what is a critique of the social brain hypothesis

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Article contents

The social brain hypothesis and human evolution.

• Robin I. M. Dunbar Robin I. M. Dunbar Department of Experimental Psychology, University of Oxford
• https://doi.org/10.1093/acrefore/9780190236557.013.44
• Published online: 03 March 2016

Primate societies are unusually complex compared to those of other animals, and the need to manage such complexity is the main explanation for the fact that primates have unusually large brains. Primate sociality is based on bonded relationships that underpin coalitions, which in turn are designed to buffer individuals against the social stresses of living in large, stable groups. This is reflected in a correlation between social group size and neocortex size in primates (but not other species of animals), commonly known as the social brain hypothesis, although this relationship itself is the outcome of an underlying relationship between brain size and behavioral complexity. The relationship between brain size and group size is mediated, in humans at least, by mentalizing skills. Neuropsychologically, these are all associated with the size of units within the theory of mind network (linking prefrontal cortex and temporal lobe units). In addition, primate sociality involves a dual-process mechanism whereby the endorphin system provides a psychopharmacological platform off which the cognitive component is then built. This article considers the implications of these findings for the evolution of human cognition over the course of hominin evolution.

• social brain
• social neuroscience
• brain evolution
• mentalizing
• theory of mind

Introduction

Primates have unusually large brains for body size compared to all other vertebrates. The conventional explanation for this is known as the “social brain hypothesis,” which argues that primates need large brains because their form of sociality is much more complex than that of other species (Byrne & Whiten, 1988 ). This does not mean that they live in larger social groups than other species of animals (in fact, they don’t), but rather that their groups have a more complex structure. In exploring the nature of this unique kind of primate sociality, this article shall argue that, so far, social neuroscience has barely scratched the surface of what is actually involved in what it means to be social. To borrow an analogy, social neuroscience has devoted its time to examining the bricks and mortar in great detail but has so far overlooked the complexity of the building that lies at the real heart of primate (and human) sociality.

The original idea for the social brain dates back to the 1970s, when a number of primatologists suggested that primate intelligence might be related to the demands of their more complex social world (Jolly, 1969 ; Humphrey, 1976 ; Kummer, 1982 ), and the name itself was later coined by the neuroscientist Lesley Brothers ( 1990 ). The primary evidence in support of the social brain hypothesis comes from the fact that, across primates, there is a correlation between mean social group size and more or less any measure of brain size one cares to use (Fig. 1 ) (Dunbar, 1992 , 1998 ; Barton, 1996 ; Barton & Dunbar, 1997 ; Dunbar & Shultz, 2007 ; Dunbar, 2011a ), although the relationship improves as the measure of brain size is focused more toward the frontal lobes (Joffe, 1997 ; Dunbar, 2011a ). In this respect, primates differ from almost all other mammals and birds: in most birds and nonprimate mammals, large brains are associated not with social group size but with a monogamous mating system (Shultz & Dunbar, 2007 , 2010a , b ; Pérez-Barbería et al., 2007 ). Note that in Figure 1 there appears to be an obvious grade difference between the apes and the monkeys. This suggests that apes require a proportionately larger brain than monkeys to deal with groups of the same size, implying that their form of sociality requires more computing power to handle. More careful analysis has since revealed that there are similar grade differences among the monkeys, with a clear distinction between more and less intensely social species. As indicated in Figure 1 , extrapolating from the relationship between group size and neocortex size in apes predicts a natural social group size for humans of around 150 (Dunbar, 1993 ). There is considerable evidence for the existence of such a group size in terms of both natural human groupings (e.g., community sizes in small scale societies) and personal social networks (Dunbar, 2008 , 2011b ).

Figure 1. Mean social group size plotted against relative neocortex volume (indexed as the ratio of neocortex volume to the volume of the subcortical brain) in anthropoid primates. Filled circles: apes (including humans); unfilled circles: monkeys. The regression lines indicate grades of increasing socio-cognitive complexity (indexed by the increasing density of the line). (Redrawn from Dunbar, 2014 .)

Secondary support for the social brain hypothesis comes from neuroimaging studies, which have recently shown that the size of an individual’s living group (macaques: Sallet et al., 2011 ) or personal social network (humans: Lewis et al., 2011 ; Powell et al., 2012 , 2014 ; Kanai et al., 2012 ) correlates with the size of core regions of the brain, mostly in the temporal and, especially, the frontal lobes. These regions turn out to be essentially those involved in the mentalizing, or theory of mind, neural network. This is an important finding because it demonstrates that the social brain hypothesis applies not just at the level of the species but also at the level of the individual. Individuals with more processing capacity in core brain units have proportionately larger social networks.

Historically, a number of alternative ecological and developmental hypotheses have been proposed for why primates have such large brains (for an overview, see Dunbar, 2012b ). Among these, the importance of foraging skills, and especially the role of social learning of foraging skills, has attracted a great deal of interest (e.g., Reader et al., 2011 ). This is not the place to discuss the ensuing debates in detail, but some points of clarification are desirable. It is important to note at the outset that everyone agrees that foraging skills have played an important role in primate evolution; the critical question is whether these have been the main, or only, driver of increases in brain size or whether they are an evolutionary by-product of large brains evolving for other (perhaps mainly social) reasons because the same cognitive skills (causal reasoning, predictive reasoning, planning, etc.) underpin both kinds of behavioral outcomes.

In fact, ecology lies at the heart of all explanations for brain evolution, including the social brain hypothesis: the core differences between them are (1) whether animals solve their ecological problems by individual trial-and-error learning or do so socially and (2) which particular ecological problem (foraging or avoidance of predation) is the more fundamental selection pressure (i.e., the evolutionary driver). What makes the social brain hypothesis intrinsically social is that it claims that animals solve their ecological problems by first solving the problem of group cohesion and coordination. One reason for thinking this is that the primary ecological problem faced by primates (and probably most other animals) is the risk of predation (either by predator species or by conspecific raiders) rather than how to find food (as important as this is in the life of any animal). Primates, like most other animals, solve the predation risk problem by living in groups (van Schaik, 1983 ; Shultz et al., 2004 ; Shultz & Finlayson, 2010 ) and have opted to do so by evolving an unusual form of bonded sociality to maintain group coherence through time (Dunbar & Shultz 2010 ). In effect, primates solve the predation problem indirectly by first solving the problem of creating coherent, stable, coordinated social groups. The issue thus comes down to the task demands of foraging versus social coordination.

A second issue we need to clarify is that the social brain hypothesis has sometimes been seen as simply the quantitative relationship between social group size and brain size shown in Figure 1 . In fact, it should properly be seen in systemic terms as a set of causally related functional behavioral relationships. Animals need to solve a variety of ecological problems in order to be able to survive and reproduce successfully, and primates solve these problems communally in a way that requires them to solve a number of social and physiological problems first. In effect, primates establish the means to solve the ecological problem (an alliance or coalition) ahead of its need, and the capacity to form coalitions in anticipation of their future need seems to be a unique feature of monkey and ape behaviour (Harcourt, 1992 ). It is this that gives rise to the unique form of primate sociality that we refer to as “bonded sociality”, in contrast to the more casual groupings found in most other species of birds and mammals where social groups (herds) can fragment and come together relatively easily (Dunbar & Shultz, 2010 ).

Maintaining the coherence and cohesion of bonded social groups through time is very demanding because animals have to be able to override the natural tendency for the stresses of social life to drive them apart (Dunbar 2010a , 2012a ), and the social brain hypothesis argues that this comes down to resolving various tensions and stresses in both dyadic relationships and the collective set of relationships formed within a social group. To do this, monkeys and apes require novel cognitive skills, and these cognitive skills in turn require appropriate hardware (or wetware in this case) to underpin them. Hence, the relationship between brain size and group size is indirect, and the real functional relationship in the social brain hypothesis is that between brain (or brain region) size and/or wiring and social cognitive abilities or competences that allow primates to manage relationships (Dunbar, 1998 , 2011a , 2012a ). In effect, group size is an emergent property of how well the animals solve the problems associated with living in close proximity.

In other words, in contrast to all the alternative ecological hypotheses that have been proposed (for overviews, see Dunbar 2011a , 2012b ), the social brain hypothesis is a two-step explanation for the evolution of large brains in primates. In contrast to all alternative hypotheses, it explicitly claims that primates are doing something radically different to all other species of animals. The ultimate evolutionary driver is not simply the capacity to engage socially or live in large groups but the extent to which this allows the animals to solve the problems associated with successful survival and reproduction. The proximate mechanism involves solving the coordination problem that lies at the heart of maintaining cohesive social groups. To the extent that primates solve this second problem (group coordination), they also solve the first (predation risk).

What’s So Social About Primate Sociality?

All mammals and birds are, of course, social in some generic sense. The central premise of the social brain hypothesis is that sociality in anthropoid primates (and perhaps a very small number of other mammalian families, including elephants, the dolphin family, and maybe the camel family, that also live in complex, multi-level social systems: Hill et al., 2008 , Shultz & Dunbar, 2010a ) is a step up from this: it involves a more bonded form of sociality built around intense dyadic relationships (friendships) (Silk, 2002 ; Dunbar & Shultz, 2010 ; Massen et al., 2010 ). This form of bonded sociality is a response to the need to handle the stresses that arise when animals live in close proximity and cannot escape these pressures simply by leaving (i.e., by group fission). Living in groups creates significant stresses (mainly due to harassment from conspecifics) that radically affect female fitness (Dunbar, 1980 , 1988 ; Hill et al., 2000 ; Smuts & Nicholson, 1989 ; Roberts & Cords, 2013 ) via an endocrinological mechanism that is now relatively well understood. Among other effects, social stress destabilizes the female menstrual endocrinology system and results in amenhorrea (temporary infertility) (Bowman et al., 1978 ; Abbott et al., 1986 ). Unless animals are able to find solutions that buffer them against these and other costs, group fission is inevitable because the cumulative costs for low-ranking females in terms of lost reproduction can become intense. These stresses are a linear function of group size: the more animals there are in the group, the more individuals one can be harassed by. Moreover, sociality itself is costly: for both primates (Dunbar, 1991 ; Lehmann et al., 2007 ) and humans (Roberts & Dunbar, 2011 ; Miritello et al., 2013 ), relationships require the investment of considerable quantities of time for their maintenance, and this time cost is more or less proportional to the number of individuals involved multiplied by relationship quality (Sutcliffe et al., 2012 ). This is partly because the mechanism involved in creating and servicing relationships involves the endorphin system: the more frequently this is activated, the stronger the relationship. We'll return to this later.

The endogenous stresses that the animals face from living in groups act as a constraint on group size because they create centrifugal forces that, if not defused, will eventually cause the group to break up. In species that do not have bonded social systems (most non-monogamous birds and mammals), these stresses are resolved by individuals simply leaving one group to join a smaller one on an ad hoc basis (the bees-around-a-honeypot model of sociality). This solution is not available to species that live in bonded social systems because of the resistance to individuals transferring between groups created by bonded relationships: members of a group do not tolerate strangers.

Anthropoid primates deal with these stresses by forming defensive alliances mediated by social grooming (Dunbar, 1980 ; Silk et al., 2003 , 2009 ; Wittig et al., 2008 ), and this in turn gives rise to highly structured social networks (Dunbar, 2008 , 2012b ; Lehmann & Dunbar, 2009 ). It is this “decision” to use coalitions as a buffer for the stresses of group living that seems to create the complexity that is widely recognized as characteristic of primate societies. This social world is more complex to handle than the physical world, partly because it is dynamic and in a constant state of flux, and partly because it involves phenomena (other individuals’ mind states) that cannot be perceived directly but instead have to be inferred (Dunbar, 2010a , 2011a , 2012b ). In effect, social systems of this kind are implicit social contracts. For a group to be stable through time, its members have to be willing to allow each other to have a fair (though not necessarily equal!) share of the benefits of sociality. Failure to hold back on prepotent actions that would offer immediate benefits to oneself (such as stealing someone else’s newly discovered food item or displacing someone from a safe roosting site) risks driving others away and destabilizing group cohesion.

One explanation for the grade structure observed in Figure 1 is that this reflects a step-change in the complexity of primate social relationships and the behaviors that underpin them as neocortex volume increases. Indeed, across primates, neocortex size correlates with increasing use of sophisticated mating strategies, larger grooming cliques, higher frequencies of tactical deception, and the formation of coalitions (Pawłowski et al., 1998 ; Kudo & Dunbar, 2001 ; Byrne & Corp, 2004 ; Dunbar & Shultz, 2007 ), as well as increasing complexity of both visual (Dobson, 2009 ) and vocal (McComb & Semple, 2005 ) communication repertoires. One example of this is that cognitively more advanced species like macaques are aware of third-party relationships and refrain from attacking or exploiting another animal when they know that individual has powerful allies, even when those allies are not physically present (Datta, 1983 ). Computational models suggest that managing third-party relationships is more demanding in terms of information processing time than managing simple dyadic relationships (Dávid-Barrett & Dunbar, 2013 ). Similarly, playback experiments have demonstrated that baboons (another cerocpithecine) can integrate at least two different relationship dimensions (kinship and dominance) simultaneously, an ability that may be beyond cognitively less well-endowed species (Bergman et al., 2003 ).

There has been a near-universal tendency to assume that the social groups of all animals are “of a kind.” However, in anthropoid primates, grooming networks become increasingly substructured as the number of individuals in the group increases, especially so in species that have larger neocortices (Kudo & Dunbar, 2001 ; Hill et al., 2008 ; Lehmann et al., 2009 ; Lehmann & Dunbar, 2009b ). In effect, these species are able to maintain two qualitatively distinct kinds of relationship simultaneously: intimate relationships with principal grooming partners (allies) and weaker ones with other group members. In this respect, monkey and ape relationships resemble the two-tier structure of human social relationships, where parallel distinctions are drawn between weak and strong “ties” (Granovetter, 1973 ; Sutcliffe et al., 2012 ) and, cutting across the weak/strong divide, between family and friends (Curry et al., 2013 ; Roberts & Dunbar, 2011 ; Roberts et al., 2014 ). This gives the social systems of anthropoid primates (and those of a small number of other mammals) a layered structure (Hill et al., 2008 ) similar to that found in humans (Zhou et al., 2005 ; Hamilton et al., 2007 ; Dunbar et al., 2015 ). While in both humans and primates an individual’s relationships with the other members of their social group can be ranked on a simple continuum based on frequency of interaction (or emotional closeness: Sutcliffe et al., 2012 ; Roberts et al., 2014 ), these nonetheless cluster into quite discrete layers of very distinctive size, as shown in Figure 2 . The numerical sizes of these grouping layers seem to be common to both human social networks and the structure of primate social groups (Hill et al., 2008 ), and one explanation for the differences in social complexity between species may be the number of layers that can be maintained as a coherent, stable system.

Figure 2. The circles of acquaintanceship for normal human adults. Ego indicates the subject of the network. Normal adult humans are embedded in a series of hierarchically inclusive layers of friendship, with each successive layer enclosing a larger number of individuals at a progressively lower level of emotional closeness. The layers have very distinct sizes, with a scaling ratio that approximates three (each layer is three times the size of the layer immediately inside it). The average sizes of each layer are indicated by the numbers against each circle in Figure 2 , although there is considerable individual variation. The circle of ~150 corresponds to the number of individuals with whom one has reciprocated relationships of trust, obligation, and reciprocity. Beyond the 150 layer there are at least two further layers: the layer of acquaintances (totaling ~500 individuals) and the number of faces one can put names to (~1500 individuals). While the two innermost layers (at 5 and 15) tend to be densely interconnected and constitute a single subnetwork, the remaining layers typically consist of more isolated sets of subnetworks (work colleagues, different sets of hobby club friends, church friends, distant family, etc.) for whom the only connection is via Ego. Each of the four innermost layers is typically split between extended family members and unrelated friends, with an overall ratio of about 50:50 (Sutcliffe et al., 2012 ).

In humans at least, there is evidence suggesting that the size of an individual’s personal social network correlates with their mentalizing competences, indexed as the ability to solve multiple-individual false belief tasks (Stiller & Dunbar, 2007 ; Lewis et al., 2011 ; Powell et al., 2012 ). Mentalizing, perhaps the archetypal form of social cognition, is the ability to handle other individuals’ mind states simultaneously and forms a naturally recursive sequence from first order intentionality (I know my own mind state) through second order (I know that A knows something—otherwise known as formal theory of mind) to a maximum of around fifth order (I know that A knows that B knows that C knows that D knows something) in most normal adult humans (Stiller & Dunbar, 2007 ). Since mentalizing competences (the number of different mind states one can have in mind at the same time) correlate with the volume of core areas in the frontal lobes (Lewis et al., 2011 ; Powell et al., 2012 , 2014 ), it follows that maintaining larger social groups is more demanding in terms of the need to allocate neural resources to those regions of the brain implicated in this task.

Further evidence that social cognition is likely to impose limits on social group size comes from an agent-based model that used processor time to assess the cognitive demands of different levels of information processing associated with managing relationships: this demonstrated not only that more complex information processing is more demanding but, more importantly, that this in turn sets limits on the size of group that can be maintained (Dàvid-Barrett & Dunbar, 2013 ; see also McNally et al., 2012 , Moreira et al., 2013 ). It may be no coincidence, then, that the social brain graph in fact consists of a series of socio-cognitive grades (Dunbar, 1993 , 2011a ; Lehmann et al., 2007 ).

There is evidence that social cognition is itself significantly more demanding than more conventional forms of cognition. We have shown, using both reaction time experiments and fMRI in humans, that mentalizing tasks (those that involve modeling the mental states of other individuals [for more details, see below]) are cognitively more demanding than equivalent non-mentalizing (i.e., purely factual memory) tasks and involve the recruitment of more neural circuitry, and that the magnitude of this difference increases with the complexity of the proposition being processed (Lewis et al., forthcoming ). One reflection of the fact that social cognition may be very costly is that it seems to develop much more slowly than more conventional instrumental cognition. In humans, emotional cue recognition (Deeley et al., 2008 ) and aspects of social cognition such as theory of mind (Blakemore & Choudhury, 2006 ; Henzi et al., 2007 ) can take as long as two decades to mature: their developmental progress seems to map onto the slow process of myelinization in the frontal lobes, which in humans is not completed until well into the third decade (Sowell et al., 2001 , 2003 ; Gogtay et al., 2004 ). Socialization seems to play an important role in this: Joffe ( 1997 ) showed that, across primates, the best predictor of the non-V1 neocortex volume is the length of the period of socialization (the period between weaning and puberty), suggesting that a considerable amount of practice over a lengthy period is required to develop the skills that underpin the social brain. These findings suggest that social skills require conscious thought in frontal lobe units before they eventually become automated and localized elsewhere in the cortex or subcortical regions (in humans, as late as the mid-20s). In other words, merely having a big computer (i.e., brain) is not enough: the hardware requires programming, and this is in large part dependent on extensive social experience. This is social learning on a dramatic scale and may explain why social learning appears to be so important in primates (Reader et al., 2011 ). A useful by-product of this is that the cognition that underpins social learning in this context then becomes available for the exchange of factual information about foraging among adults. Although this has sometimes been interpreted as the driver of brain evolution on the basis of correlational evidence (Reader & Laland, 2002 ; Reader et al., 2011 ; Pasquaretta et al., 2015 ), it could, in fact, just as easily be a consequence rather than the cause of brain evolution—a possibility that, surprisingly perhaps, never seems to have been considered.

