Primates – such as lemurs, monkeys and humans – can perform a diverse range of cognitive skills, from memory to processing speech. But how did this diversity of cognitive skills evolve?

To answer this question, scientists often compare the brain sizes of different species and measure how this relates to their social behaviors or ecology in the wild. But using the whole brain as a measure of global cognitive capacities seems crude in the light of modern cognitive neuroscience studies, which have shown that specific parts of the brain are responsible for certain cognitive skills.

It is unclear how relevant the findings of cognitive neuroscience studies, which test animals in a laboratory, are to real life situations and evolution. To address this gap, Bouret et al. integrated methods from both behavioral ecology and cognitive neuroscience to examine the size of primate brain regions.

The team studied brain images from 16 primate species, focusing on two regions that have been linked to specific cognitive functions in laboratory experiments. They examined the frontal pole, which is involved in metacognition (the ability to be aware of and assess one’s own thoughts), and the dorsolateral prefrontal cortex, which is involved in working memory (the ability to temporarily store information to solve a problem).

Bouret et al. then compared the size of these regions to socio-ecological factors: how much each species performs complex social interactions and how hard it is to find food in the wild, by measuring their population density and daily travel distance, respectively. This revealed that the volume of the frontal pole is larger in species that experience more complex social interactions and in species that struggle to find food – two tasks thought to require metacognition. The dorsolateral prefrontal cortex, however, is only larger in species that have difficulty foraging, which might require a strong working memory to plan travel routes.

These findings suggest that laboratory experiments linking cognitive skills to specific parts of the brain are reliable enough to predict the size of these regions across wild primates. The work of Bouret et al. also helps bridge the gap between cognitive neuroscience and behavioral ecology, and demonstrates how these two disciplines can be combined to investigate the evolution of cognition.

Numerous studies have addressed the mechanisms underlying the evolution of cognitive abilities in primates, using brain size as a proxy (Chambers et al., 2021; DeCasien et al., 2022; Powell et al., 2017; van Schaik et al., 2021). One of the major hypotheses, referred to as the ‘social brain’ hypothesis, proposes that social interactions require higher cognitive skills such that the complexity of social interactions played a central role in the increase in brain size during primate evolution (Dunbar, 1998). Another major theory, referred to as the ‘ecological brain’ hypothesis, proposes that the increase in brain size and corresponding cognitive skills was driven by the need to build mental representations in order to forage efficiently (Milton, 1981; Milton, 1993). Critically, both of these theories are based on the assumption that the whole brain volume is a good proxy for overall cognitive abilities, and that these global abilities can benefit social interactions or foraging, across species in an evolutionary framework. But clearly, the level of precision of such neuro-cognitive scenario remains limited, given the precision with which neuroscience and psychology have characterized the brain functional anatomy and cognition, respectively.

Imaging studies in humans have identified a network of brain regions involved in social interactions, and this network is often referred to as the social brain (Stanley and Adolphs, 2013). Recent comparative studies have shown that it also existed in macaques (Rushworth et al., 2013; Sallet et al., 2011; Testard et al., 2022). Along the same lines, laboratory studies have proposed that specific brain regions (e.g. cingulate cortex) were underlying foraging in humans and macaques (e.g. Hayden et al., 2011; Kolling et al., 2012). These studies, however, have only been conducted in a handful of species and in very artificial conditions, such that the relation between these brain regions and natural behavior or evolution remains elusive. In terms of the cognitive processes involved, ‘social interactions’ and ‘foraging’ remain poorly specific and they probably involve myriads of more elementary cognitive operations. For example, social interactions involve some form of categorization of facial expressions and metacognition, and the brain regions associated with these elementary cognitive operations (e.g. frontal pole or temporo-parietal junction) are usually part of the ‘social brain’, i.e., the set of brain regions associated with social interactions (de Gelder, 2023; de Gelder and Poyo Solanas, 2021; Deen et al., 2023; Devaine et al., 2017; Gallagher and Frith, 2003; Rushworth et al., 2013). Along the same lines, foraging is thought to require spatio-temporal mental representations of food availability and values, and these elementary cognitive operations rely upon a distinct set of brain regions, including the hippocampus and the ventromedial prefrontal cortex (VMPFC) (Lin et al., 2015; Louail et al., 2019; Rosati, 2017; Vikbladh et al., 2019; Zuberbühler and Janmaat, 2010). Finally, the brain systems potentially involved in social cognition and foraging partially overlap, especially in the frontal lobes (Barbey et al., 2014; Gallagher and Frith, 2003; Kolling et al., 2012; Mansouri et al., 2017; Rudebeck et al., 2006; Yoshida et al., 2012). This partial overlap between the networks involved in social and foraging cognition in laboratory conditions is in line with the idea that some elementary cognitive operations are generic enough to be involved in multiple contexts (Garcia et al., 2021; Shultz and Dunbar, 2022). This is typically the case for executive functions (e.g. working memory), which allow to organize behavior over space and time to reach a goal, and rely upon several regions of the prefrontal cortex (Fuster, 2008; Luria, 1973).

Given the complexity encountered in natural environments, it is hazardous to extrapolate these findings in artificial laboratory conditions to the decision-making processes (in both social and ecological dimensions) occurring in wild primates, for which the diversity of social interactions (e.g. number of individuals an animal can remember, degree of social awareness, i.e. knowledge and representation of the dominance hierarchy, kinship relations, or friendship associations) and foraging behaviors (e.g. food processing techniques, knowledge of harvesting schedules, extractive foraging) require much more cognitive flexibility. One can wonder to what extent these neuro-cognitive concepts (i.e. elementary cognitive operation and their underlying cerebral substrate) can be used as theoretical building blocks to understand the diversity of natural behaviors reported in the wild and their evolution across species. In terms of evolution, the functional heterogeneity of distinct brain regions is captured by the notion of ‘mosaic brain’, where distinct brain regions could show a specific relation with various socio-ecological challenges, and therefore have relatively separate evolutionary trajectories (Barton and Harvey, 2000; DeCasien and Higham, 2019). But these relations between specific brain regions and socio-ecological variables across species provide little insight into the cognitive processes that could be at play, because they do not necessarily map onto specific neuro-cognitive processes identified in rigorous laboratory conditions. Thus, it remains challenging to bridge the gap between (1) laboratory studies relating brain regions to elementary cognitive operations in individual species and (2) studies relating brain regions to socio-ecological challenges through an evolutionary scenario (i.e. across numerous species).

In order to bridge this gap, we designed this study to derive and test predictions where a set of well-identified elementary cognitive operations and their associated brain regions (as studied in laboratory conditions) could be involved in more natural functions (as assessed in wild animals living in their natural environments). We focused on two well-known cognitive operations, both considered as executive functions, and involving distinct regions of the prefrontal cortex: metacognition, which involves the frontal pole (FP), and working memory, which involves the dorso-lateral prefrontal cortex (DLPFC), as demonstrated by extensive work in humans and macaques (Fuster, 2008; Mansouri et al., 2017; Passingham et al., 2012). Based on the literature, we hypothesized that FP and metacognition would be associated both with social interactions (by supporting theory of mind, i.e. the ability of an individual to conceptualize others’ states of mind) and with foraging (by enabling complex planning) (Devaine et al., 2017; Fleming and Dolan, 2012; Frith, 2007; Mansouri et al., 2017). For example, group hunting, which involves both social and foraging functions, only occurs in a few primate species where metacognition is thought to be particularly developed (Boesch, 1994; Conard et al., 2020; Garcia et al., 2021; Schaik, 2016). Concerning the DLPFC, given its very clear implication in working memory and planning, we speculated that it would be critically involved in foraging. Its potential relation with social interactions, however, was less clear: on one hand, DLPFC is rarely associated with social interactions in laboratory studies (Frith, 2007; Fuster, 2008; Passingham et al., 2012; Sallet et al., 2013). But, on the other hand, working memory and planning could readily be involved in complex social interactions in more natural conditions.

