (e.g., 4 , 5 ), addiction (e.g., 6 ), and cognitive processes—among them executive function (e.g., 7 , 8 ), attention (e.g., 9 ), reaction time (RT) (e.g., 10 ), and working memory (e.g., 11 ). It has been suggested that playing video games can have cognitive, motivational, emotional, and social benefits 12 .

A number of studies have shown positive relationships between playing video games and cognitive-motor skills (e.g., 13 , 14 , 15 , 16 , 17 ). For example, Boot et al. 13 showed that expert gamers are better than non-gamers in tracking moving objects, in detecting change, and in task switching. Colzato et al. 15 reported that experienced gamers who play first-person shooter games—action games that are played from a first-person view—were more accurate at an N-back task and reacted faster to go signals in a stop-signal task without compromising stopping performance, than non-gamers. Another study 17 used a stop-change paradigm (a variation of the stop-signal task with the addition of a cue to not only inhibit a response but to initiate another) and demonstrated that, compared with non-gamers, experienced first-person shooter players reacted faster in the go condition and in the change conditions without compromising accuracy.

While the abovementioned findings are promising, there are a number of methodological concerns that undermine our ability to show a causal relationship between playing video games and improved cognitive and motor performance. It is not clear, for example, whether the relationship between gaming and performance is caused by the gaming experience or if it represents pre-existing differences that lead to a self-selection effect, causing certain individuals to choose to play video games 13 . Boot et al. 18 suggested that several methodological shortcomings may undermine the positive effects of playing video games on cognitive/motor performance. Specifically, for studies that aim at examining differences between gamers and non-gamers, covert recruiting of participants is of importance.

Boot et al. 18 emphasize that gamers should not know that they are recruited for a study about gamers or about the benefits of playing video games, as this might bias the results. To prevent that bias from occurring, researchers should not ask participants about their video game-playing experience before the study. This methodological argument is supported by the concept of psychological suggestion. Psychological suggestion refers to a process by which individuals or environmental cues influence the way we think and behave 19 . Suggestions can be deliberate (e.g., directly influencing one's thought, beliefs, or behaviors), or unintentional (e.g., given by certain cues given by individuals or that are present in the environment). Examples of such unintentional cues can be found in various domains. For example, jurors' verdicts are affected by judges’ expectations of guilt and by subtle differences in the way they give instructions to the jury 20 . Ziv and colleagues 21 provided an example from the motor learning domain, where participants’ expectancies of success were manipulated by changing the task-success criterion. In their study, participants who practiced with an easy success criterion putted golf balls more accurately in a transfer task compared with participants who practiced with a difficult success criterion. In the abovementioned studies, subtle cues led to changes in decision making in jurors as well as in individuals who performed a motor task (golf putting). Similarly, gamers who learn that they are about to participate in a study on gamers' abilities, and who believe that gaming may be related to higher cognitive and motor performance, might expect to perform better—and indeed do so.

One theory that can explain how psychological suggestion works—and in the context of the current study how unintentional psychological suggestion can lead to changes in gamers' task performance, is the response expectancy theory 22 , 23 . Response expectancies can be defined as “the anticipation of automatic, subjective, and behavioral responses to particular situational cues” ( 23 , p. 69), and they can be a product of suggestion. Such response expectancies can lead individuals to automatically change their behavior in accordance with their expectancies 24 . For example, Clifasefi et al. 25 showed that telling participants that they are receiving a drug that enhances mental alertness and cognitive function, when they actually received a placebo, led to improved performance in a cognitive task (compared with participants who were told they were given a placebo). Similarly, Foroughi et al. 26 showed that individuals who were recruited overtly for a cognitive training session (i.e., recruitment flyer mentioned that training can improve cognitive function) improved their cognitive performance after a one-hour training session compared with participants who were recruited covertly (i.e., neutral recruitment flyer) for the same training and showed no improvements.

In accordance with psychological suggestion and response expectancy theory, Boot et al. 27 suggest that participants' expectancies can affect the results of studies. Gamers, for example, may expect to perform well in certain cognitive/motor tasks if they believe that there is a positive relationship between gaming and performance, and if they are aware of the fact that they were recruited for a certain study because they play video games. Such expectancy effects can also occur in video game training studies in which participants are told that such training should lead to improved performance in various cognitive tasks (e.g., 28 ).

To assess the effects of this possible bias directly, we devised a study in which a group of gamers and a group of non-gamers were covertly recruited and were asked to perform certain cognitive/motor tasks either before or after answering a video-games questionnaire. Covert recruitment can be accomplished, for example, by inserting the questions regarding gaming habits within various unrelated questions (e.g., questions about religious beliefs and preferred temperatures) 17 . However, it is even better to avoid asking such questions at all. In our study, we used an online participant recruitment platform that allowed us to recruit gamers and non-gamers without asking any preliminary questions.

Therefore, the purpose of the current study was to examine whether asking participants about their gaming experience prior to participation in the study affects their performance. We hypothesized that (a) asking gamers about their gaming experience before the study will lead to better performance in reaction time (RT)-based tasks compared to asking the same questions after the study; (b) asking non-gamers about their gaming experience before the study compared to after the study will not affect their performance in RT-based tasks; and (c) there will be no differences between gamers and non-gamers in a digit-span memory task.

The second hypothesis requires an explanation. First, although response expectancies and suggestions can be both positive and negative, there are relatively little data regarding these effects in simple cognitive-motor tasks. In addition, the few studies that examined these effects on motor performance showed contrasting results. While Ziv et al. 21 showed positive, not negative, effects in a golf-putting task, Fillmore and Vogel-Sprott 29 reported both negative and positive changes in the performance of a pursuit-rotor task corresponding to suggestions of negative or positive effects of caffeine (when the participants actually drank a decaffeinated drink). In addition, Harrell and Juliano 30 showed the opposite effect of placebo caffeine in a finger-tapping task (improved performance when told caffeine impairs performance, reduced performance when told caffeine enhances performance). Finally, we did not know whether non-gamers believe that gaming is related to performance of such tasks, or if such beliefs are necessary for the effect to occur. Therefore, we adopted a cautious approach in developing this hypothesis. The third hypothesis is based on the view that, as compared with attention and information processing capacity, working memory capacity is expected to be affected to a lesser extent by suggestions or response expectancy (in the context of the beliefs of gamers). Indeed, Boot et al. 27 have reported that expectancies that playing video games will improve memory stores are relatively low.

We selected RT-based tasks because these tasks are expected to produce better processing speeds, attentional control, and visuomotor transformation, which appear to be more elevated in gamers than non-gamers. Working memory, on the other hand, may be positively affected by gaming to a lesser extent, albeit some improvement might be expected as the memory network and the attentional network share overlapping neural pathways (e.g., dorsal attentional pathways 31 .

Pre-registration and raw data repository

The study’s main questions and hypotheses, experimental conditions and groups, and dependent variables, as well as the handling of outliers and data exclusion, sample size, and statistical analyses, were all pre-registered on aspredicted.org and can be accessed online ( https://aspredicted.org/wp53f.pdf ). Any deviations from the pre-registration are noted. Analyses that were not pre-registered are reported in the Exploratory Analyses sub-section of the Results section. We removed one hypothesis listed in our pre-registration (i.e., that gamers who play first-person shooter games will have faster reaction times but will make a similar number of errors in RT-based tasks, since the sample size of first-person shooter players playing over 10 h per week was too small ( n  = 14) compared with those playing less than three hours per week ( n  = 119). The raw dataset used for the statistical analyses can be accessed online as well on OSF ( https://osf.io/s2vcz/?view_only=88caada978f141f787684cc2e63b7673 ).

The results are reported for each of the experimental tasks separately. The RT data for all three RT-based tasks are presented in Fig.  1 .

Mean RTs for the choice-RT task ( a ), the Simon task ( b ), and the alternate task-switching task ( c ), for the four experimental groups (figure created using R software). NG-B Non-gamers, questionnaire at the beginning, NG-E Non-gamers, questionnaire at the end, G-B Gamers, questionnaire at the beginning, G-E Gamers, questionnaire at the end. Note that the y-axis limits differ between graphs. Errors bars represent standard error. Small light-gray circles represent individual participants.