Neuropsychology and the Social Brain

In primates, the neocortex accounts for a very large proportion of total brain volume (50–80%, compared to 10–40% in all other mammals) (Finlay et al., 2001 ). This probably explains why even total brain volume on its own gives a reasonable correlation with group size and other social variables in primates—subject to some error variance introduced by species like the gorilla and orangutan that have unusually large cerebella and relatively small neocortices and for whom neocortex size gives a significantly better prediction of community size than does total brain size (Dunbar, 1992 , 2011a ). The fit is improved by excluding striate cortex (the primary visual area, V1, in the occipital lobe: see Fig. 3 ) (Joffe & Dunbar, 1997 ), and it is improved still further by narrowing the focus down to the frontal lobes (Dunbar, 2011a ), implying that the automated processing of incoming perceptual stimuli is not itself a major component of the social brain processes—and why would it be, given that it is the meaning attached to these percepts rather than the percepts themselves that lies at the heart of complex sociality? Since the successive visual processing areas (V1 through V5/MT) scale isometrically with each other up through the occipital and parietal lobes (Dougherty et al., 2003 ; Yan et al., 2009 ), it is likely that the fit would be improved still further by excluding these and other basic perceptual processing regions in the brain (i.e., by focusing mainly on the social cognition circuits in the frontal and temporal lobes). Nonetheless, the fact that the brain acts as a distributed processing network may explain why many of the comparative analyses reveal respectable correlations between social behavior and relatively large brain regions like the neocortex.

Figure 3. The main brain regions involved in mentalizing (the “theory of mind network”). PFC, prefrontal cortex; ACC, anterior cingulate cortex (buried within the cortex); TPJ, temporoparietal junction; STS, superior temporal sulcus; V1, primary visual cortex (striate cortex) in occipital lobe. Dashed arrows indicate the principal connections of the “theory of mind” network.

A number of analyses have shown that executive function skills also increase with brain (or brain region) volume (Dunbar et al., 2005 ; Deaner et al., 2006 ; Shultz & Dunbar, 2010b ; Reader et al., 2011 ). Inevitably, these analyses rely on extremely coarse anatomical resolutions and so have not allowed us to narrow down the cortical circuits involved in any detail (although the availability of more sophisticated imaging techniques may offer new opportunities in this respect; see Mars et al., 2014 ). In the only serious attempt to address this issue to date, Passingham and Wise ( 2012 ) concluded that some brain regions (notably the dorsal prefrontal cortex and the frontal pole [Brodman area 10 at the very center of the forehead]; Fig. 3 ) are crucial for causal evaluation and strategic planning in anthropoid primates. However, their analysis was inevitably based on a very small sample of species. That said, the question as to what function(s) these competences subserve remains open: they may well be generic skills required for all forms of decision-making. All the experimental tests on which these studies are based (“odd-one out” problems, mapping tasks, analogical reasoning, causal reasoning) involve tasks that are essentially instrumental (mainly foraging tasks) rather than social ones. The problem for comparative psychology has always been that genuinely social tasks are not easy to devise: they tend to have long time delays to their outcomes (sometimes on the scale of a lifetime; see Silk et al., 2003 , 2009 ), and experimentalists require an immediately measurable outcome. This has been compounded by a long-held and widespread assumption that, in the wild, animals do very little other than sleep and search for food. Historically, there has been no incentive to devise more complex tasks.

If the different social and ecological uses to which primates put their brains depend on essentially the same cognitive mechanism (and, in particular, the same second-order cognitive processes such as causal reasoning, one-trial learning, analogical reasoning, comparison between two or more alternative projections into the future; Passingham & Wise, 2012 ), it may not be too surprising that there is evidence to support both the instrumental and the social hypotheses. However, a task analysis suggests that, while certain kinds of cognition are likely to be common to all the functional hypotheses for primate brain evolution, there is a natural asymmetry among the hypotheses. The kinds of cognition required to support bonded relationships may allow social (i.e., cultural) transmission of information or novel foraging behaviors, but the reverse is probably not the case; similarly, the kinds of cognition required to support social transmission of foraging skills would likely allow individual trial-and-error learning of foraging behavior, but the reverse is not the case. This is especially likely to be true to the extent that the real complexity of social relationships depends on the need to model other individuals’ minds and behavior in a virtual mental state space, something that seems to be cognitively very demanding even for humans (Lewis et al., forthcoming ). Some evidence to support this suggestion is provided by one of the few experimental studies to compare social and instrumental cognitive skills across primate species directly: Herrmann et al. ( 2007 ) found striking differences between humans and great apes in performance on social tasks but much less so on instrumental tasks.

This suggests (1) that the cognitive demands of instrumental tasks are significantly less than those of social tasks and (2) that the ability to manage social tasks depends crucially on frontal lobe volume (in particular). It would seem that only the social hypotheses would naturally provide for the other hypotheses as emergent by-products. This is not to say that cognitive evolution did not begin with solving simple ecological problems like food-finding (it almost certainly did), but rather to suggest that the demands of social cognition have resulted in additional more sophisticated cognitive competences being added to this mix and that these have, in turn, then allowed more sophisticated food-finding strategies.

In the previous section, it was suggested that mentalizing may be central to complex sociality in humans because it allows individuals to work with virtual representations of other individuals in a mental state space. Meta-analyses of a large number of neuroimaging studies of theory of mind in humans have identified the medial and/or orbitofrontal prefrontal cortex (PFC) as being differentially activated during mentalizing tasks in more than 90% of studies, the temporoparietal junction in 58%, the anterior cingulate cortex in 55%, and superior temporal sulcus (STS) in 50%; other regions that were less commonly activated included the amygdala and the insula (13% of studies in both cases) (Carrington & Bailey, 2009 ; see also Gallagher & Frith, 2003 ; van Overwalle, 2009 ; Apperly, 2012 ). Figure 3 shows the relative locations of these regions in the brain. It is well known that lesions in the prefrontal cortex specifically disrupt social skills, whereas those elsewhere typically do not (Kolb & Wishaw, 1996 ), while the role of the prefrontal cortex and the temporoparietal areas in managing false belief tasks (the benchmark for theory of mind) has been confirmed experimentally using transcranial magnetic stimulation to knock these regions out during experimental tasks (Costa et al., 2008 ). Recently, Makinodan et al. ( 2012 ) reported that mice that had been socially isolated immediately after weaning exhibited irrecoverable functional deficits in both the prefrontal cortex and its myelination, indicating that there may be a critical period that is vital for neurotypical development in a region that is crucial for normal adult social behavior.

This network also appears to be present in at least the catarrhine primates (Rushworth et al., 2013 ), although it is unlikely that it is capable of producing fully functional theory of mind sensu stricto in these species. What it probably does allow is perspective-taking, and that may be an important evolutionary and developmental precursor for full-blown theory of mind as well as being functionally essential for much of what is involved in the social interactions of nonhuman primates. There is considerable evidence that great apes, at least, are able to take others’ perspective into account (Hare et al., 2000 , 2001 ), and perspective-taking is probably crucial to managing monogamous pair-bonded relationships, since monogamy requires close coordination between the pair in a way that is not as necessary in the more fluid social systems that characterize most birds and mammals. Perspective-taking may thus have provided the initial step that started the evolutionary process that eventually gave rise to the evolution of full-blown mentalizing (Dunbar, 2011b ). This would explain why large brains are associated with monogamous mating systems rather than with group size in birds and non-primate mammals (Shultz & Dunbar, 2007 ; Pérez-Barbería et al., 2007 ).

In humans, damage to these prefrontal regions is associated with dramatic (and usually catastrophic) changes in personality and empathy, commonly resulting in socially inappropriate behavior (Adolphs, 1999 ) as well as more directly utilitarian responses on emotionally salient moral dilemmas such as the “trolley task” (Koenigs et al., 2007 ). More broadly, there is evidence from clinical studies that lesions in the prefrontal cortex tend to disrupt the processing (manipulation) of knowledge as well as social skills, whereas lesions in the temporal cortex tend to disrupt factual knowledge but leave the processing of social knowledge unaffected (Roca et al., 2010 ; Woolgar et al., 2010 ). Low densities of gray matter in the prefrontal cortex have also been linked to socially dysfunctional conditions such as schizophrenia (Lee et al., 2004 ; Yamada et al., 2007 ). More importantly for present purposes, individual differences in mentalizing competences in normal human adults correlate with the volume of neural matter in the key regions of the theory of mind network, especially those in the frontal lobes (Lewis et al., 2011 ; Powell et al., 2010 , 2014 ).

Seeley et al. ( 2007 ) have suggested that the regions associated with mentalizing constitute two distinct functional networks: an “executive control” network (involving mainly the dorsolateral prefrontal cortex and parietal areas) and an “emotional salience” network (involving mainly the anterior insular cortex and the anterior cingulate cortex, the amygdala and the hypothalamus), although the former may be specifically associated with rational thinking (“fluid IQ”) rather than social cognition per se (Woolgar et al., 2010 ). Nonetheless, emotion and cognition are not entirely independent of each other: the anterior insula and the medial prefrontal cortex are included in both networks, suggesting some level of interaction between the two networks (Craig, 2009 ).

The prefrontal cortex seems to be crucially involved in the management of social relationships in both humans (Powell et al., 2010 , 2012 , 2014 ; Lewis et al., 2011 ; Kanai et al., 2012 ) and macaques (Sallet et al., 2013 ). More importantly, perhaps, Powell et al. ( 2012 ) have shown, using path analysis, that there is a clear causal sequence here: individual differences in orbitofrontal cortex volume determine mentalizing competences (how well individuals do on multi-level/multi-individual false belief tasks), and mentalizing competences in turn determine the individual’s social network size. In humans, the medial and mid-prefrontal cortex is also associated with moral judgment, critical assessment, and core executive functions related to self-control, deception, and lying (MacDonald et al., 2000 ; Karton & Bachmann, 2011 ), all of which are associated with both social skills in general and theory of mind in particular.

This relationship between mentalizing competences and the volume of the frontal lobe in humans seems to be mirrored in the comparative evidence from primates. It is generally accepted that monkeys do not have theory of mind (second order intentionality) and are thus effectively first order intentional (they are aware of their own mental states, but not those of other individuals). In contrast, there is some evidence to suggest that great apes do understand others’ mind states (chimpanzees: O’Connell & Dunbar, 2003 ; Hare et al., 2000 , 2001 ; Crockford et al., 2012 ; orangutans: Cartmill & Byrne, 2007 ): although they are certainly not as good at formal theory of mind (the ability to pass false belief tests) as 6-year-old children (almost all of whom are fully expert on the task), they are about as good as 4-year-olds (most of whom are on the verge of acquiring this skill). By contrast, normal adult humans have been repeatedly shown to cope with fifth order intentionality (Kinderman et al., 1998 ; Stiller & Dunbar, 2007 ; Powell et al., 2010 ). For the limited data available, these competency levels turn out to map linearly against frontal lobe volume (Fig. 4 ). Figure 4 also plots the putative position of other monkey and great ape species for whom frontal lobe volume data are available on the assumption that their mentalizing competences are the same as those of the other members of their respective taxa. Notice how all these points cluster very tightly around the regression line: no monkey has a frontal lobe volume large enough to move it up to second order, and no great ape has one small enough to move it down to first order or large enough to move it up to third order. These data seem to tell us that mentalizing competences (whatever they actually are) are a function of the absolute volume of the frontal lobes (and most likely gray matter regions within the prefrontal cortex). It is important to appreciate that we still do not really understand what theory of mind (or mentalizing, more generally) actually involves cognitively (Roth & Leslie, 1998 ). Nonetheless, it seems to provide us with a convenient and reliable natural scale of social cognitive abilities, whatever the actual cognitive mechanisms involved may be.

Figure 4. Mentalizing competences (indexed as the maximum achievable order of intentionality) of six Old World monkey and four great ape species, plotted against frontal lobe volume. Monkeys are generally assumed to be first order intentional; experimental evidence suggests that chimpanzees and orangs are just about second order intentional, whereas adult humans are fifth order intentional. Species for whom mentalizing competences have been estimated experimentally (left to right: chimpanzees, orangutans, and humans) are indicated by solid symbols; species for whom mentalizing competences are not known but who are assumed to have the same mentalizing competences as other members of their taxonomic family are indicated by open symbols. (Redrawn from Dunbar, 2009 . Frontal lobe volume data from Bush & Allman, 2004 .)

Indeed, the conventional mentalizing (or intentionality) scale essentially treats all competences below full theory of mind (i.e., second order intentionality) as a homogeneous set. This is almost certainly a radical oversimplification. Sperber & Wilson ( 1986 ) argued that there is a series of finer scale gradations at the lower end of this scale (see also Gärdenfors, 2012 ). This makes sense in the light of the fact that, behaviorally, some species of animals (baboons, macaques, spider monkeys) seem to be socially and cognitively more complex than other species (e.g., colobines, howlers, antelope) (Deaner et al., 2006 , 2007 ; Shultz & Dunbar, 2010b ), despite the fact that on the conventional scale all would be regarded as first order intentional. Unpacking the lower end of the scale may allow us to evaluate better the cognitive differences between the different nonhuman species.

Passingham and Wise ( 2012 ) have pointed out that anthropoid primates are characterized by the evolution of entirely new regions in the prefrontal cortex (in particular Brodman area 10, the frontal pole; Fig. 3 ) that are not present in prosimians or other mammals (see also Sallet et al., 2013 ). They argue that these new regions allowed monkeys and apes to engage in cognitive strategies that other mammals (including prosimian primates) are unable to master. These include one-trial learning (as opposed to more laborious forms of association learning), propositional reasoning, and the capacity to compare the future consequences of two or more alternative behavioral strategies (Passingham & Wise, 2012 ). Among the anthropoid primates, it seems that only the callitrichids (marmosets and tamarins) lack area 10—which might account for this taxon’s unusually labile social system, which can flip rapidly between monogamy, polygamy, polygynandry, and polyandry (Dunbar, 1995a ,b; Opie et al., 2013 ), and the fact that their neocortex:group size ratio is completely out of line with those of all obligately monogamous primates (Dunbar, 2010b ).

So far in this section, we have focused in a rather conventional way on the neuroanatomy of sociality. There is, however, an important aspect of the neurobiology of primate sociality that we need to consider, and this has to do with the role played by neuroendocrines. Much fuss has been made of the role of oxytocin in social relationships (Insel & Young, 2001 ); this mechanism is certainly widely distributed among mammals and has been shown to correlate with some aspects of social behavior in both chimpanzees (Crockford et al., 2013 , 2014 ) and humans (Kosfeld et al., 2005 ) (for an overview, see Dunbar 2010c ). However, the oxytocin system habituates very quickly (Dunbar, 2010c ). More importantly, it is an endogenous response that appears to be insensitive to relationship quality or quantity: it causes individuals to act more or less affiliatively depending on the expression of the relevant gene, but it does not allow them to influence the responses of the individuals with whom they interact. It has been argued that the very unusual kind of bonded social relationships that are found in anthropoid primates (Silk, 2002 ; Shultz & Dunbar, 2010a ; Massen et al., 2010 ) necessitated a more robust bonding mechanism, and this involved exploiting the endorphin system (van Wimersma Greidanus et al., 1988 ; Panksepp et al., 1997 ; Depue & Morrone-Strupinsky, 2005 ; Curley & Keverne, 2005 ; Broad et al., 2006 ; Barr et al., 2008 ; Dunbar, 2010b ; Machin & Dunbar, 2011 ; Resendez et al., 2013 ).

In primates, endorphin activation is triggered by social grooming (Keverne et al., 1989 ), and we have been able to show, using positron emission tomography (PET), that light stroking of precisely the kind that so characteristically defines social grooming in primates also triggers endorphin activation in the human brain, and frontal lobe in particular (Nummenmaa et al., under review). It seems likely that this mechanism is mediated by the afferent c-tactile neurons, a unique set of unmyelinated (hence slow) neurons that respond only to slow stroking and which are not associated with a return motor loop from the brain (Olausson et al., 2010 ; Morrison, 2012 ; Vrontou et al., 2013 ). The significance of this is that the endorphin system responds exogenously (i.e., it is triggered in the recipients of grooming by their social partners, rather than merely endogenously in the groomer as is the case for oxytocin) and so is more responsive to both the quantity of time invested in a relationship and the number of social partners. An endorphin agonist, such as morphine, increases the attractiveness ratings of faces as well as the motivation for continuing to view them, whereas antagonists like naltrexone decrease both (Chelnokova et al., 2014 ). Similarly, PET studies reveal that the density of μ ‎-receptors (the opioid receptors that have a particular affinity for β ‎-endorphins) in core areas of the brain correlate with both the size of personal social network (Nummenmaa et al., under review) and an individual’s attachment style (Nummenmaa et al., in press). These findings suggest a central role for endorphins in the processes that underpin social relationships.

Building a close relationship with someone requires time, and there is a strong correlation between time devoted to socializing with an individual and willingness to support or offer help to that individual in both monkeys (Dunbar, 1980 , 2012a ) and humans (Roberts & Dunbar, 2011 ; Curry & Dunbar, 2013 ; Sutcliffe et al., 2012 ; Curry et al., 2013 ). By triggering endorphin activiation, time spent interacting—grooming in the case of primates, engaging in laughter (Dunbar et al., 2012b ) and perhaps other activities as well as affective touch (Nummenmaa et al., in press) in the case of humans—probably sets up an emotional attachment that allows a very rapid response based on a quantitative index of the quality of the relationship.

In sum, primate social bonding seems to involve a two-process mechanism. In effect, the endorphin system is used to create an internal psychopharmacological platform that enables the individuals to develop a more cognitive long-term relationship that involves reciprocity, obligation, and trust (Sutcliffe et al., 2012 ). The latter, of course, is where the social brain comes in, but it is important to appreciate that beneath the simple group–brain size correlation there is a more complex neurobiological story as well as a more complex behavioral superstructure that is supported by these neurological mechanisms.

Neuropsychological research offers considerable potential for understanding both the processing demands of different kinds of cognition and how these relate to neurological pathways in the brain, and hence to the volumetric demands on different brain units and their interconnections (see also Mars et al., 2014 ). Although there has been considerable interest in social cognition in the recent neuroimaging literature, much of it has typically been concerned with judgments of trustworthiness or with reward and punishment in simple dyadic contexts (e.g., Knoch et al., 2006 ; Behrens et al., 2008 ; Lebreton et al., 2009 ). While this clearly provides valuable insight into how such judgments are made, it does not really capture the richness of the social world in which humans and other primates live. Nor does it engage with the question of just how and why humans differ from other primates, or why anthropoid primates differ from other mammals not just in cognitive abilities but also in their social style. It is these issues that need to be addressed, and so far they have been conspicuous by their absence from the literature on brain evolution.

