Humans have much larger brains than other primates, but it is not clear exactly when and how this difference emerged during evolution. Some scientists believe that the expansion of a part of the brain called the neocortex – which handles sight, hearing, conscious decision-making and language – drove the increase in the size of the human brain. Newer studies have challenged that idea.

One way to learn more about how humans evolved bigger brains is to compare the size of the brain, and specific parts of the brain, between humans and our closest relatives: non-human primates. To make accurate comparisons, scientists must account for many factors. Closely related primates may have more similar traits because they more recently shared a common ancestor. This means the evolutionary relationships between species need to be considered. Larger animals also tend to have larger brains so it is important to consider body size, too.

Now, Miller at al. show that the human brain is much larger than expected even after accounting for these factors, and that increases in brain size accelerated over the course of early human evolution. In the analyses, the brain and skull sizes of different living primate species, like chimpanzees and gorillas, and fossils of extinct primates, including Neanderthals, were compared using mathematical models.

These findings suggest that larger brains provided fitness advantages that led to large brain sizes in modern humans and Neanderthals. These increases in brain size were not driven by disproportionate growth in the neocortex alone, but rather by increases in the size of many parts of the brain. Increases in the relative size of the cerebellum, which is essential for balance and movement, were also important.

Primates vary almost a thousand-fold in endocranial volume – a measure which closely approximates brain size – ranging from 1.63 mL in mouse lemurs (Isler et al., 2008) to 1478 mL in humans (Robson and Wood, 2008). Body size is perhaps the most important statistical predictor of brain size across primates, with larger bodied species having larger brains, but substantial variation remains after accounting for the effects of body size (Isler et al., 2008). While numerous comparative studies have sought to identify ecological, behavioral, and cognitive correlates of this variability (Barton, 1999; MacLean et al., 2014; DeCasien et al., 2017; Powell et al., 2017; Noonan et al., 2018), much less is known about the evolutionary patterns and processes that generated extant variation in brain size within the primate clade, how these differ for different components of the brain, or the degree to which the brain phenotypes of particular species, such as humans, are the result of exceptional patterns of evolutionary change.

A common approach to investigating human uniqueness is to test whether humans fall ‘significantly’ far from a regression line, for example by regressing brain size on body mass (Azevedo et al., 2009; de Sousa et al., 2010; Herculano-Houzel and Kaas, 2011). One surprising recent result reported from such an analysis is that the mass of the human brain is only 10% greater than expected for a primate of human body mass (Azevedo et al., 2009). However, such non-phylogenetic methods may give misleading results because they fail to incorporate trait co-variation among species that results from shared evolutionary history. Valid analysis requires methods that account for phylogeny both when estimating scaling parameters and when evaluating deviations from scaling patterns exhibited by individual species (Garland and Ives, 2000; Ross et al., 2004; Organ et al., 2011). An additional source of error arises if the species being investigated is included in the regression model (e.g. Azevedo et al., 2009), particularly when, as for humans, the phenotypic trait lies at the extreme of the distribution for the other species in the analysis. This procedure would reduce the magnitude of deviations from expected trait values for lineages that have undergone exceptional change, and in the case of humans, would bias the results toward failing to detect uniqueness.

Comparative methods make it possible to incorporate phylogeny into analyses and to model phenotypic evolution in ways that uncover hitherto hidden patterns. Such methods are now being applied to a wide variety of traits (e.g. Vining and Nunn, 2016; Pagel, 1999; Orme, 2013), including brain size. Pagel (2002) estimated phylogenetic scaling parameters to characterize the evolutionary trajectory of endocranial volume (ECV) in fossil hominins. His analyses revealed that ECV evolution accelerated towards the present. As this analysis did not account for body size, it is not clear to what extent this pattern reflects changes in brain size independent of body size. Montgomery et al. (2010) used ancestral state reconstruction with fossil data to demonstrate a directional trend in primate brain size evolution and to identify branches in the primate phylogeny along which exceptional evolutionary change occurred. They found that while the absolute change in the mass of the human brain was exceptional, the rate of change relative to body size was not. Phylogenetic methods have also been used to examine how specific brain components evolved and the extent to which the branch leading to humans exhibited unusual amounts or rates of change in the size of these components (Barton and Venditti, 2013; Barton and Venditti, 2014). Recently, Lewitus (2018) suggested that comparative analyses of neuroanatomical data can be improved by incorporating and comparing results from different evolutionary models.

Here, we use phylogenetic methods to model the evolution of brain size and to identify exceptional evolutionary change along phylogenetic branches. We employ three methods: The first method models trait evolution both as a multi-optima Ornstein-Uhlenbeck (OU) process (which incorporates stabilizing selection and drift) and as a Brownian motion process (Felsenstein, 1985), and then compares the fit of the two models. In cases where the OU model is favored, exceptional patterns of trait evolution are indicated by recent shifts in adaptive optima in humans’ (or other species’) evolutionary lineage. In cases where the Brownian model is favored, we apply our second method, which is a phylogenetic outlier test that uses phylogenetic generalized least squares (PGLS) to predict a phenotype for a species and then compares observed and predicted values. With this method, we can assess whether humans are a phylogenetic ‘outlier’ relative to expectations based on their phylogenetic position and trait covariation in other primate species. Our last method tests for directional and accelerating evolution by fitting phylogenetic scaling parameters to data on deviation from trait expectations and evolutionary time, building on previous efforts with these approaches (Pagel, 2002).