To evaluate the relevance of laboratory-based neuro-cognitive concepts for understanding which cognitive operations could be mobilized to overcome specific socio-ecological challenges in the wild and their variability across species, we addressed the following central question: to what extent does the functional mapping between brain regions and specific executive functions in laboratory conditions relate to variability in socio-ecological variables at the interspecific level? For this purpose, it is necessary to go beyond the handful of species usually studied in well-controlled laboratory conditions, not only to evaluate the reliability of the relations between neuro-anatomical measures and socio-ecological factors across primates, but also to quantify the contribution of phylogeny.

To address this question, we measured the volume of the two prefrontal regions of interest (FP and DLPFC) in 16 primate species and evaluated the influence of key socio-ecological factors on the size of these regions. Based on the known positive relation between the size of a given brain region (or the number of corresponding neurons) and the relative importance of its associated function, both within and across species, we assume that the size of each brain region can be taken as a proxy for the weight of its associated function on behavior (Barks et al., 2015; Barton and Harvey, 2000; DeCasien and Higham, 2019; Ferrucci et al., 2022; Herculano-Houzel, 2017; Herculano-Houzel et al., 2016; Louail et al., 2019; Maguire et al., 2000; Sallet et al., 2011; Testard et al., 2022). Anatomical studies have shown that these regions were relatively well conserved between humans and macaques, both in terms of cytoarchitectonics and connectivity profiles (Amiez et al., 2019; Petrides et al., 2012; Sallet et al., 2013). Here, these two regions were chosen and identified based on functional maps and reliable macroscopic landmarks, rather than cytoarchitectonic criteria, to maximize the reliability of the comparative approach (see also Louail et al., 2019). Finally, the cognitive functions in which they are involved appear relatively well conserved between macaques and humans (Ferrucci et al., 2022; Fuster, 2008; Mansouri et al., 2017; Passingham and Sakai, 2004). The 16 primate species were chosen to cover a wide range of phylogenetic distances coupled to a diversity of socio-ecological variables. We compared the influence of several combinations of these socio-ecological variables on the variability in size of each brain region of interest, and interpreted it in the light of its known function in laboratory conditions. Thus, for a given functional region, the differences in regional volume across species provide a reliable index of the differences in neuronal count, and therefore of the skill level in the corresponding function. Even though comparative studies often used scaled measures (i.e. relative to the whole brain) to account for potential allometric effects, we favored absolute measures because scaling procedures would distort the relation between volume and neural counts (Barton et al., 1995; DeCasien et al., 2022; Gabi et al., 2016; Herculano-Houzel, 2017; Herculano-Houzel et al., 2008; Krebs et al., 1989; Smaers et al., 2017; Smaers and Soligo, 2013). Moreover, the extent to which other brain regions could be affected by socio-ecological variables, and therefore affect scaled measures, is difficult to evaluate. We therefore used several procedures to evaluate the specificity of the effects of socio-ecological factors on FP and DLPFC. First, we compared the influence of socio-ecological variables not only on FP and DLPFC but also on the whole brain. Second, we included the whole brain volume as a covariate along with the socio-ecological variables to account for FP and DLPFC volumes. Altogether, these procedures provide a reliable indication of the specificity of the influence of socio-ecological variables on the FP and DLPFC volumes, used as proxies for inter-species differences in metacognitive and working memory skills, respectively. used as proxies for inter-species differences in metacognitive and working memory skills. Thus, we could evaluate the strength of the relation between specific regions of the prefrontal cortex and specific socio-ecological variables across species, as observed in the wild, thereby complementing laboratory studies and bridging the gap between cognitive neurosciences, behavioral ecology, and primate evolution.

We evaluated the influence of 11 socio-ecological variables on the size of the whole brain as well as two specific brain regions involved in executive functions: the FP and the DLPFC. Altogether, our results showed that all three cerebral measures strongly correlated and were influenced by the same set of socio-ecological variables: body mass, daily traveled distance, and population density. As expected from evidence suggesting brain-body covariation (Martin, 1981), body mass had a strong and reliable influence on all brain measures. Daily traveled distance, a proxy for how challenging is foraging, also had a clear positive influence on all brain measures. Finally, population density, a proxy for how challenging and complex are social interactions, was generally less powerful at explaining neuro-anatomical variability, but it was also the variable showing the greatest difference in effect size across brain regions. For the FP, the influence of population density was similar to that of daily traveled distance, but it was much weaker for the whole brain, and it even failed to reach significance for the DLPFC. Thus, our data are generally compatible with the idea that the evolution of executive functions relying upon FP and DLPFC is driven both by ecological and by social constraints. Critically, the relative influence of these constraints seems to vary across regions, in line with our hypothesis based on the cognitive functions with which they are associated in laboratory conditions. Thus, neuro-cognitive entities as defined in laboratory conditions can readily be articulated with socio-ecological processes in the wild, in order to better understand primate cognition and behavior in natural conditions.

As we and others have done before (within and across species), we assume that the size of a given brain region provides a good proxy of the strength of its influence (through its known cognitive function) on behavior (Barton and Harvey, 2000; DeCasien and Higham, 2019; Louail et al., 2019; Maguire et al., 2000). Indeed, the size of a brain region provides a reliable estimate of the number of neurons allocated to its function, given the known positive relation between number of cortical neurons and cognitive skills in primates (Herculano-Houzel, 2017; Herculano-Houzel, 2018). Here, we restricted our analysis to a set of specific brain regions, namely the FP and the DLPFC, assuming that the difference in size of these brain regions across species could readily be used as proxies for the relative importance of their corresponding functions (namely, metacognition for the FP and working memory for the DLPFC). Using that tool, we explored the relation between neuro-cognitive entities (e.g. FP/metacognition vs DLPFC/working memory) and socio-ecological variables, based on the assumption that the more a given cognitive function would be required to face a given socio-ecological challenge, the bigger the corresponding brain region would be.

Our aim here was clearly not to provide an accurate identification of anatomical boundaries across brain regions in individual species, as others have done using finer neuro-anatomical methods in humans and macaques (e.g. Petrides et al., 2012). Moreover, we cannot affirm that our conclusions in gyrencephalic species could be extended to lissencephalic species. Indeed, we acknowledge that since the definition of the regions of interest we used is based on gyri and sulci, it was therefore impossible to include lissencephalic primates in our sample, even if previous laboratory studies in marmosets could identify DLPFC and FP using both cytoarchitectonic and functional criteria (Dias et al., 1996; Dureux et al., 2023; Roberts et al., 2007; Wong et al., 2023). Nonetheless, since our comparative study and the associated phylogenetic analysis required at least 10–15 species, it could not have been conducted with only laboratory species and these invasive approaches, and we therefore focused on gyrencephalic species.