Social Cognition and Human Evolution

Human evolution has always been viewed through the lens of anatomy and archaeology, with a clear focus on the “stones and bones” of the archaeological record. While this has spawned an interest in the cognitive aspects of human evolution (sometimes referred to as cognitive archaeology; Renfrew & Zubrow, 1994 ), in practice the focus has been on task analyses of the demands of tool-making (e.g., Gowlett, 2006 ). More recently attempts have been made to relate these to mentalizing abilities (Barham, 2010 ; Cole, 2012 ). However, Gamble et al. ( 2011 ) and Gowlett et al. ( 2012 ) remind us that the processes of evolution, and human evolution in particular, do not proceed through material culture as such but through the behavior and minds of the people who made the material culture. Here, social cognition is likely to play an especially important role, and, difficult as this may be to study, it needs to be given much more attention.

Although archaeologists have shied away from grappling with social and cognitive evolution, our growing knowledge of the finer details of the cognitive differences between both human and other primates and, at the level of individual differences, within humans offers the possibility of a more principled approach. Given the explicit quantitative relationships between social and cognitive traits and brain (or brain region) volumes, it may, for example, be possible to make more informed inferences about human cognitive evolution. We do not, of course, have access to soft tissue morphology from fossil species, but there has been a long tradition within paleoanthropology of making inferences about brain composition from the impressions created on the inside of the skull by the brain (Bruner, 2010 ; Bruner et al., 2003 ). More importantly, perhaps, the tight allometric scaling between brain regions in living primates allows us to make inferences about the sizes of these units in fossil specimens, given observed cranial volumes. It is, of course, necessary to be cautious in interpreting individual cases, given that there are well-known exceptions to these allometric relationships in living primates (e.g., the large cerebella and small neocortices of the gorilla and orangutan that we noted earlier). Other exceptions include the impact that latitude has on the size of the visual system in both modern humans (Pearce & Dunbar, 2012 ; Pearce & Bridge, 2013 ) and Neanderthals (Pearce et al., 2013 ), which in the latter case at least results in a smaller neocortex than would be predicted on the basis of cranial volume. Nonetheless, such extrapolations from general equations can tell us something about the overall pattern of evolution. What is important here is that these trajectories are not open-ended: we know roughly where the trajectory started (essentially, the brain composition and cognition of great apes) and where it ended (those of modern humans); our problem is to infer how the changes that must have occurred are strung out between these two endpoints. This will be illustrated here with just two contrasting examples.

The easiest and most secure extrapolation is that for social group size, since the social brain relationship is robust and empirically well substantiated. Using standard allometric equations to interpolate from cranial volume to neocortex volume, we can estimate the community sizes for individual fossil specimens of the main hominin species (Fig. 5 ). The community sizes for living chimpanzees are shown on the left side of the graph for comparison. Two things may be noted. First, for most of early human evolution (the australopithecine phase, represented by the genus Australopithecus and its allies) predicted community sizes do not differ from those observed in living chimpanzees. In effect, early hominins were just ordinary great apes. Second, community size undergoes a rapid increase with the appearance of the genus Homo at around 2 million years ago, stabilizes for about a million and a half years, and then increases rapidly and exponentially through archaic humans ( Homo heidelbergensis and allies) into modern humans. To the extent that community size represents the outcome of the cognitive processes that underpin the social brain, these data reflect the pattern of change in cognition over time.

Figure 5. Median (±50% and 95% ranges) social group for the main hominin species, in temporal order of appearance. Social group is estimated by interpolating through a series of equations from cranial volume, via brain size and neocortex size, to group size (using the relationship shown for apes in Fig. 1 ). The equations are given in Aiello & Dunbar ( 1993 ) and Gowlett et al. ( 2012 ). The values are for individual fossil specimens. The equivalent values for individual chimpanzee populations, based on actual community sizes, are shown on the left. (After Gowlett et al., 2012 .)

We can, however, go one step further by considering cognition directly in the form of mentalizing competences, bearing in mind that these are almost certainly simply an emergent index of more conventional forms of cognition. Given that these appear to correlate linearly with the size of the frontal lobe, and, in general, brain units all correlate with total brain volume, it is in principle a simple matter of interpolating through a series of equations from cranial volume to mentalizing abilities. These are plotted in the same way for all major hominin species in Figure 6 . The values for Neanderthals are corrected to take account of their larger occipital lobes and smaller frontal lobes, reflecting their relatively larger visual system (Pearce et al., 2013 ). Once again, our benchmarks are provided by great apes at level 2 intentionality and modern humans at level 5, and our problem is simply to decide the pattern of change between these two fixed points.

Figure 6. Median (±50% and 95% ranges) mentalizing competences, indexed as the maximum achievable level of intentionality, for the main hominin species, in temporal order of appearance. Mentalizing competences are estimated by interpolating through a series of equations from cranial volume, via brain size and frontal lobe volume, to intentionality level (using the relationship for Fig. 5 , and the equation for mentalizing competences from Dunbar 2010a ). The values are for individual fossil specimens. (After Dunbar, 2015 .)

Two points may be noted from this graph. First, once again, australopithecines were simply jobbing great apes, with no particular pretensions to advanced cognition. Second, all fossil anatomically modern humans (i.e., members of our own species) typically achieve level 5 intentionality, but no archaic humans (including Neanderthals) were likely to have done so. To be sure, all of these would have made level 4 intentionality, which, in the grand scheme of things, is itself pretty impressive: they would not have been intellectual slouches by any means. In cognitive terms, they would have been in the same bracket as the lower end of the normal distribution for modern human adults, and at about the same intellectual level as young teenagers. However, this key difference between archaic and modern humans would have had crucial implications in respect to their capacities for both language and culture.

In normal adult humans, individual differences in the ability to manage complex multi-clause sentences correlates one-to-one with mentalizing competences (Oesch & Dunbar, under review). In other words, mentalizing competences seem to determine how complex our language can be, and this would have had inevitable consequences both for the length of the propositional chains that Neanderthals could have managed and, hence, for the complexity of the stories they told. It may also have had implications for the complexity of the culture that these species would have been able to produce, and this at least seems to be borne out by the archaeological evidence. Attempts to claim that Neanderthal culture was as complex as that of contemporary anatomical modern humans (e.g., Zilhão et al., 2010 ) notwithstanding, the fact is that neither the Neanderthals nor the other archaic humans produced cultural artefacts that were nearly as sophisticated as those of contemporary anatomically modern humans (Klein, 1999 ). Neanderthal tools lacked both the technical sophistication of those developed by modern humans (multi-component tools like bows and arrows or spear-throwers) and the capacity to miniaturize (fine bone and flint points that functioned as arrowheads, buttons, awls, needles), and there is no evidence at all to suggest that they ever produced the kinds of “frivolous” material culture (Venus figurines, toys, cave paintings) that modern humans began to produce in abundance around the time the Neanderthals went extinct (Dunbar, 2015 ). This may be associated with the fact that several genes associated with both brain enlargement and neural efficiency in humans show evidence for strong recent selection (Burki & Kaessmann, 2004 ; Evans et al., 2005 ; Mekel-Bobrov et al., 2005 ; Uddin et al., 2008 ; Wang et al., 2008 ). This does not, of course, mean that Neanderthals were, as a result, in any sense intellectually primitive: it simply means they were not yet quite in the same league as modern humans, and this necessarily has consequences for what they could accomplish in social, cultural, and ecological terms.

On a more general note, human evolution provides a framework within which modern human behavior and cognition can be understood. It can tell us why we ended up the way we are, and so provide insights into the design, and perhaps flexibility, of the human mind. The importance of this historical framework is frequently overlooked in psychology, with its emphasis on the mechanisms and development of behavior in the here and now. Asking how and why we got to be the way we are can tell us a great deal about those mechanisms, especially when seen against a background of primate cognitive and social evolution. And it should remind us, above all, that human social evolution, like that of all primates, is not simply about individual traits but about how these traits enable us to live in an extensive, complex, highly dynamic social world.

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2 The social brain hypothesis: An evolutionary perspective on the neurobiology of social behaviour

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This chapter defends a version of the social brain hypothesis, focusing especially on the cognitive powers involved in the ‘Theory of Mind’ that humans are thought to deploy in order to attribute mental states to each other. The size of the human brain is not a new discovery (and does not need any sophisticated technology to establish it): the innovative contribution of neuroscience to this area of study, however, has been to enable experiments in which the brain activation of subjects doing various ‘social cognition’ tasks can be examined and mapped. The discovery that parts of the prefrontal cortex are consistently activated by Theory of Mind tasks provides the social brain hypothesis with support. The fact that it is this area of the brain that has become larger as human brain size has increased over time also fits nicely within a theory that regards the demands of social cognition as a central ‘driver’ of the evolution of the human brain.

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The social brain hypothesis and its implications for social evolution.

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The social brain hypothesis was proposed as an explanation for the fact that primates have unusually large brains for body size compared to all other vertebrates: Primates evolved large brains to manage their unusually complex social systems. Although this proposal has been generalized to all vertebrate taxa as an explanation for brain evolution, recent analyses suggest that the social brain hypothesis takes a very different form in other mammals and birds than it does in anthropoid primates. In primates, there is a quantitative relationship between brain size and social group size (group size is a monotonic function of brain size), presumably because the cognitive demands of sociality place a constraint on the number of individuals that can be maintained in a coherent group. In other mammals and birds, the relationship is a qualitative one: Large brains are associated with categorical differences in mating system, with species that have pairbonded mating systems having the largest brains. It seems that anthropoid primates may have generalized the bonding processes that characterize monogamous pairbonds to other non-reproductive relationships ('friendships'), thereby giving rise to the quantitative relationship between group size and brain size that we find in this taxon. This raises issues about why bonded relationships are cognitively so demanding (and, indeed, raises questions about what a bonded relationship actually is), and when and why primates undertook this change in social style.

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10.1080/03014460902960289

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Ann Hum Biol

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Animals, Biological Evolution, Brain, Cognition, Humans, Primates, Social Behavior

The Social Intelligence Hypothesis

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• First Online: 31 July 2017
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• Published: 15 June 2018

Sociality does not drive the evolution of large brains in eusocial African mole-rats

• Kristina Kverková 1 ,
• Tereza Bělíková 1 ,
• Seweryn Olkowicz 1 ,
• Zuzana Pavelková 1 ,
• M. Justin O’Riain 2 ,
• Radim Šumbera 3 ,
• Hynek Burda 4 ,
• Nigel C. Bennett 5 &
• Pavel Němec   ORCID: orcid.org/0000-0003-0277-0239 1  

Scientific Reports volume  8 , Article number:  9203 ( 2018 ) Cite this article

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• Neuroscience

The social brain hypothesis (SBH) posits that the demands imposed on individuals by living in cohesive social groups exert a selection pressure favouring the evolution of large brains and complex cognitive abilities. Using volumetry and the isotropic fractionator to determine the size of and numbers of neurons in specific brain regions, here we test this hypothesis in African mole-rats (Bathyergidae). These subterranean rodents exhibit a broad spectrum of social complexity, ranging from strictly solitary through to eusocial cooperative breeders, but feature similar ecologies and life history traits. We found no positive association between sociality and neuroanatomical correlates of information-processing capacity. Solitary species are larger, tend to have greater absolute brain size and have more neurons in the forebrain than social species. The neocortex ratio and neuronal counts correlate negatively with social group size. These results are clearly inconsistent with the SBH and show that the challenges coupled with sociality in this group of rodents do not require brain enlargement or fundamental reorganization. These findings suggest that group living or pair bonding per se does not select strongly for brain enlargement unless coupled with Machiavellian interactions affecting individual fitness.

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Introduction

The social brain hypothesis (SBH) contends that the demands imposed on individuals by living in cohesive social groups exert a selection pressure favouring the evolution of large brains and complex cognitive abilities 1 . It was originally proposed to explain the exceptional cognitive abilities in primates, but it has since been extended to a wider range of vertebrate taxa, including cetaceans, carnivores, bats, insectivores, ungulates, various birds and cichlids (for a review see 2 , 3 ). While the SBH has gained great traction in evolutionary anthropology, what the underlying mechanisms are, or how broadly it applies to other animals remains an area of active research. Recent studies incorporating phylogenetic corrections and more stringent measures have failed to provide strong support 4 , 5 , 6 , 7 and even new analyses in primates, incorporating a substantially larger number of species and phylogenetic uncertainty, challenge its validity 8 , 9 . An exception is a recent study reporting larger brain size in cetaceans living in mid-sized groups 10 . The hypothesis has only recently been tested in rodents for the first time and the results revealed that, in ground squirrels, sociality is not associated with larger relative brain size, but that social species tend to have larger bodies and correspondingly absolutely larger brains 6 , suggesting that a possible link between body size and sociality may be mediating the effect on brain size.

Over the past decades, different factors have been proposed as the main driving force of cognitive enhancement mediated by sociality in birds and mammals generally, and primates in particular (reviewed in 2 , 11 ). The original idea emphasized competition and tactical deception (as reflected in the name “Machiavellian intelligence”) 12 , but the mechanism was later reformulated by Dunbar and Shultz 13 , 14 as the need to maintain group cohesion through individual recognition and affiliative interactions to diffuse conflict. According to this latter view, cognitively demanding social behaviours are believed to take the form of behavioural coordination and pair bond formation in non-primates, but might become generalized to all group members in primates (reviewed in 2 ). Mating system thus represents another domain of sociality that is pertinent to brain evolution. Indeed, association between monogamy and larger relative brain size has been reported in ungulates, carnivores, and birds 13 , 15 . Cooperative breeding itself is another factor that has been suggested as potentially facilitating large brain evolution 15 , 16 , 17 (but see 18 , 19 ).

Despite recent progress in comparative methods that take phylogenetic relatedness into account, broad comparative studies, while allowing for greater statistical power, remain inherently prone to spurious findings due to large variations in ecology and life history traits, the unrecognized influence of hidden variables, heterogeneity in evolutionary trajectories and selection pressures, and data inconsistencies across datasets 3 , 9 , 20 , 21 . One way to limit the effects of biological heterogeneity and statistical interference is to study brain evolution within closely related but behaviourally diverse clades 21 . Here, we use this approach and test the SBH in African mole-rats (Rodentia: Bathyergidae). This group is ideal to provide insights into some of the unanswered questions without introducing confounding factors associated with differences in general biology and ecology that have been implicated in brain size evolution. Major factors besides sociality include substrate use, habitat complexity, diet and foraging mode, activity pattern, home range, developmental mode and maternal investment (for a review, see 20 ). Mole rats are uniform in most of these traits. They are all strictly subterranean, burrowing and feeding on underground parts of plants 22 , 23 , 24 , 25 , 26 , but cover the whole social spectrum, from strictly solitary to the remarkably social cooperative breeders, warranting the term “eusocial” 27 , 28 . They all give birth to altricial young and from the limited information available, it seems there are no systematic differences in maternal investment (gestation length, litter size, lactation length) connected to sociality 29 . The naked mole-rat is somewhat exceptional, though, in having substantially larger litters than the other species 30 . Solitary species, however, seem to be seasonal breeders 31 , 32 , 33 , in contrast to mostly aseasonally breeding social species 34 , 35 , 36 . Sociality also goes hand in hand with larger burrow systems and thus increased “home range”, but reliable data for all species are not available and there is substantial intraspecific variation 37 , 38 .

Solitary mole-rats are highly territorial and aggressive towards conspecifics. Their affiliative social interactions are confined to short periods of time during the breeding season and maternal care for juveniles, which disperse shortly after weaning 31 , 32 , 33 . Social species live in stable, multigenerational families in which only few individuals (often just a single bonded pair) reproduce and most of their offspring stay permanently within the family as non-reproductive helpers. Typically, members of this cohesive group cooperate through digging and maintaining the burrows, foraging for food and bringing it to communal storage, engaging in colony defence against intruders and predators, and taking care of the pups – grooming, huddling, returning them to the nest chamber when they wander off and providing them with cecotrophs 22 , 39 , 40 , 41 , 42 , 43 . In the genus Cryptomys the groups tend to be smaller and much less stable, especially in the mesic parts of the range 44 . Moreover, social mole-rats, in contrast to solitary ones, seem to be monogamous 45 , 46 , 47 , 48 , which is another purported driver of cognitive abilities in non-primate mammals 13 . There is also evidence of individual recognition 43 , 49 and elaborate vocalization and social interactions in the social species 30 , 50 , 51 , 52 so these are not just simple aggregations. Mole-rat sociality is based on long-term (lifelong in eusocial species) pair bonds and stable social relationships among all members of an extended family 27 , 28 , 53 . Due to limited opportunity for dispersal and new burrow formation, there seems to be little flux in the composition of the social group, especially in eusocial species, colonies of which are characterized by extensive overlap of adult generations and permanent (lifelong) philopatry 27 . Importantly, manipulative or Machiavellian behaviour is likely selected against in mole-rat colonies with monopolized reproduction because it would harm an individual’s inclusive fitness.

While social environment is a complex system, where various components come into play, some patterns in the data could provide insight into their relative importance. The general prediction is that monogamous social species of mole-rats should have bigger brains than solitary species. If social bonding, individual recognition, maintaining group cohesion and cooperation exert the major selection pressure 13 , 54 , then the eusocial species with extremely high reproductive skew towards a single breeding pair might be expected to show the largest brains and cognitive potential, since they live in the largest and most cohesive groups, with a decreasing trend towards the solitary end of the social spectrum. If, however, the competitive aspect of sociality is more important, eusocial species should not face a pressure to increase brain size, since outcompeting other colony members would not improve an individual’s fitness. Mole-rats that are still social, but not with such an extreme reproductive skew (genus Cryptomys ) 44 , 55 , could perhaps be expected to show greater cognitive capacities and larger brains, since they could potentially benefit by becoming dominant and taking over or starting their own colony, or realise their direct fitness by extra-colonial paternity 55 . However, as noted above, it is highly unlikely that complex Machiavellian interactions are present in mole-rats. No difference in brain size between the groups would thus indirectly point to these competitive interactions being the most important factor.

The social organization of eusocial mole-rats resembles that of eusocial insect societies in several aspects, such as monopolization of reproduction 27 , 28 and division of labour among non-reproductive group members 39 , 56 , 57 , 58 (but see 59 , 60 ). Alternative hypotheses for social brain evolution have been recently developed for (eu)social insects 61 and African mole-rats have been suggested as a possible vertebrate group where they may apply. The distributed cognition hypothesis (DCH) seems to be particularly pertinent, as its predictions are opposite to those of the SBH. It assumes that in multi-generational colony groups characterized by high reproductive skew and therefore subjected to strong colony-level selection, members can rely on social communication to supplement individual cognition. The hypothesis therefore predicts relaxed selection for individual cognitive abilities and reduced brain investment in such (eu)social species 61 . If cooperative information sharing among individual mole-rats outweighs within-colony conflicts, solitary species should have the largest brains, with a decreasing trend toward the eusocial end of the social spectrum, where the potential for “distributed cognition” is highest.