Using the first two methods, we investigate the evolution of absolute brain size and brain size relative to body mass within primates. Absolute brain volume has been shown to predict cognitive ability in primates better than other metrics that account for body mass (MacLean et al., 2014; Deaner et al., 2007). However, brain size is highly correlated with body size (Isler et al., 2008), and as such it is difficult to interpret the significance of brain size alone. Additionally, accounting for body mass gives more insights into the significance of brain size in life history processes, as relative brain size better approximates relative investment in cognitive ability. Accounting for body mass is also important as the relationship between this trait and brain size is associated with scaling effects that reflect conservation of neural function, such as preservation of somatosensory acuity across large surface areas (St Wecker and Farel, 1994) and compensation for increased neural conduction distances in larger animals through (i) larger neuron and axon sizes, increased myelination, and increased white matter volume, all of which result in reduced neuron density (Barton, 2012; Wang et al., 2008; Collins et al., 2013) and (ii) increased neural resources devoted to prediction-based sensorimotor control that result from escalating neural conduction delays as body size increases (More et al., 2010). Other measures of relative brain size such as encephalization quotients, ratios, and residuals have been used in the past, but all make theoretical assumptions about the underlying relationship between brain and body size evolution that may not hold. Using relative measures can bias parameter estimates and is not recommended as a good statistical practice (Freckleton, 2002). Instead, an empirical approach is preferred in which the covariation of brain size with body size is accounted for within a statistical model that also accounts for phylogenetic history (such as PGLS).

We also apply the first two phylogenetic comparative methods to investigate the evolution of major brain structures involving the neocortex, cerebellum, and medulla. It is widely assumed that the neocortex expanded disproportionately relative to other brain structures during the evolution of anthropoid primates and most particularly in human evolution (Kriegstein et al., 2006; Geschwind and Rakic, 2013; Florio and Huttner, 2014). Surprisingly however, direct tests of this hypothesis are lacking, despite the focus of much evolutionary and developmental neuroscience on the neocortex as the site of interest for understanding human uniqueness and its developmental mechanisms (Mitchell and Silver, 2017). Recent evidence suggests that the cerebellum may have contributed more to human brain evolution than previously appreciated: it underwent rapid evolutionary expansion in the great ape clade including hominins (Barton and Venditti, 2014; Smaers et al., 2018) and has been implicated in shape changes of the brain in hominin fossil endocasts (Kochiyama et al., 2018; Neubauer et al., 2018). Molecular evidence now corroborates the proposal that selection on cerebellar function was an important feature of hominoid and hominin brain evolution (Sousa et al., 2017), with changes in protein-coding genes implicated in cerebellar development more likely to have evolved adaptively in apes than those implicated in neocortical development (Harrison and Montgomery, 2017). It therefore appears that the neocortex and cerebellum have had different evolutionary trajectories in primate evolutionary history. More research is needed to document and understand these patterns.

We examined volumetric change in the neocortex and cerebellum relative to both body mass and the volume of the rest of the brain. As a check to establish whether changes in evolutionary patterns for relative neocortex and cerebellum size are primarily attributable to changes in those structures or to changes in the rest of the brain, we investigated the evolution of the rest of the brain relative to body mass. We also conducted analyses of the volume of the medulla relative to body mass and the volume of the rest of the brain. The relative volume of the medulla does not vary significantly across clades (Barton, 2000) and as such it has not been attributed a major role in brain expansion. For the analyses of fossil species, brain component volumes are not available; thus, analyses of these lineages are restricted to overall brain size (ECV).

Although our main focus is on broad patterns across primate phylogeny and on the extent to which human brain evolution fits or departs from these patterns, we also examined brain evolution in other species that are considered to be unusually large-brained, such as the aye-aye (Daubentonia) and capuchins (Cebinae) (Isler et al., 2008; Pagel and Harvey, 1989). Our analyses also help to identify other primate species that have experienced exceptional expansion or reduction of the brain or its components, generating new questions for future research on exceptional brain evolution in primates.

We used our third method to characterize patterns of brain evolution in humans and extinct hominins. Pagel (2002) conducted similar analyses of raw ECV. Our analyses advance his findings in two ways. First, we incorporate body mass as a predictor. Second, we focus on the deviation from brain size expectations, based on the PGLS methods used to assess outlier status. Our findings therefore provide insights to the evolutionary trajectory of exceptional hominin ECV relative to primate-wide brain-body mass scaling relationships.

Our phylogenetic analyses revealed that the human brain is 238% larger than the size expected for a primate of similar body mass and phylogenetic position. The exceptional size of the human brain was achieved through progressive scaling shifts towards larger size over several million years of hominin evolution, and the evolution towards increased brain size relative to expectations based on primate scaling patterns accelerated over time. These findings add an important dimension to previous observations of gradual phyletic increases in hominin brain size. Du et al. (2018) fit six evolutionary models to within- and between-lineage change in hominin brain sizes (random walk, gradualism, stasis, punctuated equilibrium, stasis-random walk and stasis-gradualism), obtaining the best fit for a gradualism model. However, their non-phylogenetic analysis did not test explicitly for accelerating directional increase. Our findings extend the results obtained by Pagel (2002) on absolute cranial volume, as the pattern of accelerating evolution is found even after accounting for body size. The pattern of accelerating brain size increase documented here is consistent with hypotheses that postulate a co-evolutionary positive feedback process driving human brain evolution, such as feedback between brain size and culture or language (Wills, 1993; Deacon, 1998) or between the brain sizes of conspecifics engaged in a socio-cognitive evolutionary arms race (Dunbar, 1998; Miller, 2011).

While humans clearly have the largest relative brain size among extant primates, anatomically modern humans were closely matched by H. neanderthalensis. However, even when accounting for the close phylogenetic relationship between humans and H. neanderthalensis and the exceptionally large brain of the latter, the human brain is still much larger than expected: humans were identified as strongly supported outliers when their ECV (relative to body mass) was predicted by phenotypic data from all primates, including H. neanderthalensis. This pattern was not reciprocal, however; H. neanderthalensis was not significantly different from other primates when H. sapiens was included in the model.

Significant variation exists between estimates of ECV and body mass made from different fossil specimens of the same hominin species (Robson and Wood, 2008). Thus, using single specimens to represent a species would not be a good statistical practice. We used a dataset in which almost all mean species values were calculated from multiple fossil specimens (Table 1). Unfortunately, we could not explicitly account for intraspecific variation in our analyses, as the multi-optima OU model fitting approach and the outlier test are unable to account for variation in both a trait and predictor variable. It would therefore be worthwhile to revisit our analyses as new phylogenetic comparative methods that can account for intraspecific variation become available. Additionally, data quality will likely improve over time. More hominin fossils will be discovered, increasing sample sizes for estimated ECV and body mass.