Choice-RT task

A two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] revealed no group effect, F (1, 127) = 0.47, p  = 0.49, \(\eta_{p}^{2}\)  = 0.00 and no Questionnaire Timing effect, F (1, 127) = 1.68, p  = 0.20, \(\eta_{p}^{2}\)  = 0.01. In addition, no significant interaction was found, F (1, 127) = 0.59, p  = 0.44, \(\eta_{p}^{2}\)  = 0.01. The mean choice RT was 367.72 ± 62.73 ms.

Mean correct responses

There were no differences between questionnaire delivery time (before or after the task) in gamers (Mann–Whitney U = 638.00, p  = 0.74; mean: 23.73 ± 0.41) and non-gamers (Mann–Whitney U = 358.50, p  = 0.26; mean: 23.71 ± 0.53). There were also no differences in total correct responses between gamers and non-gamers (Mann–Whitney U = 2172, p  = 0.76).

A two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] revealed a significant interaction, F (1, 127) = 7.30, p  = 0.01, \(\eta_{p}^{2}\)  = 0.05, as can be seen in Fig.  2 . The mean RT of the non-gamers was higher when the questionnaire was delivered before performing the task (515.40 ± 70.26 ms) compared with after the task (479.51 ± 47.57 ms; Cohen’s d  = 0.61). In contrast, the mean RT of gamers was lower when the questionnaire was delivered before the task (487.26 ± 57.75 ms) compared with after the task (510.98 ± 70.57 ms, Cohen’s d  = 0.37). There was no Group effect, F (1, 127) = 0.02, p  = 0.88, \(\eta_{p}^{2}\)  = 0.00, and no Questionnaire Timing effect, F (1, 127) = 0.30, p  = 0.58, \(\eta_{p}^{2}\)  = 0.00.

The interaction between group (gamers vs. non-gamers) and the questionnaire delivery time (before vs. after the task) of the mean RT during the Simon task (error bars represent 95% confidence intervals) (figure created using Microsoft Excel).

There were no differences in the questionnaire delivery time (before or after the task) between gamers (Mann–Whitney U = 608.00, p  = 0.66; mean: 22.60 ± 1.32) and non-gamers (Mann–Whitney U = 404.50, p  = 0.81; mean: 22.35 ± 1.41). There were also no differences in total correct responses between gamers and non-gamers (Mann–Whitney U = 2309.50, p  = 0.29).

Alternate task-switching task

A two-way ANOVA [Group × Questionnaire Timing (before or after tasks)] revealed no group effect, F (1, 123) = 0.77, p  = 0.38, \(\eta_{p}^{2}\)  = 0.01, no Questionnaire Timing effect, F (1, 123) = 0.53, p  = 0.47, \(\eta_{p}^{2}\)  = 0.00, and no interaction, F (1, 123) = 3.12, p  = 0.08, \(\eta_{p}^{2}\)  = 0.03. The mean RT for this task was 967.43 ± 184.01 ms.

There were no differences in the questionnaire delivery time (before or after the task) between gamers (Mann–Whitney U = 495.50, p  = 0.83; mean: 21.93 ± 2.01) and non-gamers (Mann–Whitney U = 361.00, p  = 0.79; mean: 21.42 ± 2.73). There were also no differences in total correct responses between gamers and non-gamers (Mann–Whitney U = 1758.00, p  = 0.77).

Digit span task

A two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] revealed no group effect, F (1, 127) = 2.32, p  = 0.13, \(\eta_{p}^{2}\)  = 0.02, no Questionnaire Timing effect, F (1, 127) = 0.22, p  = 0.64, \(\eta_{p}^{2}\)  = 0.00, and no interaction, F (1, 127) = 0.70, p  = 0.70, \(\eta_{p}^{2}\)  = 0.00. The mean correct response was 5.88 ± 1.81.

Mean highest number of digits before the first error

A two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] revealed no group effect, F (1, 127) = 1.32, p  = 0.25, \(\eta_{p}^{2}\)  = 0.0 and no Questionnaire Timing effect, F (1, 127) = 0.63, p  = 0.43, \(\eta_{p}^{2}\)  = 0.01. In addition, no significant interaction was found, F (1, 127) = 0.64, p  = 0.43, \(\eta_{p}^{2}\)  = 0.01. The mean highest number of digits before the first error was 6.69 ± 1.93.

Stepwise multiple regression and LASSO regression analyses

We entered the following independent variables to the regression equations: hours per week playing video games; playing first-person shooter games, strategy games, and role-playing games; years playing video games; beliefs regarding a connection between playing video games and task performance; and, knowledge of media reports on a connection between playing video games and task performance. Table 1 presents the findings for both the stepwise and LASSO regressions. As can be seen in Table 1 , regardless of the type of regression used, the models led to a low R 2 of under 0.06.

Exploratory analyses

Gender differences.

We did not expect that gender differences would affect our results, and therefore we did not include an analysis such differences in our preregistration. However, we wanted to make sure that this assumption was indeed the case, and thus performed independent t-tests for all dependent variables in order to assess differences between males and females. Our assumption was correct, as all of these tests were statistically insignificant with low effect sizes (see Table 2 ).

Alternate task switching task including all data

For the alternate task-switching task, we decided before the study to remove all RT values over 1500 ms. However, because there was no time limit to the stimulus, durations of over 1500 ms may have been valid as well. Therefore, we ran the two-way ANOVA [Group × Questionnaire Timing (before or after the tasks)] without excluding values over 1500 ms. This analysis revealed a significant interaction, F (1, 127) = 4.35, p  = 0.04, \(\eta_{p}^{2}\)  = 0.03, as can be observed in Fig.  3 . A post-hoc analysis showed that the non-gamers reduced their RT from 1135.45 ± 605.75 ms when the questionnaire was completed before performing the tasks to 911.01 ± 161.57 ms when the questionnaire was answered after performing the tasks (Cohen’s d  = 0.51). In contrast, the gamers had similar RTs in the beginning questionnaire (1007.94 ± 272.63 ms) and the end questionnaire (1054.83 ± 332.94 ms). There was neither a Group effect, F (1, 127) = 0.02, p  = 0.90, \(\eta_{p}^{2}\)  = 0.00, nor a Questionnaire Timing effect, F (1, 127) = 1.86, p  = 0.18, \(\eta_{p}^{2}\)  = 0.01.

The interaction between group (gamers vs. non-gamers) and the questionnaire delivery time (before vs. after the task) of the mean RT during the alternate task-switching task (error bars represent 95% confidence intervals) (figure created using Microsoft Excel).

Beliefs about a connection between playing video games and the ability to perform cognitive-motor tasks

An independent t-test revealed no differences between the beliefs of gamers and non-gamers regarding the connection between playing video games and the ability to perform cognitive-motor tasks, t (128) = 1.44, p  = 0.15, Cohen’s d  = 0.25. The mean response (on a scale of 1–10) was 8.05 ± 1.93 and 7.60 ± 1.64, for gamers and non-gamers, respectively. The median for both groups was eight.

Awareness of media reports on the benefits of video games

In non-gamers (< 3 h of play per week), 25 participants reported that they were aware of media reports discussing the benefits of video games in regards to the performance of cognitive-motor tasks, and 32 participants reported that they were not aware of such reports. In gamers (> 10 h of play per week), 43 reported that they were aware and 30 reported that they were not aware of these reports. However, a χ 2 test revealed no differences between the groups, χ 2 (1) = 2.90, p  = 0.09, φ = 0.15.

Bayesian analyses of null results

In null-hypothesis significance testing, a lack of significance does not allow us to demonstrate the probability of the null hypothesis itself 32 . Therefore, we used Bayesian statistics to assess the probability of the null hypotheses for the dependent variables that did not produce significant main effects or interactions. The Bayes factors supporting the null hypothesis (BF 01 ) compared to the possible combinations of main effects and interactions are presented in Table 3 .

We also analyzed the Bayes factors to exclude the interaction effect alone. This analysis showed that the Bayes factors for excluding the interaction were 16.51, 6.3, 15.70, and 14.94 for the choice-RT task RT, the alternate task switching RT, the correct response, and the highest number of digits before first error in the digit-span task, respectively.