Most comparative studies dealing with the SBH published to date have focused on relative brain mass or volumes of specific brain regions (particularly the neocortex) and the results were largely based on the analysis of previously published data 5 , 13 , 54 , 62 , 63 , 64 , 65 . In this study, we test predictions of the SBH and the DCH, using new, unprecedentedly comprehensive data on brains of 11 species representing all six existing genera of mole-rats. In light of recent studies on cognition 66 , 67 and neuronal scaling rules 68 , 69 , it becomes clear that regarding cognitive abilities as a function of relative brain size is a gross oversimplification, and might be even misleading 70 . There are at least two factors at play – brain size and neuronal density 69 , 71 . Thus, at neuroanatomical level, more cognitive power can be achieved by increasing brain size or size of specific brain regions, or by increasing the neuronal density without that necessarily manifesting as a substantial increase in volume. Investigating a broad range of brain size measures enables us to pinpoint which brain parts, if any, are under selection, or if the whole brain responds in concert.

Absolute and relative brain size

While it might be possible that subterranean microphthalmic mammals are somehow aberrant in the way their brains are built, we show that this is not a concern in the choice of mole-rats as our model group. With the exception of the naked-mole rat, bathyergids do not significantly differ from other rodents in either their allometric brain-body relationship or previously published neuronal scaling rules (Fig. S 1 ). Notably, the naked mole-rat not only has a smaller brain than expected for a rodent of its body size, but also a lower number of neurons than predicted for its brain size.

The studied species range in average body mass from 38 g to 908 g and in average brain mass from 0.44 g to 3.81 g (Fig.  1 , Table  S1 ). Solitary species have significantly larger body mass than social species (posterior mean = 1.1089, CI = [0.1481, 2.2049], pMCMC = 0.0321, lambda mean = 0.75; for other comparisons, see Table  S2 ). Likewise, absolute brain mass tends to be higher in solitary species, although the difference is not significant (posterior mean = 0.6486, CI = [−0.0018, 1.4556], pMCMC = 0.0741, lambda mean = 0.84; for other comparisons, see Table  S2 ) (Fig.  2a,b ).

Body size, brain size and number of neurons for the mole-rat species examined. ( a ) The phylogeny of the 11 African mole-rat species included in the analyses with body mass (the left tree) and brain mass (the right tree) mapped as a continuous trait with the ancestral states reconstructed using the phytools package in R. The topology of the tree follows a published report 113 . ( b ) Dorsal and lateral views of representative brains are accompanied by information concerning total numbers of brain neurons (yellow), numbers of pallial neurons (blue) and brain mass (red). M, million. Scale bar, 10 mm. Species names are colour-coded by sociality: red – eusocial, green – social, blue – solitary.

Absolute and relative brain size by sociality. Bar plots illustrating the differences in absolute ( a , b ) and relative brain size ( c , d ) between social and solitary (left column graphs) and eusocial, social and solitary species of African mole-rats (right column graphs). Note that solitary mole-rats tend to have absolutely, but not relatively larger brains than social ones. Relative brain size is expressed as a residual from the brain-on-body regression line for Rodentia, with 1 added to get positive numbers. Data are represented as mean ± SEM.

Relative brain size, a measure previously shown to be associated with sociality 2 , 13 , 72 (expressed as a residual from the regression line for rodents) shows no connection to the social system in mole-rats (Fig.  2c,d ; for statistics, see Table  S2 ).

Volumetric analyses

To assess whether there is any evidence of mosaic evolution (disproportional enlargement of specific brain parts, see e.g. 73 ) in response to selective pressures associated with sociality, we measured the volumes of 14 brain regions and determined the scaling rules for those structures with brain size (Tables  S1 and S3 ). All measured volumes correlate significantly and very tightly with whole brain volume (Fig.  3 ). In fact, brain volume accounts for over 90% of variance in all structure volumes measured, except for the amygdala ( R 2  = 0.86) (Table  S3 ). We then compared relative volumes of these brain structures between sociality grades. Not surprisingly, given the high proportion of variance explained by brain size, relative volumes of all the structures are independent of sociality (Table  S4 ). Mole-rats are thus no exception to the broad rule that conserved scaling rules explain an overwhelming proportion of variance in brain region volumes, as has been clearly shown in a much larger sample of mammals 74 .

Scaling of selected brain structures with brain volume. Log-transformed structure volumes are plotted against log-transformed total brain volumes. The diencephalon volume was calculated as the sum of the thalamic and hypothalamic volumes, the mesencephalon volume as the sum of the tectal and tegmental volumes. Fitted lines and coefficients of determination are taken from the OLS regressions of species averages. Note that all structures scale very predictably with total brain volume. BS, Bathyergus suillus ; CH, Cryptomys hottentotus ; CN, Cryptomys natalensis ; CP, Cryptomys pretoriae ; FA, Fukomys anselli ; FD, Fukomys damarensis ; FI , Fukomys darlingi ; FM, Fukomys mechowii ; GC, Georychus capensis ; HA, Heliophobius argenteocinereus ; HG, Heterocephalus glaber .

The neocortex ratio [C R : neocortex volume/(brain volume − neocortex volume)] has been traditionally used as a proxy for intelligence in tests of the SBH. We found that in mole-rats, there are no significant differences between the social categories, but there is a potential trend towards higher C R in solitary species (Fig.  4 , Table  S2 ). C R also decreases significantly with maximum group size (PGLS: −0.0278, p = 0.0294; Fig.  5a ) and mean group size (PGLS: −0.0358, p = 0.0218; Fig.  5d ), but the relationship is not significant after removing the naked mole-rat from the analysis (maximum group size: −0.0297, p = 0.0721; mean group size: −0.0337, p = 0.1405).

Neocortex ratio by sociality. Bar plots illustrating the differences in neocortex ratio (the ratio of neocortex volume to the rest of the brain volume) between ( a ) social and solitary, and ( b ) eusocial, social and solitary species of African mole-rats. Data are represented as mean ± SEM. Note that solitary species tend to have higher neocortex ratios than social ones.

The relationship of selected neuronal correlates of cognitive capacity and social group size. Scatter plots showing negative correlation between neocortex ratio ( a , d ), number of brain neurons ( b , e ), number of cortical neurons ( c , f ) and maximum ( a – c ) and mean group size ( d – f ). The fitted lines represent the phylogenetic least squares regressions.

Number of neurons

Neuronal numbers in the whole brain and specific brain regions are presented in Fig.  1 and Table  S5 , results of the statistical analyses in Table  S6 . Mole-rats generally conform to the neuronal scaling rules previously established for rodents 75 (Fig. S 1b,c ). Solitary species tend to have higher absolute numbers of neurons compared to social species (Table  S6 ; Fig.  6a,b ). Importantly, this difference is most pronounced and statistically significant in the number of cortical neurons (posterior mean = 0.7928, CI = [0.0694, 1.5191], pMCMC = 0.0396, lambda mean = 0.48) and neurons in the subcortical forebrain (posterior mean = 0.6884, CI = [0.0306, 1.3882], pMCMC = 0.0480, lambda mean = 0.44), i.e., solitary species have significantly more neurons in the forebrain (posterior mean = 0.7603, CI = [0.0405,1.4421], pMCMC = 0.0332, lambda mean = 0.48) (Fig.  6c,d ).

Neuronal approximations of cognitive capacity by sociality. Bar plots illustrating the differences in the average number of brain neurons ( a , b ), the average number of forebrain neurons ( c , d ), neuronal index ( e , f ) and the ratio of cortical neurons to brain stem neurons ( g , h ) between social and solitary (left column graphs) and between eusocial, social and solitary species of African mole-rats (right column graphs). Note that solitary mole-rats have significantly more forebrain neurons and tend to have more brain neurons and higher cortical neurons ratios than social ones. The neuronal index is expressed as a residual from the neurons-on-body mass regression line for Rodentia, adjusted by adding the largest negative value to get positive numbers. Data are represented as mean ± SEM; asterisk marks a significant difference (95% confidence interval does not include 0).

Consistent with these results, the number of brain neurons decreases with both maximum group size (PGLS: −0.2167, p = 0.0322; Fig.  5b ) and mean group size (PGLS: −0.2804, p = 0.0492; Fig.  5e ), although this relationship is not significant after removing the naked mole-rat from the analysis (maximum group size: −0.1423, p = 0.1048; mean group size: −0.1643, p = 0.133). Number of cortical neurons also decreases with maximum group size (PGLS: −0.2724, p = 0.0019; Fig.  5c ) and mean group size (PGLS: −0.3680, p = 0.0021; Fig.  5f ), and, notably, this relationship remains significant even when analysed without the naked mole-rat (maximum group size: −0.2905, p = 0.0272; mean group size: −0.2342, p = 0.018).

Numbers of neurons contained in the brain regions examined correlate significantly and very tightly with their mass (Table  S3 ) and, because the size of these regions scales highly predictably with brain size (Fig.  7b ), also with brain mass (Fig.  7a ). Numbers of neurons relative to the brain mass do not differ between the social grades in the whole brain or any of the five brain parts (Table  S6 ).

Scaling of neuronal numbers and volumes of major brain divisions with brain mass. ( a ) Number of neurons contained in the brain divisions plotted as a function of brain mass. ( b ) Division volumes plotted as a function of brain mass, for comparison. Data points correspond to species averages. Coefficients of determination are reported for the OLS regressions. See caption to Fig.  3 for abbreviations.

We also examined residuals from the neurons-body regression line for rodents, essentially the neuronal index proposed by Herculano-Houzel 76 as an adequate proxy for cognitive abilities, and the ratio of cortical neurons to the neurons in brain stem, another index of cognitive power, analogous to the neocortex ratio (Fig.  6e–h ). No significant differences were found between the solitary and social groups for either the neuronal index (posterior mean = −0.2467, CI = [−1.7914, 1.2497], pMCMC = 0.72, lambda mean = 0.07; for other comparisons, see Table  S2 ) or cortical neurons ratio (posterior mean = 0.4113, CI = [−0.0363, 0.8235], pMCMC = 0.0585, lambda mean = 0.36; for other comparisons, see Table  S2 ), although there is a trend for higher cortical neurons ratio in solitary species (Fig.  6g,h ).

The analyses performed in this study do not indicate a positive association between the neuroanatomical correlates of brain information processing capacity and sociality in African mole-rats. Despite examining measures ranging from overall brain size to neuronal numbers, we found no differences between the social grades in any of the relative measures, whether previously reported (relative brain size, neocortex ratio) 13 , 62 , or tested for the first time (neuronal index, cortical neurons ratio). The few significant differences we revealed relate to absolute measures and were in favour of solitary mole-rats. Most importantly, solitary species have more neurons in the forebrain than social ones. Because the forebrain subserves higher cognitive functions and because the number of forebrain neurons is one of the major determinants of brain computational capacity 69 , 71 , 77 , the high number of forebrain neurons likely endows solitary species with improved cognitive abilities and increased behavioural flexibility. General cognitive abilities aside, it could be hypothesized that social mole-rats would have relatively larger brain areas related to individual recognition and/or emotional processing, such as olfactory areas or the amygdala 78 , 79 . This is not the case, however. Brain structure scaling is very conservative in mole-rats and we found no evidence of mosaic evolution. These results show that social living that entails maintaining group cohesion, individual recognition, behavioural coordination, monogamous pair bonding and cooperative breeding does not drive the evolution of large brains harbouring large numbers of neurons in African mole-rats. Importantly, our failure to find support for the SBH is not due to lack of statistical power. If that were the case, there would be no significant results and the trends would be in the opposite direction.

Although the debate about the importance of relative vs. absolute brain size for cognition is still ongoing and recent evidence for both is available 66 , 80 , our results do not support the SBH in any case. Since we included both absolute and relative measures of whole brains and several brain regions, the results are not tied to any particular assumptions about the neural substrate for cognitive capacity. Drawing an analogy with insect eusociality (see Introduction), it is tempting to interpret the lower number of forebrain neurons in social mole-rats as evidence supporting the DCH. The very fact that the naked mole rat, the species that forms the largest colonies of up to 295 members 24 and in which non-breeding individuals of both sexes are physiologically suppressed from reproduction 81 , 82 , has the smallest brain and the lowest number of neurons (both in absolute and relative terms; Tables  S1 and S5 , Fig. S 1 ) is in line with the hypothesis. However, in contrast to DCH predictions 61 , a reduced brain size and lower numbers of neurons were not observed in the other eusocial species, in which reproductive skew is maintained solely by incest avoidance 43 or by combination of incest avoidance and a suppression of female reproductive physiology 83 . While it is well possible that physiological reproductive suppression of non-breeders is necessary to achieve the level of group selection needed to relax the selection for individual cognitive abilities, alternative explanations cannot be excluded. For instance, the small, hairless and semi-poikilotermic 84 naked mole-rat may face more severe metabolic constraints than its larger hairy relatives. All other differences between social and solitary species reported in this study seem to be attributable to differences in body size. Taken together, the results obtained in this study are inconsistent with the SBH and do not provide a sound support for the DCH, they highlight the importance of viewing body size not just as a confounding factor to be corrected for, but as intrinsically connected with and driving brain size and computational capacity. Technically, body size is tightly coupled to absolute brain size and that, in turn, with the total number of neurons. There is substantial evidence and growing consensus that the total number of neurons and their densities are decisive for brain computational power 67 , 69 , 71 , 77 . Moreover, it has been posited that increased numbers of neurons lead to increased brain complexity, as neurons are the brain’s “computational units” and more neuronal assemblies can be created, a notion supported by recent experimental evidence in mice 85 .

The special case of mole-rats might also provide an insight into a more general problem with the SBH. Considering that, across vertebrates, the single best determinant of brain size is body size 86 , we might have to deal with a confounding factor responsible for driving both sociality and larger bodies. Because the evolution of group-living is generally believed to have evolved as a response to predation 3 , 87 , which can select for greater body size 88 , 89 , and a growing body of evidence suggests that predation also directly selects for larger brains, it has been suggested by van der Bijl and Kolm (2016) that predation may confound the SBH by causing spurious correlation between sociality and brain size 3 . The subterranean niche confers relative protection from predators and predation is not a driver of social evolution in mole-rats (see below). Therefore, we argue that low predation pressure in subterranean burrows may partly explain the lack of positive relationship between the correlates of brain processing capacity and sociality in African mole-rats.

These findings add to the series of recent papers that have reported no link between relative brain size and sociality in mammals 5 , 6 , 8 , 9 , 80 (but see 10 ) and fish 4 , 90 . However, they are in stark contrast to previous studies in primates, cetaceans, carnivores and insectivores 62 , 63 , 64 , 65 , 91 that have found a positive relationship between C R and social group size. In mole-rats, the trend goes in the opposite direction: solitary species tend to have larger C R and C R tends to correlate negatively with group size. This makes sense in light of the findings of Schillaci 92 , who reports that C R in primates correlates highly positively with body size and is not a significant predictor of group size, after controlling for body size. In other words, C R is in fact indicative of absolute brain size, and that is what drives the correlation in primates. Interestingly, a recent test of the SBH in another rodent group (ground squirrels of the tribe Marmotini) 6 , revealed that there is no link between relative brain size and sociality, but that social species tend to be larger and hence have absolutely larger brains. This relationship between body mass and sociality (and, correspondingly, the neocortex ratio) is opposite in mole-rats, and thus contrary to the SBH. Once again, these results point to a tight coupling between body size and absolute brain size. The latter seems to be generally linked with the brain’s intrinsic complexity: the proportional and absolute size of the neocortex, the number of cortical areas and the total number of cortical neurons increase with absolute brain size (for reviews, see 93 , 94 ).

The results presented here in no way challenge the existence of more subtle neurobiological differences between solitary and social mole-rats. Indeed, differences in neuropeptide receptor distributions and densities and in adult hippocampal neurogenesis were reported 95 , 96 , 97 , though only limited data on a handful of species are currently available. Likewise, our findings cannot rule out that sociality does select for larger brains in mole-rats, as all we can observe is the end result of all selective pressures and constraints put together. Some hidden factors might be confounding the results, since not enough reliable data is available on all aspects of life-history in mole-rats. However, from the information available, there does not seem to be a systematic difference in maternal investment (gestation length, litter size, weaning age) between social and solitary species 29 . Solitary species, however, are seasonal breeders, in contrast to mostly aseasonally breeding social species 31 , 32 , 33 , 34 , 35 , 36 . To our knowledge, this has not been previously linked to differences in brain size, but it is another difference that cannot be separated from sociality and deserves further investigation.

Furthermore, it is possible that solitary mole-rats are subject to selection for larger size, or that social mole-rats face some constraints on body and/or brain size that the solitary ones are free from. Factors contributing to mole-rat sociality, or lack thereof, are still not well understood, although the aridity food distribution hypothesis is currently the prevalent explanation 53 (for alternative explanations, see 27 ). Social mole-rats, generally living in harsher environments with fewer resources, may be prevented from attaining larger body (and brain) size due to the need to reduce energetic demands. Brains are metabolically expensive 98 and, simultaneously, excavating the burrow systems, especially in hard soils, carries an enormous energetic cost 99 . Lowering the metabolic demands might therefore be of utmost importance. Smaller body size and communal foraging means improving the chances of subsisting on scarce and dispersed food sources. The fact that this reduction in body size is not accompanied by an increase of relative brain size (which would result purely from decelerated brain mass reduction compared to body mass reduction) suggests that sociality does not exert enough selective pressure on brain size to outweigh these metabolic constraints. This is not to say that sociality does not act on cognitive abilities, but its importance may be more limited than generally assumed by the SBH.

To conclude, the absence of any evidence for selection acting on larger brain size or higher neuronal numbers in eusocial mole-rats, the pinnacle of cooperative breeding in vertebrates, weakens the notion that behavioural coordination or stable bonding is cognitively demanding and drives the evolution of cognitive capacity across vertebrates 13 . The fact that the challenges coupled with sociality do not entail brain enlargement or fundamental reorganization in this group resonate with an alternative view that dyadic and polyadic social interactions might not require flexible cognitive solutions in real-time, but could be solved by simpler evolved rules-of-thumb 100 . To our knowledge, there is no evidence that mole-rats engage in any Machiavellian interactions. But even if they were involved in sophisticated strategies like formation of coalitions or tactical deception, such behaviours would not increase individual fitness in species with monopolized reproduction; hence Machiavellian interactions should not effectively select for larger brains and improved cognitive abilities in eusocial mole-rats. Taken together, mole-rat sociality involves most putative drivers of cognitive abilities except for Machiavellian interactions. Therefore, our findings suggest, albeit indirectly, that Machiavellian interactions rather than social bonding and cooperation underlie the previously found link between social complexity and brain size.

