The brains of mammals consist of the same basic structures, but each of these structures varies from one species to the next. A given structure may be larger in one species than another, for example. It may contain different numbers or sizes of cells. It may even have different connections to other brain regions. By comparing individual brain structures between species, we can map how the mammalian brain has evolved.

Smaers et al. have now done this for the cerebellum, a structure at the back of the brain. The mammalian cerebellum consists of three main areas: the vermis, paravermis, and the lateral hemispheres. Smaers et al. show that in apes, dolphins and seals, the lateral hemispheres are unusually large relative to the cerebellum as a whole. This could indicate that these three groups of animals share a common ancestor with enlarged lateral hemispheres. Yet, genetic studies suggest that this is not the case.

Another possibility is that apes, dolphins and seals independently evolved enlarged lateral hemispheres. This may have given rise to a trait that proved beneficial for each of them. But what might this be? Studies in people suggest that the lateral hemispheres help to support some forms of learning. Apes, dolphins and seals are among only a few species of mammal with the ability to learn new calls and vocalizations. The expansion of the lateral cerebellum may therefore have contributed to the evolution of vocal learning, and this may have occurred independently on at least three separate occasions.

Future work should extend this analysis to other cognitive skills, as well as to other species. Bats, for example, would be of particular interest because of their ability to echolocate. Finally, the lateral hemispheres consist of several subregions that play different roles in learning and information processing. Further experiments should explore whether different subregions have increased in size in different species.

The brain is the anatomical substrate of behavior. In turn, the behaviors of a species are closely linked to the ecological context and evolutionary history of that species. Large-scale evolutionary modifications in the brain therefore provide essential information about the factors that shape species’ diversification patterns. Changes in neurobiological features that directly relate to higher-order cognitive capacities are particularly relevant as they underpin adaptive behaviors such as tool manipulation (Krützen et al., 2005; Boesch and Boesch, 1990), flexible problem solving (Benson-Amram et al., 2016), planning for the future (Raby et al., 2007; Osvath and Osvath, 2008), and sophisticated communication systems (Janik, 2013).

Even though it is commonly agreed that instances of intelligent behavior have evolved independently in different lineages of mammals (Roth and Dicke, 2005; Roth, 2015), it is unclear whether such convergent behavioral abilities arose from modifications of common neural systems or whether lineage-specific contingency has shaped particular brain circuits according to unique socioecological conditions. This uncertainty has led to different perspectives on what defines ‘intelligence’. Comparative psychologists describe it as a domain-general problem solving ability that comprises associative-learning. Such ‘general intelligence’ has been proposed to equip species with the ability to make mental models of the environment, develop actions based on abstract notions of associations between percepts, and to generate goals from current contexts (Spearman, 1904; Duncan et al., 2000; Genovesio et al., 2014). A different view holds that intelligence comprises the aggregate of cognitive modules of special abilities that evolved within a species in response to specific environments (Barkow et al., 1995; Cosmides and Tooby, 1992). Under this view, intelligence evolved to perform specific computational strategies that are tailored to solve the task demands of ancestrally recurrent adaptive problems (Cosmides et al., 2010).

Here, we examine the mammalian cerebellum to address whether convergent evolution of mammalian cognitive capacities are scaffolded by modifications of this common neural system. The cerebellum may be especially informative in uncovering the coevolution of brain structure and cognition for several reasons. First, unlike the more commonly investigated cerebral cortex, the cerebellum’s structural, connectional, functional, and developmental anatomy is relatively uniform (Larsell, 1970) and is therefore ideal for the comparison of homologous neural circuits across species (Smaers, 2014a). Second, the lateral extension of the cerebellum to form distinct hemispheres arose early in mammalian evolution (Figure 1), providing the opportunity to investigate the diversification pattern of a newly evolving neural system. Third, through its connectional integration with heteromodal association areas in the cerebrum, the lateral cerebellum has been hypothesized to be involved in the generation of domain-general higher-order models of mental activity. A central working hypothesis in this context is that the cerebellum imposes a type of cognitive control over information processing that consists of automating sequences of thoughts and actions (Schmahmann, 1997). This cerebellar-type cognitive control may underpin many aspects associated with ‘intelligent’ behavior, such as working memory, executive function, and the development of behavioral learning models (Strick et al., 2009). Finally, the strong connectional and functional integration between the lateral cerebellar hemisphere and the cerebrum is also evident in developmental modularity. Whereas the medial cerebellum develops early, the lateral cerebellum develops later in tandem with cerebral association areas (Tiemeier et al., 2010; Altman and Bayer, 1997).