The current study examined whether cognitive/motor task performance in gamers and non-gamers was affected by whether they completed a video-games questionnaire prior to performing those tasks. We had three hypotheses. First, we expected that asking gamers about their gaming experience before the study would lead to better performance in RT-based tasks compared with asking the same questions after the study. This hypothesis was partially supported. Gamers had faster RTs when they performed the Simon task (but not the other two RT-based tasks) after answering the video-games questionnaire compared with before answering the questionnaire. The Bayes factors associated with these tasks mostly suggested that the data are more likely to be accurate under the null hypothesis (except for inconclusive findings regarding the models with the separate main effects) (see Table 3 ). Second, we hypothesized that this effect would not be found in non-gamers. This hypothesis was not supported by our data. In the Simon task, non-gamers had faster RTs when performing the task before completing the questionnaire compared with after answering the questionnaire. In addition, our exploratory analysis showed that this also occurred in the alternate task-switching task. These results suggest that answering a video-games questionnaire before performing such tasks may have an adverse effect on performance in non-gamers. Finally, our hypothesis that similar effects would not be found for the digit-span memory task was supported by the data of the current experiment.

The finding that the timing of questionnaire delivery affects both gamers and non-gamers can explain, at least in part, the observed differences between groups in previous studies in which all participants answered a video-games questionnaire prior to their participation in the study. According to the results obtained in our study, not only do gamers perform better after answering questions about their gaming habits, but non-gamers perform worse after answering such questions. In fact, our data suggest that it is possible that the effect on non-gamers is greater than the opposite effect on gamers, since answering questions about video-games habits negatively affected the non-gamers in two tasks—the Simon task and the alternate task-switching task (although this is an exploratory finding), whereas this only positively affected gamers in the Simon task. Moreover, there were no differences between groups in the participants' responses to the question “Do you think there is a connection between playing video games and the ability to perform cognitive-motor tasks, such as the ones you just performed?”. In both groups, the mean response was ~ 7.5–8 (on a scale of 1—not at all to 10—very much so). In addition, there were no differences between groups in the number of participants who were familiar with media reports or research regarding the benefits of playing video games in relation to the ability to perform cognitive-motor tasks. Both gamers and non-gamers appeared to believe that playing video games can enhance performances of cognitive-motor tasks, and therefore it is possible that the video-games questionnaire caused gamers to perform better and caused non-gamers to perform worse. Therefore, the results of our study support the concept of psychological suggestion as well as the response expectancy theory.

Similar effects can be found in the literature on stereotypes and test performance. These effects suggests that individuals show suboptimal task performance when they know they are "expected to be" weak at that task 33 . The belief that one is supposed to be weak at a task can be due to prior experience, common knowledge, media reports, or a direct manipulation. All those causes are forms of psychological suggestion—a phenomenon in which what individuals are made to believe, think, or feel can influence their cognition and patterns of behavior positively or negatively 19 . For example, Beilock et al. 34 (Exp. 1) randomly assigned 40 male expert golfers to a stereotype-threatened group or a control group and asked them to putt from three distances. The participants in the stereotype group were told that women tend to perform these putting tasks better than men and that these differences are supported by statistics from the Professional Golf Association and the Ladies Professional Golf Association. While there were no differences in putting performances between groups in a pre-test, golfers in the stereotype-threatened group performed worse in a post-test compared with their counterparts in the control group.

Psychological suggestion can affect, among other factors, the motivation of the participants. Therefore, it is possible that once gamers realized that the study is about gaming, their motivation to perform better was directly elevated. This elevated motivation may have led to behavioral changes that led to the improved performance in gamers when they performed the tasks after answering the video games questionnaire. Regardless of the reason for the elevated motivation, it has been shown that such motivation can increase focus on the task at hand, and therefore leading to improved performance and learning 35 . While the effects of psychological suggestion and/or motivation are plausible mechanisms for improved performance, the actual underlying mechanism are still to be examined directly in additional studies.

In the current study, the effects of the questionnaire timing on performance in both gamers and non-gamers were found only in some of the performed tasks. In the digit-span task, despite previous studies showing improved working memory performance in gamers (e.g., N-back task 15 ), we did not expect any differences between gamers and non-gamers in memorizing digits, as this is not usually a beneficial attribute in video games. However, it is possible that working memory may still benefit from video games, as this cognitive function could be facilitated by improvement in attention or processing speed since the brain networks mediating memory functions and executive function appear to overlap 36 . There were no differences between groups and conditions in the choice-RT task as well. We suspect that this is because the task was too easy, and therefore was not sensitive enough to account for the possible priming effect of the questionnaire. The Simon task was of moderate difficulty and presented the greatest effect of questionnaire timing. Finally, the alternate task-switching task, the most challenging of the three RT-based tasks, showed a questionnaire timing effect only for the non-gamers in the exploratory post-hoc analysis. It is possible that task difficulty serves as a moderator for such stereotype effects 37 , 38 . Barber et al. 37 , for example, showed that negative age-based stereotyping negatively affected the gait of older adults in a difficult gait task but not in a simple gait task. Additional studies that examine the effects of playing video games on generic or practical cognitive-motor performance should address task difficulty as a possible moderator.

Finally, it is possible that our inconsistent and relatively modest findings are because the priming effect itself was subtle as it required participants to indirectly realize that the study is about video gaming (when the questionnaire was introduced prior to the performance of the tasks). It is possible that larger and more consistent effect sizes would have been found if participants were overtly recruited for this study. However, it was our purpose to examine the effects of subtle and indirect cues on performance, and thus we chose covert recruitment and indirect cues. It should also be noted that in psychological research, as Funder and Ozer 39 suggested, “small effect sizes from large- N studies are the most likely to reflect the true state of nature” (p. 164), and that “Smaller effect sizes are not merely worth taking seriously. They are also more believable” (p. 166). The results of our study, taking into account variability in human behavior, the large sample size, and the relatively subtle intervention, are in line with the abovementioned statements.

Strengths of the current study

The primary strength of the current study is the covert recruitment of participants. The online participant recruitment platform we used ( www.prolific.co ) allows the researcher to employ many variables to exclude or include participants based on preliminary answers they supplied when they registered on the website (e.g., demographics, health, hobbies). Furthermore, the researcher can exclude participants who had participated in previous studies completed by the researcher. Once a study is published on that website, participants receive a message that they are eligible to participate, but they do not know the criteria for participation. Hence, the participants in the current study did not know that this was a study that examined the relationships between playing video games and cognitive-motor performance, nor did they participate in any of our previous studies in which similar tasks were used. This is a major methodological issue in video-game research 18 , and therefore we believe that our methodology allowed us to provide meaningful answers to our research questions.

Another strength of the current study is the large sample size and ample statistical power. Many of the studies that compared gamers to non-gamers used relatively small sample sizes [e.g., 36 participants 17 , 21 participants 13 , 35 participants 14 ]. In the current study, we were able to recruit 131 participants, who provided us with at least 80% of statistical power. Finally, conducting the study online ensured that it was double-blinded. In addition to the covert recruitment, the researchers in such an online study do not have any contact with the participants, and thus cannot influence their performance in any way.

One final strength is the computerized randomization to experimental groups. This randomization is performed without the knowledge or the intervention of the researchers, and therefore prevents bias in assigning participants to groups.

Limitations of the current study

One limitation of the current study is that the sample size did not include enough participants who were first-person shooter players or action video-game players. In previous studies, it was mainly playing action video games that was associated with improved cognitive-motor performance. However, we would have been required to implement an overt recruitment process of participants to specifically recruit those participants, and that would have prevented us from answering our research questions.

Second, in an online study, variables such as type and size of keyboard, screen size, participants’ motivation, and environmental conditions cannot be controlled. However, all the participants used a computer to complete the experimental tasks and did not use a smartphone or a tablet. In addition, Woods et al. 40 suggested that large sample sizes in online studies can make up for the relative lack of control.

Third, it is possible that self-selection bias led participants who received an invitation to participate in a study on RT and memory. Such self-selection may create a sample that consider themselves as proficient at such tasks. However, if this was the case, our findings may suggest that both negative and positive priming can lead to differences in performance even in a biased sample of participants who perform such tasks well.

Finally, in order to maintain covert recruitment, we could not ask detailed questions about videogame playing habits prior to the study. Therefore, we have no knowledge of the distribution of playing time over the week. It is possible that some of the participants play mostly on weekends (similar to massed practice) while others distribute their playing time more evenly throughout the week (similar to distributed practice). Massed and distributed practice may affect learning differently (e.g., 41 ), and thus this can be an important moderating variable that should be examined in additional studies on gaming.