The hominin phylogeny will also likely become better resolved and more complete. We accounted for some phylogenetic uncertainty by repeating our analyses with an alternate phylogeny. The use of different phylogenies influenced outcomes of some statistical tests, as the Brownian model favored when we used the hominin phylogeny and OU model was favored when we used the alternate hominin phylogeny. However, we found that all of the OU models we fit inferred the same pattern of evolution towards larger ECV along the human lineage. The results of our outlier tests and PGLS model fitting – which assume a Brownian mode of evolution – also detected this pattern on different phylogenies. Collectively, these results indicate that our findings are likely to be robust to variations in assumed evolutionary relationships, and potentially to assumptions about the mode of evolution.

It is widely assumed that primate brain size evolution in general, and the large size of the human brain in particular, reflects expansion of the neocortex relative to other brain structures (Kriegstein et al., 2006; Rakic, 2009). Our results contradict this assumption: human neocortical volume was exceptionally large relative to body mass, but not exceptional relative the volume of the rest of the brain. We documented only one shift in neocortex size relative to the rest of the brain during primate evolution: an increase at the origin of all haplorrhines. This shift may be related to the visual specializations of haplorrhines for high-acuity photic vision, mediated by extensive cortical visual areas that make up over 50% of the cortex in these species (Drury et al., 1996; Barton, 1998; Barton, 2007). On branches postdating the split between haplorrhines and strepsirrhines, neocortex size is largely predictable from its scaling relationship to the rest of the brain, in line with the proposed importance of cortical-subcortical connectivity in primate brain evolution (Whiting and Barton, 2003).

In contrast, we found that the cerebellum increased in size relative to the rest of the brain on the branch leading to apes. This finding is consistent with the results of recent studies implicating the cerebellum, and especially the lateral cerebellum, in brain expansion in apes and some other mammalian lineages (Barton and Venditti, 2014; Smaers et al., 2018; MacLeod et al., 2003). Our findings also reinforce the argument that subcortical structures should be given greater consideration in studies of mammalian brain evolution and cognition (Barton, 2012; Miller and Clark, 2018). Cerebellar specialization in apes may have been initiated by the demands on motor control and route-planning imposed by arboreal below-branch locomotion and/or by complex extractive foraging (Barton and Venditti, 2014; Barton, 2012). The fact that shifts in the relative size of neocortex and cerebellum occurred on different parts of the tree supports the theory of mosaic brain evolution (Barton and Harvey, 2000) and suggests that no single adaptive hypothesis is likely to be capable of accounting for primate brain evolution; rather, different selection pressures, on different information-processing capacities, likely operated at different times on different lineages.

Consistent with previous studies, we found that the medulla expanded in humans (positive outlier status for medulla volume relative to body mass), but to a lesser degree than other structures (negative outlier status for medulla volume relative to the rest of the brain). Relative to body mass, medulla volume has been shown to be much less variable across taxa than other brain structures, particularly compared to the neocortex and cerebellum. For example, unlike neocortex and cerebellum, medulla volume does not differ significantly between insectivores, strepsirrhines and haplorrhines (Barton, 2000). Accordingly, we found that after controlling for either body mass or brain size, the evolution of the medulla was not modulated by selection towards a stationary optimum in the primate clade. These results further support mosaic brain evolution (Barton and Harvey, 2000), and also suggest that scaling constraints related to connectivity with other brain regions (Montgomery et al., 2016) was less critical for the medulla than for the neocortex and cerebellum.

Several non-human primate species exhibited exceptional brain evolution in one trait or another, but only humans showed exceptional brain evolution for multiple brain components. As predicted, we detected shifts towards larger brain size on the terminal branches leading to D. madagascariensis, and on the branch leading to the Cebinae clade. Large brain size in Daubentonia and Cebinae has been attributed to extractive foraging and tool use (Kaufman, 2005; Melin et al., 2014; Parker, 2015). Although not one of our a priori expectations, we also documented shifts towards smaller brain size on branches leading to several clades, including Alouatta. We also found that two Gorilla species exhibit a smaller brain or neocortex size relative to body mass than expected. Given the extremely large body mass of Gorilla species, these unique traits may be the byproduct of a body mass increase rather than a reduction in brain size. Also unexpectedly, two Pan troglodytes sub-species were found to have exceptionally large and small ECV relative to body mass respectively. However, because more closely related species are weighed more heavily when BayesModelS generates distributions of predicted trait values, sister taxa deviating from expectations in opposite directions could result in both taxa being identified as outliers, even if they both conform to patterns of brain-body scaling for other primates. If the trait distributions for each species overlap significantly, then accounting for intraspecific variation in future analyses could remedy this problem.

The unexpected patterns that we observed amongst non-human primates raise several questions for further research. Given the well-established positive correlation between overall brain size and extended life history (Isler and van Schaik, 2009; Sol, 2009; González-Lagos et al., 2010), what are the life history implications of mosaic shifts in the sizes of different structures, and do these support any specific interpretations of the correlation between brain size and life histories? One hypothesis, the developmental costs hypothesis, is that large brains simply take longer to grow and mature, leading to extended periods of maternal investment and slower maturation, with other life history correlates of brain size being byproducts of developmental prolongation. Support for this hypothesis is provided by the finding that, amongst mammals, the durations of gestation and lactation have independent effects on pre- and postnatal brain growth, and once these effects are accounted for, other life history correlates are non-significant (Barton and Capellini, 2011). Despite their generally correlated evolution (MacLeod et al., 2003), we found shifts in the relative size of neocortex and cerebellum on different parts of the phylogenetic tree. Because these two structures have different developmental trajectories, the developmental costs hypothesis predicts different life history correlates; this prediction has now received support (Powell et al., 2019). Further work is needed to establish exactly what developmental changes allowed for the neocortex and cerebellum to rest-of-brain scaling rules to change at the origin of haplorrhines and hominoids, respectively.