Figure 1

Download asset Open asset

We investigate the extent to which mammalian lateral cerebellar hemispheres evolved in coordination with the rest of the cerebellum, whether they are correlated with measures of domain-general cognitive performance, and what patterns underlie their evolutionary diversification. We primarily focus on the relative measure of lateral to medial cerebellar volume to account for the functional, connectional, and developmental modularity of the cerebellum (see more details in Materials and Methods). Volumetric measurements of cerebellar partitions were used because cerebellar volume is a nearly linear function of its number of neurons (Herculano-Houzel, 2010), and by extension, its relative investment in particular information processing loops (Herculano-Houzel, 2010).

Phylogenetic scaling of lateral to medial cerebellar volume indicates a positive scaling trend (F = 294.6, p<0.001, λ = 0.945) with a slope that is higher than unity (95% confidence interval: 1.167:1.478) (Figure 2). Residuals were considered as measures of ‘relative lateral to medial cerebellar size’ (or ‘lateral-medial cerebellar reorganization’). The ratio of the observed to the predicted values range from 2.3 to 4.4 in apes, cetaceans and pinnipeds, from 0.6 to 0.7 in feliformes, and from 0.2 to 0.3 in artiodactyls. Phylogenetic regression analysis also demonstrated that lateral-medial cerebellar reorganization is a significant predictor of domain-general cognition in primates (F = 15.670, p=0.001, Figure 2)

Figure 2

Download asset Open asset

Figure 2—source data 1

Brain data used in the analyses.

https://doi.org/10.7554/eLife.35696.005

Download elife-35696-fig2-data1-v2.docx

The evolutionary history of lateral-medial cerebellar reorganization was quantified using a Bayesian reversible-jump Ornstein-Uhlenbeck (‘OU’) approach (Uyeda and Harmon, 2014) (Figure 3). This analysis indicates five shifts in mean value with a posterior probability (‘PP’)>0.8. These regime shifts occurred at the root branches of the apes, the cetartiodactyls, the cetaceans (note that our sample includes toothed whales only), the pinnipeds, and the feliformes (Figure 3, Figure 3—figure supplement 1). The signal-to-noise ratio of this estimated pattern is 52.34, demonstrating that the analysis has high effect size and high power. Phylogenetic analysis of covariance (Smaers and Rohlf, 2016) indicates that these shifts represent significant differences in the intercept of lateral to medial cerebellar scaling (i.e. grade shifts; Table 1). Specifically, apes, toothed whales and pinnipeds are not significantly different from each other, but each (and as a group) are significantly different from others. Furthermore, feliformes and artiodactyls are not significantly different from each other, but each (and as a group) are significantly different from others. These results demonstrate that the six regimes identified by OU modelling constitute three significantly different grades (in order of magnitude of relative lateral to medial cerebellar size): apes, toothed whales, and pinnipeds (grade 1); rest of the sample (grade 2); artiodactyls and feliformes (grade 3). The equality of slopes assumption of analysis of covariance is upheld (grade 1 versus grade 2: F = 0.009, p=0.925; grade 1 versus grade 3: F = 0.088, p=0.771; grade 2 versus grade 3: F = 0.836, p=0.367).

Figure 3 with 3 supplements see all

Download asset Open asset

Figure 3—source data 1

Tree used in the analyses.

https://doi.org/10.7554/eLife.35696.010

Download elife-35696-fig3-data1-v2.txt

The evolutionary history of lateral-medial cerebellar reorganization was also examined by visualizing the evolutionary trait space in an ancestral phenogram (Figure 3). Ancestral states were inferred using a multiple variance Brownian motion (‘mvBM’) approach (Smaers et al., 2016). Results using a standard BM and a reversible-jump BM method yielded similar results (Figure 3—figure supplement 2). Lineage-specific amounts of evolutionary change were also estimated using the mvBM approach and compared against a null model of gradual evolution to obtain estimates of how much faster lineages evolve relative to a gradual model of evolution. These results are visualized in Figure 3 and presented in full in Figure 3—figure supplement 3. Results using a reversible-jump BM method yielded similar results (Figure 3—figure supplement 3).