The results of the current study suggest that asking participants about their gaming experience before they perform cognitive-motor tasks can either positively or negatively affect their performance, depending on whether they are gamers or non-gamers. In addition, task difficulty is a probable moderator of these effects. The results obtained in our study have methodological implications for future research that examines the differences between gamers and non-gamers, and for research in video-game training aimed at facilitating cognitive-motor performance. Finally, these findings support the concept of psychological suggestion and the response expectancy theory.

Participants

We used G*Power 42 to perform a priori power analysis for our two-way analysis of variance (ANOVA) [Group (gamers/non-gamers) × Questionnaire Delivery Time (before/after the performance of tasks comparing)]. To the best of our knowledge, no previous studies have directly examined the effects of suggestion or response expectancy on simple cognitive/motor tasks in gamers and non-gamers. However, there are studies from the motor learning literature showing that enhanced expectancies of success which are caused by providing easy criteria of success (e.g., 21 , 43 ) or visual illusions that lead to a perceived larger target 44 can lead to improved performance and learning, with effect sizes varying from moderate (Cohen's d  = 0.54; 44 to large (Cohen’s d  = 0.8 21 , calculated from the reported \(\eta_{p}^{2}\)  = 0.14). Because these studies had a small sample size ( N between 36 and 45), effect sizes could have been overestimated (the Winner’s curse 45 . Therefore, in our study we took a more cautious approach and selected a moderate effect size (Cohen’s d  = 0.5/Cohen’s f  = 0.25) for our power analysis. We entered this effect size into the power analysis with the following parameters: alpha (two-sided) = 0.05, power = 0.80, allocation ratio 1:1. The results of the power analysis suggested that 128 participants are required to detect differences between groups or to find an interaction with 80% power.

Therefore, our goal was to recruit 128 participants between the ages of 18–35 years and to randomize them to four groups of 32 participants each: (a) gamers who answered a video-games questionnaire at the beginning of the study (G-B), (2) gamers who answered a video-games questionnaire at the end of the study (G-E), (c) non-gamers who answered a video-games questionnaire at the beginning of the study (NG-B), and (d) non-gamers who answered a video-games questionnaire at the end of the study (NG-E).

We recruited participants through Prolific ( www.prolific.co )—an online participant database platform that allows the researcher to use various exclusion and inclusion criteria (based on information individuals provide in their profile) and allows the participants to participate in an online study from their own computer.

In such an online study, we cannot know if the participants who begin the study will complete it. Therefore, we recruited 160 participants in two projects. In one project we recruited 80 participants who, according to their information on Prolific, play video games more than 13 h per week (a more stringent criterion than our pre-registered requirement of > 10 h per week), and in another project we recruited 80 participants who play video games less than three hours per week. We were aware of the possibility that the information individuals entered when they created an account on Prolific may not be current, and indeed, out of 159 participants, only 110 matched our pre-registered criteria: 70 participants who reported playing over 10 h per week and 40 participants who reported playing fewer than three hours per week. Therefore, we added 28 participants in another project in order to increase the number of non-gamers. This addition led to a total of 187 participants who completed the study. Out of those, 131 participants (27 females, one participant who did not report gender, mean age = 23.51 ± 4.33 years) matched our gamers and non-gamers inclusion criteria, and they are analyzed in the current study: 34 participants in the G-B group (one female, one unreported gender), 39 participants in the G-E group (seven females), 28 participants in the NG-B group (10 females), and 30 participants in the NG-E group (nine females). It is important to note that the participants were not recruited based on information entered when signing up to our specific study. The participants on Prolific.ac answer general questions regarding demographics, hobbies, health, etc. when joining the database. Based on these data, we were able to filter participants who filled in specific responses. However, the participants did not know why they received an invitation to participate. This allowed us to covertly recruit participants for the study, without them knowing that the study had anything to do with gaming.

Randomization to groups was performed automatically by the web-based platform. Importantly, the prospective participants in Prolific did not know that they were recruited based on their video game playing habits. The participants also reported being fluent in English and were paid 2.5 British Pounds for their participation. The study was approved by the Ethics Committee of The Academic College at Wingate (approval # 303), and all participants filled out an electronic informed consent form on the study’s website prior to their participation. In addition, all methods were performed in accordance with the relevant guidelines and regulations.

Participants were asked to perform the following four tasks.

In this task, the participants pressed as quickly as possible the “j” key if the word “right” appeared on the right side and the “f” key if the word “left” appeared on the left side of a centralized cross on the computer screen. The words “right” or “left” were presented for 900 ms, followed by 600 ms during which only the centralized cross was displayed 46 , 47 .

This task is a variation of the choice-RT task. The words “right” or “left” could be displayed on either the right or the left side of the cross. The participants were required to press the “j” key if they saw the word “right” (even if it appeared on the left side of the cross) and to press the “f” key if they saw the word “left” (even if it appeared on the right side of the cross) 48 , 49 . Similar to the choice-RT task, the words “right” or “left” were presented for 900 ms, followed by 600 ms during which only the centralized cross was displayed.

In this task, a square or a rectangle in either a blue or green color appeared at the top or at the bottom of the screen. If a shape appeared at the top of the screen, the participants were asked to press the “f” key if the shape was blue and the “j” key if the shape was green (regardless of whether it was a square or a rectangle). However, if the shape appeared at the bottom of the screen, participants were asked to press the “f” key if the shape was a square and the “j” key if the shape was a rectangle (regardless of the color). In this task, each stimulus was presented for an unlimited duration until a key press was recorded. The above-mentioned three RT tasks are presented in Fig.  4 .

An example of the choice-RT task ( a ), the Simon task ( b ), and the alternate task-switching task ( c , d ) (figure created using Microsoft PowerPoint).

Digit-span memory task

In this task, participants were asked to remember the digits that were presented to them on the screen. The first number included three digits and each consecutive number had one additional digit up to 11 digits. Digits were shown one at a time for a period of one second each. Digits were randomly selected using a random number sampling of the digits 0 to 9 without replacement up to 10 digits. For the 11-digit number an additional (duplicate) digit was randomly added to the 10 digits. All digit randomizations were conducted in R 50 . If the random sample included a series of ascending or descending numbers (e.g., 1, 3, 5; 7, 5, 3; 3, 6, 9; 8, 6, 4) the series was deleted, and another random sample was generated. A similar approach to the presentation of this task has been used in previous experiments (e.g., 51 ).

This study was conducted online using a web-based platform ( www.gorilla.sc 52 ). This platform is integrated with the participants' database ( www.prolific.co ) and the participants perform the experiment on their own computer. Web-based studies have been shown to provide accurate measures of RT that are similar to those attained in lab-based studies (e.g., 53 , 54 ).

After the completion of an electronic informed consent form that was presented at the beginning of the study, participants in the G-B and NG-B groups answered a questionnaire regarding their video game playing habits. Specifically, they were asked how many hours they spend playing video games per week in general, and how many hours they specifically spend playing first-person shooter games, strategy games, or role-playing games. The participants chose one answer from a list (I do not play video games, 1–3 h, 4–6 h, 7–9 h, 10–12 h, 13 h per week or more). In addition, they were asked how many years they have been playing video games (< 1 year, 1–2 years, 3–4 years, 5–6 years, > 7 years). Participants in the G-E and NG-E groups answered a neutral questionnaire with the same number of questions (e.g., how many hours per week do you watch TV, how long is your commute to work, how many books have you read in the last year). After answering the questionnaires, the participants were familiarized with the four tasks in a counterbalanced order. Each participant performed one block of eight trials of the three RT-based tasks (i.e., choice-RT, Simon task, alternate task-switching task) and one block of four trials of the digit span task that consisted of remembering one digit, two digits, three digits, and four digits.

After completing the familiarization stage, the main part of the study began. For the three RT-based tasks, the participants performed two blocks of 24 trials each. For the digit span task, they performed two blocks starting with three digits and ending with 11 digits (in increments of one). The four tasks were presented in a counterbalanced order between participants. After completing the four tasks, participants in the G-E and NG-E answered the same video game playing habits questionnaire. In addition, all four groups answered the following two questions: (a) “Do you think there is a connection between playing video games and the ability to perform cognitive-motor tasks, such as the ones you just performed?” (answers on a scale of 1—not at all, to 10—very much so), and (b) “Are you familiar with media reports or research regarding the benefits of playing video games in relation to the ability to perform cognitive-motor tasks?” (yes or no). These two questions were presented to all groups at the end of the study, because if they were presented at the beginning of the study they could have explicitly exposed the study’s purpose 18 . In all of the questions presented throughout the experiment, the option to answer, “Prefer not to say” was included as well.