Future stringent tests assessing the validity and generality of the SBH should encompass both (i) broad-scale comparative analyses incorporating various measures of social complexity as well as ecological and life-history variables including potentially confounding factors (such as appropriate proxies of predation pressure) and (ii) studies of variation in brain composition among closely related species that have similar ecologies and life-history traits but exhibit different levels of sociality. It will be equally important to direct further efforts to move from using readily measured traits such as brain size to more reliable proxies for cognitive abilities such as neuronal numbers and sizes of brain regions involved in specific behaviours. Integration of these approaches will provide deeper insights into the causal relationship between brain processing capacity and sociality.

African mole-rats (Bathyergidae) are endemic to sub-Saharan Africa. They form a monophyletic group within the rodent clade Ctenohystrica. Recently, it was suggested that the naked mole-rat Heterocephalus glaber be moved into its own family Heterocephalidae based on the time of divergence and distinctive morphological and genetic traits 101 . Since this taxonomical revision does not change the phylogenetic relationships in any way, all the species are treated here as belonging to the monophyletic family Bathyergidae.

Eleven species of African mole-rats were examined: the Cape dune mole-rat Bathyergus suillus (BS), silvery mole-rat Heliophobius argenteocinereus (HA), Cape mole-rat Georychus capensis (GC), common mole-rat Cryptomys hottentotus (CH), Natal mole-rat Cryptomys natalensis (CN), highveld mole-rat Cryptomys pretoriae (CP), Ansell’s mole-rat Fukomys anselli (FA), Mashona mole-rat Fukomys darlingi (FI), Damaraland mole-rat Fukomys damarensis (FD), giant mole-rat Fukomys mechowii (FM) and naked mole-rat Heterocephalus glaber (HG). All animals were adults and in the case of cooperatively breeding species non-reproductive individuals. Reproductive animals are usually the largest in the colony and can even substantially increase their body size after gaining reproductive status 102 , 103 , 104 , 105 . On the other hand, it is highly unlikely that reproductive status has any significant effect on absolute brain size or composition, because all reproductive animals are recruited from helpers well after reaching maturity. As reproductive individuals were not available in sufficient numbers and for all species, they were excluded from the analysis because including them could introduce a potential bias in relative brain size. Both sexes were close to equally represented (females: 52, males: 49, unknown: 4), with at least one male and one female of each species for each analysis. Animals were obtained either from colonies in the University of Duisburg-Essen, the University of South Bohemia (České Budějovice) and the University of Cape Town or wild-caught and housed at the University of Pretoria and University of Cape Town. Details on origin and use of experimental animals are provided in Table  1 .

Animals were killed by halothane overdose and perfused transcardially with heparinized phosphate-buffered saline, followed by 4% phosphate-buffered paraformaldehyde (PFA). Brains were dissected and weighed immediately after perfusion, post-fixed overnight in the same fixative, and stored in 0.5% PFA or in anti-freeze solution at −20 °C until further processing.

All experimental procedures were conducted in accordance with the Guidelines for Animal Care and Treatment of the European Union, and were approved by the animal care and ethics representatives of the Faculty of Science of Charles University in Prague, University of Duisburg-Essen and University of Pretoria (AUCC 030110-002, AUCC 040702-015 and AUCC 000418-006). Captive animals originated from breeding colonies, the maintenance of which was approved by the Veterinary Office of the City of Essen, Germany (AZ: 32-2-1180-71/328) and by Ministry of Agriculture of the Czech Republic (22395/2014-MZE 17214); wild animals were collected under permit from the relevant Nature Conservation authorities of Gauteng, Western Cape and Northern Cape Provinces, South Africa. All efforts were made to minimize animal numbers and suffering.

Given the lack of a generally accepted measure of social complexity and problems associated with even simple measures such as group size 106 , we decided to adopt a simple approach and treat sociality either as a binary variable (solitary: BS, GC, HA; social: all others), or a categorical variable with tree levels (solitary: BS, GC, HA; social: CH, CN, CP, FI; eusocial: FA, FD, FM, HG). While crude, it is not subject to intraspecific variation and research effort bias and the categories also roughly correspond to group size 24 . The categories were delimited based on reproductive skew (the number of overlapping generations). Although it remains controversial whether solitary or social life-style is ancestral for African mole-rats 27 , eusociality has evolved at least two times, once in the naked mole-rat and once within the genus Fukomys (Fig.  1a ). Social group size (see electronic supplementary material, Table  S7 ) was also used in a subset of analyses for the sake of comparison with earlier studies.

Relative brain size

A total of 106 animals were used to investigate the brain-body scaling in African mole-rats. The interspecific allometry of brain mass was determined by ordinary least square (OLS) linear regression of brain mass on body mass. Brain-body allometry at the order level (Rodentia) was used to calculate residuals for mole-rats. This relationship was based on a separate dataset of brain and body masses for rodent species (n = 414) collated from the literature (for references, see Dataset S1). The regression line is thus kept independent of the data and provides an unbiased reference.

Volumetric analysis

Forty-five brains were used to perform the volumetric analysis. Brains were embedded in gelatine blocks fixed in sucrose-paraformaldehyde solution (30% sucrose, 4% PFA) and sectioned on a cryostat in the coronal plane at a thickness of 60 µm. Every second section was mounted on a slide and stained with cresyl violet. Total brain volume and the volume of 14 distinct regions of the brain (olfactory bulbs, olfactory cortices, neocortex, entorhinal cortex, hippocampus, amygdala, striatum, septum, thalamus, hypothalamus, midbrain tectum, midbrain tegmentum, cerebellum and medulla oblongata) were determined. Contours of the brain and the measured regions were drawn from the sections using a camera lucida. These drawings were then digitized using a Wacom tablet and the areas measured using the Scion Image software. The total area of the drawn structures was multiplied by the section thickness and sampling ratio to obtain the structure volume. Final volumes were then corrected for shrinkage. The extent to which a brain shrinks during histological processing is different in each brain. To obtain comparable values, each structural volume was multiplied by a correction factor (C ind ) calculated for each brain as follows: C ind  = volume of the perfused brain/sum of serial section volumes. The volume of the perfused brain was calculated by dividing the brain mass by the fixed brain tissue density (1.036 g/cm 3 ) 107 . Note that brain mass does not change significantly within the first hours of fixation 107 . Because all brains used in this study were weighed immediately after perfusion, i.e., after very short fixation, these measurements correspond to mass/volume of fresh brain.

Isotropic fractionator

Three brains per each species (33 in total) were used for quantification of total numbers of cells, neurons and nonneuronal cells using the isotropic fractionator method 108 . Brains were postfixed in 4% PFA for at least two weeks. After fixation, brains were dissected into the following five compartments: olfactory bulbs, cerebral cortex (including the underlying white matter and comprising the neocortex, hippocampus, olfactory cortices such as piriform and entorhinal cortex, and pallial amygdala), subcortical forebrain (comprising the diencephalon, caudate putamen, nucleus accumbens, globus pallidus, ventral pallidum, olfactory tubercle and septum), cerebellum, and brain stem (comprising the mesencephalon and medulla oblongata). Each dissected brain division was homogenized in 40 mM sodium citrate with 1% Triton X-100 using Tenbroeck tissue grinders (Wheaton, Millville, NY, USA). When turned into an isotropic suspension of isolated cell nuclei, homogenates were stained with the fluorescent DNA marker DAPI, adjusted to a defined volume, and kept homogenous by agitation. The total number of nuclei in suspension, and therefore the total number of cells in the original tissue, was estimated by determining the density of nuclei in small fractions drawn from the homogenate. At least four 10 µl aliquots were sampled and counted using a Neubauer improved counting chamber (BDH, Dagenham, Essex, UK) with an Olympus BX51 microscope equipped with epifluorescence and appropriate filter settings; additional aliquots were assessed when needed to reach the coefficient of variation among counts ≤0.15. Once the total cell number was known, the proportion of neurons was determined by immunocytochemical detection of the neuronal nuclear marker NeuN 109 . This neuron-specific protein was detected by an anti-NeuN rabbit polyclonal antibody (Merck Millipore, dilution 1:800). The binding sites of the primary antibody were revealed by a secondary anti-rabbit antibody conjugated with Alexa Fluor 594 (Life Technologies, Carlsbad, CA, USA; dilution 1:400). An electronic hematologic counter (Alchem Grupa, Torun, Poland) was used to count simultaneously DAPI-labelled and NeuN-immunopositive nuclei in the Neubauer chamber. A minimum of 500 nuclei was counted to estimate the percentage of double-labelled neuronal nuclei. Numbers of nonneuronal cells were derived by subtraction.

Data analysis

All data analyses were performed in R Studio with R 3.3.2. 110 . Prior to statistical analyses data were log-transformed. For estimating the differences between social and non-social species (sociality as a fixed effect), we used Bayesian generalised linear mixed models with Markov chain Monte Carlo (MCMC) estimation in the package MCMCglmm 111 , with phylogenetic correction and multiple measurements per species taken into account as random effects. The lambda parameter was estimated for each MCMC model. This parameter potentially varies between 0, indicating that the trait evolution is independent of phylogeny, and 1, indicating that the traits are evolving according to Brownian motion on the given phylogeny, while intermediate values correspond to an effect of phylogeny weaker than under the Brownian model 112 . Mole-rat phylogeny was constructed from a published report 113 . Each model was run for 5 million iterations, with a burnin of 5000, and a thinning interval of 1000, that means approximately 5000 estimations were sampled. Convergence was confirmed by visual inspection of trace plots. Estimates of the differences between the levels of sociality were calculated from a posterior distribution created by subtracting the estimates for each level obtained during each MCMC iteration. Parameter estimates were considered statistically significant when 95% credible intervals (CI) did not include 0.

All linear regression coefficients, used to describe allometric scaling relationships, were determined by the ordinary least squares (OLS) method from species averages. For analyses of the relationship between selected brain measures and social group size, phylogenetic least squares (PGLS) method implemented in the R package nlme 114 was used with Pagel’s lambda model for scaling the phylogenetic variance-covariance matrix. Statistical significance was evaluated at α level of 0.05.

Relative sizes and indexes of cognitive power were calculated as follows: relative brain size as a residual from the brain-body mass OLS regression for 414 species of rodents, excluding mole-rats; relative volumes of brain regions as residuals from the OLS regression of the brain region volume on the whole brain volume; relative numbers of neurons as residuals from the neurons-brain mass OLS regression for mole-rats; the neuronal index as residuals from the neurons-body mass OLS regression for rodents 76 , excluding mole-rats; the cortical neurons ratio as the ratio of the number of cortical neurons to the number of brain stem neurons.

Data availability

All data generated or analysed during this study are included in this published article (and its Supplementary Information files).

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Acknowledgements

This study was supported by the Czech Science Foundation (14–2758 S, to P.N.), Grant Agency of Charles University (325515, to K.K.) and the European Social Fund and the state budget of the Czech Republic (CZ.1.07/2.3.00/30.0022, to S.O.). We thank Kate Arnold, Thomas Bugnyar and W. Tecumseh Fitch for reading of the manuscript and discussions, Heiko Frahm, Marcela Lucová and Ivana Rašpličková for their assistance with experiments. P.N. thanks Jennifer U.M. Jarvis for her hospitality during his stay in Cape Town.

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P.N., K.K., H.B. and N.C.B. designed the research; H.B., R.S., M.J.O. and N.C.B. provided experimental animals; P.N., K.K. T.B., S.O. and Z.P. collected the data; K.K. analysed the data and all authors wrote the paper.

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Kverková, K., Bělíková, T., Olkowicz, S. et al. Sociality does not drive the evolution of large brains in eusocial African mole-rats. Sci Rep 8 , 9203 (2018). https://doi.org/10.1038/s41598-018-26062-8

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The Social Brain Hypothesis and Human Evolution

Primate societies are unusually complex compared to those of other animals, and the need to manage such complexity is the main explanation for the fact that primates have unusually large brains. Primate sociality is based on bonded relationships that underpin coalitions, which in turn are designed to buffer individuals against the social stresses of living in large, stable groups. This is reflected in a correlation between social group size and neocortex size in primates (but not other species of animals), commonly known as the social brain hypothesis, although this relationship itself is the outcome of an underlying relationship between brain size and behavioral complexity. The relationship between brain size and group size is mediated, in humans at least, by mentalizing skills. Neuropsychologically, these are all associated with the size of units within the theory of mind network (linking prefrontal cortex and temporal lobe units). In addition, primate sociality involves a dual-process mechanism whereby the endorphin system provides a psychopharmacological platform off which the cognitive component is then built. This article considers the implications of these findings for the evolution of human cognition over the course of hominin evolution.

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Social Brain, Distributed Mind

To understand who we are and why we are, we need to understand both modern humans and the ancestral stages that brought us to this point. The core to that story has been the role of evolving cognition — the social brain — in mediating the changes in behaviour that we see in the archaeological record. This volume brings together two powerful approaches — the social brain hypothesis and the concept of the distributed mind. The volume compares perspectives on these two approaches from a range of disciplines, including archaeology, psychology, philosophy, sociology and the cognitive and evolutionary sciences. A particular focus is on the role that material culture plays as a scaffold for distributed cognition, and how almost three million years of artefact and tool use provides the data for tracing key changes in areas such as language, technology, kinship, music, social networks and the politics of local, everyday interaction in small-world societies. A second focus is on how, during the course of hominin evolution, increasingly large spatially distributed communities created stresses that threatened social cohesion. This volume offers the possibility of new insights into the evolution of human cognition and social lives that will further our understanding of the relationship between mind and world.

Is the Social Brain Theory Applicable to Human Individual Differences? Relationship between Sociability Personality Dimension and Brain Size

Assessing sources of error in comparative analyses of primate behavior: intraspecific variation in group size and the social brain hypothesis, the social brain.

The first discussion of a relationship between sociality and intelligence came in the middle of the twentieth century, especially by Humphrey who suggested that living socially demanded intellectual abilities above and beyond those required by an animal’s ecology. This led to the Social Intelligence Hypothesis, and then the Machiavellian Intelligence Hypothesis, both proposing that sociality was the main driver of the superior intellect of primates, especially humans. Two key challenges for this hypothesis are that sociality is difficult to quantify and cognition is not well tested by problem solving. More importantly, as data from more species have been examined, the analyses increasingly fail to show that sociality explains variation in brain size, even in primates. I conclude that appealing as this hypothesis is, it does not do a very compelling job of explaining variation in brain size.

Theory of mind in autism, schizophrenia, and in-between

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Understanding primate brain evolution

We present a detailed reanalysis of the comparative brain data for primates, and develop a model using path analysis that seeks to present the coevolution of primate brain (neocortex) and sociality within a broader ecological and life-history framework. We show that body size, basal metabolic rate and life history act as constraints on brain evolution and through this influence the coevolution of neocortex size and group size. However, they do not determine either of these variables, which appear to be locked in a tight coevolutionary system. We show that, within primates, this relationship is specific to the neocortex. Nonetheless, there are important constraints on brain evolution; we use path analysis to show that, in order to evolve a large neocortex, a species must first evolve a large brain to support that neocortex and this in turn requires adjustments in diet (to provide the energy needed) and life history (to allow sufficient time both for brain growth and for ‘software’ programming). We review a wider literature demonstrating a tight coevolutionary relationship between brain size and sociality in a range of mammalian taxa, but emphasize that the social brain hypothesis is not about the relationship between brain/neocortex size and group size per se ; rather, it is about social complexity and we adduce evidence to support this. Finally, we consider the wider issue of how mammalian (and primate) brains evolve in order to localize the social effects.

Coevolution of cultural intelligence, extended life history, sociality, and brain size in primates

Explanations for primate brain expansion and the evolution of human cognition and culture remain contentious despite extensive research. While multiple comparative analyses have investigated variation in brain size across primate species, very few have addressed why primates vary in how much they use social learning. Here, we evaluate the hypothesis that the enhanced reliance on socially transmitted behavior observed in some primates has coevolved with enlarged brains, complex sociality, and extended lifespans. Using recently developed phylogenetic comparative methods we show that, across primate species, a measure of social learning proclivity increases with absolute and relative brain volume, longevity (specifically reproductive lifespan), and social group size, correcting for research effort. We also confirm relationships of absolute and relative brain volume with longevity (both juvenile period and reproductive lifespan) and social group size, although longevity is generally the stronger predictor. Relationships between social learning, brain volume, and longevity remain when controlling for maternal investment and are therefore not simply explained as a by-product of the generally slower life history expected for larger brained species. Our findings suggest that both brain expansion and high reliance on culturally transmitted behavior coevolved with sociality and extended lifespan in primates. This coevolution is consistent with the hypothesis that the evolution of large brains, sociality, and long lifespans has promoted reliance on culture, with reliance on culture in turn driving further increases in brain volume, cognitive abilities, and lifespans in some primate lineages.

Why Humans Aren’t Just Great Apes

Although we share many aspects of our behaviour and biology with our primate cousins, humans are, nonetheless, different in one crucial respect: our capacity to live in the world of the imagination. This is reflected in two core aspects of our behaviour that are in many ways archetypal of what it is to be human: religion and story-telling. I shall show how these remarkable traits seem to have arisen as a natural development of the social brain hypothesis, and the underlying nature of primate sociality and cognition, as human societies have been forced to expand in size during the course of our evolution over the past 5 million years.

When Individuals Do Not Stop at the Skin

This chapter examines contemporary hunter-gatherer societies in Africa and elsewhere in light of the social brain and the distributed mind hypotheses. One question asked is whether African hunter-gatherers offer the best model for societies at the dawn of symbolic culture, or whether societies elsewhere offer better models. The chapter argues for the former. Theoretical concepts touched on include sharing and exchange, universal kin classification, and the relation between group size and social networks. The chapter offers reinterpretations of classic anthropological notions such as Wissler's age-area hypothesis, Durkheim's collective consciousness and Lévi-Strauss's elementary structures of kinship. Finally, the chapter outlines a theory of the co-evolution of language and kinship through three phases (signifying, syntactic and symbolic) and the subsequent breakdown of the principles of the symbolic phase across much of the globe in Neolithic times.

Absolute, not relative brain size correlates with sociality in ground squirrels

The social brain hypothesis (SBH) contends that cognitive demands associated with living in cohesive social groups favour the evolution of large brains. Although the correlation between relative brain size and sociality reported in various groups of birds and mammals provides broad empirical support for this hypothesis, it has never been tested in rodents, the largest mammalian order. Here, we test the predictions of the SBH in the ground squirrels from the tribe Marmotini. These rodents exhibit levels of sociality ranging from solitary and single-family female kin groups to egalitarian polygynous harems but feature similar ecologies and life-history traits. We found little support for the association between increase in sociality and increase in relative brain size. Thus, sociality does not drive the evolution of encephalization in this group of rodents, a finding inconsistent with the SBH. However, body mass and absolute brain size increase with sociality. These findings suggest that increased social complexity in the ground squirrels goes hand in hand with larger body mass and brain size, which are tightly coupled to each other.