The same procedures were used to analyze relative cerebellum size (residuals of total cerebellar volume to the volume of the rest of the brain). These analyses revealed no regime shifts indicating PP >0.8, and only a single regime shift with PP >0.5 (the ancestral lineage of the zebu (Bos taurus indicus): PP = 0.76). Two other regimes shifts had 0.2 < PP < 0.5: the root of musteline carnivorans (0.38), and cercopithecine primates (0.29) (Figure 3—figure supplement 1). For relative cerebellar size observed/predicted values ranges from 1.2 to 1.6 for musteline carnivorans, from 0.7 to 0.9 for cercopithecine primates, and 0.5 for the zebu. Phylogenetic analysis of covariance (‘pANCOVA’) confirms that these regimes form significantly different grades (Supplementary file 1). Phylogenetic regression analysis demonstrated that relative cerebellum size is not a significant predictor of domain-general cognition in primates (lateral-medial: F = 2.122, p=0.163).

The difference in rate of evolution between lateral-medial cerebellar reorganization and relative cerebellum size was tested using a Q-mode approach (Adams, 2014). Rates were found to be significantly different (p=0.002) at a ratio of 2.9 (lateral to medial cerebellum σ² = 0.00753, relative cerebellum size σ² = 0.00261). Figure 4 visualizes this rate difference using a standard BM MCMC procedure (Revell, 2012). Similar results were found using mvBM and rjBM procedures.

Figure 4

Download asset Open asset

We also assessed potential grade shifts in the size of the medial cerebellum relative to the rest of the brain in order to ascertain whether the described convergent trend between apes, toothed whales and pinnipeds may be confounded by different patterns of evolution in the medial cerebellum (e.g. the medial may be exceptionally small in some clades but not others, confounding the lateral to medial comparison). Results indicate that apes, toothed whales and pinnipeds are not significantly different in relative medial cerebellum size (apes versus toothed whales and pinnipeds: F = 1.06, p=0.31; toothed whales versus apes and pinnipeds: F = 1.11, p=0.30; pinnipeds versus apes and toothed whales: F = 0.07, p=0.79). Furthermore, analysis of lateral cerebellum size versus rest of brain size yields similar results as the lateral to medial comparison in that apes, toothed whales and pinnipeds are not significantly different from each other, but are different from other mammals (apes versus toothed whales and pinnipeds: F < 0.01, p=0.96; toothed whales versus apes and pinnipeds: F = 1.13, p=0.29; pinnipeds versus apes and toothed whales: F = 0.56, p=0.45; apes, toothed whales and pinnipeds versus others (holding constant artiodactyls and feliforms): F = 5.06, p=0.01). These results confirm that the convergent lateral to medial reorganization among these clades is not due to a differential effect on medial and/or lateral cerebellum size, but rather, that it is due to a similarly convergent pattern of lateral to medial reorganization.

Results demonstrate a combination of parallel and convergent evolutionary events in mammals leading to relative lateral cerebellar expansion. This evolutionary pattern encompasses distantly related lineages - apes, toothed whales, and pinnipeds. The congruence between apes and pinnipeds demonstrates parallel evolution as they derive from a similar ancestral condition. The congruence with toothed whales demonstrates convergence as they derive from a different ancestral condition (evident in the artiodactyls grade with smaller relative lateral cerebellar size). These results bear on the interpretation of the evolution of cognition, modularity in brain evolution, and the occurrence of neurodevelopmental shifts across species.