Data exclusion

During pre-registration, we decided that for the choice-RT and the Simon task, RT values of over 1000 ms would be removed because they represented RTs that were longer than the presentation of the stimulus (900 ms). However, this did not occur. For the alternate task-switching task, based on our pre-registration, RT values of over 1500 ms were removed. This resulted in a removal of 17 blocks (out of a total of 262 blocks, 6.5% of the blocks). If there were more than 50% incorrect key presses in a block of 24 trials, the block was deleted, as this most likely shows that the participant did not understand the task. This happened only three times in the Simon task (1.1% of blocks), and eight times in the alternate task-switching task (3.1% of the blocks). During pre-registration, we also decided that if there were over 50% incorrect key presses in both blocks of two of the three RT-based tasks for one participant, this participant would be removed from the study. However, this did not occur with any of the participants.

Data analyses

For each of the three RT-based tasks we measured RTs (ms) and the number of correct responses. These were averaged for the two blocks of trials in each task. For the digit span task, we measured the maximum number of digits remembered before the first error and the total number of correct answers. These two variables were averaged for the two blocks of trials.

Based on skewness and kurtosis values, RTs were mostly normally distributed and were analyzed using a 2-way ANOVA [Group (gamers/non-gamers) × Timing of questionnaire (before/after tasks)]. The number of correct key presses in the three RT-based tasks was not normally distributed, and because there is no non-parametric equivalent for a two-way ANOVA we used the Mann–Whitney test to examine, for the group of gamers and non-gamers separately, the differences in correct responses between the condition in which the video-games questionnaire was completed before performing the task and the condition in which it was presented after performing the tasks. The variables measured in the digit span memory task were normally distributed and were analyzed using two-way ANOVAs like those used for the analyses of RTs.

In our pre-registration, we wanted to conduct the statistical analyses separately for each type of game played (e.g., general, first-person shooter, strategy games, role-playing). However, the separate sample sizes were too small, and therefore these analyses could not be performed. We also performed a stepwise multiple regression to examine whether video game playing habits and conceptions of the effects of video games on performance could predict RTs and correct responses in the performed tasks. For this analysis only, we used the data of all 187 participants who completed the study. Because stepwise regression can lead to overfitting and over-estimation of models, we also conducted LASSO (Least Absolute Shrinkage and Selection Operator) regression—an accepted alternative to stepwise regression that deals with such problems 55 . To better understand the non-significant effects or interactions, we used Bayesian statistics in our exploratory analyses.

Statistical analyses were conducted using the SPSS version 25 (SPSS Statistics, IBM, USA), R 50 for LASSO regression, and JASP 56 for all Bayesian analyses. Bonferroni post-hoc analyses and 95% confidence intervals were used for post-hoc testing when appropriate, and alpha was set at 0.05.

Data availability

The raw data for this study is available in a raw data repository: https://osf.io/s2vcz/?view_only=88caada978f141f787684cc2e63b7673 . The pre-registration is available here: https://aspredicted.org/wp53f.pdf .

Wijman, T. The World’s 2.7 Billion gamers will spend $159.3 Billion on games in 2020; The market will surpass $200 Billion by 2023 (2020). https://newzoo.com/insights/articles/newzoo-games-market-numbers-revenues-and-audience-2020-2023/

Greitemeyer, T. The spreading impact of playing violent video games on aggression. Comput. Hum. Behav. 80 , 216–219 (2018).

Article   Google Scholar  

Kühn, S. et al. Does playing violent video games cause aggression? A longitudinal intervention study. Mol. Psychiatry 24 , 1220–1234 (2019).

Article   PubMed   Google Scholar  

Russoniello, C. V., Fish, M. & O’Brien, K. The efficacy of casual videogame play in reducing clinical depression: A randomized controlled study. Games Health J. 2 , 341–346 (2013).

Tortolero, S. R. et al. Daily violent video game playing and depression in preadolescent youth. Cyberpsychol. Behav. Soc. Netw. 17 , 609–615 (2014).

Article   PubMed   PubMed Central   Google Scholar  

Triberti, S. et al. What matters is when you play: Investigating the relationship between online video games addiction and time spent playing over specific day phases. Addict. Behav. Rep. 8 , 185–188 (2018).

PubMed   PubMed Central   Google Scholar  

Mayer, R. E., Parong, J. & Bainbridge, K. Young adults learning executive function skills by playing focused video games. Cogn. Dev. 49 , 43–50 (2019).

Wang, P., Zhu, X.-T., Qi, Z., Huang, S. & Li, H.-J. Neural basis of enhanced executive function in older video game players: An fMRI study. Front. Aging Neurosci. 9 , 382 (2017).

Irons, J. L., Remington, R. W. & McLean, J. P. Not so fast: Rethinking the effects of action video games on attentional capacity. Aust. J. Psychol. 63 , 224–231 (2011).

Deleuze, J., Christiaens, M., Nuyens, F. & Billieux, J. Shoot at first sight! First person shooter players display reduced reaction time and compromised inhibitory control in comparison to other video game players. Comput. Human Behav. 72 , 570–576 (2017).

Waris, O. et al. Video gaming and working memory: A large-scale cross-sectional correlative study. Comput. Human Behav. 97 , 94–103 (2019).

Granic, I., Lobel, A. & Engels, R. C. The benefits of playing video games. Am. Psychol. 69 , 66–78 (2014).

Boot, W. R., Kramer, A. F., Simons, D. J., Fabiani, M. & Gratton, G. The effects of video game playing on attention, memory, and executive control. Acta Psychol. 129 , 387–398 (2008).

Clark, K., Fleck, M. S. & Mitroff, S. R. Enhanced change detection performance reveals improved strategy use in avid action video game players. Acta Psychol. 136 , 67–72 (2011).

Colzato, L. S., van den Wildenberg, W. P., Zmigrod, S. & Hommel, B. Action video gaming and cognitive control: Playing first person shooter games is associated with improvement in working memory but not action inhibition. Psychol. Res. 77 , 234–239 (2013).

Green, C. S. & Bavelier, D. Effect of action video games on the spatial distribution of visuospatial attention. J. Exp. Psychol. Hum. Percept. Perform. 32 , 1465–1478 (2006).

Steenbergen, L., Sellaro, R., Stock, A.-K., Beste, C. & Colzato, L. S. Action video gaming and cognitive control: Playing first person shooter games is associated with improved action cascading but not inhibition. PLoS ONE 10 , e0144364 (2015).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Boot, W. R., Blakely, D. P. & Simons, D. J. Do action video games improve perception and cognition?. Front. Psychol. 2 , 226 (2011).

Michael, R. B., Garry, M. & Kirsch, I. Suggestion, cognition, and behavior. Curr. Dir. Psychol. Sci. 21 , 151–156 (2012).

Hart, A. J. Naturally occurring expectation effects. J. Pers. Soc. Psychol. 68 , 109–115 (1995).

Ziv, G., Ochayon, M. & Lidor, R. Enhanced or diminished expectancies in golf putting—Which actually affects performance?. Psychol. Sport Exerc. 40 , 82–86 (2019).

Kirsch, I. Response expectancy as a determinant of experience and behavior. Am. Psychol. 40 , 1189 (1985).

Kirsch, I. Response expectancy theory and application: A decennial review. Appl. Prev. Psychol. 6 , 69–79 (1997).

Kirsch, I. & Lynn, S. J. Automaticity in clinical psychology. Am. Psychol. 54 , 504–515 (1999).

Article   CAS   PubMed   Google Scholar  

Clifasefi, S. L., Garry, M., Harper, D. N., Sharman, S. J. & Sutherland, R. Psychotropic placebos create resistance to the misinformation effect. Psychon. Bull. Rev. 14 , 112–117 (2007).