Another area of interest concerns the cases we found of brain or brain component size reduction. Montgomery et al. (2010) found that brain size reductions were rare during primate evolution, and that there was a general trend for brain size to increase across multiple branches of the phylogeny. This raises questions for future work concerning the causes, developmental mechanisms and functional implications of specific types of size reduction, such as those that we uncovered in brain size relative to body size in Alouatta and other clades, and in neocortex size relative to the rest of the brain in N. larvatus.

Finally, a key question that has attracted considerable attention concerns the ecological and social drivers of brain size and structure across large-scale evolutionary radiations. It has become increasingly apparent that correlations between overall brain size and behavioral ecology needed to be treated with caution (Powell et al., 2017; Healy and Rowe, 2007; Wartel et al., 2018). However, as suggested by the hypothesis of mosaic brain evolution, correlations between ecology and individual, less functionally heterogenous brain components may be more reliable and robust (Barton and Venditti, 2014; Barton, 2012; Barton, 2007; Whiting and Barton, 2003; Montgomery et al., 2016; Barton et al., 1995). Our analyses focused on gross subdivisions within the brain, and we suggest that further insights could be obtained by applying the phylogenetic methods used in this paper to more fine-grained neuro-anatomical data, using this approach to tease apart the contributions of correlated and mosaic change among brain components (Melin et al., 2014) and by incorporating ecological, behavioral, and developmental predictor variables that may account for additional variation in the traits of interest.

In conclusion, we provided robust evidence for directional and accelerating selection towards larger brain size over the course of human evolution, resulting in the human brain being exceptionally large for a primate of similar body mass. We also found that the sizes of human brain components – including the neocortex, cerebellum, and the rest of the brain – are not larger or smaller than expected relative to the size of the rest of the brain, but all are larger than expected for a primate of similar body mass. These results suggest that relative neocortical expansion is not a hallmark of our species. The diversity of evolutionary patterns for various brain components that we observed within primates suggests that no single factor fully explains primate brain evolution; instead, comparative research should investigate how different selection pressures influenced the evolution of different neuroanatomical components at different times on different parts of the phylogenetic tree. Additionally, future work should seek to analyze the evolution of other brain traits, including neuronal composition, using similar phylogenetic comparative methods that account for the non-independence of data from related species.

All data used in our analyses are provided as supplementary material.

1. 1. Araújo A
   2. Arruda M
   3. Alencar A
   4. Albuquerque F
   5. Nascimento M
   6. Yamamoto M

   (2000) Body weight of wild and captive common marmosets (Callithrix jacchus)

   International Journal of Primatology 21:317–324.

   https://doi.org/10.1023/A:1005433722475

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

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

   Evolutionary Anthropology: Issues, News, and Reviews 19:114–118.

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

   • Google Scholar
3. 1. Azevedo FA
   2. Carvalho LR
   3. Grinberg LT
   4. Farfel JM
   5. Ferretti RE
   6. Leite RE
   7. Jacob Filho W
   8. Lent R
   9. Herculano-Houzel S

   (2009) Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain

   The Journal of Comparative Neurology 513:532–541.

   https://doi.org/10.1002/cne.21974

   • PubMed
   • Google Scholar
4. 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
5. 1. Barton RA

   (1998) Visual specialization and brain evolution in primates

   Proceedings of the Royal Society of London. Series B: Biological Sciences 265:1933–1937.

   https://doi.org/10.1098/rspb.1998.0523

   • Google Scholar
6. Book

   1. Barton RA

   (1999) The Evolutionary Ecology of the Primate Brain

   Cambridge: Cambridge University Press.

   https://doi.org/10.1017/CBO9780511542466.010

   • Google Scholar
7. 1. Barton RA

   (2000)

   Move Why Anim. Travel Groups

   204–237, Primate brain evolution: cognitive demands of foraging or of social life, Move Why Anim. Travel Groups, University of Chicago Press.

   • Google Scholar
8. 1. Barton RA

   (2007) Evolutionary specialization in mammalian cortical structure

   Journal of Evolutionary Biology 20:1504–1511.

   https://doi.org/10.1111/j.1420-9101.2007.01330.x

   • PubMed
   • Google Scholar
9. 1. Barton RA

   (2012) Embodied cognitive evolution and the cerebellum

   Philosophical Transactions of the Royal Society B: Biological Sciences 367:2097–2107.

   https://doi.org/10.1098/rstb.2012.0112

   • Google Scholar
10. 1. Barton RA
    2. Capellini I

    (2011) Maternal investment, life histories, and the costs of brain growth in mammals

    PNAS 108:6169–6174.

    https://doi.org/10.1073/pnas.1019140108

    • PubMed
    • Google Scholar
11. 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
12. 1. Barton RA
    2. Venditti C

    (2013) Human frontal lobes are not relatively large

    PNAS 110:9001–9006.

    https://doi.org/10.1073/pnas.1215723110

    • PubMed
    • Google Scholar
13. 1. Barton RA
    2. Venditti C

    (2014) Rapid evolution of the cerebellum in humans and other great apes

    Current Biology 24:2440–2444.

    https://doi.org/10.1016/j.cub.2014.08.056

    • PubMed
    • Google Scholar
14. 1. Bush EC
    2. Allman JM

    (2003) The scaling of white matter to gray matter in cerebellum and neocortex

    Brain, Behavior and Evolution 61:1–5.

    https://doi.org/10.1159/000068880

    • PubMed
    • Google Scholar
15. 1. Bush EC
    2. Allman JM

    (2004) The scaling of frontal cortex in primates and carnivores

    PNAS 101:3962–3966.

    https://doi.org/10.1073/pnas.0305760101

    • PubMed
    • Google Scholar
16. 1. Butler MA
    2. King AA

    (2004) Phylogenetic comparative analysis: a modeling approach for adaptive evolution