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The social brain hypothesis and its implications for social evolution

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• 1 Institute of Cognitive & Evolutionary Anthropology, University of Oxford, Oxford, UK. [email protected]
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• DOI: 10.1080/03014460902960289

The social brain hypothesis was proposed as an explanation for the fact that primates have unusually large brains for body size compared to all other vertebrates: Primates evolved large brains to manage their unusually complex social systems. Although this proposal has been generalized to all vertebrate taxa as an explanation for brain evolution, recent analyses suggest that the social brain hypothesis takes a very different form in other mammals and birds than it does in anthropoid primates. In primates, there is a quantitative relationship between brain size and social group size (group size is a monotonic function of brain size), presumably because the cognitive demands of sociality place a constraint on the number of individuals that can be maintained in a coherent group. In other mammals and birds, the relationship is a qualitative one: Large brains are associated with categorical differences in mating system, with species that have pairbonded mating systems having the largest brains. It seems that anthropoid primates may have generalized the bonding processes that characterize monogamous pairbonds to other non-reproductive relationships ('friendships'), thereby giving rise to the quantitative relationship between group size and brain size that we find in this taxon. This raises issues about why bonded relationships are cognitively so demanding (and, indeed, raises questions about what a bonded relationship actually is), and when and why primates undertook this change in social style.

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What Does “the Social Brain” Really Mean?

The term can be misleading, but it speaks to the importance of social cognition..

Posted March 16, 2020

Time and time again, I hear the term the social brain when people talk about social cognition . It’s a catchy phrase that captures how important social behavior is to the brains of social species, like us. However, this catchy terminology can also be misleading.

I try not to use it, but sometimes I give in to its efficiency in communicating that a vital function of the brain in social species is regulating social behavior. Essentially, the social brain suggests that there is a set of specialized brain regions for computing socially relevant information. In this blog post, I’ll unpack this and along the way hopefully convince you that this term can easily create and maintain a distorted view of how the brain computes social cognition.

The main concept underlying the social brain initially came from observing that lesioning certain areas of the brain leads to substantial impairments in social behavior. This concept has since been reinforced by many functional neuroimaging (fMRI) studies in people. The social brain has become a popular term to capture the important involvement of several of these brain regions in mediating social behavior. Largely, the social brain generally refers to the temporal parietal junction, posterior superior temporal sulcus, medial prefrontal cortex, anterior cingulate cortex, amygdala, and other regions implicated in social functions. These brain areas are reliably engaged (more so than other areas) when we are thinking about others, interacting with them, putting ourselves in their shoes, deciding to help them, cooperating or competing with them, and so on. Basically, these are the basic elements of being human.

However, the imprecision of the term is paramount. The mere fact that these brain areas are consistently activated when we interact socially does not mean that they are really specialized for social behavior. Instead, it is just as likely that these brain areas are driven by complex cognitive operations, such as inferential reasoning, predictive thinking, and reward valuation, that also happen to underlie core aspects of social functions. If one were to examine how nerve cells convey information by emitting electrical signals in one of these brain areas during social and non-social behaviors, we would likely find an immensely wide range of neural signals, ranging from non-social to social information, and everything in between.

Moreover, establishing the selectivity of a brain region to social over non-social computations is inherently complicated by the method used. For example, if we were to identify brain areas that show greater activation for social (compared to non-social) events, certain areas might appear to be selective to social functions. But if we were to zoom in to the level of ion channels necessary for nerve cells to send electrical signals in one of these brain areas, it would be humorous to consider whether such ion channels are selective to social functions – they’re not. Labeling brain areas as selective to social stimuli, then, would essentially mean ignoring the fact that the molecular and cellular, and sometimes even circuit-level building blocks of these brain areas are commonly shared in both social and non-social domains. How, then, could we claim functional specialization of these areas to social functions?

So, what is the real value, if any, in using the expression the social brain when we talk about social cognition in the brain? I think the merit behind this term is the emphasis on the importance of social interaction in our daily lives. Many central cognitive functions of the brain, such as valuation of rewards, emotion , and executive control, frequently take place in social contexts. In highly social species, the most common environment in which our brain does its work is a social environment. I would argue that the brains of highly social primate species, like us, are built to navigate and increase survival fitness in their social environments. Many brain regions may belong to the social brain by virtue of our brains being fine-tuned over evolution to operate in social settings. This point references Dr. Robin Dunbar’s famous Social Brain Hypothesis, where the term “social brain” is used to associate brain size with social complexity in primates and signifies the importance of social engagement in shaping primate brain evolution. Perhaps it really is true that one cannot understand the brain without taking into account its long evolutionary history.

Steve W. C. Chang, Ph.D., is an assistant professor of Psychology and Neuroscience at Yale University. He studies the neurobiology underlying social interaction.

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Is attachment theory actually important for romantic relationships?

by Marissa Nivison, Sheri Madigan, The Conversation

There has been a recent surge of attention toward attachment theory: from TikTok videos to online quizzes that claim to "assess your attachment style." It's become a hot topic, especially in the context of romantic relationships, with some articles claiming that one person (or partner's) attachment styles are the reason why relationships fail.

As experts in developmental and clinical psychology focusing on attachment theory, we seek to provide an accessible resource to better understand the science of attachment, and what it means for one's romantic relationships.

What is attachment?

Attachment theory stems from the field of developmental psychology. It is the notion that in the first year of life, the ways in which a parent and caregiver respond to a child's needs shape a child's expectation of relationships across their lifespan.

In research, attachment has been associated with well-being across the lifespan including: mental and physical health, brain functioning and even romantic relationships .

How is attachment related to romantic relationships?

Among professionals in the field, there is diversity in perspectives regarding how attachment relates with romantic relationships. As developmental psychologists, we tend to think that attachment is associated with romantic relationships through what we call the " internal working model ."

In childhood, when a parent is consistent and responsive in tending to their child, the child learns that their parent can be counted on in times of need. These expectations and beliefs about relationships are then internalized as a blueprint, sometimes in popular media referred to as a " love map ." Just like how an architect uses a blueprint to design a building, a child's attachment to their parents provides a blueprint for understanding how to approach other relationships.

Based on this blueprint, people develop expectations of how relationships should work, and how other important people in their life, including partners, should respond to their needs.

Sometimes attachment is also described in terms of attachment "styles." There are two overarching types of attachment: secure and insecure . Those with a secure attachment style tend to have expectations that their attachment figures (and later, partners) will be responsive, sensitive and caring in times of distress. People with secure "blueprints" find it easier to build new structures (i.e., relationships) with the same design.

People with insecure blueprints—such as disorganized, avoidant or anxious attachment styles—may face relationship challenges when their current relationship doesn't align with their childhood experiences , and may need to renovate their blueprint design together with their partner.

Whether you think about attachment as a style or a love map, they both are related to expectations of relationships, which are shaped by past experiences.

In research we see that people who had consistent, reliable and sensitive parents are more likely to have more positive relationships—including friendships , teacher-child relationships and yes, romantic relationships too .

Relationships with parents and relationships with partners

Although we do see in research that better childhood relationships are associated with better romantic relationships, there is still a large part of the population who have good relationships with partners, despite their history of lower-quality relationships with their parents.

It is possible for romantic relationships to serve as a "healing relationship" and improve one's own internal working model of relationships. Specifically, when a partner is consistently sensitive, responsive and available, a person may begin to adjust their blueprint and develop new expectations from relationships. Attachment theory consistently supports the idea that one's patterns of attachment can change .

So, all in all, the answer is no: Your relationship with your parents influences but does not determine the quality of your romantic relationships.

Is attachment the reason why my relationships don't work out?

It is possible that your expectations of a romantic relationship may not align with the expectations of your partner, and may affect the quality of the relationship. For example, sometimes individuals with insecure attachments may withdraw when they are upset, but their partner who has a secure attachment may be upset that their partner is not coming to them for comfort.

Thinking through your own attachment history and expectations of relationships may be a great opportunity for self-reflection , but it is important to remember that attachment is only one part of a relationship. Communication, trust and respect, to name a few, are also critically important aspects of a relationship.

Can I improve my attachment expectations?

The short answer: Yes. Improving attachment quality has been one of the cornerstones of attachment theory and research since its conception. Most commonly, attachment is targeted in childhood through interventions , but also in adulthood through individual therapy, or various forms of couples therapy, such as emotionally focused therapy or the Gottman method .

It is also possible that through positive relationships you may be able to improve your own expectations of relationships. There are many different avenues to explore, but improvement is always possible.

In sum, attachment can be an important factor in romantic relationships , but it is not a "catch-all" to be blamed for why relationships may not work out. Thinking about your own expectations for relationships and talking through those with your partner may do great things in improving the quality of your relationships.  

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• Philos Trans R Soc Lond B Biol Sci
• v.371(1693); 2016 May 5

What does the interactive brain hypothesis mean for social neuroscience? A dialogue

Hanne de jaegher.

1 Department of Logic and Philosophy of Science, IAS-Research Centre for Life, Mind, and Society, University of the Basque Country, Av. De Tolosa 70, 20018 San Sebastián, Spain

2 Department of Informatics, Centre for Computational Neuroscience and Robotics, and Centre for Research in Cognitive Science, University of Sussex, Brighton, UK

Ezequiel Di Paolo

3 Ikerbasque, Basque Foundation for Science, Bilbao, Spain

Ralph Adolphs

4 Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA

5 Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA

6 Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA

A recent framework inspired by phenomenological philosophy, dynamical systems theory, embodied cognition and robotics has proposed the interactive brain hypothesis (IBH). Whereas mainstream social neuroscience views social cognition as arising solely from events in the brain, the IBH argues that social cognition requires, in addition, causal relations between the brain and the social environment. We discuss, in turn, the foundational claims for the IBH in its strongest form; classical views of cognition that can be raised against the IBH; a defence of the IBH in the light of these arguments; and a response to this. Our goal is to initiate a dialogue between cognitive neuroscience and enactive views of social cognition. We conclude by suggesting some new directions and emphases that social neuroscience might take.

1. Introduction

In the context of recent advances in social neuroscience, particularly the availability of methods for investigating brain activity in complex situations, including live interaction, novel research questions emerge. Do brains in interaction function the same way as in non-interactive situations? Are social interactions simply more complex scenarios involving more dynamical kinds of processing, but not essentially different from non-interactive cases? Or do live social engagements between people engender novel phenomena, which prompt us to reconsider brain function?

A recent proposal strongly vouches for the last option: brains work differently in social interactive situations. And, moreover, the dynamics of the interaction itself play important roles in cognitive function [ 1 ].

We investigate some implications of this view by raising critical questions. This will take the form of a dialogue between the authors of this paper—not to settle the issues or to iron out wider conceptual disagreements once and for all, but to progress, if not to a final common ground, then hopefully to some useful inroads into it.

2. The interactive brain hypothesis and how social neuroscientists should view it (H.D.J. and E.D.P.)

Two of us (H.D.J. and E.D.P.) argue that embodiment and interaction are partly, but fundamentally, constitutive of social cognition. This view is captured by the interactive brain hypothesis (IBH): ‘The IBH … proposes that social interaction processes play enabling and constitutive roles in the development and in the ongoing operation of brain mechanisms involved in social cognition, whether the person is engaged in an interactive situation or not ’ ([ 1 ], p. 2). We use here the terminology introduced in [ 2 ]: an enabling factor is causally necessary for a phenomenon to occur, while a constitutive factor is part of what makes the phenomenon what it is. While a hypothesis rather than a scientific claim, the IBH stimulates a novel perspective on how social neuroscientists should construe information processing that generates social behaviour. The unit of analysis is no longer delimited to the brain, but broadened to include aspects of the social environment with which the brain interacts or has interacted.

There is a range of hypotheses about the role that embodiment and social context play in social cognition. The weakest claim is that social interaction is methodologically useful in social neuroscience, as it provides ecological validity and engages research participants. Another weak claim is that social interaction needs to be considered as providing important contextual modulation of the social brain. To our knowledge, nobody disagrees with these claims. They are now actively pursued in social neuroscience, as evidenced in the other contributions in this issue; we do not further treat them here.

A stronger claim is that social interaction facilitates particular kinds of brain processes: that is, there is a strong enabling role for social interaction [ 2 ]. For instance, it is well known that development in the absence of social interaction (severe social deprivation, in humans or other mammals) results in a highly abnormal brain with highly abnormal cognition [ 3 ]. This fact also suggests important constraints on the design of artificial cognitive systems. To our knowledge, this claim is also uncontentious. We do not discuss this enabling aspect of social cognition here either.

The hypothesis we discuss here concerns an occurrent instance of social cognition. We claim that a normal adult human brain in isolation is insufficient for a typical instance of social cognition. Of course, we acknowledge that the brain plays a large, and probably major, role in social cognition. 1 But processes occurring in that brain at the time of an instance of social cognition are, in the typical case, not fully constitutive of social cognition: additional events are also required. Those additional events involve relations between the brain and (parts of) the rest of the world. It is important to note that we are not claiming that events external to the brain, in isolation, make a determining contribution. It is the relations between such external events, the body, and the brain that matter, or rather, it is only within these relations, which are not merely contextual, that we can make sense of brain function during most social cognition. In such cases, there is no factoring out of such extra-brain elements without removing at the same time something essential to social cognition as such.

What are those instances of social cognition where we expect extra-neural relational patterns to play a constitutive role? Certainly, those involving direct interaction with others. But also those instances involving the presence of others to various degrees (physical, virtual, etc.) and which predispose us to engage interactively, even if we do not or cannot actualize such dispositions.

Consider an example where, we argue, social interaction plays a constitutive role in the performance of a social task. This is the perceptual crossing experiment by Auvray et al. [ 4 ] ( figure 1 ). Two blindfolded participants are told to freely move a sensor (a computer mouse) along a shared virtual line. In this virtual ‘world’, each participant can encounter two different kinds of objects, one fixed in space and two moving objects. One of the moving objects corresponds to the scanning sensor of the other participant, and the other is attached to this sensor at a fixed distance, like a ‘shadow’. In terms of their trajectories, the moving objects are indistinguishable. Whenever her sensor encounters an object on this line, the participant receives an on/off tactile stimulus—a tap on the finger, which is the same for each kind of object. This situation is symmetrical for both participants. Note that when a participant's sensor encounters the shadow of the other participant, only the first participant will receive a tactile stimulus. When the two sensors meet, both participants receive a stimulus simultaneously.

Set-up for perceptual crossing experiment. Both participants are isolated, each controlling the position of a sensor along a shared virtual one-dimensional line using a computer mouse. The squares on each side of the line represent the objects that can be sensed by each participant, respectively. Objects are identical in size. When the sensor touches an object the participant gets a tactile feedback on the finger (green circle). Each participant can sense only three objects, a static one (black square), the sensor of the other participant (red square) and a ‘shadow’ object that copies exactly the movement of the other's sensor at a fixed distance (blue square). (Copyright © 2010 De Jaegher et al. [ 2 ]. Licenced under Creative Commons Attribution 3.0 Unported, http://creativecommons.org/licences/by/3.0 ).

Participants are instructed to click the mouse button whenever they judge they are in contact with the other participant. As a result, statistically, mouse clicks tend to concentrate on each other's sensors (65.9% of clicks) and not on the identically moving shadow objects (23%). This means that participants can find each other by ‘perceptually crossing’ their scanning activities. However, the authors find that the probability of clicking following a stimulus is approximately the same whether this stimulation comes from the other's sensor or its shadow. Auvray et al . explain this result in terms of the collective dynamics of the interactive configuration. Each participant attempts to re-scan many times an object that is apparently moving, by making back and forth mouse movements. When this scanning involves an encounter between both sensors, the participants tend to continue this activity for a long time. By contrast, when one of them is scanning the other's shadow, which is moving independently of this scanning movement, this ‘encounter’ is short-lived. This means that the dyadic system is organized such that sensor–sensor encounters are more frequent than sensor–shadow encounters, which explains why participants can ‘find each other’ in spite of the sensory ambiguity [ 2 ]. For this reason, we consider that the interaction process here plays a constitutive role in how the social task is organized and performed.

As this and other examples show, we cannot always assume that social interactions are mere inputs to cognitive systems, but rather, in general, we need to consider as relevant both individual and interactive mechanisms from the start [ 2 ]. The IBH proposes that social interactions are at the basis of social skills more often than usually assumed. Therefore, the point of the IBH is to provide a research guide to specify which social events and social relations, as well as what kind of brain activity, matter and how, to particular instances of social cognition. As methodologies for investigating brain activity during free interactions continue to develop, it is necessary to theorize about these questions, especially as we approach everyday situations involving emergent collective patterns, jointly authored actions, and multiple brains and bodies in coordination. With the IBH we question the tacit assumption that the best way to approach this challenge is to try to understand brain function in isolation by assigning to all extra-neural variation a role exclusively as inputs or outputs. Instead, we propose to leave open to investigation the conditions under which this assumption may be valid as a limiting case.

3. Arguments against the interactive brain hypothesis (R.A.)

The other one of us (R.A.) disagrees with the above view because it seems to disregard a natural partition to causal interactions in the world. That is, there is a much more direct and dense set of causal interactions internal to the brain, than between brain and external environment. Disregarding this fact leads to a concern that the IBH renders unclear the specific role of social neuroscience (as opposed to social psychology or sociology or behavioural studies of crowd behaviour) in explaining social behaviour.

In order to understand cognition, we need to partition cognitive systems. In particular, we need to partition them into those parts that should be analysed as inputs to the system, those that are the outputs from the system, and those that are actually implementing the cognition. We do the same thing with computers running programs: there is a causal interaction with the world that can be treated as input, there is processing internal to the computer, and there is causal interaction with the world that can be treated as output.

The IBH follows the more dynamical systems view that much of situated cognition has adopted [ 5 , 6 ], and claims that this partitioning does not reflect how cognition actually takes place in the world. Unlike the classical computer metaphor, there is a more or less continuous stream of causal interaction between brain and the world, and, in the case of the social world, ‘outputs' from a brain influence ‘inputs' (i.e. one person influences another) in such a tightly coupled way that it becomes impossible to distinguish input from output. Instead, say advocates of the IBH, one should treat the whole system (two or more people interacting) as a single, dynamically coupled system. Cognition is constituted by the events in my brain, the events in the other person's brain, and the causal relations between them: the whole system matters.

I am sympathetic with what motivates the IBH, and of course I agree that the classical computer metaphor is inadequate. Indeed, modern cognitive neuroscience acknowledges that much of cognition is ‘active’: we continuously move our eyes to redirect visual input [ 7 ], we continuously shift attention to redirect what information is processed, we continuously interact with the environment, especially in the case of a social encounter [ 8 , 9 ]. Current enthusiasm about Bayesian or predictive coding approaches [ 10 , 11 ] reflects this acknowledgement. But I am confused about how and why one would need to adopt the IBH, as opposed to one of its weaker forms, to incorporate these facts. To make my confusion more transparent, consider three properties of a person that we might want to understand: their observable behaviour, their cognition and their conscious experience. Let's consider these in the light of an experiment that IBH advocates have mustered: the experiment by Auvray et al . [ 4 ], discussed above (cf. figure 1 ). In this study, participants find each other's sensors, even though they themselves appear unaware of whether they are finding a sensor or a shadow. One could quibble about various aspects of this example as a good example of the IBH in action (it is not particularly ‘ecologically valid’; the fact that the subjects cannot explicitly distinguish sensor from shadow does not show that their brains are not representing this distinction, just unconsciously; etc.), but let us take it as an example nonetheless. Now the question is: what exactly does this experiment show? It shows that coupled causal interactions between two people are required to explain something—what is that? As far as I can tell, it is only a certain aspect of behaviour. Yes, the behaviour of the system cannot be explained only by events in individual brains. Me riding a bicycle also cannot be explained only by events in my brain. Much of our behaviour comes about through complex causal interactions between our brains and the world, and social behaviour is no exception.