Foroughi, C. K., Monfort, S. S., Paczynski, M., McKnight, P. E. & Greenwood, P. Placebo effects in cognitive training. Proc. Natl. Acad. Sci. 113 , 7470–7474 (2016).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Boot, W. R., Simons, D. J., Stothart, C. & Stutts, C. The pervasive problem with placebos in psychology: Why active control groups are not sufficient to rule out placebo effects. Perspect. Psychol. Sci. 8 , 445–454 (2013).

Tiraboschi, G. A., Fukusima, S. S. & West, G. L. An expectancy effect causes improved visual attention performance after video game playing. J. Cogn. Enhanc. 3 , 436–444 (2019).

Fillmore, M. & Vogel-Sprott, M. Expected effect of caffeine on motor performance predicts the type of response to placebo. Psychopharmacology 106 , 209–214 (1992).

Harrell, P. T. & Juliano, L. M. Caffeine expectancies influence the subjective and behavioral effects of caffeine. Psychopharmacology 207 , 335–342 (2009).

Zhou, D. et al. The neural mechanism of spatial-positional association in working memory: A fMRI study. Brain Cogn. 152 , 105756 (2021).

Dienes, Z. Bayesian versus orthodox statistics: Which side are you on?. Perspect. Psychol. Sci. 6 , 274–290 (2011).

Gentile, A., Boca, S. & Giammusso, I. ‘You play like a Woman!’ Effects of gender stereotype threat on Women’s performance in physical and sport activities: A meta-analysis. Psychol. Sport Exerc. 39 , 95–103 (2018).

Beilock, S. L., Jellison, W. A., Rydell, R. J., McConnell, A. R. & Carr, T. H. On the causal mechanisms of stereotype threat: Can skills that don’t rely heavily on working memory still be threatened?. Pers. Soc. Psychol. Bull. 32 , 1059–1071 (2006).

Wulf, G. & Lewthwaite, R. Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning. Psychon. Bull. Rev. 23 , 1382–1414 (2016).

Witt, S. T., van Ettinger-Veenstra, H., Salo, T., Riedel, M. C. & Laird, A. R. What executive function network is that? An image-based meta-analysis of network labels. Brain Topogr. 34 , 598–607 (2021).

Barber, S. J., Hamel, K., Ketcham, C., Lui, K. & Taylor-Ketcham, N. The effects of stereotype threat on older adults’ walking performance as a function of task difficulty and resource evaluations. Psychol. Aging 35 , 250–266 (2020).

Keller, J. Stereotype threat in classroom settings: The interactive effect of domain identification, task difficulty and stereotype threat on female students’ maths performance. Br. J. Educ. Psychol. 77 , 323–338 (2007).

Funder, D. C. & Ozer, D. J. Evaluating effect size in psychological research: Sense and nonsense. Adv. Methods Pract. Psychol. Sci. 2 , 156–168 (2019).

Woods, A. T., Velasco, C., Levitan, C. A., Wan, X. & Spence, C. Conducting perception research over the internet: A tutorial review. PeerJ 3 , e1058 (2015).

Cecilio-Fernandes, D., Cnossen, F., Jaarsma, D. A. & Tio, R. A. Avoiding surgical skill decay: A systematic review on the spacing of training sessions. J. Surg. Educ. 75 , 471–480 (2018).

Faul, F., Erdfelder, E., Lang, A.-G. & Buchner, A. G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39 , 175–191 (2007).

Palmer, K., Chiviacowsky, S. & Wulf, G. Enhanced expectancies facilitate golf putting. Psychol. Sport Exerc. 22 , 229–232 (2016).

Witt, J. K., Linkenauger, S. A. & Proffitt, D. R. Get me out of this slump! Visual illusions improve sports performance. Psychol. Sci. 23 , 397–399 (2012).

Button, K. S. et al. Power failure: Why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14 , 365–376 (2013).

Smith, E. E. Choice reaction time: An analysis of the major theoretical positions. Psychol. Bull. 69 , 77–110 (1968).

Burle, B., Vidal, F., Tandonnet, C. & Hasbroucq, T. Physiological evidence for response inhibition in choice reaction time tasks. Brain Cogn. 56 , 153–164 (2004).

Simon, J. R. & Wolf, J. D. Choice reaction time as a function of angular stimulus-response correspondence and age. Ergonomics 6 , 99–105 (1963).

Lu, C.-H. & Proctor, R. W. The influence of irrelevant location information on performance: A review of the Simon and spatial Stroop effects. Psychon. Bull. Rev. 2 , 174–207 (1995).

R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).

Woods, D. L. et al. Improving digit span assessment of short-term verbal memory. J. Clin. Exp. Neuropsychol. 33 , 101–111 (2011).

Anwyl-Irvine, A. L., Massonnié, J., Flitton, A., Kirkham, N. & Evershed, J. K. Gorilla in our midst: An online behavioral experiment builder. Behav. Res. Methods 52 , 388–407 (2020).

Crump, M. J., McDonnell, J. V. & Gureckis, T. M. Evaluating Amazon’s Mechanical Turk as a tool for experimental behavioral research. PLoS ONE 8 , e57410 (2013).

Article   CAS   PubMed   PubMed Central   ADS   Google Scholar  

Hilbig, B. E. Reaction time effects in lab-versus Web-based research: Experimental evidence. Behav. Res. Methods 48 , 1718–1724 (2016).

Ranstam, J. & Cook, J. LASSO regression. J. Br. Surg. 105 , 1348–1348 (2018).

JASP Team. JASP (verion 0.16.1) [Computer software] (2022).

Download references

Author information

Authors and affiliations.

Motor Behavior Laboratory, The Academic College at Wingate, 4290200, Netanya, Israel

Gal Ziv & Ronnie Lidor

Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium

You can also search for this author in PubMed   Google Scholar

Contributions

G.Z.: Conceptualization, Methodology, formal analysis, writing—original draft. R.L. and O.L.: Conceptualization, writing—reviewing and editing. All authors reviewed the manuscript.

Corresponding author

Correspondence to Gal Ziv .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Ziv, G., Lidor, R. & Levin, O. Reaction time and working memory in gamers and non-gamers. Sci Rep 12 , 6798 (2022). https://doi.org/10.1038/s41598-022-10986-3

Download citation

Received : 13 December 2021

Accepted : 07 April 2022

Published : 26 April 2022

DOI : https://doi.org/10.1038/s41598-022-10986-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Reaction Time Essays

The influence of auditory expectations on perceptual judgments: echoes of belief, popular essay topics.

• American Dream
• Artificial Intelligence
• Black Lives Matter
• Bullying Essay
• Career Goals Essay
• Causes of the Civil War
• Child Abusing
• Civil Rights Movement
• Community Service
• Cultural Identity
• Cyber Bullying
• Death Penalty
• Depression Essay
• Domestic Violence
• Freedom of Speech
• Global Warming
• Gun Control
• Human Trafficking
• I Believe Essay
• Immigration
• Importance of Education
• Israel and Palestine Conflict
• Leadership Essay
• Legalizing Marijuanas
• Mental Health
• National Honor Society
• Police Brutality
• Pollution Essay
• Racism Essay
• Romeo and Juliet
• Same Sex Marriages
• Social Media
• The Great Gatsby
• The Yellow Wallpaper
• Time Management
• To Kill a Mockingbird
• Violent Video Games
• What Makes You Unique
• Why I Want to Be a Nurse
• Send us an e-mail

• Free Essays
• Reaction Essay
• Reaction Time Of People With ADHD and Without ADHD

Use 15% OFF on 1st order!

Reaction Time of People with ADHD and Those Without ADHD

Attention Deficit Hyperactivity Disorder (ADHD) is a mental illness characterized by inattention or the loss of attention as well as impulsive and hyperactive behavior. ADHD is an urgent problem since it occurs in 3–10% of children in the general population (Andreou et al., 2007; Kofler et al., 2013). The clinical manifestations of this disorder in adults are given much less attention, although in numerous cases and to some extent, the symptoms of ADHD remain even in adulthood (Valko et al., 2010; Torre, Barroso, Le?n-Carri?n, Mestre, & Bozalet, 2012). Still, Kofler et al., (2013) have concluded that children who suffer from ADHD usually demonstrate reduced performance with slower reaction time as compared to those without ADHD. However, recent studies have examined that children with ADHD show greater reaction time variability in comparison with a control group of children (Tamm et al., 2012; Karalunas, Geurts, Konrad, Bender, & Nigg, 2014). From these contradictive findings, it remains unclear how ADHD affects reaction time in adulthood. The current paper is aimed to explore the difference in the reaction time of adults with ADHD and those without ADHD.