    The American Naturalist 164:683–695.

    https://doi.org/10.1086/426002

    • PubMed
    • Google Scholar
17. 1. Collins CE
    2. Leitch DB
    3. Wong P
    4. Kaas JH
    5. Herculano-Houzel S

    (2013) Faster scaling of visual neurons in cortical areas relative to subcortical structures in non-human primate brains

    Brain Structure and Function 218:805–816.

    https://doi.org/10.1007/s00429-012-0430-5

    • PubMed
    • Google Scholar
18. 1. Cooper N
    2. Thomas GH
    3. Venditti C
    4. Meade A
    5. Freckleton RP

    (2016) A cautionary note on the use of ornstein uhlenbeck models in macroevolutionary studies

    Biological Journal of the Linnean Society 118:64–77.

    https://doi.org/10.1111/bij.12701

    • PubMed
    • Google Scholar
19. 1. de Sousa AA
    2. Sherwood CC
    3. Mohlberg H
    4. Amunts K
    5. Schleicher A
    6. MacLeod CE
    7. Hof PR
    8. Frahm H
    9. Zilles K

    (2010) Hominoid visual brain structure volumes and the position of the lunate sulcus

    Journal of Human Evolution 58:281–292.

    https://doi.org/10.1016/j.jhevol.2009.11.011

    • PubMed
    • Google Scholar
20. Book

    1. Deacon TW

    (1998)

    The Symbolic Species: The Co-Evolution of Language and the Brain

    WW Norton & Company.

    • Google Scholar
21. 1. Deaner RO
    2. Isler K
    3. Burkart J
    4. van Schaik C

    (2007) Overall brain size, and not encephalization quotient, best predicts cognitive ability across non-human primates

    Brain, Behavior and Evolution 70:115–124.

    https://doi.org/10.1159/000102973

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

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

    Nature Ecology & Evolution 1:0112.

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

    • Google Scholar
23. 1. Drury HA
    2. Van Essen DC
    3. Anderson CH
    4. Lee CW
    5. Coogan TA
    6. Lewis JW

    (1996) Computerized mappings of the cerebral cortex: a multiresolution flattening method and a surface-based coordinate system

    Journal of Cognitive Neuroscience 8:1–28.

    https://doi.org/10.1162/jocn.1996.8.1.1

    • PubMed
    • Google Scholar
24. 1. Du A
    2. Zipkin AM
    3. Hatala KG
    4. Renner E
    5. Baker JL
    6. Bianchi S
    7. Bernal KH
    8. Wood BA

    (2018) Pattern and process in hominin brain size evolution are scale-dependent

    Proceedings of the Royal Society B: Biological Sciences 285:20172738.

    https://doi.org/10.1098/rspb.2017.2738

    • Google Scholar
25. Book

    1. Dunbar R

    (1998)

    Grooming, Gossip, and the Evolution of Language

    Harvard University Press.

    • Google Scholar
26. 1. Fan Y
    2. Wu R
    3. Chen MH
    4. Kuo L
    5. Lewis PO

    (2011) Choosing among partition models in bayesian phylogenetics

    Molecular Biology and Evolution 28:523–532.

    https://doi.org/10.1093/molbev/msq224

    • PubMed
    • Google Scholar
27. 1. Felsenstein J

    (1985) Phylogenies and the comparative method

    The American Naturalist 125:1–15.

    https://doi.org/10.1086/284325

    • Google Scholar
28. 1. Fitzpatrick JL
    2. Almbro M
    3. Gonzalez-Voyer A
    4. Hamada S
    5. Pennington C
    6. Scanlan J
    7. Kolm N

    (2012) Sexual selection uncouples the evolution of brain and body size in pinnipeds: brain evolution in pinnipeds

    Journal of Evolutionary Biology 25:1321–1330.

    https://doi.org/10.1111/j.1420-9101.2012.02520.x

    • Google Scholar
29. 1. Florio M
    2. Huttner WB

    (2014) Neural progenitors, neurogenesis and the evolution of the neocortex

    Development 141:2182–2194.

    https://doi.org/10.1242/dev.090571

    • PubMed
    • Google Scholar
30. 1. Freckleton RP

    (2002) On the misuse of residuals in ecology: regression of residuals vs. multiple regression

    Journal of Animal Ecology 71:542–545.

    https://doi.org/10.1046/j.1365-2656.2002.00618.x

    • Google Scholar
31. 1. Garland T
    2. Ives AR

    (2000) Using the past to predict the present: confidence intervals for regression equations in phylogenetic comparative methods

    The American Naturalist 155:346–364.

    https://doi.org/10.1086/303327

    • PubMed
    • Google Scholar
32. 1. Geschwind DH
    2. Rakic P

    (2013) Cortical evolution: judge the brain by its cover

    Neuron 80:633–647.

    https://doi.org/10.1016/j.neuron.2013.10.045

    • PubMed
    • Google Scholar
33. 1. González-Lagos C
    2. Sol D
    3. Reader SM

    (2010) Large-brained mammals live longer

    Journal of Evolutionary Biology 23:1064–1074.

    https://doi.org/10.1111/j.1420-9101.2010.01976.x

    • PubMed
    • Google Scholar
34. 1. Gordon AD

    (2006) Scaling of size and dimorphism in primates II: macroevolution

    International Journal of Primatology 27:63–105.

    https://doi.org/10.1007/s10764-005-9004-1

    • Google Scholar
35. 1. Grabowski M
    2. Voje KL
    3. Hansen TF

    (2016) Evolutionary modeling and correcting for observation error support a 3/5 brain-body allometry for primates

    Journal of Human Evolution 94:106–116.

    https://doi.org/10.1016/j.jhevol.2016.03.001

    • PubMed
    • Google Scholar
36. 1. Hansen TF

    (1997) Stabilizing selection and the comparative analysis of adaptation

    Evolution 51:1341–1351.