The mapping between the regions measured here and either the specific cognitive operations of interest (metacognition vs working memory) or the socio-ecological variables (e.g. social or foraging complexity) is probably not exclusive. First, we are aware of the complexity of identifying neural processes underlying such high-level cognitive functions, and even in well-controlled laboratory conditions, the delimitation of exact boundaries of cortical regions remains challenging (Mansouri et al., 2017; Petrides, 2005; Sallet et al., 2013). Rather, we tried to maximize the reliability of the landmarks that could be identified in all species to compare the relative size of areas that are essentially used as proxies for their known function, as defined in laboratory conditions in macaques and humans. The underlying assumption that we used was that the functional anatomy of the prefrontal cortex was enough conserved in primates, such that traits that were common between humans and macaques were also shared with other primates (Amiez et al., 2019; Bludau et al., 2014; Sallet et al., 2013). Of course, the neural mechanisms underlying metacognition and working memory are extremely complicated and they involve myriads of other brain regions (e.g. temporal and parietal cortices, as well as subcortical systems). Moreover, the exact boundary between FP and DLPFC as functional entities remains difficult to assess, even in laboratory conditions (Boorman et al., 2009; Gallagher and Frith, 2003; Genovesio et al., 2014; Koechlin, 2016; Passingham and Sakai, 2004; Preuss and Wise, 2022). But the contribution of the FP to metacognition and of the DLPFC to working memory is so strong and reliable in humans and laboratory primates that it seems reasonable to assume that, given the relative level of conservation of prefrontal cortex anatomical organization across primates, FP and DLPFC should also play a key role in metacognition and working memory in other primate species. As for our previous study using a similar approach, this method was reliable enough to capture meaningful and specific effects of interest (Louail et al., 2019). Thus, even if we make no strong claim regarding the specific boundaries of FP and DLPFC, the anatomo-functional difference between these regions, as characterized in a few species used in laboratory experiments, is reliable enough to be captured by the variability in socio-ecological variables in a larger set of primates.

In previous studies, the specificity of the relation between a given brain region and socio-ecological variables has often been evaluated using relative measures, such that the variable represents the relative increase in volume of the region relative to the rest of the brain (e.g. Krebs et al., 1989; Barton et al., 1995). This approach enables to detect changes in relation to allometric relations, which are often interpreted in terms of significant evolutionary events (major reorganization of the brain). By contrast, changes in regional volume that remain within allometric proportions (in the brain) are interpreted as a non-specific effect, and therefore negligible from an evolutionary perspective (Barton and Harvey, 2000; Barton et al., 1995; DeCasien et al., 2022; Krebs et al., 1989; Smaers et al., 2017). But even if we share the intuition of specificity captured by relative measures, we decided to use absolute measures because relative measures have several theoretical implications that we find problematic. First, relative measures imply that the effect of interest should be negligible in the rest of the brain, compared to the area of interest. But we made no predictions about the rest of the brain given that most cognitive operations involve networks rather than unique regions. For example, since working memory also relies upon parietal cortices, we expect parietal cortices to also show a positive relation with ecological constraints. Thus, the theoretical effect size of a relative measure, given the functional specificity hypothesis, would require identifying the relative volume of all regions involved in working memory (for DLPFC) and metacognition (for FP), which is clearly beyond the scope of this study. As it will be discussed in the next paragraph, we believe that the comparison between DLPFC, FP, and whole brain measures provides a good index of specificity of the effects, and they can be interpreted more directly. Second, the interpretation of relative measures implies that, all other things being equal, a pool of neurons involved in a given function is less efficient when surrounded by a large number of neurons, compared to the situation where the same pool is surrounded by fewer neurons. For example, the hippocampus occupies a much larger fraction of the brain in rodents compared to primates, such that based on a relative size measurement, one would conclude that rodents have better spatial and episodic memory skills compared to primates. And even if the comparison remains difficult, there is clearly no evidence for that in behavioral data (Crystal, 2009; de Cothi et al., 2022). In summary, even if we acknowledge that relative measures provide an index of the specificity of the relation between socio-ecological variables and regional brain volumes, we find relative measures much more difficult to interpret and we chose to use absolute measures of cerebral volumes to stay away from these considerations. Indeed, the number of neurons in a given brain region should increase if its function can promote survival, irrespective of what happens in other regions.

We did evaluate the specificity of the effects by comparing the influence of socio-ecological variables not only between the two specific prefrontal regions but also with the whole brain volume. At first sight, the very strong correlation between the volume of the whole brain and that of both FP and DLPFC implies that the specificity of the influence of socio-ecological factors on these prefrontal regions should be limited, and indeed, the variability of all three measures (FP, DLPFC, and whole brain) is predicted by the same model, i.e., the same combination of socio-ecological variables. This is in line with the idea that executive functions are strongly inter-related and it can prove difficult to demonstrate their independence, even within a single species in controlled conditions (Miyake et al., 2000; Völter et al., 2022). But in spite of this strong correlation and in line with our prediction based on laboratory studies, we could still find significant differences between FP and DLPFC in terms of relation with population density, an index of social complexity.

How reliable is the difference we reported between brain regions? Even if the same combination of variables accounted for the relative size of all three brain measures, there was a difference in the weight of population density, proxy for social challenges, across the three regions. Indeed, for the FP, the effect of population density was as strong as that of daily traveled distance: both were significant, and their effect size (beta weight) was of the same order of magnitude. Also, they showed a similarly small sensitivity to the ‘leave-one-out’ procedure. Finally, standardized beta weights for population density were greater than for daily traveled distance. By contrast, for the DLPFC, the effect of population density failed to reach significance (p=0.07), its beta weight was one order of magnitude smaller than that of daily traveled distance and standardized beta weights for population density were smaller than for daily traveled distance. Finally, this relatively weak influence of population density on DLPFC volume was confirmed by the ‘leave-one-out’ procedure, since the effect of population density on the size of the DLPFC was reliably marginal across all combination of species used to fit the model. Interestingly, the whole brain volume (which is strongly correlated with both FP and DLPFC) seems to show an intermediate tendency: even if the effect of population density appears smaller than that of daily traveled distance (beta weight is one order of magnitude smaller), it is clearly significant, and the ‘leave-one-out’ procedure indicated that it was only slightly less reliable than daily traveled distance, with a significant decrease in model fit quality for four vs one combination of species. Altogether, this indicates that even if the influence of population density cannot be ruled out for any of the three brain measures, it appears quantitatively smaller for the DLPFC than for the FP. Note that the relative weight of population density also decreased (relative to that of daily traveled distance) when we used a less conservative estimate of FP volumes, which included a dorsal part potentially overlapping with the DLPFC. This is also compatible with the fact that the influence of population density seems to be of intermediate magnitude in the whole brain, which includes both FP and DLPFC. Again, we acknowledge that this method has limitations and that further studies, including more species, would be necessary to evaluate the nature and the dynamics of underlying evolutionary processes.

To what extent do these conclusions depend upon the specific sample of species used here? The results of the ‘leave-one-out’ analysis showed that our sample, even if relatively limited, was sufficient to evaluate the relative weight of socio-ecological variables on specific brain regions in primates. Interestingly, the relation between brain region volumes and socio-ecological variables was not completely independent from the sample of species, as previously shown in a study conducted on a larger sample (Powell et al., 2017). In line with the main analysis (PGLS with all species included), the influence of body mass and daily traveled distance appears very reliable (no more than one species caused the model to fail when removed). The influence of population density appeared less reliable, since it was sensitive to the removal of up to three to four species (as a function of brain region, see previous paragraph). But the influence of individual species is nearly impossible to interpret, because no apparent obvious pattern emerges. Indeed, the species without which the model failed was not always the same (e.g. humans and baboons have very distinct values for population density, 56 vs 7.5). Note, however, that getting clear intuitions from the data remains difficult because the PGLS includes the covariance induced by phylogeny to the linear relation across variables. We see no reason to exclude humans (as done in previous studies, e.g. DeCasien and Higham, 2019), because even if the brain of H. sapiens is much bigger than others, a general biological law describing the relation between socio-ecology and the brain in primates should also apply to humans (Gabi et al., 2016; Herculano-Houzel, 2017). Moreover, even if the human brain is much bigger than that of other primates, the global organization of the frontal cortex appears qualitatively similar in humans and other primates (Barrett et al., 2020; Gabi et al., 2016; Gabi et al., 2016; Roumazeilles et al., 2020; Sallet et al., 2013). One potential issue with humans is the difficulty to evaluate socio-ecological variables, given that modern human populations show a tremendous geographical variability in terms of socio-ecological variables. But the same problematic applies to other modern primate species (including those confronted to intense anthropization of their habitat) (McKinney et al., 2023). Thus, the actual potential limitation (which concerns all species) is the reliability with which socio-ecological variables were estimated for each species, given the amount of intra-specific variability. Note, there is also a significant amount of intra-specific variability at the level of the brain, both in humans and in non-human primates, but this intra-specific variability was shown to be negligible compared to inter-specific variability (DeCasien and Higham, 2019; Louail et al., 2019; Maguire et al., 2000; Testard et al., 2022). Thus, we acknowledge that intra-specific variability could introduce noise in the inter-specific relation between brain measures and socio-ecological variables, but the fact that clear relations could be established with our sample indicates that this source of intra-specific variability was limited enough relative to the inter-species relation between neuroanatomical measures and socio-ecological variables. In other words, the error with which these variables were estimated at the level of individual species was small enough to allow us to study the relation of interest across species, and from that perspective, there is no reason to exclude any species from that analysis and our sample appears reliable enough to characterize the relation between neuroanatomical features and socio-ecological variables across primates.