With age, the profile of disturbances changes among people with ADHD. Specifically, in childhood, hyperactivity is more pronounced, whereas among adults, the problems associated with the weakening of control over the performance of actions gain greater prominence (Kofler et al., 2013). From the neurophysiological point of view, people with ADHD manifest the dysfunction of one of the most important control systems. It has been assumed that this system is responsible for planning, initiating, maintaining, and coordinating the actions most appropriate under current conditions (Tamm et al., 2012). ADHD disturbs the processes of maintaining attention, sustainability, and selectivity, which leads to inattention, while the violation of the suppression of prepared actions is usually expressed in impulsiveness (Karalunas et al., 2014). As a result, adults diagnosed with ADHD suffer from its manifestation on the regular basis, and this may significantly affect the quality of their life.

Currently, this disorder is considered because of the developmental disorders of the nervous system that stem from childhood. There is an assumption that in adulthood, primary ADHD cannot develop (Valko et al., 2010). Thus, attention to this disorder, which is traditionally observed among children, presents a great social problem for adult patients since it negatively affects the adaptation processes (Andreou et al., 2007). Moreover, adults with ADHD are much more likely to get into accidents, change jobs, and they are more likely to suffer from addictive disorders as compared to their healthy peers (Weafer, Milich, & Fillmore, 2011). Despite the fact that adults with ADHD almost do not have any cognitive deficiencies, they still suffer in such areas as academic achievements and education. For example, such patients are less likely to receive higher education, and, as it can be expected, they tend to occupy lower professional positions. Still, the key symptoms of ADHD are the failure to focus attention for a sufficiently long time and impulsive behavior (Weafer et al., 2011). In this regard, it is vital to identify if there any differences in reaction time between adults with ADHD and those without it.

There is no single conclusion concerning the reaction time among ADHD patients in comparison with healthy adults. Some empirical studies demonstrate that because of impulsivity, people with ADHD become impatient and react quicker than someone without this disorder (Kofler et al., 2013). However, multiple researchers have studied elevated reaction time variability among participants with ADHD and concluded that this phenomenon is a robust (Tamm et al., 2012). Furthermore, children with ADHD prove elevated reaction time variability throughout numerous empirical studies (Kofler et al., 2013; Karalunas et al., 2014). Therefore, reaction time variability among ADHD patients is not simply a widening of the reaction time distribution but a look at the phenomenon as if it has specific properties that express its appearance.

Some neuropsychological studies have explained how impulsiveness appears and works among children. For instance, Valko et al. (2010) have indicated that disturbances among children with ADHD and motor disorders are associated with suspected changes in neurophysiological reactivity with respect to both external and internal stimuli. In the mature type of cortical-subcortical connection, the cortex of the large hemispheres and frontal lobes acquires the ability to control the ascending from the sub cortex by activating influences. The essence of this process is that the activation influences, optimal in their intensity, are directed to the right place at the right time, thus mobilizing the nerve centers, necessary to perform this particular activity. Hence, this process, which is called controlled activation, is associated with the formation of a local activation system and it may affect reaction time.

Furthermore, inattention can be explained based on redundancy processes. Children with ADHD receive excessive stimulation, and since they are not able to filter the incoming sensory information selectively, this results in the formation of redistribution of the cortex, attention disturbances, and distraction (Epstein et al., 2011). The manifestations of ADHD are explained from two opposite positions such as the excess of the influence of activating systems of the central nervous system and their insufficiency (Gooch, Snowling, & Hulme, 2012). According to the theories of functional insufficiency of activating brain systems (Tamm et al., 2012), their low level, together with the weakness of inhibiting control mechanisms, leads to the fact that children with ADHD are distracted by the stimuli that are ignored by healthy children. Thus, this may explain why the former show greater reaction time variability in comparison with their healthy peers.

Unfortunately, ADHD negatively affects the whole process of performing a task. Additionally, in performing tasks, children with ADHD have fewer correct answers, more omissions, and more erroneous reactions than healthy children do, which indices decrease as the trial lasts (Gooch et al., 2012). Notably, in the case of correct answers, the reaction time among children with ADHD is greater than in the children of the control group. However, the number of erroneous or premature reactions in the above examples can be considered as an indicator of impulsivity among children with ADHD. Thus, the higher number of erroneous reactions in children with ADHD suggests that when they have the urge to react, they are less likely to inhibit premature or repeated reactions if compared to others. Thus, such differences can be also represented by the speed component of decision-making processes.

Children with ADHD prefer to complete their tasks as quickly as possible, leaving the quality of work in the background, which indicates a weakening of volitional control. At the same time, they have an increase in reaction time before the task is completed (Epstein et al., 2011). These factors altogether demonstrate that such children are worse performers than their healthy peers are. The probable explanation of erroneous reactions might lie in too fast reactions to the task and the inattentiveness of children with ADHD. Thus, it could be assumed that adults with ADHD, as well as children with this disorder, will have a faster reaction time when performing a task than those without it.

However, the inconsistency in the results of some studies indicates that reaction time variability another possible manifestation of ADHD. For example, Kofler et al., (2013) overviewed more than 300t studies on the analysis of reaction time variability among people diagnosed with ADHD and concluded that a greater proportion of such people had showed slow responses that had been mixed with fast ones in some cases. This conclusion allows assuming that there is an inconsistent pattern of the reaction time of people with ADHD.

Based on the literature review, the key purpose of the current experiment was to explore the difference in the reaction time between people diagnosed with ADHD and those without it. Specifically, this study has aimed to provide support for the suggestion of Kofler et al. (2013) that adults diagnosed with ADHD should demonstrate reduced performance with higher reaction time as compared to healthy individuals. Thus, it is predictable that a person diagnosed with ADHD has a faster reaction time than someone without this disorder does. With the purpose of confirming this hypothesis, the experimental study was conducted.

Participants

The study used a total of 160 participants, with an average age of 29.44 years ( SD = 10.90) with minimum and maximum ages of 17 and 58 respectively. There were 79 (49.37%) male and 81 (50.63%) female participants of this experiment. Based on the diagnostic criteria of ADHD, subjects were divided into two groups – ADHD group (33 women and 37 men) and Control group (48 women and 42 men). All participants aged 17 years and above were eligible for the current research. In addition, all participants required to sign a consent form (see Appendix A). There was no reward or any other compensation for the involvement in the experiment, and all subjects volunteered in this study.

Design and Procedure

An experimental research design was employed. To test a hypothesis, overall recorded data were analyzed. In addition, a between-groups design was used. The reaction time was measured as the dependent variable (DV) and researchers calculated the average times in seconds it had taken every participant to complete a simple reaction time task. The ADHD diagnosis (withwithout) of the participants was measured as an independent variable (IV) in the current experiment. Since the study sample was selected from people who had been diagnosed with ADHD, it was an ethical challenge to collect the data from these participants. By filling the consent forms, participants have agreed to participate in the experiment on the anonymous basis, which is ethically recommended in involving people with ADHD to this research. Additionally, to identify whether the participant has or has not this disorder, they were asked to take the Fast Task Survey. Response time was measured by a simple reaction time task. This task required pressing a key on a keyboard. Additionally, it detected the ability to inhibit the behavioral reaction of performing the action of pressing itself. A simple reaction time task required approximately 10 minutes to complete.

All effects declared reliable from initial independent samples t -test have p less than the alpha level of .05. For the current statistical analyses, the variables group (ADHD/Control) and reaction time were used. The group is a between-group variable, and reaction time is an independent variable. An independent samples t-test [ t (160) = 3.08; p = 0.002] showed that reaction time in the ADHD group ( M = 0.96; SD = 0.11) was significantly higher than reaction time in the control group ( M = 0.51; SD = 0.09) (see Figure 1).

These results confirm research’s expectations, as people diagnosed with ADHD have showed the quicker reaction time as compared to healthy individuals. Therefore, the faster reaction time when performing a simple reaction time task is considered as the reflection of increased impulsivity since such performance productivity occurs when the subject performs an action in the conditions when it is required. The increase in the number of reaction times could be considered as a correlate of an impulsive behavior among people with ADHD (Tamm et al., 2012; Kofler et al., 2013). However, Figure 1 demonstrates numerous separate cases that have been excluded from the analysis. It may be presumed that these cases appear to show the reaction time variability in both groups.