The cerebellum has been hypothesized to be involved in the domain-general automation of associative learning abilities (Schmahmann, 1997) and, according to the results of the present analyses, may be a target of evolution leading to the emergence of ‘intelligent’ behaviors. Specifically, the contribution of the cerebellum’s role to cognition has been hypothesized to lie in the extrapolation of error-based motor learning processes to domains beyond motor control such as social cognition (Sokolov et al., 2017). According to theoretical models of cerebellar function, the cerebellum receives a copy of the motor command and generates a representation (‘internal model’) of the expected consequences of that command (Sokolov et al., 2017; Ito, 2008; Moberget and Ivry, 2016). Such sensory prediction (‘forward model’) allows for predictive control of motor actions which results in smooth and automated execution (Koziol et al., 20132014). In humans, evidence has been found that this process, in addition to being active in relation to motor actions, may also be active in semantic processing (Moberget et al., 2014), and social cognition (Van Overwalle et al., 2014). Here, we demonstrate a significant relationship between lateral-medial cerebellar reorganization and a measure of domain-general cognitive capacity across primates (Figure 2). This measure of cognitive ability in primates quantifies the performance of animals on nine tasks that are independent of perceptual bias and contextual confounds (Deaner et al., 2006). This positive correlation provides indirect support for the hypothesis that the cerebellum is involved in cognition, and further suggests that this association may have deep evolutionary roots.

Ideally, a comparative test between lateral cerebellar expansion and cognition would include all mammalian species. Unfortunately, Primates are the only order for which a standardized measure of higher cognitive capacity is available that matches available cerebellar data. Beyond primates, the analysis of vocal production learning (‘VPL’) has, however, also produced useful insights into the evolution of higher order learning capacities. VPL is defined as the ability to modify existing vocalizations and to imitate novel sounds not belonging to the innate repertoire (Janik and Slater, 2000). VPL aligns with ‘cerebellar-type’ error-based learning in that it results in smooth and automated execution after combining individual motor actions into a more complex automated model. In humans, the link between cerebellum and vocal learning is supported by an association between lateral cerebellar activation patterns and semantic processing (Moberget et al., 2014). In mammals, only a few species are known to be capable of VPL: humans, bats (Vernes, 2017), elephants (Stoeger et al., 2012), seals (Ralls et al., 1985; Ravignani et al., 2016), dolphins (Reiss and McCowan, 1993), and toothed whales (Foote et al., 2006). Apart from bats (not included in our sample due to a lack of available data on cerebellar hemispheres), our analyses indicate that all these species have increased lateral cerebella (though note that although our analysis demonstrates that elephants indicate a pronounced trend of lateral cerebellar expansion, this trend is not significant in our sample). The congruence between our results on multiple independent evolutionary expansion events of the mammalian lateral cerebellum and the occurrence of the capacity for VPL is further support of an evolutionary association between changes in cerebellar processing and cognition.

A potential link between such cognitive specializations and convergent lateral cerebellar expansion suggests that repeated selection on a common neural substrate has, at least partly, characterized mammalian brain evolution in relation to cognition. This, however, does not exclude the concomitant occurrence of lineage-specific adaptive specializations in other parts of the brain, such as the neocortex. The available information suggests that different orders of mammals may have neocortices that are organized in fundamentally different ways. Primates, for example, have evolved a suite of novel fronto-parietal cortical areas that form a network that is functionally, hodologically, and developmentally linked. Crucially, this primate-specific network is not observed in other mammals (Preuss, 2007; Preuss, 1995; Preuss and Goldman-Rakic, 1991a; Preuss and Goldman-Rakic, 1991b). Such order-specific specializations lend support to the hypothesis that species differences in intelligence can, at least partly, be understood as an interaction between aggregates of domain-specific abilities, together with changes in domain-general processing. Indeed, our current results demonstrate that those cerebellar partitions that are hypothesized to underpin domain-general associative learning abilities have repeatedly been subject to directional selection, suggesting that modification of common neural systems may contribute significantly to the evolution of animal intelligence. The nature of cerebellar microstructural circuit anatomy (fairly uniform across mammals) and hypothesized function (to automate and streamline cerebral information processing) lends support to our conclusion that its domain-general features have been repeatedly subject to selection, irrespective of the precise organization of the cerebral cortex in different orders. Across orders, the evolution of the mammalian brain may hereby be characterized by something new (cerebral specializations) and something borrowed (cerebellar convergences).