The stronger sensitivity of the FP to the variable ‘population density’ is reminiscent of the social brain hypothesis, i.e., the idea that social challenges favored the evolution of larger brains, and especially larger neocortex size, with species living in larger groups having bigger brains to deal with the associated complex social interactions (Dunbar, 1998). Critically, the size of the FP and the corresponding development of metacognitive skills is not exclusively related to population density (proxy for social interactions) but also to daily traveled distance (proxy for foraging complexity). Thus, these data are also compatible with the ecological brain hypothesis (Milton, 1981). This dual relation between FP size, social, and ecological constraints might be accounted for by the fact that in the wild, social and ecological constraints remain strongly related, even if we did not find strong correlations between those factors in our data set. In reality, it is difficult to treat ecological and social factors as if they were disconnected (Henke-von der Malsburg et al., 2020), and cognitive abilities associated with foraging are likely to play a role in social foraging tactics too (Street et al., 2017). Indeed, greater population density implies a greater inter-individual competition for food, and thus potentially increases in daily traveled distances, and/or the development of sophisticated foraging skills to deal with the increase in scramble competition. But from a cognitive point of view, this also implies that the benefits associated with increased FP volume, and presumably an increase in metacognitive skills, could be related to both social and ecological functions. Indeed, as pointed out earlier, the development of executive functions in general, and metacognitive skills in particular, could have been a critical leverage to allow the development of both complex social interactions (through the use of theory of mind) and complex foraging (through flexible, context-dependent planning) (Garcia et al., 2021; Shultz and Dunbar, 2022). Thus, our work indicates that the notions of ‘social brain’ and ‘ecological brain’ should not be mutually exclusive, even when considering specific brain regions. Rather, we confirm intuitions based on laboratory studies that the FP, through its role in metacognition, belongs to both sets of brain regions defined as the social brain and the ecological brain, respectively. Further work would be needed to capture the metacognitive processes underlying social and foraging processes in the wild, as well as their interactions.

The size of the DLPFC, which we used as a proxy for working memory and planning skills, showed a significant relation with daily traveled distance (proxy for foraging complexity), and to a lesser extent with population density (proxy for social complexity). This suggests that the cognitive functions at play in the DLPFC, i.e., working memory and planning, are critical for foraging in primates, especially when they need to travel long distances, and presumably have to deal with more complex navigation strategies. This is clearly in line with the global idea of the ecological brain hypothesis, but here we provide a critical insight into the specific neuro-cognitive operations associated with foraging strategies. The weaker influence of population density, a marker of social complexity, might be surprising at first sight, because a priori working memory and planning could also be strongly involved in complex social interactions (Garcia et al., 2021). But it is in line with laboratory data showing that social interactions seem to rely much more upon rostro-medial prefrontal regions compared to DLPFC (Fleming and Dolan, 2012; Frith, 2007; Sallet et al., 2011; Testard et al., 2022). In other words, this work confirms the specificity of the ‘social brain’ as defined in laboratory conditions as a set of structures specifically involved in social interactions (Frith, 2007; Rushworth et al., 2013). Critically, again, social and ecological functions are tightly intermingled in primates’ natural environment and more specific studies would be needed to clarify how DLPFC-related functions such as working memory are involved in natural conditions, when animals need to face both ecological and social challenges.

This new set of results can be integrated in the general framework of the primate mosaic brain evolution, i.e., the different distinct structures varying in size both within and between species, and reflecting selection for cognitive skills. In line with recently published papers (DeCasien et al., 2022; Smaers et al., 2021; van Schaik et al., 2021), we argue that the relation between ecology, neurobiology, and cognition can be better captured with more specific brain measures, and more specific cognitive operations, than by using the cruder measure of brain size. We believe that comparisons between brain regions have the potential to identify which patterns of brain region evolution can provide insights into the evolution of cognition. Our new set of results are in agreement with a previous study we conducted on another brain region, the VMPFC, critically involved in value-based decision-making (Louail et al., 2019). This study was conducted on 29 brain scans from only five species, such that we could not use a PGLS and instead included phylogenetic distance as a co-regressor, along social and ecological variables. As we did here, we also identified the combination of socio-ecological variables that best predicted neuro-anatomical variability across species. Interestingly, the pattern reported for the whole brain was similar to that of the current study, and the weight of the ecological variable (daily traveled distance) was one order of magnitude greater than the influence of the social variable (group size). As it was the case here, VMPFC and whole brain volumes were strongly correlated (r=0.99), but the size of the VMPFC was predicted by a distinct set of ecological variables (dietary quality and weaning age), with little modulation by group size. Thus, our results from these two combined studies suggest that specific neuro-cognitive entities, established in laboratory studies and defined by a conjunction of specific brain regions and cognitive operations, can be related to specific socio-ecological challenges that animals face in natural conditions.

In conclusion, our results confirm that the size of specific brain regions can be related to socio-ecological variables through the cognitive operations relying on these regions in laboratory conditions. Thus, our approach which aims at articulating cognitive operations reported in laboratory settings with real socio-ecological challenges should provide a clear insight into the neuro-cognitive operations at play in the wild, as well as their evolution in primates. Conversely, integrating realistic socio-ecological challenges can provide a strong insight into the evolution of specific brain functions in primates. Of course, we would need to provide a clearer model regarding how facing socio-ecological challenges can rely upon specific and dynamic cognitive operations, but we believe that this is a critical first step that helps building a common theoretical framework to cross boundaries across behavioral ecology and cognitive neurosciences.

The sources of brain images are provided in the manuscript (methods section), and the measurements that we made are available on the Dryad website (https://doi.org/10.5061/dryad.qfttdz0s4). Socio-ecological data and their sources are fully available in the manuscript and supporting files.