    https://doi.org/10.1111/j.1558-5646.1997.tb01457.x

    • Google Scholar
37. 1. Harrison PW
    2. Montgomery SH

    (2017) Genetics of cerebellar and neocortical expansion in anthropoid primates: a comparative approach

    Brain, Behavior and Evolution 89:274–285.

    https://doi.org/10.1159/000477432

    • PubMed
    • Google Scholar
38. 1. Healy SD
    2. Rowe C

    (2007) A critique of comparative studies of brain size

    Proceedings of the Royal Society B: Biological Sciences 274:453–464.

    https://doi.org/10.1098/rspb.2006.3748

    • Google Scholar
39. 1. Herculano-Houzel S
    2. Kaas JH

    (2011) Gorilla and orangutan brains conform to the primate cellular scaling rules: implications for human evolution

    Brain, Behavior and Evolution 77:33–44.

    https://doi.org/10.1159/000322729

    • PubMed
    • Google Scholar
40. 1. Ho LST
    2. Ané C

    (2013) Asymptotic theory with hierarchical autocorrelation: Ornstein–Uhlenbeck tree models

    The Annals of Statistics 41:957–981.

    https://doi.org/10.1214/13-AOS1105

    • Google Scholar
41. 1. Isler K
    2. Christopher Kirk E
    3. Miller JM
    4. Albrecht GA
    5. Gelvin BR
    6. Martin RD

    (2008) Endocranial volumes of primate species: scaling analyses using a comprehensive and reliable data set

    Journal of Human Evolution 55:967–978.

    https://doi.org/10.1016/j.jhevol.2008.08.004

    • PubMed
    • Google Scholar
42. 1. Isler K
    2. van Schaik CP

    (2009) The expensive brain: a framework for explaining evolutionary changes in brain size

    Journal of Human Evolution 57:392–400.

    https://doi.org/10.1016/j.jhevol.2009.04.009

    • PubMed
    • Google Scholar
43. Book

    1. Jeffreys H

    (1998)

    The Theory of Probability

    Oxford: Oxford University Press.

    • Google Scholar
44. 1. Kass RE
    2. Raftery AE

    (1995) Bayes factors

    Journal of the American Statistical Association 90:773–795.

    https://doi.org/10.1080/01621459.1995.10476572

    • Google Scholar
45. 1. Kaufman JA

    (2005) Anatomicalanalysis of an aye-aye brain (Daubentonia madagascariensis, primates: prosimii)combining histology, structural magnetic resonance imaging, and diffusion-tensorimaging

    The Anatomical Record Part A 287:1026–1037.

    https://doi.org/10.1002/ar.a.20264

    • Google Scholar
46. 1. Kochiyama T
    2. Ogihara N
    3. Tanabe HC
    4. Kondo O
    5. Amano H
    6. Hasegawa K
    7. Suzuki H
    8. Ponce de León MS
    9. Zollikofer CPE
    10. Bastir M
    11. Stringer C
    12. Sadato N
    13. Akazawa T

    (2018) Reconstructing the neanderthal brain using computational anatomy

    Scientific Reports 8:6296.

    https://doi.org/10.1038/s41598-018-24331-0

    • PubMed
    • Google Scholar
47. 1. Kriegstein A
    2. Noctor S
    3. Martínez-Cerdeño V

    (2006) Patterns of neural stem and progenitor cell division may underlie evolutionary cortical expansion

    Nature Reviews Neuroscience 7:883–890.

    https://doi.org/10.1038/nrn2008

    • PubMed
    • Google Scholar
48. 1. Lewitus E

    (2018) Inferring evolutionary process from neuroanatomical data

    Frontiers in Neuroanatomy 12:.

    https://doi.org/10.3389/fnana.2018.00054

    • PubMed
    • Google Scholar
49. 1. MacLean EL
    2. Hare B
    3. Nunn CL
    4. Addessi E
    5. Amici F
    6. Anderson RC
    7. Aureli F
    8. Baker JM
    9. Bania AE
    10. Barnard AM
    11. Boogert NJ
    12. Brannon EM
    13. Bray EE
    14. Bray J
    15. Brent LJ
    16. Burkart JM
    17. Call J
    18. Cantlon JF
    19. Cheke LG
    20. Clayton NS
    21. Delgado MM
    22. DiVincenti LJ
    23. Fujita K
    24. Herrmann E
    25. Hiramatsu C
    26. Jacobs LF
    27. Jordan KE
    28. Laude JR
    29. Leimgruber KL
    30. Messer EJ
    31. Moura AC
    32. Ostojić L
    33. Picard A
    34. Platt ML
    35. Plotnik JM
    36. Range F
    37. Reader SM
    38. Reddy RB
    39. Sandel AA
    40. Santos LR
    41. Schumann K
    42. Seed AM
    43. Sewall KB
    44. Shaw RC
    45. Slocombe KE
    46. Su Y
    47. Takimoto A
    48. Tan J
    49. Tao R
    50. van Schaik CP
    51. Virányi Z
    52. Visalberghi E
    53. Wade JC
    54. Watanabe A
    55. Widness J
    56. Young JK
    57. Zentall TR
    58. Zhao Y

    (2014) The evolution of self-control

    PNAS 111:E2140–E2148.

    https://doi.org/10.1073/pnas.1323533111

    • PubMed
    • Google Scholar
50. 1. MacLeod CE
    2. Zilles K
    3. Schleicher A
    4. Rilling JK
    5. Gibson KR

    (2003) Expansion of the neocerebellum in hominoidea

    Journal of Human Evolution 44:401–429.

    https://doi.org/10.1016/S0047-2484(03)00028-9

    • PubMed
    • Google Scholar
51. 1. Melin AD
    2. Young HC
    3. Mosdossy KN
    4. Fedigan LM

    (2014) Seasonality, extractive foraging and the evolution of primate sensorimotor intelligence

    Journal of Human Evolution 71:77–86.

    https://doi.org/10.1016/j.jhevol.2014.02.009

    • PubMed
    • Google Scholar
52. Book

    1. Miller G

    (2011)

    The Mating Mind: How Sexual Choice Shaped the Evolution of Human Nature

    Anchor.