The potential link between lateral cerebellar expansion and cognition across species, may further inform on the nature of the cognitive differences across species. In primates, the lateral cerebellum’s role in cognitive processing is tightly linked to higher-order visual and somatosensory cortical association areas (Preuss, 2007). Primates evolved specializations for visual grasping and reaching that involved extensive integration of visual, motor, and somatosensory processing (Wise et al., 1997; Felleman and Van Essen, 1991). Accordingly, cortical association areas involved in these specializations are connected to lateral cerebellar lobules (O'Reilly et al., 2010; Ramnani et al., 2006; Glickstein et al., 2011; Sokolov et al., 2014; Schmahmann and Pandya, 1991). The nature of cortico-cerebellar hodology in toothed whales and pinnipeds is, however, less well described. Although previous research provides indirect evidence for the expansion of cortical association areas in these groups (van Kann et al., 2017; Hof and Van Der Gucht, 2007; Hof et al., 2005; Turner et al., 2017; Sawyer et al., 2016), more research is needed. It is clear, however, that cortical information processing in toothed whales is geared more towards auditory processing (echolocation), while in pinnipeds, as in terrestrial carnivorans and primates, it is geared more towards vision. Rather than evolving echolocation to navigate a poorly visible aquatic niche, pinnipeds evolved exceptional somatosensory and visual sensitivity (Hanke et al., 2009; Scholtyssek et al., 2008; Dehnhardt et al., 1998). The differences between toothed whales (auditory specializations for echolocation) and primates and pinnipeds (visual and somatosensory specializations) accord with preliminary observations that the enlargement of the lateral cerebellum in toothed whales may be due to the expansion of different lobules within the hemisphere, as compared with apes and pinnipeds. Whereas hemispheric expansion in apes is most likely due to the enlargement of lobule HVII (Balsters et al., 2010) (which connects closely to cortical association areas such as temporal, parietal and prefrontal cortex, which are involved in visually-guided actions), hemispheric expansion in toothed whales may be due to enlargement of lobule HIX (Jansen and Jansen, 1969) (possibly more closely connected to auditory processing). Given that visual and somatosensory specializations are prominent in both primates and pinnipeds, we speculate that hemispheric expansion in pinnipeds is also due to lobule HVII. Similarly, visually dominant birds such as crows, parrots, and woodpeckers have enlarged visually projecting lobules VI–IX (in the medial cerebellum; birds do not have lateral extensions of the cerebellum, see Figure 1), likely related to their repertoire of visually guided goal-directed beak behavior (Sultan, 2005).

The lower than expected relative lateral cerebellar size in artiodactyls and felines should be confirmed with increased sampling. Considering that the lateral expansion of cerebellar lobules is a feature that first arose in early mammals, the artiodactyls and felines may, in fact, better reflect an early ancestral condition. In this scenario, the extent of the convergent and parallel expansion in apes, toothed whales, and pinnipeds observed here would be an underestimation of the true trend.

Previous studies on cerebellar evolution have mainly considered the cerebellum as a whole. These studies have suggested that the cerebellum underwent rapid size increase (relative to the cerebral cortex considered as a whole) in apes (Barton and Venditti, 2014), and that the elephant has the largest relative cerebellum size of all mammals (Maseko et al., 2012). After accounting for scaling differences with the rest of brain size, our results confirm that relative cerebellum size shows a grade shift in apes (pANCOVA, F = 5.432, p=0.024), but we found no evidence to support the conclusion that relative cerebellar size is significantly expanded in elephants compared to other mammals (pANCOVA, F = 2.426, p=0.126). However, Bayesian reversible-jump estimation of multi-peak OU models indicates that neither of these patterns are the most dominant in characterizing the macroevolutionary landscape of relative cerebellum size. Rather, the macroevolutionary landscape of relative cerebellar size is primarily characterized by a significant decrease in the zebu (observed/allometrically predicted value = 0.52) and cercopithecine primates (observed/allometrically predicted value = 0.85), and an increase in the musteline carnivorans (observed/allometrically predicted value = 1.44) (SI1, SI5). Furthermore, a comparison of the rate of evolution of lateral-medial cerebellar reorganization against relative cerebellum size shows a significantly higher rate for lateral-medial reorganization (Figure 4), suggesting that the main result of selective pressure in mammalian cerebellar evolution has led to modular changes within the cerebellum (likely related to reciprocal loops of information processing with particular cerebral networks), rather than global changes in cerebellum size relative to overall brain size or overall cerebral cortex (or ‘neocortex’) size. These results also accord with the cerebellum’s functional, connectional, and developmental modularity.