1. 1. Bouret S
   2. Paradis E
   3. Prat S
   4. Castro L
   5. Perez P
   6. Gilissen E
   7. Garcia G

   (2024) Dryad Digital Repository

   Primate PFC comparison: Neuroanatomical measures.

   https://doi.org/10.5061/dryad.qfttdz0s4

1. 1. Akaike H

   (1974) A new look at the statistical model identification

   IEEE Transactions on Automatic Control 19:716–723.

   https://doi.org/10.1109/TAC.1974.1100705

   • Google Scholar
2. 1. Amiez C
   2. Sallet J
   3. Hopkins WD
   4. Meguerditchian A
   5. Hadj-Bouziane F
   6. Ben Hamed S
   7. Wilson CRE
   8. Procyk E
   9. Petrides M

   (2019) Sulcal organization in the medial frontal cortex provides insights into primate brain evolution

   Nature Communications 10:3437.

   https://doi.org/10.1038/s41467-019-11347-x

   • PubMed
   • Google Scholar
3. 1. Arnold C
   2. Matthews LJ
   3. Nunn CL

   (2010) The 10kTrees website: a new online resource for primate phylogeny

   Evolutionary Anthropology 19:114–118.

   https://doi.org/10.1002/evan.20251

   • Google Scholar
4. 1. Barbey AK
   2. Colom R
   3. Paul EJ
   4. Chau A
   5. Solomon J
   6. Grafman JH

   (2014) Lesion mapping of social problem solving

   Brain 137:2823–2833.

   https://doi.org/10.1093/brain/awu207

   • PubMed
   • Google Scholar
5. 1. Barks SK
   2. Calhoun ME
   3. Hopkins WD
   4. Cranfield MR
   5. Mudakikwa A
   6. Stoinski TS
   7. Patterson FG
   8. Erwin JM
   9. Hecht EE
   10. Hof PR
   11. Sherwood CC

   (2015) Brain organization of gorillas reflects species differences in ecology

   American Journal of Physical Anthropology 156:252–262.

   https://doi.org/10.1002/ajpa.22646

   • PubMed
   • Google Scholar
6. 1. Barrett RLC
   2. Dawson M
   3. Dyrby TB
   4. Krug K
   5. Ptito M
   6. D’Arceuil H
   7. Croxson PL
   8. Johnson PJ
   9. Howells H
   10. Forkel SJ
   11. Dell’Acqua F
   12. Catani M

   (2020) Differences in frontal network anatomy across primate species

   The Journal of Neuroscience 40:2094–2107.

   https://doi.org/10.1523/JNEUROSCI.1650-18.2019

   • PubMed
   • Google Scholar
7. 1. Barton RA
   2. Purvis A
   3. Harvey PH

   (1995) Evolutionary radiation of visual and olfactory brain systems in primates, bats and insectivores

   Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 348:381–392.

   https://doi.org/10.1098/rstb.1995.0076

   • PubMed
   • Google Scholar
8. 1. Barton RA
   2. Harvey PH

   (2000) Mosaic evolution of brain structure in mammals

   Nature 405:1055–1058.

   https://doi.org/10.1038/35016580

   • PubMed
   • Google Scholar
9. 1. Bludau S
   2. Eickhoff SB
   3. Mohlberg H
   4. Caspers S
   5. Laird AR
   6. Fox PT
   7. Schleicher A
   8. Zilles K
   9. Amunts K

   (2014) Cytoarchitecture, probability maps and functions of the human frontal pole

   NeuroImage 93 Pt 2:260–275.

   https://doi.org/10.1016/j.neuroimage.2013.05.052

   • PubMed
   • Google Scholar
10. 1. Boesch C

    (1994) Cooperative hunting in wild chimpanzees

    Animal Behaviour 48:653–667.

    https://doi.org/10.1006/anbe.1994.1285

    • Google Scholar
11. 1. Boorman ED
    2. Behrens TEJ
    3. Woolrich MW
    4. Rushworth MFS

    (2009) How green is the grass on the other side? Frontopolar cortex and the evidence in favor of alternative courses of action

    Neuron 62:733–743.

    https://doi.org/10.1016/j.neuron.2009.05.014

    • Google Scholar
12. Book

    1. Borden NM
    2. Stefan C
    3. Forseen SE

    (2015)

    Imaging anatomy of the human brain: a comprehensive atlas including adjacent

    Structures: Springer Publishing Company.

    • Google Scholar
13. 1. Chambers HR
    2. Heldstab SA
    3. O’Hara SJ

    (2021) Why big brains? A comparison of models for both primate and carnivore brain size evolution

    PLOS ONE 16:e0261185.

    https://doi.org/10.1371/journal.pone.0261185

    • PubMed
    • Google Scholar
14. 1. Clutton-Brock TH

    (1989) Review lecture: mammalian mating systems

    Proceedings of the Royal Society of London. B. Biological Sciences 236:339–372.

    https://doi.org/10.1098/rspb.1989.0027

    • PubMed
    • Google Scholar
15. 1. Conard NJ
    2. Serangeli J
    3. Bigga G
    4. Rots V

    (2020) A 300,000-year-old throwing stick from Schöningen, northern Germany, documents the evolution of human hunting

    Nature Ecology & Evolution 4:690–693.

    https://doi.org/10.1038/s41559-020-1139-0

    • PubMed
    • Google Scholar
16. 1. Crystal JD

    (2009) Elements of episodic-like memory in animal models

    Behavioural Processes 80:269–277.

    https://doi.org/10.1016/j.beproc.2008.09.009

    • PubMed
    • Google Scholar
17. 1. DeCasien AR
    2. Williams SA
    3. Higham JP

    (2017) Primate brain size is predicted by diet but not sociality

    Nature Publishing Group 1:1–7.

    https://doi.org/10.1038/s41559-017-0112

    • Google Scholar
18. 1. DeCasien AR
    2. Higham JP

    (2019) Primate mosaic brain evolution reflects selection on sensory and cognitive specialization

    Nature Ecology Evolution 01:1–14.

    https://doi.org/10.1038/s41559-019-0969-0

    • Google Scholar
19. 1. de Cothi W
    2. Nyberg N
    3. Griesbauer E-M
    4. Ghanamé C
    5. Zisch F
    6. Lefort JM
    7. Fletcher L
    8. Newton C
    9. Renaudineau S
    10. Bendor D
    11. Grieves R
    12. Duvelle É
    13. Barry C
    14. Spiers HJ

    (2022) Predictive maps in rats and humans for spatial navigation

    Current Biology 32:3676–3689.

    https://doi.org/10.1016/j.cub.2022.06.090

    • PubMed
    • Google Scholar
20. 1. de Gelder B

    (2023) Social affordances, mirror neurons, and how to understand the social brain

    Trends in Cognitive Sciences 27:218–219.

    https://doi.org/10.1016/j.tics.2022.11.011

    • PubMed
    • Google Scholar
21. 1. DeCasien AR
    2. Barton RA
    3. Higham JP

    (2022) Understanding the human brain: insights from comparative biology

    Trends in Cognitive Sciences 26:432–445.

    https://doi.org/10.1016/j.tics.2022.02.003

    • PubMed
    • Google Scholar
22. 1. Deen B
    2. Schwiedrzik CM
    3. Sliwa J
    4. Freiwald WA

    (2023) Specialized networks for social cognition in the primate brain

    Annual Review of Neuroscience 46:381–401.

    https://doi.org/10.1146/annurev-neuro-102522-121410

    • PubMed
    • Google Scholar
23. 1. de Gelder B
    2. Poyo Solanas M

    (2021) A computational neuroethology perspective on body and expression perception

    Trends in Cognitive Sciences 25:744–756.

    https://doi.org/10.1016/j.tics.2021.05.010

    • Google Scholar
24. 1. Devaine M
    2. San-Galli A
    3. Trapanese C
    4. Bardino G
    5. Hano C
    6. Saint Jalme M
    7. Bouret S
    8. Masi S
    9. Daunizeau J

    (2017) Reading wild minds: A computational assay of theory of mind sophistication across seven primate species

    PLOS Computational Biology 13:e1005833.

    https://doi.org/10.1371/journal.pcbi.1005833

    • Google Scholar
25. 1. Dias R
    2. Robbins TW
    3. Roberts AC

    (1996) Dissociation in prefrontal cortex of affective and attentional shifts

    Nature 380:69–72.

    https://doi.org/10.1038/380069a0

    • PubMed
    • Google Scholar
26. Book

    1. Dixson AF
    2. Press OU

    (2012) Primate sexuality: comparative studies of the prosimians, monkeys, apes, and humans

    Oxford University Press.