Now, to see the limits of this example, ask yourself what the answer would be with respect to conscious experience. Is the coupled system of two people interacting supposed to be aware of the distinction between sensors and shadows? Surely not. One good reason is that whatever it is about the system that is generating the behaviour of the system under consideration here seems far too meager an example of processing to count as cognition. The two people's brains in the experiment are each processing information so as to generate cognition and conscious experience. The entire system generates a unique behaviour, but that is it. There is not in addition any kind of collective ‘cognition’ generated for the same reason that there is not in addition any kind of collective consciousness generated (intuitions here may of course diverge; see [ 12 – 14 ]). The reason is that the causal interactions at the systems level that explain the behaviour are far too thin to constitute cognition. Cognition requires an extraordinarily dense and complex set of causal interactions that are part of an extensive processing architecture. Whatever exactly one's view on what cognition is, it is far more than a reflex, far more than a fixed action pattern, and instead is a highly inferential, context-dependent and flexible form of information processing. So far as we know, only the brains of certain animals can generate examples of it. The causal interactions between those brains and the rest of the world are simply too ‘thin’.

This brings me to my core objection against the IBH as a hypothesis about cognition: in widening the causal base of cognition, it negates a distinction that is critical to understand cognition, the distinction between those causal events internal to the brain, and those constituting a brain's relations with the rest of the world. This distinction is huge. A brain's 80 billion or so neurons, or a much larger number of compartments of those neurons (opinions vary on what to consider the basic processing units in the brain) all causally interact, at multiple time scales. A single cortical neuron gets input from perhaps 10 000 other cells and participates in networks at local and global scales. Needless to say, we do not understand exactly how information is processed in the brain, but clearly it depends on very dense, very complex, sets of biological causal interactions between networks of cells in the brain. By comparison, the path of causal inputs to the brain (or outputs from it to the world) is extremely sparse. There are only a million axons from the eye going into the brain. There are many more axons between processing stages deeper in the brain (indeed, there are more axons from higher-level brain regions back down to lower-level regions, such as the ‘feedback’ from visual cortex to thalamus, than in the opposite ‘feed-forward’ direction). In the auditory system, there are only about 3000 cells that transduce sound in each ear. Yet from this, through considerably more complex processing internal to the brain, we can hear music. Inside the brain there is cognition and conscious experience. Outside the brain there are causal relations, and indeed some of those causal relations can be fairly complex and reciprocal. But they are not part of the brain's computations and they are also not constitutive of cognition.

Indeed, cognition does not require concurrent causal interaction with the world at all: we can think, calculate the product of two numbers and generate images with our eyes closed in a quiet room, or while dreaming. Moreover, a lot of such internal cognition is social: we think and dream about other people all the time. All of the occurrent causal events that constitute such examples of cognition must be limited to what happens inside our skulls (or perhaps also our bodies). The IBH deals with this problem by including in its substrate for cognition not only those causal relations occurring between brain and the world at the time of the cognition, but also relations between brain and the world that happened in the past. This has always struck me as a rather desperate move that brings us back to how we began this section. Yes, of course, cognition depends on the history of causal interactions with the world. Had my causal history been very different, my cognition would also be very different. But the reason for this difference should be apparent: the only mechanism by which my cognition could be changed in the light of a different causal history is through the brain. Change my causal history, you change the brain and hence cognition. All this shows is that causal history is one particular kind of ‘input’ to the brain over time (albeit one that might ultimately ground what it is that the brain's representations are about; see the concluding section).

It may well be that ‘causal density’ as I have described it above is just a proxy for another property that is more fundamental to cognition. Perhaps it is computational complexity. Perhaps it is something that requires much more clarification, like ‘ownership’ for a person. Perhaps it is something like ‘manipulability’ that could ground our concept of causation; one could imagine manipulability as experimental manipulability by us, or as biological manipulability in terms of what is accessible to evolution or development. Much more debate will be needed to develop arguments for any of these, but for present purposes causal density serves as a simple and intuitive metric.

There is no question that there are collective social phenomena that emerge from causal relations between multiple people and their shared environment. Group behaviours, politics and the stock market are all examples of this. Each has its proper domain of study required to explain phenomena that emerge at those macroscopic levels. Disciplines like political psychology and economics tackle that. None of these truisms, however, challenge the neuroscience of social cognition: the proper domain of study to explain social cognition is the individual brain. The social neuroscientist does not also need to be studying the stock market. Even if the stock market were a cognitive system (unlikely in my view), then this still does not undermine the study of individual brains to understand social cognition. If eventually we engineer a computer so advanced that it has cognition, we would not also need to understand the cognition happening inside the brains of the people who built that computer. The reasons for the distinctions in all these examples are the same: they are just separate systems. Perhaps there is stock market cognition, AI and human cognition. I can study them individually, and add chimpanzee cognition and pet dog cognition. What is important is to partition the world into systems, the internal constituents of which interact in ways that do not require also knowing how they interact with the rest of the world. Our understanding of the world requires such partitioning, and the disciplines that have arisen to explain how the world works reflect those partitions. The cognitive holism that the IBH envisions erases real distinctions and, if carried through all the way, would make understanding cognition intractable because it is everywhere.

4. In defence of the interactive brain hypothesis (E.D.P. and H.D.J.)

Let us consider the IBH at its most radical: the claim that the dynamics of social interaction play constitutive roles in social cognition. The developmental version seems less controversial, although its implications are not trivial (see e.g. [ 15 ]). In fact, for any environmental factor to developmentally shape the function of brain processes, it cannot be systematically just an informational input. To play an informational role strictly requires the stationary functional context of the system for which a signal serves as an input. Hence, the developmental IBH also necessitates the possibility of interaction dynamics playing more than just informational roles.

Turning to the constitutional version of the IBH, we first must stress what the claim is. We defend that the dynamical processes involved in social interactions, which implicate not just extra-neural processes but also relational processes between participants (and their surroundings) can be a constitutive part of the processes of social cognition as they are enacted by the individual participants involved [ 1 , 2 ]. The strong version of the IBH simply hypothesizes that this possibility is, in general, a widespread plausibility . This can be criticized in two ways: the extension from possibility to plausibility is not empirically warranted, or the very claim of possibility is wrong. The criticisms of the previous section are centred on the second option. If this possibility claim is wrong, then the constitutive version of IBH falls with it and only the developmental version remains.

We discuss three aspects in support of the constitutive claim: (i) the non-decomposability of neural and extra-neural processes during interaction, (ii) the functional role of interaction dynamics, (iii) the irreducibility of interactive phenomena such as meaning generated in social interaction and the co-authorship of interactive acts.

(a) Entanglement

The brain-internal causal density argument discussed above seems compelling only if we assume that brains are ‘nearly decomposable’ systems [ 16 ] with respect to body and environment. Nearly decomposable systems interact with other systems without losing their functionality or altering significantly their internal causal relations. Considering the brain in this way means to treat its couplings with body and environment as inputs. There are solid arguments against the disposability of body and environment for normal brain function. Some are based on the abundant evidence of the entangled neural, body and environmental dynamics in a wide range of cognitive performance [ 17 ]. A more conceptual argument is Thompson and Cosmelli's critique of the brain-in-the-vat thought experiment [ 18 ]. They argue that it is inconceivable for a brain to retain its functionality if separated from body and the world.

We could assume that the causal support given by body and environment does not constrain neural function and so, at least functionally, the brain could be considered independent. But even in such a case, we cannot infer near-decomposability from the evidence of inner causal density alone. We must also demonstrate that inner processes are not dominated, shaped or regulated in their function by external processes, i.e. that coupling with the world does not involve nonlinear interactions across a significant range of timescales. In short, the inner complexity of the brain, which is of course undisputed, is not a deciding factor between the two interpretations discussed here: interactional processes as input versus interactional processes as constitutive of social cognitive function.

Consider the evidence of the entanglement of brain and interaction dynamics observed in dual-scanning experiments [ 19 ]. According to Simon ([ 20 ], p. 204) a nearly decomposable system ‘[separates] the high-frequency dynamics of a hierarchy—involving the internal structure of the components—from the low-frequency dynamics—involving interactions among components’. But this precisely is not the case during inter-brain synchronization in live interactions. Using dual electroencephalogram (EEG) scanning during an imitation task with interactors visibly moving their hands freely and allowing spontaneous synchrony and turn-taking, Dumas et al . [ 21 ] found inter-brain phase synchronization in the alpha–mu (8–12 Hz), beta (13–30 Hz) and gamma (31–48 Hz) bands. How can social interaction affect neural oscillation phase in two distinct brains at frequencies more than one order of magnitude faster than the interactive movements?

Leaving aside the question of what role (if any) might be played by such cross-scale synchronization, the evidence suggests that interaction patterns produce an entanglement between the brains of the participants. Internally, the wave of influence across various temporal and spatial scales may travel from low to high frequencies via variations in neuronal excitability [ 22 – 25 ]. These top-down effects, evidenced also in arrhythmic cross-frequency couplings [ 26 ], have been associated with different cognitive phenomena, notably with the control of visual attention [ 27 – 29 ]. From here it is not a big leap to suggest that inter-brain synchronization at high frequencies [ 21 , 30 , 31 ] is due to high-to-low frequency integration and low-to-high frequency enslavement, with the difference that, instead of slow neural oscillations, the processes ‘at the top of the hierarchy’ are the emergent rhythms of social interaction. This seems the simplest interpretation of the data, not the only possible one. But until disproven, it is not a bad idea to follow Occam's advice.

This interpretation is in line with calls to investigate the braided coordination of neural, behavioural and social processes [ 32 , 33 ]. It also coheres with cumulative evidence of the brain–body as an interaction-dominant system (the opposite of a nearly decomposable one), based on findings of correlations of neural and behavioural variability across a wide range of time scales [ 34 , 35 ]. Interaction-dominant systems are characterized by the causal inextricability of the various processes involved, as well as the unpredictability of the behaviour of the whole from knowledge of the parts in isolation. Evidence of interaction-dominance has also been found to involve extra-neural factors, e.g. in agent–tool systems [ 36 ] and during social interaction [ 37 – 40 ].

In view of this evidence, our suggested explanation of multiscale inter-brain synchronization engendered by emergent interaction patterns seems plausible. This allows us to make two points. The first, which is negative, is that this evidence casts doubt on the causal density argument against the IBH. Indeed, it would seem that at least under some conditions, brain, body and interactive activity are under mutual causal influence, despite (or thanks to) the density of causal linkages in the brain. The second, neutral point raised by entanglement is that if social interaction can have such an influence on brain activity, then it is clearly possible that the interactive influence on brain dynamics during instances of social cognition is of a functional kind. To this positive possibility we turn next.

(b) Functional roles for social interaction

Evidence of entanglement suggests that we should discard the view of interaction patterns as mere inputs to compartmentalized brain processes. But it does not yet say whether this more complex picture is sufficient to warrant the interpretation that interaction dynamics can be constitutive of the functional aspects of social cognition. What kind of cognitive ‘work’ could be done by social interaction? This question cannot be answered in general terms. Each case will merit its own response. But at least in some cases we can provide a story. This is the importance of experiments like perceptual crossing, mentioned before [ 4 ]. In it the ecological situation is maximally simplified without eliminating a key factor: the free control of the social interaction dynamics by the participants. We do not think that this is an example of ‘just behaviour’, if by this is meant that no sense-making is involved. It is a powerful exemplar that, thanks to its simplicity, can help us think differently about individual and interactive processes in more complex cases.

The perceptual crossing task is anything but simple. It is only after the performance has been explained that it appears so. In fact, described in strict computational terms, it is a highly ambiguous, type II problem [ 41 ], i.e. a problem where stimuli must be actively discriminated spatially and qualitatively using only temporal and proprioceptive cues (all ‘objects' found in the virtual space produce the exact same on–off tactile stimulation). The task set to the participants is no less complex than typical discrimination tasks. In fact, it is untypically difficult, because the two moving objects that the participant can encounter (the other participant's sensor and shadow) move identically. Distinguishing them would require, from an individual perspective, not only a complex strategy for testing socially contingent reactions in these objects but also measuring these reactions in a highly ambiguous sensory space.

The fact that this computationally tough problem can be resolved with relative ease in the presence of interactive dynamics does not make this too meager an example of social cognition. That its difficulty deflates dramatically once we understand the collective dynamics is precisely the theoretically pregnant point of the experiment.

The type II regularities in the sensory signals that could help distinguish sensors from shadows are statistically invisible in the absence of a systematic sampling strategy. One way to solve the task is to implement a strategy that successfully transforms type II signals into type I data, i.e. into non-relational and unambiguous inputs [ 41 ]. A type I signal by itself contains enough information to determine the next course of action towards the resolution of the task. This route towards solving the task involves a biased sampling of the raw sensory streams, such that the task is rid of its ambiguities. Were this biased sampling to be implemented in the participants' brains, we would not hesitate to acknowledge that the processes involved are responsible for the core cognitive workload required to solve the problem. In other words, to solve the perceptual crossing task using this strategy is to find the way of biasing the sampling of sensory inputs so as to transform them from type II into type I.

Now this sampling bias is precisely what is achieved by the collective dynamics, i.e. by the interactive combination of individual strategies. As shown by Auvray et al . [ 4 ], the interaction process biases the statistical presentation of sensory stimulus towards much more frequent encounters with the other participant's sensor, and not the shadow. Mutual scanning of sensors produces mutual sensory feedback and a permanence in the shared spatial region. This is more stable than one participant unidirectionally scanning the shadow of the other, who is unaware of this scanning and continues the search in other areas; thus, the scanned shadow object quickly disappears. This is not done consciously by the participants but by the relation between their correlated movements. This cognitive work is neither given externally (in which case, we would be right in attributing the solution of the problem to a third party) nor is it generated internally within the participants' brains. It is produced by the self-organized collective dynamics in which they participate but whose properties do not correspond to individual properties of either agent on its own, or to a linear aggregation of these. The task is transformed from type II to type I—it is solved —by the interaction process. There is no need for the participants' brains to represent the distinction between sensor and shadow at all to solve the task. The participants reap the benefits and deal with quasi-disambiguated, type I stimuli: ‘if it moves but stays nearby (repeated crossings), then click’. If a process instantiates the solution to a cognitive problem it constitutes an instance of cognition. This is what social interaction does in perceptual crossing.

Further empirical confirmation that social interaction can play constitutive roles in social cognition is provided by a variation of the perceptual crossing experiment by Froese et al . [ 42 ]. This variation involves a more sophisticated social cognitive faculty, that of recognizing the other as an agent. The authors found that if they instructed the participants in a perceptual crossing task to cooperate as a team in finding each other, through several repeated interactions, the probability of clicking on the other's sensor grew to twice as much as that of clicking on the shadow object (in the original experiment these probabilities are approximately the same; the difference in absolute clicks is given by the interactively skewed probability of encountering each object). This means that participants develop a better way of ‘telling’ if they are in contact with another agent, for instance, by using prototypical, co-authored regularities in the interaction patterns, which in turn would confirm the direct co-presence of the participants. Some pairs developed clear turn-taking patterns. As the authors say, these co-authored patterns turned ‘the individual epistemic task of agency detection into a social pragmatic task aimed at mutual coordination’ ([ 42 ], p. 4). As mutual recognition is a fundamental aspect of a wide range of cases of social cognition, its social constitution in as simple a situation as perceptual crossing is suggestive of an interactive sharing of socio-cognitive processes in other cases.

(c) Irreducibility

The examples of entanglement and cognitive functionality evidenced in at least some cases of social interaction are indicative of phenomena that cannot be fully determined by what goes on in the individual participants' brains and bodies. But there is also an important sense in which the acts and meanings that are cognized about in social cognition are themselves part of emergent interactive phenomena, and not simply a summation of individual attributes (such as moods, intentions, etc.). To cognize socially, in the enactive understanding of the term, is to skillfully engage in the multiple demands and possibilities of the social world, many of which are directly or indirectly emergent from social interactions. During interactive encounters, this skillful engagement does not in general necessitate tracking evidence that allows us to infer the mental states of others. Often such mental states do not directly impact on what is immediately required at the present moment, or they are directly evident in the acts and responses of the others. Crucially, in such situations of interactive engagement, it is not individual cognizing and behaviour that sufficiently determines the relevant phenomena: both social acts and meanings are constituted socially and during the interactive encounter—think of a handshake, or the act of giving/receiving an object. The interactive constitution of social acts and meanings is a joint cognitive process that necessitates, but is under-determined by, individual cognition; the remainder of determination is given by the relational dynamics of the interactive encounter. We call this process participatory sense-making [ 43 ].

Consider escalation as a simple example of what we mean by irreducibility in the case of interactive phenomena. Typically, escalation involves an antagonistic pattern of interaction, sustained in time, increasing in intensity, and potentially spiralling out of control. Past conflictive interactions can predispose the onset of escalation even when the interaction partners do not individually intend to engage in an antagonistic exchange (see for instance, [ 44 ]). Sometimes escalation arises spontaneously as a result of interactive patterns. An example is given by Shergill et al . [ 45 ]. The interaction is quite minimal and involves participants applying a downward push with a finger on the other one's hand; an operation that is then repeated, alternating roles. Participants are instructed to apply the same force as the perceived external force applied to them. As the turns alternate, the absolute amount of force escalates. The suggestion is that participants tend to underestimate the force they apply: self-generated force is perceived as weaker than externally generated force. Participants compensate by increasing force in the next round, resulting in escalation. Simple as this explanation is, it provides a good model for more general situations: escalation can originate unintentionally by a reciprocal configuration in which a perceived mismatch between one's own ‘moves' and those that we are subject to by external action.

The full explanation in this case combines individual and relational factors: a tendency to underestimate one's own force and the configuration of the alternating interaction pattern. Remove either factor and the explanation fails. Moreover, the explanation does not involve any high-level awareness of the escalating pattern or deliberate intention to initiate escalation. Like the perceptual crossing situation, the onset of escalation just happens as part of the collective dynamics.

This simple case exemplifies how interactional dynamics are not fully under the control of the participants. There is no escalation module in the brain or an individual intention to escalate in general. It also shows that an important aspect of social meaning can relate to these emergent patterns. Escalation is often associated with the generation of negative affect, which undoubtedly relates to interactional history, but as we can see, can emerge as novel social meaning due to the interaction itself, and not to any individual intentions. Similar processes where social meaning is generated by interactional patterns were already described by Gregory Bateson in terms of schismogenesis and feedback [ 46 ], and taken up in psychotherapeutic contexts (see e.g. [ 47 – 49 ]). The objects of social meaning are themselves interactively generated as well as apprehended.