You can get reaction paper writing help from seasoned writers here!

The present study has examined the difference in the reaction time of people with ADHD and healthy controls. Thus, findings have showed that participants with ADHD display a higher reaction time in performing a simple reaction time task. Hence, participants’ performance on the measures of impulsivity, or the reaction time, depends whether people have ADHD or not. Our results have also confirmed our main hypothesis and suggestion of Kofler et al. (2013) that because of impulsivity, people with ADHD become impatient and react quicker than someone without this disorder.

Based on the literature review, the findings of the research explain a decisive role in the formation of a modern concept of ADHD. Additionally, the disorder was usually characterized as a primary defect, while attention deficit was considered with an abnormally short period of its retention on some object or action (Kofler et al., 2013). Along with such typical manifestations of the syndrome as attention deficit, the impulsiveness of motor and verbal reactions as well as hyperactivity in adults with ADHD, the need for a substantially more positive reinforcement for the formation of behavioral patterns was noted. Moreover, the deficiency of attention is mainly manifested in the violations of maintained attention (Kofler et al., 2013). For example, the deficiency of attention is explored as the impossibility of performing long monotonous tasks, in increasing the number of errors with increasing the duration of the task (Karalunas et al., 2014). It has been suggested that behavioral and cognitive disorders are associated with this syndrome, with a constitutional predisposition of the neurological nature, which causes a decrease in inhibitory control, a violation of modulation of activity levels (Epstein et al., 2011). Still, the results indicate that adults with ADHD are more likely to develop faster reactions in performing tasks if compared to healthy individuals.

The severity of the symptoms of ADHD varies with age (Tamm et al., 2012). Furthermore, attention breakdown is the main symptom of this condition. To the greatest extent, such properties of attention as stability, the ability for concentration, and distribution are lower among people diagnosed with ADHD. However, our finding indicates that such people become impatient and react quicker than someone without this diagnosis. Thus, it is possible to suggest that with age, people with ADHD have an ability to develop cognitive strategies for analyzing the conditions of tasks and focus on their implementation, which can lead to a faster reaction time in performing a task.

Still, one of the limitations of our study is the absence of other important variables in performing this task such as errors, productivity, and so on. Many researchers contribute to the fact even when completing some task faster, people with ADHD make many mistakes because of their inattentiveness (Epstein et al., 2011; Kofler et al., 2013). Thus, future studies should maintain this limitation and analyze the overall process of performing the test by people with ADHD. Moreover, the function of attention is associated with the concept of brain control functions such as the regulation and control of mental activity (Valko et al., 2010). Hence, a complex approach to the analysis of data, including the exploration of reaction time, errors, and productivity in performing the task, would allow accessing the differences between people with ADHD and those without it.

Among children with ADHD, such disorders manifest themselves as difficulties in maintaining a certain behavioral response during a relatively long time, difficulties in distributing attention, and so one (Epstein et al., 2011). Thus, an experimental procedure also should vary on time, whereas participant need to complete some tasks during long periods and other – during short ones. Such a manipulation can show the endurance of people with ADHD to react and perform tasks during a relatively long time.

The main problem with ADHD is the excessive variability in the rate and amplitude of the level of activating systems and their reactivity. An inadequate synchronization of various aspects of these systems was observed in many studies (Epstein et al., 2011; Valko et al., 2010; Tamm et al., 2012). According to Gooch et al. (2012), children are unable to regulate their activity in response to the demand of a situation. At the heart of attention, deficits are the immaturity of the regulatory systems of the brain stem and front-thalamic levels. There is evidence that the violation of attention in children with ADHD is based on the discrepancy between the ages of the degree of functional maturity of the brain’s regulatory systems (Gooch et al., 2012). Recently, the notion of the role of the brain’s three main regulatory systems, such as associative cortical zones, subcortical, and stem structures, in the implementation of arbitrary forms of behavior has gained increasing importance (Weafer et al., 2011). Thus, these conclusions are a good point for grounding our empirical results, and they could explain why the reaction time of people with ADHD is faster than that of their healthy peers.

In the light of our findings, we suggest that a probable explanation of the faster reaction time of participants with ADHD might also relate to the response motor timing demands of a simple reaction time task. Furthermore, Kofler et al. (2013) have suggested that in similar tasks, it is vital to apply inhibitory control over reaction time in order to make responses that are more sensitive so they could fall within a narrow time window. Moreover, this explanation is consistent with the results that people with ADHD have shortfalls on the tasks that involve other precise motor timing (Epstein et al., 2011). This also explains that the peculiarities of psychophysiological mechanisms of ADHD development in the exploration of sensorimotor reactions and behavioral response. Particularly, because of their impulsivity, people with ADHD can have issues with mastering other activities and the ability to create strategies for action and control. This also may suggest that these people prefer to complete tasks as quickly as possible, which indicates a weakening of volitional control.

The future research should focus on the exploration of the treatment effects for decreasing reaction time among people with ADHD. This need is caused by the fact that whatever task is processed, it remains in the focus of attention for a short time, which means that the limited resources of people with ADHD are freed, which demonstrates a large total productivity. Thus, reaction time plays a significant role in performing other cognitive tasks, and it can indirectly influence the productivity of completing them.

Related essays

• The Role Of Military In Nigerian And Brazilian Politics
• Live Jazz Music Performed
• Additional Skills and Abilities
• How Technology Impacts Bullying in Schools
• Admission Essay
• Book Review
• Comparative Essays
• Economics Essays
• Film Review
• History Essay
• Medicine Essay
• Narrative Essays
• Personal Statement
• Politics Essays
• Research Paper
• Research Proposal
• Sociology Essay

Limited time offer

your first order with code: first15off

• 79% of first time clients use our reliable service again.
• We guarantee your sensitive data protection!
• All papers are written by our writers without using prewritten topics!
• All essays are written according to your personal requirements.
• Customers' satisfaction rate 89.8% based on 800+ clients feedback.
• We are ready to handle the tasks of variable difficulty and timing!
• 12 point font size
• Double-spaced pages
• 300 words/page
• Text aligned left
• One-inch margins

Still haven't found the perfect essay? Let a skillful expert help you!

The online eclipse experience: People on X get creative, political and possibly blind

The 2024 total eclipse is caused by a rare alignment in celestial spheres that will send millions of people in the path of totality outside to peer at the sky.

People have booked their Airbnbs years in advance , eclipse glasses are selling out and forecasters have been warning of cloudy skies for weeks.

Regardless of how otherworldly this event is and how much planning people have dedicated to experiencing it in person, the internet is being the internet and providing an eclipse experience of its own.

GIFs, quips and skits are flooding social media platforms like X on Monday.

Here are some of the best social media reactions to the eclipse:

Solar eclipse 2024 live updates: See latest weather forecast, what time it hits your area

Forgot your eclipse glasses? So did the internet

Proper eye safety is recommended for looking at the sun during an eclipse, and several places like Warby Parker and public libraries have been giving them away for free.

But some poor souls didn't secure their pair in advance, and the internet knows it:

"I don’t have eclipse glasses and I don’t trust myself not to look at the sun," one user posted on X.

"During the eclipse, it’s important not to stare at the Sun directly, as it will take this as a provocation. Look away to the sides to indicate clearly that you are not a threat to the Sun. Do NOT run away; this will activate the Sun’s predation instinct, and then God help us all," another posted .

Viral moment: Looking back (but not directly at) Donald Trump's 2017 solar eclipse moment

Some people are making their own eclipses

*flips ipad around*, of course, it's getting political.

Several supporters of former President Donald Trump have also made their own footage showing Trump eclipsing President Joe Biden , indicating their hope for the 2024 presidential election.

"Biggest Event of 2024," wrote one user , with a picture of Trump "eclipsing" Biden attached.

But many people are reminiscing about the 2017 eclipse, when Trump seemingly looked up at the sky without glasses .

Biden joined in poking fun at him in a post saying, "don't be silly, folks – play it safe and wear protective eyewear," a presumed nod to Trump's viral moment.

Contributing: Eric Lagatta, Natalie Alund