    https://doi.org/10.1093/acprof:osobl/9780199544646.001.0001

    • Google Scholar
27. 1. Dunbar RIM

    (1998) The social brain hypothesis

    Evolutionary Anthropology 6:178–190.

    https://doi.org/10.1002/(SICI)1520-6505(1998)6:5<178::AID-EVAN5>3.3.CO;2-P

    • Google Scholar
28. 1. Dureux A
    2. Zanini A
    3. Selvanayagam J
    4. Menon RS
    5. Everling S

    (2023) Gaze patterns and brain activations in humans and marmosets in the Frith-Happé theory-of-mind animation task

    eLife 12:e86327.

    https://doi.org/10.7554/eLife.86327

    • PubMed
    • Google Scholar
29. 1. Ferrucci L
    2. Nougaret S
    3. Ceccarelli F
    4. Sacchetti S
    5. Fascianelli V
    6. Benozzo D
    7. Genovesio A

    (2022) Social monitoring of actions in the macaque frontopolar cortex

    Progress in Neurobiology 218:102339.

    https://doi.org/10.1016/j.pneurobio.2022.102339

    • PubMed
    • Google Scholar
30. 1. Fleming SM
    2. Dolan RJ

    (2012) The neural basis of metacognitive ability

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 367:1338–1349.

    https://doi.org/10.1098/rstb.2011.0417

    • PubMed
    • Google Scholar
31. 1. Frith CD

    (2007) The social brain?

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 362:671–678.

    https://doi.org/10.1098/rstb.2006.2003

    • PubMed
    • Google Scholar
32. Book

    1. Fuster JM

    (2008) The prefrontal

    Cortex: Academic Press.

    https://doi.org/10.1016/B978-0-12-373644-4.00002-5

    • Google Scholar
33. 1. Gabi M
    2. Neves K
    3. Masseron C
    4. Ribeiro PFM
    5. Ventura-Antunes L
    6. Torres L
    7. Mota B
    8. Kaas JH
    9. Herculano-Houzel S

    (2016) No relative expansion of the number of prefrontal neurons in primate and human evolution

    PNAS 113:9617–9622.

    https://doi.org/10.1073/pnas.1610178113

    • PubMed
    • Google Scholar
34. 1. Gallagher HL
    2. Frith CD

    (2003) Functional imaging of “theory of mind.”

    Trends in Cognitive Sciences 7:77–83.

    https://doi.org/10.1016/s1364-6613(02)00025-6

    • PubMed
    • Google Scholar
35. 1. Garcia C
    2. Bouret S
    3. Druelle F
    4. Prat S

    (2021) Balancing costs and benefits in primates: ecological and palaeoanthropological views

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 376:20190667.

    https://doi.org/10.1098/rstb.2019.0667

    • PubMed
    • Google Scholar
36. 1. Genovesio A
    2. Wise SP
    3. Passingham RE

    (2014) Prefrontal–parietal function: from foraging to foresight

    Trends in Cognitive Sciences 18:72–81.

    https://doi.org/10.1016/j.tics.2013.11.007

    • Google Scholar
37. 1. Grafen A

    (1989) The phylogenetic regression

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 326:119–157.

    https://doi.org/10.1098/rstb.1989.0106

    • PubMed
    • Google Scholar
38. 1. Hayden BY
    2. Pearson JM
    3. Platt ML

    (2011) Neuronal basis of sequential foraging decisions in a patchy environment

    Nature Neuroscience 14:933–939.

    https://doi.org/10.1038/nn.2856

    • Google Scholar
39. 1. Henke-von der Malsburg J
    2. Kappeler PM
    3. Fichtel C

    (2020) Linking ecology and cognition: does ecological specialisation predict cognitive test performance?

    Behavioral Ecology and Sociobiology 74:12.

    https://doi.org/10.1007/s00265-020-02923-z

    • Google Scholar
40. 1. Herculano-Houzel S
    2. Collins CE
    3. Wong P
    4. Kaas JH
    5. Lent R

    (2008) The basic nonuniformity of the cerebral cortex

    PNAS 105:12593–12598.

    https://doi.org/10.1073/pnas.0805417105

    • PubMed
    • Google Scholar
41. 1. Herculano-Houzel S
    2. Kaas JH
    3. de Oliveira-Souza R

    (2016) Corticalization of motor control in humans is a consequence of brain scaling in primate evolution

    The Journal of Comparative Neurology 524:448–455.

    https://doi.org/10.1002/cne.23792

    • PubMed
    • Google Scholar
42. 1. Herculano-Houzel S

    (2017) Numbers of neurons as biological correlates of cognitive capability

    Current Opinion in Behavioral Sciences 16:1–7.

    https://doi.org/10.1016/j.cobeha.2017.02.004

    • Google Scholar
43. 1. Herculano-Houzel S

    (2018) Embodied (embrained?) cognitive evolution, at last!

    Comparative Cognition & Behavior Reviews 13:91–94.

    https://doi.org/10.3819/CCBR.2018.130009

    • Google Scholar
44. 1. Isler K
    2. van Schaik CP

    (2012) Allomaternal care, life history and brain size evolution in mammals

    Journal of Human Evolution 63:52–63.

    https://doi.org/10.1016/j.jhevol.2012.03.009

    • Google Scholar
45. 1. John JP
    2. Yashavantha BS
    3. Gado M
    4. Veena R
    5. Jain S
    6. Ravishankar S
    7. Csernansky JG

    (2007) A proposal for MRI-based parcellation of the frontal pole

    Brain Structure & Function 212:245–253.

    https://doi.org/10.1007/s00429-007-0157-x

    • PubMed
    • Google Scholar
46. 1. Koechlin E

    (2016) Prefrontal executive function and adaptive behavior in complex environments

    Current Opinion in Neurobiology 37:1–6.

    https://doi.org/10.1016/j.conb.2015.11.004

    • PubMed
    • Google Scholar
47. 1. Kolling N
    2. Behrens TEJ
    3. Mars RB
    4. Rushworth MFS

    (2012) Neural mechanisms of foraging

    Science 336:95–98.

    https://doi.org/10.1126/science.1216930

    • PubMed
    • Google Scholar
48. 1. Krebs JR
    2. Sherry DF
    3. Healy SD
    4. Perry VH
    5. Vaccarino AL

    (1989) Hippocampal specialization of food-storing birds

    PNAS 86:1388–1392.

    https://doi.org/10.1073/pnas.86.4.1388

    • PubMed
    • Google Scholar
49. 1. Levy R
    2. Goldman-Rakic PS

    (2000) Segregation of working memory functions within the dorsolateral prefrontal cortex

    Experimental Brain Research Experimentelle Hirnforschung Expérimentation Cérébrale 133:23–32.

    https://doi.org/10.1007/s002210000397

    • PubMed
    • Google Scholar
50. 1. Lin WJ
    2. Horner AJ
    3. Bisby JA
    4. Burgess N

    (2015) Medial prefrontal cortex: adding value to imagined scenarios

    Journal of Cognitive Neuroscience 27:1957–1967.

    https://doi.org/10.1016/j.neuron.2005.01.010

    • Google Scholar
51. 1. Louail M
    2. Gilissen E
    3. Prat S
    4. Garcia C
    5. Bouret S

    (2019) Refining the ecological brain: Strong relation between the ventromedial prefrontal cortex and feeding ecology in five primate species

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 118:262–274.

    https://doi.org/10.1016/j.cortex.2019.03.019

    • PubMed
    • Google Scholar
52. Book

    1. Luria AR

    (1973)

    The Working

    Brain: Basic Books (AZ.