Social interaction processes can be very hard to disentangle from individual brain and body dynamics. They can also play specific functional roles in the solution of a cognitive task. And they can give rise to objects, meanings and actions that are irreducibly interactive. These complex realities in no way eliminate the possibility of scientific inquiry. On the contrary, in some cases they result in simpler explanations than those that are unduly constrained to be skull-bound, as we witness in the case of escalation and perceptual crossing. Far from making social cognition fuzzier and mysterious, the IBH in fact seeks to provide a more objective foundation, one that is more amenable to scientific observation and experimental manipulation.

5. Response (R.A.)

My co-authors are correct in taking me to reject the possibility, not just the plausibility, of the IHB in what I wrote in §3. However, this depends on three concepts, whose relations were argued for only in the vaguest terms; let me say a bit more about them here in responding to the arguments of §4. The three concepts are cognition, causation and explanatory domain (for a discipline). Very roughly, my idea was that features of causation (causal density) put constraints on cognition, and that this put constraints on what social neuroscience can study in order to understand cognition.

None of us has defined what we mean by cognition, although I alluded to two other concepts as perhaps providing some reference: computation and consciousness. If cognition is taken to be processing that could at least potentially contribute to the contents of our conscious experience [ 50 ], then I found it implausible that the coupled system of the Auvray experiment had cognition. H.D.J. and E.D.P. do not seem to have the same concept of cognition, and instead theirs may rely more on how coordination (of behaviour, of meaning) arises from social interactions. I am unclear on what my co-authors mean by cognition; but I am also unclear on what I myself mean. So I think this is one obvious way forward in our discussion: insofar as all of us are vague on what we mean by cognition, it opens the way for a revised understanding of this concept that might reconcile our apparent differences.

How would one go about revising a concept of cognition? One place to begin would be by taking the term as relative to a discipline. This brings us to the topic of ‘explanatory domain’. The points made in the previous section all argue that the study of the brain alone is insufficient to understand the kind of coupling we see in social interactions. I found the example of the Auvray experiment too detached from what happens in the brain, but H.D.J. and E.D.P. argue it is not simple, not an atypical example, and not reducible to events in the brain and events outside the brain. In short, the suggestion of §4 is that social neuroscience could gain more traction on how it uses the concept of cognition to explain behaviour, if it incorporated relations with extra-neural events into its domain of study. This is an empirical suggestion: social neuroscientists should try to take this stance, and see how far they get with it. Will it be helpful in explaining human social behaviour, or will it create complications if we widen the discipline of social neuroscience in this way? This seems like a reasonable practical position. If cognition is somewhat relativized to a discipline in this way, shifts in the explanatory domain of the discipline would result in corresponding shifts in the concept of cognition.

The final issue concerns causation: I felt that this was much ‘denser’ in the brain than between brain and environment, but the only metric I offered were sheer numbers of axons. H.D.J. and E.D.P. argue that this is not the right metric, because even very small physical connections can result in profound influences. I think they are right. This then leaves me to retreat to something other than causal density as the distinguishing feature that delimits processing in the brain from processing involving events outside the brain. The only other good metric that comes to mind is something like ‘evolvability’ or ‘manipulability by evolution’. That is, there is a strong intuition that evolution can direct changes in cognition through changes in the brain, but not changes in the physical environment. Unfortunately, this intuition only works for the non-social environment. For the social environment, there is instead a strong plausibility that brains co-evolved, and so cognition indeed could evolve through changes across multiple brains.

To summarize: my current concept of cognition, however ill-defined, is squarely centred on the brain. But I have not made a serious attempt to revise this, and it is possible that such a revision would result in a concept with more utility. The argument that causal density specifies nature's joints for a cognitive system is problematic because actual physical density of connections is probably not the right metric. These considerations lead to the conclusions of the next section, on which all three of us agree.

6. Suggestions for social neuroscience

In writing this article, all three of us acknowledge that understanding the brain and cognition is incredibly hard. All approaches should have the provisional status of ‘hypothesis'—something the IBH explicitly does have, but standard information processing views usually do not. We agree that historical views of cognition as computation over representations are unlikely to adequately describe how brains work. Rather than defining cognition as that kind of information processing that is unique to brains, we would prefer to think of cognition as involving brains in some way yet to be fully understood, possibly including causal relations between brains, bodies, other people and even the non-social environment. Evolution has made use of whatever substrates are available to generate flexible behaviour, and we simply do not know yet what those substrates are.

The IBH can be seen as making a practical claim for scientists, namely that there is a more compact explanation of human social behaviour if we adopt the interactive stance than if we stick with the classical input–output stance. Consider how far one could push the classical brain-in-a-vat thought experiment. There have been recent experiments using optogenetics in mice that manipulate brain activity so precisely that they literally reconstitute the pattern of neuronal activity that would have been evoked by encoding an actual sensory stimulus [ 51 ]. Such experimentally created patterns of activity in the brain cause the mouse to behave as if it remembered an actual stimulus. While this experiment seems to show that we can understand cognition and behaviour as divorced from the environment, it actually points to the value of the IBH as a framework to understand what is happening. Suppose the experiment attempted to recreate the pattern of activity involved in an actual, reciprocal, social encounter. It quickly becomes apparent that to do so would require mimicking the other animal as a social stimulus. But as the other animal responds to our experimental mouse, this is not a fixed input, but rather a complex, time-varying input embedded in causal loops with the very behaviour we wish to experimentally control. Our surrogate ‘input pattern’ ends up being not only extremely complex, but in fact cannot be specified in the absence of an analysis of the first mouse as involved in a socially coupled interaction, which itself can show emergent dynamical patterns that do not reduce to the activity of the mice. This conclusion is consonant with Cosmelli & Thompson's suggestions about the brain-in-a-vat thought experiment [ 18 ]. The upshot is that we may be able to describe some aspects of cognition reasonably well as input–output transformations by the brain, whereas others cannot be so described. Social cognition typically may be of the latter kind.

This leads us to consider the practical issue of which are the criteria for delineating the systems under study. One possible reading of the IBH could be that everything matters, and so there is no right decomposition into causal systems that could illuminate a particular instance of social cognition. This is certainly not the intended reading. 2 To highlight the embeddedness of brain activity within a behaving and interacting body is not to render social neuroscience a hopeless endeavour. It is to raise awareness that certain assumptions, such as the assumption of decomposability, can be problematic if formulated uncritically. There exist many experimental approaches that would allow the simultaneous study and manipulation of neural and interactive dynamics, as we have mentioned.

Besides these practical claims, the IBH can also be seen as a foundational claim about what grounds social cognition. From input–output transformations alone, meaning cannot emerge; for what do the inputs and outputs stand for? The IBH assigns meaning from the wider perspective of (i) social interaction and (ii) the wider enactive theory about cognition as sense-making that it forms part of (e.g. [ 55 ]). From an enactive perspective, meaning emerges in virtue of historical and concurrent patterns of interaction. That something like this must happen, for example, as an infant learns her very first words seems uncontentious. Associating the word ‘book’ with seeing a book presumably works by the infant and another person both looking at the book and saying and hearing the word book. But why does she learn the word book? Why is it meaningful to her at all? Here, the wider framework of participatory sense-making out of which the IBH grew makes claims about how cognizers encounter the world as meaningful (which is how enactivists define cognition) [ 43 , 55 ]. Certainly, there is room for rich further debate here. The key future challenge for the enactive approach is to develop further concepts and hypotheses, and to continue to articulate them in ways that make contact with other frameworks. In this way, concepts like ‘participatory sense-making’ can be articulated into domain-specific claims, hypotheses and explanations that relate to conceptions of meaning, as they vary between relevant disciplines. Formulating the IBH is an attempt to do precisely this for the field of social neuroscience.

A closing question is where to find a home for social neuroscience in all of this. The difficulty arises when social neuroscience attempts to study that which underlies behaviour and cognition, when it attempts to explain how meaning is generated, and why social cognition and social behaviour exhibit particular forms and features. After all, social cognition shows substantial differences if we compare a dog, a chimpanzee or, for that matter, people from different cultures or at different ages. How can we explain these differences—differences that render social behaviour meaningful for individuals of each species, culture and epoch, but less meaningful as we cross between these. Perhaps the largest contribution of the IBH to social neuroscience is to show that it is impossible to answer these questions if the only data we entertain are from a single adult brain in one species. That is, we need to consider species-typical social interactions not just in the context of meaningful situations, but also in the context of evolution and development. Echoing the well-known ethological refrain that ‘nothing makes sense in biology except in the light of evolution’ ([ 56 ], p. 125), we would urge that social neuroscience should incorporate at least comparative neuroscience, developmental neuroscience, and input from sciences that study social interaction into its domain of study (see e.g. [ 57 ]).

Acknowledgements

We thank Guillaume Dumas, Frederick Eberhardt, Riitta Hari and the reviewers for their comments on the manuscript.

1 This is not to say that we think that understanding the brain is sufficient for understanding cognition. From an enactive perspective, what matters is the embodied subject in relation to her world or meaningful environment. Interestingly, the same kind of argument in favour of the IBH presented here in the context of social neuroscience could be made in the context of many approaches to embodied cognition that remain methodologically individualistic. For these, the body is crucial for the mind, meaning the individual body and not its engagement in social interactions. From the enactive perspective, by contrast, both body and social engagement are primordial [ 1 , 2 ].

2 The situation is not unlike other debates in biology. Arguments for the importance of non-genetic factors in evolution and development (e.g. [ 52 , 53 ]) are often met with the criticism that one cannot study every conceivable causal factor scientifically. But there are positive counter-proposals that distinguish between different causal roles. For instance, Woodward [ 54 ] suggests that one can discriminate between causal factors according to different criteria such as their stability or non-contingency, specificity and appropriateness for the level of explanation. Manipulability could be another factor for this kind of consideration.

Authors' contributions

E.D.P. and H.D.J. are the primary authors of §§2 and 4; R.A. is the primary author of §§3 and 5; all authors contributed equally to §§1 and 6, and all authors provided substantial input to all sections of the paper.

Competing interests

We have no competing interests.

H.D.J. is funded by a Ramón y Cajal Fellowship, RYC-2013-14583. R.A. was supported in part by a Conte Center grant from the National Institute of Mental Health (USA).

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Two key brain systems are central to psychosis, Stanford Medicine-led study finds

When the brain has trouble filtering incoming information and predicting what’s likely to happen, psychosis can result, Stanford Medicine-led research shows.

April 11, 2024 - By Erin Digitale

People with psychosis have trouble filtering relevant information (mesh funnel) and predicting rewarding events (broken crystal ball), creating a complex inner world. Emily Moskal

Inside the brains of people with psychosis, two key systems are malfunctioning: a “filter” that directs attention toward important external events and internal thoughts, and a “predictor” composed of pathways that anticipate rewards.

Dysfunction of these systems makes it difficult to know what’s real, manifesting as hallucinations and delusions. 

The findings come from a Stanford Medicine-led study , published April 11 in  Molecular Psychiatry , that used brain scan data from children, teens and young adults with psychosis. The results confirm an existing theory of how breaks with reality occur.

“This work provides a good model for understanding the development and progression of schizophrenia, which is a challenging problem,” said lead author  Kaustubh Supekar , PhD, clinical associate professor of psychiatry and behavioral sciences.

The findings, observed in individuals with a rare genetic disease called 22q11.2 deletion syndrome who experience psychosis as well as in those with psychosis of unknown origin, advance scientists’ understanding of the underlying brain mechanisms and theoretical frameworks related to psychosis.

During psychosis, patients experience hallucinations, such as hearing voices, and hold delusional beliefs, such as thinking that people who are not real exist. Psychosis can occur on its own and isa hallmark of certain serious mental illnesses, including bipolar disorder and schizophrenia. Schizophrenia is also characterized by social withdrawal, disorganized thinking and speech, and a reduction in energy and motivation.

It is challenging to study how schizophrenia begins in the brain. The condition usually emerges in teens or young adults, most of whom soon begin taking antipsychotic medications to ease their symptoms. When researchers analyze brain scans from people with established schizophrenia, they cannot distinguish the effects of the disease from the effects of the medications. They also do not know how schizophrenia changes the brain as the disease progresses. 

To get an early view of the disease process, the Stanford Medicine team studied young people aged 6 to 39 with 22q11.2 deletion syndrome, a genetic condition with a 30% risk for psychosis, schizophrenia or both. 

Kaustubh Supekar

Brain function in 22q11.2 patients who have psychosis is similar to that in people with psychosis of unknown origin, they found. And these brain patterns matched what the researchers had previously theorized was generating psychosis symptoms.

“The brain patterns we identified support our theoretical models of how cognitive control systems malfunction in psychosis,” said senior study author  Vinod Menon , PhD, the Rachael L. and Walter F. Nichols, MD, Professor; a professor of psychiatry and behavioral sciences; and director of the  Stanford Cognitive and Systems Neuroscience Laboratory .

Thoughts that are not linked to reality can capture the brain’s cognitive control networks, he said. “This process derails the normal functioning of cognitive control, allowing intrusive thoughts to dominate, culminating in symptoms we recognize as psychosis.”

Cerebral sorting  

Normally, the brain’s cognitive filtering system — aka the salience network — works behind the scenes to selectively direct our attention to important internal thoughts and external events. With its help, we can dismiss irrational thoughts and unimportant events and focus on what’s real and meaningful to us, such as paying attention to traffic so we avoid a collision.

The ventral striatum, a small brain region, and associated brain pathways driven by dopamine, play an important role in predicting what will be rewarding or important. 

For the study, the researchers assembled as much functional MRI brain-scan data as possible from young people with 22q11.2 deletion syndrome, totaling 101 individuals scanned at three different universities. (The study also included brain scans from several comparison groups without 22q11.2 deletion syndrome: 120 people with early idiopathic psychosis, 101 people with autism, 123 with attention deficit/hyperactivity disorder and 411 healthy controls.) 

The genetic condition, characterized by deletion of part of the 22nd chromosome, affects 1 in every 2,000 to 4,000 people. In addition to the 30% risk of schizophrenia or psychosis, people with the syndrome can also have autism or attention deficit hyperactivity disorder, which is why these conditions were included in the comparison groups.

The researchers used a type of machine learning algorithm called a spatiotemporal deep neural network to characterize patterns of brain function in all patients with 22q11.2 deletion syndrome compared with healthy subjects. With a cohort of patients whose brains were scanned at the University of California, Los Angeles, they developed an algorithmic model that distinguished brain scans from people with 22q11.2 deletion syndrome versus those without it. The model predicted the syndrome with greater than 94% accuracy. They validated the model in additional groups of people with or without the genetic syndrome who had received brain scans at UC Davis and Pontificia Universidad Católica de Chile, showing that in these independent groups, the model sorted brain scans with 84% to 90% accuracy.

The researchers then used the model to investigate which brain features play the biggest role in psychosis. Prior studies of psychosis had not given consistent results, likely because their sample sizes were too small. 

Vinod Menon

Comparing brain scans from 22q11.2 deletion syndrome patients who had and did not have psychosis, the researchers showed that the brain areas contributing most to psychosis are the anterior insula (a key part of the salience network or “filter”) and the ventral striatum (the “reward predictor”); this was true for different cohorts of patients.

In comparing the brain features of people with 22q11.2 deletion syndrome and psychosis against people with psychosis of unknown origin, the model found significant overlap, indicating that these brain features are characteristic of psychosis in general.

A second mathematical model, trained to distinguish all subjects with 22q11.2 deletion syndrome and psychosis from those who have the genetic syndrome but without psychosis, selected brain scans from people with idiopathic psychosis with 77.5% accuracy, again supporting the idea that the brain’s filtering and predicting centers are key to psychosis.

Furthermore, this model was specific to psychosis: It could not classify people with idiopathic autism or ADHD.

“It was quite exciting to trace our steps back to our initial question — ‘What are the dysfunctional brain systems in schizophrenia?’ — and to discover similar patterns in this context,” Menon said. “At the neural level, the characteristics differentiating individuals with psychosis in 22q11.2 deletion syndrome are mirroring the pathways we’ve pinpointed in schizophrenia. This parallel reinforces our understanding of psychosis as a condition with identifiable and consistent brain signatures.” However, these brain signatures were not seen in people with the genetic syndrome but no psychosis, holding clues to future directions for research, he added.

Applications for treatment or prevention

In addition to supporting the scientists’ theory about how psychosis occurs, the findings have implications for understanding the condition — and possibly preventing it.

“One of my goals is to prevent or delay development of schizophrenia,” Supekar said. The fact that the new findings are consistent with the team’s prior research on which brain centers contribute most to schizophrenia in adults suggests there may be a way to prevent it, he said. “In schizophrenia, by the time of diagnosis, a lot of damage has already occurred in the brain, and it can be very difficult to change the course of the disease.”

“What we saw is that, early on, functional interactions among brain regions within the same brain systems are abnormal,” he added. “The abnormalities do not start when you are in your 20s; they are evident even when you are 7 or 8.”

Our discoveries underscore the importance of approaching people with psychosis with compassion.

The researchers plan to use existing treatments, such as transcranial magnetic stimulation or focused ultrasound, targeted at these brain centers in young people at risk of psychosis, such as those with 22q11.2 deletion syndrome or with two parents who have schizophrenia, to see if they prevent or delay the onset of the condition or lessen symptoms once they appear. 

The results also suggest that using functional MRI to monitor brain activity at the key centers could help scientists investigate how existing antipsychotic medications are working. 

Although it’s still puzzling why someone becomes untethered from reality — given how risky it seems for one’s well-being — the “how” is now understandable, Supekar said. “From a mechanistic point of view, it makes sense,” he said.

“Our discoveries underscore the importance of approaching people with psychosis with compassion,” Menon said, adding that his team hopes their work not only advances scientific understanding but also inspires a cultural shift toward empathy and support for those experiencing psychosis. 

“I recently had the privilege of engaging with individuals from our department’s early psychosis treatment group,” he said. “Their message was a clear and powerful: ‘We share more similarities than differences. Like anyone, we experience our own highs and lows.’ Their words were a heartfelt appeal for greater empathy and understanding toward those living with this condition. It was a call to view psychosis through a lens of empathy and solidarity.”

Researchers contributed to the study from UCLA, Clinica Alemana Universidad del Desarrollo, Pontificia Universidad Católica de Chile, the University of Oxford and UC Davis.

The study was funded by the Stanford Maternal and Child Health Research Institute’s Uytengsu-Hamilton 22q11 Neuropsychiatry Research Program, FONDEYCT (the National Fund for Scientific and Technological Development of the government of Chile), ANID-Chile (the Chilean National Agency for Research and Development) and the U.S. National Institutes of Health (grants AG072114, MH121069, MH085953 and MH101779).

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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