    • Google Scholar
53. 1. Miller M
    2. Clark A

    (2018) Happily entangled: prediction, emotion, and the embodied mind

    Synthese 195:2559–2575.

    https://doi.org/10.1007/s11229-017-1399-7

    • Google Scholar
54. 1. Mitchell C
    2. Silver DL

    (2017)

    Seminars in Cell & Developmental Biology

    Enhancing our brains: Genomic mechanisms underlying cortical evolution, Seminars in Cell & Developmental Biology, Elsevier.

    • Google Scholar
55. 1. Montgomery SH
    2. Capellini I
    3. Barton RA
    4. Mundy NI

    (2010) Reconstructing the ups and downs of primate brain evolution: implications for adaptive hypotheses and homo floresiensis

    BMC Biology 8:9.

    https://doi.org/10.1186/1741-7007-8-9

    • PubMed
    • Google Scholar
56. 1. Montgomery SH
    2. Mundy NI
    3. Barton RA

    (2016) Brain evolution and development: adaptation, allometry and constraint

    Proceedings of the Royal Society B: Biological Sciences 283:20160433.

    https://doi.org/10.1098/rspb.2016.0433

    • Google Scholar
57. 1. More HL
    2. Hutchinson JR
    3. Collins DF
    4. Weber DJ
    5. Aung SKH
    6. Donelan JM

    (2010) Scaling of sensorimotor control in terrestrial mammals

    Proceedings of the Royal Society B: Biological Sciences 277:3563–3568.

    https://doi.org/10.1098/rspb.2010.0898

    • Google Scholar
58. 1. Mounier A
    2. Caparros M

    (2015) The phylogenetic status of Homo Heidelbergensis – a cladistic study of Middle Pleistocene hominins

    Bmsap 27:110–134.

    https://doi.org/10.1007/s13219-015-0127-4

    • Google Scholar
59. 1. Neubauer S
    2. Hublin JJ
    3. Gunz P

    (2018) The evolution of modern human brain shape

    Science Advances 4:eaao5961.

    https://doi.org/10.1126/sciadv.aao5961

    • PubMed
    • Google Scholar
60. 1. Noonan MP
    2. Mars RB
    3. Sallet J
    4. Dunbar RIM
    5. Fellows LK

    (2018) The structural and functional brain networks that support human social networks

    Behavioural Brain Research 355:12–23.

    https://doi.org/10.1016/j.bbr.2018.02.019

    • PubMed
    • Google Scholar
61. Book

    1. Nunn CL

    (2011) The Comparative Approach in Evolutionary Anthropology and Biology

    University of Chicago Press.

    https://doi.org/10.7208/chicago/9780226090009.001.0001

    • Google Scholar
62. 1. Nunn CL
    2. Zhu L

    (2014)

    Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology

    481–514, Phylogenetic prediction to identify “evolutionary singularities”, Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology, Springer, 10.1007/978-3-662-43550-2_21.

    • Google Scholar
63. 1. Organ C
    2. Nunn CL
    3. Machanda Z
    4. Wrangham RW

    (2011) Phylogenetic rate shifts in feeding time during the evolution of homo

    PNAS 108:14555–14559.

    https://doi.org/10.1073/pnas.1107806108

    • PubMed
    • Google Scholar
64. Software

    1. Orme D

    (2013)

    The caper package: comparative analysis of phylogenetics and evolution in R

    R Package Version.
65. 1. Pagel M

    (1999) Inferring the historical patterns of biological evolution

    Nature 401:877–884.

    https://doi.org/10.1038/44766

    • PubMed
    • Google Scholar
66. 1. Pagel M

    (2002)

    Modelling the evolution of continuously varying characters on phylogenetic trees

    In Morphology, Shape and Phylogeny, Taylor and Francis pp. 269–286.

    • Google Scholar
67. 1. Pagel MD
    2. Harvey PH

    (1989) Taxonomic differences in the scaling of brain on body weight among mammals

    Science 244:1589–1593.

    https://doi.org/10.1126/science.2740904

    • PubMed
    • Google Scholar
68. 1. Parker ST

    (2015) Re-evaluating the extractive foraging hypothesis

    New Ideas in Psychology 37:1–12.

    https://doi.org/10.1016/j.newideapsych.2014.11.001

    • Google Scholar
69. 1. Plummer M
    2. Best N
    3. Cowles K
    4. Vines K

    (2006)

    CODA: convergence diagnosis and output analysis for MCMC

    R News 6:7–11.

    • Google Scholar
70. 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: Biological Sciences 284:1765.

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

    • Google Scholar
71. Preprint

    1. Powell LE
    2. Street SE
    3. Barton RA

    (2019) Maternal investment, life histories, and the evolution of brain structure in primates

    bioRxiv.

    https://doi.org/10.1101/513051

    • Google Scholar
72. 1. Rakic P

    (2009) Evolution of the neocortex: a perspective from developmental biology

    Nature Reviews Neuroscience 10:724–735.

    https://doi.org/10.1038/nrn2719

    • PubMed
    • Google Scholar
73. 1. Rilling JK
    2. Insel TR

    (1998) Evolution of the cerebellum in primates: differences in relative volume among monkeys, apes and humans

    Brain, Behavior and Evolution 52:308–314.

    https://doi.org/10.1159/000006575

    • PubMed
    • Google Scholar
74. 1. Rilling JK
    2. Insel TR

    (1999) The primate neocortex in comparative perspective using magnetic resonance imaging

    Journal of Human Evolution 37:191–223.

    https://doi.org/10.1006/jhev.1999.0313

    • PubMed
    • Google Scholar
75. 1. Robson SL
    2. Wood B

    (2008) Hominin life history: reconstruction and evolution

    Journal of Anatomy 212:394–425.

    https://doi.org/10.1111/j.1469-7580.2008.00867.x

    • PubMed
    • Google Scholar
76. 1. Ross CF
    2. Henneberg M
    3. Ravosa MJ
    4. Richard S

    (2004) Curvilinear, geometric and phylogenetic modeling of basicranial flexion: is it adaptive, is it constrained?