    • Google Scholar
53. 1. Maguire EA
    2. Gadian DG
    3. Johnsrude IS
    4. Good CD
    5. Ashburner J
    6. Frackowiak RS
    7. Frith CD

    (2000) Navigation-related structural change in the hippocampi of taxi drivers

    PNAS 97:4398–4403.

    https://doi.org/10.1073/pnas.070039597

    • Google Scholar
54. 1. Manson JH

    (1997) Primate consortships: a critical review

    Current Anthropology 38:353–374.

    https://doi.org/10.1086/204623

    • Google Scholar
55. 1. Mansouri FA
    2. Koechlin E
    3. Rosa MGP
    4. Buckley MJ

    (2017) Managing competing goals — a key role for the frontopolar cortex

    Nature Reviews Neuroscience 18:645–657.

    https://doi.org/10.1093/brain/awq229

    • Google Scholar
56. 1. Martin RD

    (1981) Relative brain size and basal metabolic rate in terrestrial vertebrates

    Nature 293:57–60.

    https://doi.org/10.1038/293057a0

    • PubMed
    • Google Scholar
57. 1. Martins EP
    2. Hansen TF

    (1997) Phylogenies and the comparative method: a general approach to incorporating phylogenetic information into the analysis of interspecific data

    The American Naturalist 149:646–667.

    https://doi.org/10.1086/286013

    • Google Scholar
58. Book

    1. McKinney T
    2. Waters S
    3. Rodrigues MA

    (2023)

    Primates in Anthropogenic Landscapes: Exploring Primate Behavioural Flexibility Across Human

    Contexts: Springer.

    • Google Scholar
59. 1. Milton K

    (1981) Distribution patterns of tropical plant foods as an evolutionary stimulus to primate mental development

    American Anthropologist 83:534–548.

    https://doi.org/10.1525/aa.1981.83.3.02a00020

    • Google Scholar
60. 1. Milton K

    (1993) Diet and primate evolution

    Scientific American 269:86–93.

    https://doi.org/10.1038/scientificamerican0893-86

    • PubMed
    • Google Scholar
61. 1. Miyake A
    2. Friedman NP
    3. Emerson MJ
    4. Witzki AH
    5. Howerter A
    6. Wager TD

    (2000) The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis

    Cognitive Psychology 41:49–100.

    https://doi.org/10.1006/cogp.1999.0734

    • Google Scholar
62. 1. Ongür D
    2. Ferry AT
    3. Price JL

    (2003) Architectonic subdivision of the human orbital and medial prefrontal cortex

    The Journal of Comparative Neurology 460:425–449.

    https://doi.org/10.1002/cne.10609

    • PubMed
    • Google Scholar
63. 1. Pagel M

    (1999) The Maximum likelihood approach to reconstructing ancestral character states of discrete characters on phylogenies

    Systematic Biology 48:612–622.

    https://doi.org/10.1080/106351599260184

    • Google Scholar
64. 1. Paradis E
    2. Schliep K

    (2019) ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R

    Bioinformatics 35:526–528.

    https://doi.org/10.1093/bioinformatics/bty633

    • Google Scholar
65. 1. Passingham D
    2. Sakai K

    (2004) The prefrontal cortex and working memory: Physiology and brain imaging

    Current Opinion in Neurobiology 14:163–168.

    https://doi.org/10.1016/j.conb.2004.03.003

    • Google Scholar
66. Book

    1. Passingham RE
    2. Passingham RE
    3. Wise SP

    (2012) The neurobiology of the prefrontal cortex: anatomy, evolution, and the

    In: Richard EP, Steven P, editors. Wise - Google Books. Oxford University Press. pp. 1–3.

    https://doi.org/10.1093/acprof:osobl/9780199552917.001.0001

    • Google Scholar
67. 1. Petrides M

    (2005) Lateral prefrontal cortex: architectonic and functional organization

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 360:781–795.

    https://doi.org/10.1098/rstb.2005.1631

    • PubMed
    • Google Scholar
68. 1. Petrides M
    2. Tomaiuolo F
    3. Yeterian EH
    4. Pandya DN

    (2012) The prefrontal cortex: Comparative architectonic organization in the human and the macaque monkey brains

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 48:46–57.

    https://doi.org/10.1016/j.cortex.2011.07.002

    • Google Scholar
69. 1. Powell LE
    2. Isler K
    3. Barton RA

    (2017) Re-evaluating the link between brain size and behavioural ecology in primates

    Proceedings of the Royal Society B 284:20171765.

    https://doi.org/10.1098/rspb.2017.1765

    • Google Scholar
70. 1. Preuss TM
    2. Wise SP

    (2022) Evolution of prefrontal cortex

    Neuropsychopharmacology 47:3–19.

    https://doi.org/10.1038/s41386-021-01076-5

    • PubMed
    • Google Scholar
71. 1. Ramnani N
    2. Owen AM

    (2004) Anterior prefrontal cortex: insights into function from anatomy and neuroimaging

    Nature Reviews Neuroscience 5:184–194.

    https://doi.org/10.1038/nrn1343

    • Google Scholar
72. 1. Roberts AC
    2. Tomic DL
    3. Parkinson CH
    4. Roeling TA
    5. Cutter DJ
    6. Robbins TW
    7. Everitt BJ

    (2007) Forebrain connectivity of the prefrontal cortex in the marmoset monkey (Callithrix jacchus): An anterograde and retrograde tract‐tracing study

    Journal of Comparative Neurology 502:86–112.

    https://doi.org/10.1002/cne.21300

    • Google Scholar
73. 1. Rosati AG

    (2017) Foraging cognition: reviving the ecological intelligence hypothesis

    Trends in Cognitive Sciences 21:691–702.

    https://doi.org/10.1016/j.tics.2017.05.011

    • Google Scholar
74. 1. Roumazeilles L
    2. Eichert N
    3. Bryant KL
    4. Folloni D
    5. Sallet J
    6. Vijayakumar S
    7. Foxley S
    8. Tendler BC
    9. Jbabdi S
    10. Reveley C
    11. Verhagen L
    12. Dershowitz LB
    13. Guthrie M
    14. Flach E
    15. Miller KL
    16. Mars RB

    (2020) Longitudinal connections and the organization of the temporal cortex in macaques, great apes, and humans

    PLOS Biology 18:e3000810.

    https://doi.org/10.1371/journal.pbio.3000810

    • PubMed
    • Google Scholar
75. 1. Rudebeck PH
    2. Walton ME
    3. Smyth AN
    4. Bannerman DM
    5. Rushworth MFS

    (2006) Separate neural pathways process different decision costs

    Nature Neuroscience 9:1161–1168.

    https://doi.org/10.1038/nn1756

    • Google Scholar
76. 1. Rushworth MFS
    2. Mars RB
    3. Sallet J

    (2013) Are there specialized circuits for social cognition and are they unique to humans

    Current Opinion in Neurobiology 23:436–442.

    https://doi.org/10.1016/j.conb.2012.11.013

    • Google Scholar
77. 1. Sailer LD
    2. Gaulin SJC
    3. Boster JS
    4. Kurland JA

    (1985) Measuring the relationship between dietary quality and body size in primates

    Primates 26:14–27.

    https://doi.org/10.1007/BF02389044

    • Google Scholar
78. Book

    1. Saleem KS
    2. Logothetis NK

    (2007)

    A combined MRI and histology atlas of the rhesus monkey brain in stereotaxic coordinates

    Semantic Scholar.