    Journal of Human Evolution 46:185–213.

    https://doi.org/10.1016/j.jhevol.2003.11.001

    • PubMed
    • Google Scholar
77. 1. Smaers JB
    2. Turner AH
    3. Gómez-Robles A
    4. Sherwood CC

    (2018) A cerebellar substrate for cognition evolved multiple times independently in mammals

    eLife 7:e35696.

    https://doi.org/10.7554/eLife.35696

    • PubMed
    • Google Scholar
78. 1. Smith RJ

    (1993) Logarithmic transformation bias in allometry

    American Journal of Physical Anthropology 90:215–228.

    https://doi.org/10.1002/ajpa.1330900208

    • Google Scholar
79. 1. Smith RJ
    2. Jungers WL

    (1997) Body mass in comparative primatology

    Journal of Human Evolution 32:523–559.

    https://doi.org/10.1006/jhev.1996.0122

    • PubMed
    • Google Scholar
80. 1. Sol D

    (2009) Revisiting the cognitive buffer hypothesis for the evolution of large brains

    Biology Letters 5:130–133.

    https://doi.org/10.1098/rsbl.2008.0621

    • PubMed
    • Google Scholar
81. 1. Sousa AMM
    2. Zhu Y
    3. Raghanti MA
    4. Kitchen RR
    5. Onorati M
    6. Tebbenkamp ATN
    7. Stutz B
    8. Meyer KA
    9. Li M
    10. Kawasawa YI
    11. Liu F
    12. Perez RG
    13. Mele M
    14. Carvalho T
    15. Skarica M
    16. Gulden FO
    17. Pletikos M
    18. Shibata A
    19. Stephenson AR
    20. Edler MK
    21. Ely JJ
    22. Elsworth JD
    23. Horvath TL
    24. Hof PR
    25. Hyde TM
    26. Kleinman JE
    27. Weinberger DR
    28. Reimers M
    29. Lifton RP
    30. Mane SM
    31. Noonan JP
    32. State MW
    33. Lein ES
    34. Knowles JA
    35. Marques-Bonet T
    36. Sherwood CC
    37. Gerstein MB
    38. Sestan N

    (2017) Molecular and cellular reorganization of neural circuits in the human lineage

    Science 358:1027–1032.

    https://doi.org/10.1126/science.aan3456

    • PubMed
    • Google Scholar
82. 1. St Wecker PG
    2. Farel PB

    (1994) Hindlimb sensory neuron number increases with body size

    The Journal of Comparative Neurology 342:430–438.

    https://doi.org/10.1002/cne.903420309

    • PubMed
    • Google Scholar
83. 1. Stephan H
    2. Frahm H
    3. Baron G

    (1981) New and revised data on volumes of brain structures in insectivores and primates

    Folia Primatologica 35:1–29.

    https://doi.org/10.1159/000155963

    • Google Scholar
84. 1. Thalmann U
    2. Geissmann T

    (2000) Distribution and geographic variation in the western woolly lemur (Avahi occidentalis) with description of a new species (A. unicolor

    International Journal of Primatology 21:915–941.

    https://doi.org/10.1023/A:1005507028567

    • Google Scholar
85. Software

    1. Uyeda JC

    (2017) Bayou developmental version

    Github.

    https://github.com/uyedaj/bayou/tree/537e373b6c15faf6a03f21d3d642d14e567ad4d8
86. 1. Uyeda JC
    2. Harmon LJ

    (2014) A novel bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data

    Systematic Biology 63:902–918.

    https://doi.org/10.1093/sysbio/syu057

    • PubMed
    • Google Scholar
87. 1. Vining AQ
    2. Nunn CL

    (2016) Evolutionary change in physiological phenotypes along the human lineage

    Evolution, Medicine, and Public Health 2016:312–324.

    https://doi.org/10.1093/emph/eow026

    • PubMed
    • Google Scholar
88. 1. Wang SS
    2. Shultz JR
    3. Burish MJ
    4. Harrison KH
    5. Hof PR
    6. Towns LC
    7. Wagers MW
    8. Wyatt KD

    (2008) Functional trade-offs in white matter axonal scaling

    Journal of Neuroscience 28:4047–4056.

    https://doi.org/10.1523/JNEUROSCI.5559-05.2008

    • PubMed
    • Google Scholar
89. Preprint

    1. Wartel A
    2. Lindenfors P
    3. Lind J

    (2018) Whatever you want: inconsistent results is the rule, not the exception, in the study of primate brain evolution

    bioRxiv.

    https://doi.org/10.1101/454132

    • Google Scholar
90. 1. Whiting BA
    2. Barton RA

    (2003) The evolution of the cortico-cerebellar complex in primates: anatomical connections predict patterns of correlated evolution

    Journal of Human Evolution 44:3–10.

    https://doi.org/10.1016/S0047-2484(02)00162-8

    • PubMed
    • Google Scholar
91. Book

    1. Wills C

    (1993)

    The Runaway Brain: The Evolution of Human Uniqueness

    Basic Books.

    • Google Scholar

Author details

1. Ian F Miller

   1. Ecology and Evolutionary Biology, Princeton University, Princeton, United States
   2. Department of Evolutionary Anthropology, Duke University, Durham, United States

   Contribution

   Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   ifmiller@princeton.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2673-9618

2. Robert A Barton

   Evolutionary Anthropology Research Group, Department of Anthropology, University of Durham, Durham, United Kingdom

   Contribution

   Conceptualization, Data curation, Investigation, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

3. Charles L Nunn

   1. Department of Evolutionary Anthropology, Duke University, Durham, United States
   2. Duke Global Health Institute, Duke University, Durham, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9330-2873

• 7,807

  views

• 896

  downloads

• 48

  citations

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