    • Google Scholar
79. 1. Sallet J
    2. Mars RB
    3. Noonan MP
    4. Andersson JL
    5. O’Reilly JX
    6. Jbabdi S
    7. Croxson PL
    8. Jenkinson M
    9. Miller KL
    10. Rushworth MFS

    (2011) Social network size affects neural circuits in macaques

    Science 334:697–700.

    https://doi.org/10.1126/science.1210027

    • PubMed
    • Google Scholar
80. 1. Sallet J
    2. Mars RB
    3. Noonan MP
    4. Neubert FX
    5. Jbabdi S
    6. O’Reilly JX
    7. Filippini N
    8. Thomas AG
    9. Rushworth MF

    (2013) The organization of dorsal frontal cortex in humans and macaques

    The Journal of Neuroscience 33:12255–12274.

    https://doi.org/10.1523/JNEUROSCI.5108-12.2013

    • PubMed
    • Google Scholar
81. 1. Schaik CP

    (2016) The primate origins of human nature

    American Journal of Human Biology 28:950–951.

    https://doi.org/10.1002/ajhb.22939

    • Google Scholar
82. 1. Semendeferi K
    2. Armstrong E
    3. Schleicher A
    4. Zilles K
    5. Hoesen GW

    (2001) Prefrontal cortex in humans and apes: a comparative study of area 10

    American Journal of Physical Anthropology 114:224–241.

    https://doi.org/10.1002/1096-8644(200103)114:3<224::AID-AJPA1022>3.0.CO;2-I

    • Google Scholar
83. 1. Shultz S
    2. Dunbar RIM

    (2022) Socioecological complexity in primate groups and its cognitive correlates

    Philosophical Transactions of the Royal Society B 377:20210296.

    https://doi.org/10.1098/rstb.2021.0296

    • Google Scholar
84. 1. Smaers JB
    2. Soligo C

    (2013) Brain reorganization, not relative brain size, primarily characterizes anthropoid brain evolution

    Proceedings of the Royal Society B 280:e0269.

    https://doi.org/10.1098/rspb.2013.0269

    • Google Scholar
85. 1. Smaers JB
    2. Gómez-Robles A
    3. Parks AN
    4. Sherwood CC

    (2017) Exceptional evolutionary expansion of prefrontal cortex in great apes and humans

    Current Biology 27:714–720.

    https://doi.org/10.1016/j.cub.2017.01.020

    • Google Scholar
86. 1. Smaers JB
    2. Rothman RS
    3. Hudson DR
    4. Balanoff AM
    5. Beatty B
    6. Dechmann DKN
    7. Safi K

    (2021) The evolution of mammalian brain size

    Science Advances 7:18.

    https://doi.org/10.1126/sciadv.abe2101

    • PubMed
    • Google Scholar
87. 1. St Amant R
    2. Horton TE

    (2008) Revisiting the definition of animal tool use

    Animal Behaviour 75:1199–1208.

    https://doi.org/10.1016/j.anbehav.2007.09.028

    • Google Scholar
88. 1. Stanley DA
    2. Adolphs R

    (2013) Toward a neural basis for social behavior

    Neuron 80:816–826.

    https://doi.org/10.1016/j.neuron.2013.10.038

    • PubMed
    • Google Scholar
89. 1. Street SE
    2. Navarrete AF
    3. Reader SM
    4. Laland KN

    (2017) Coevolution of cultural intelligence, extended life history, sociality, and brain size in primates

    PNAS 114:7908–7914.

    https://doi.org/10.1073/pnas.1620734114

    • PubMed
    • Google Scholar
90. 1. Testard C
    2. Brent LJN
    3. Andersson J
    4. Chiou KL
    5. Negron-Del Valle JE
    6. DeCasien AR
    7. Acevedo-Ithier A
    8. Stock MK
    9. Antón SC
    10. Gonzalez O
    11. Walker CS
    12. Foxley S
    13. Compo NR
    14. Bauman S
    15. Ruiz-Lambides AV
    16. Martinez MI
    17. Skene JHP
    18. Horvath JE
    19. Unit CBR
    20. Higham JP
    21. Miller KL
    22. Snyder-Mackler N
    23. Montague MJ
    24. Platt ML
    25. Sallet J

    (2022) Social connections predict brain structure in a multidimensional free-ranging primate society

    Science Advances 8:eabl5794.

    https://doi.org/10.1126/sciadv.abl5794

    • PubMed
    • Google Scholar
91. 1. Tsujimoto S
    2. Genovesio A
    3. Wise SP

    (2011) Frontal pole cortex: encoding ends at the end of the endbrain

    Trends in Cognitive Sciences 15:169–176.

    https://doi.org/10.1016/j.tics.2011.02.001

    • PubMed
    • Google Scholar
92. 1. van Schaik CP
    2. Triki Z
    3. Bshary R
    4. Heldstab SA

    (2021) A farewell to the encephalization quotient: a new brain size measure for comparative primate cognition

    Brain, Behavior and Evolution 96:1–12.

    https://doi.org/10.1159/000517013

    • Google Scholar
93. 1. Vikbladh OM
    2. Meager MR
    3. King J
    4. Blackmon K
    5. Devinsky O
    6. Shohamy D
    7. Daw ND

    (2019) Hippocampal contributions to model-based planning and spatial memory

    Neuron 102:683–693.

    https://doi.org/10.1016/j.neuron.2019.02.014

    • PubMed
    • Google Scholar
94. 1. Völter CJ
    2. Reindl E
    3. Felsche E
    4. Civelek Z
    5. Whalen A
    6. Lugosi Z
    7. Duncan L
    8. Herrmann E
    9. Call J
    10. Seed AM

    (2022) The structure of executive functions in preschool children and chimpanzees

    Scientific Reports 12:6456.

    https://doi.org/10.1038/s41598-022-08406-7

    • Google Scholar
95. 1. Wong RK
    2. Selvanayagam J
    3. Johnston KD
    4. Everling S

    (2023) Delay-related activity in marmoset prefrontal cortex

    Cerebral Cortex 33:3523–3537.

    https://doi.org/10.1093/cercor/bhac289

    • Google Scholar
96. 1. Yoshida K
    2. Saito N
    3. Iriki A
    4. Isoda M

    (2012) Social error monitoring in macaque frontal cortex

    Nature Neuroscience 15:1307–1312.

    https://doi.org/10.1038/nn.3180

    • PubMed
    • Google Scholar
97. Book

    1. Zuberbühler K
    2. Janmaat KRL

    (2010) Foraging cognition in nonhuman primates

    In: Platt ML, Ghazanfar AA, editors. Primate neuroethology. New York: Oxford University Press. pp. 64–83.

    https://doi.org/10.1093/acprof:oso/9780195326598.003.0004

    • Google Scholar

Author details

1. Sebastien Bouret

   Team Motivation Brain & Behavior, ICM – Brain and Spine Institute, Paris, France

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   sebastien.bouret@icm-institute.org

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2279-6161

2. Emmanuel Paradis

   ISEM, Univ. Montpellier, IRD, EPHE, Montpellier, France

   Contribution

   Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Visualization, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Sandrine Prat

   UMR 7194 (HNHP), MNHN/CNRS/UPVD, Musée de l’Homme, Paris, France

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Laurie Castro

   1. UMR 7194 (HNHP), MNHN/CNRS/UPVD, Musée de l’Homme, Paris, France
   2. UMR 7206 Eco-anthropologie, CNRS – MNHN – Univ. Paris Cité, Musée de l'Homme, Paris, France

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Pauline Perez

   Team Motivation Brain & Behavior, ICM – Brain and Spine Institute, Paris, France

   Contribution

   Data curation, Formal analysis, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

6. Emmanuel Gilissen

   1. Department of African Zoology, Royal Museum for Central Africa, Tervuren, Belgium
   2. Université Libre de Bruxelles, Laboratory of Histology and Neuropathology, Brussels, Belgium

   Contribution

   Resources, Data curation, Writing - review and editing

   Competing interests

   No competing interests declared

7. Cecile Garcia

   UMR 7206 Eco-anthropologie, CNRS – MNHN – Univ. Paris Cité, Musée de l'Homme, Paris, France

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

• 827

  views

• 62

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.