The interpretation of the nature of structural volumetric reorganization in brain evolution has also been debated. Some argue that volumetric reorganization occurs predominantly in a ‘mosaic’ fashion as a result of behavioral selective pressures which is largely unconstrained by developmental patterning (Barton, 2006; Barton and Harvey, 2000; Barton, 2001). Others argue that the sequence of developmental events plays a more significant role in shaping brain reorganization such that earlier developing structures are more likely to be evolutionarily conservative than later developing structures (Finlay et al., 2001; Finlay and Darlington, 1995). Our results indicate that cerebellar reorganization occurs in alignment with developmental predictions, with the later developing partition (i.c. the lateral cerebellum) showing a higher rate of evolution and characterized by a more complex evolutionary scenario than the earlier developing partition (i.c. the medial cerebellum) (Figure 3—figure supplement 1, Figure 4). Mammalian cerebellar reorganization is therefore more consistent with modular change (i.e. reorganization predicated on developmental patterning), than it is with mosaic change (i.e. reorganization largely unconstrained by developmental patterning). In primates, lateral cerebellar evolution appears to be particularly linked with those cerebral association areas that are part of the primate-specific cerebro-cerebellar network (Smaers, 2014a; Balsters et al., 2010; Smaers et al., 2013; Smaers et al., 2011), where a similar grade shift in great apes and humans is observed (Smaers et al., 2017; Passingham and Smaers, 2014).

The occurrence of significant changes in cerebellar structural reorganization in the mammalian macroevolutionary landscape (Figure 3), combined with the higher rate of evolution for changes in lateral-medial cerebellar organization over changes in relative cerebellar size (Figure 4) draws attention to a new avenue of adaptive brain evolution. Across mammals, the total number of cerebellar neurons (excluding Purkinje neurons) correlates significantly with the number of cerebral neurons, leading to the suggestion that coordinated processing networks with the neocortex constrain cerebellar evolution(Herculano-Houzel, 2010) To date, however, such analyses have rarely accounted for modular reorganization within gross anatomical structures (such as the cerebellum and the neocortex) independently of overall size (but see Balsters et al. (2010)). The significant grade shifts in cerebellar reorganization observed here show that not all scaled up cerebella are anatomically homologous. A comparison of observed to allometrically predicted values indicate that apes, toothed whales, and pinnipeds have lateral cerebellar hemispheres that are 2.3 to 4.4 times larger than predicted relative to the medial cerebellum, while artiodactyls have lateral cerebella that are 3.3 to 4.4 times smaller than predicted. Given near isometric scaling of number of neurons and mass in the cerebellum, this implies that the relative number of neurons dedicated to automating either higher cerebral association processing (lateral cerebellum), or basic motor skills and proprioception (medial cerebellum) is similarly unevenly distributed in apes, toothed whales, and pinnipeds versus artiodactyls. Moreover, the artiodactyls in our sample have similarly sized brains than apes and pinnipeds, further demonstrating the importance of modular reorganization patterns that are independent of brain size. Other aspects of cerebellar microstructural anatomy may also exhibit functionally significant phylogenetic variation. For instance, cerebellar cells (mostly granule cells) are more densely packed in eulipotyphlans, primates, and elephants, compared to other mammals investigated (Herculano-Houzel et al., 2015). And remarkable differences in the ratio of granule cells to Purkinje neurons have been reported, with the greatest proportions of granule cells per Purkinje neuron found in primates, toothed whales, and elephants (Lange, 1975). Additionally, Golgi impregnation studies have demonstrated that cerebellar neuron morphologies vary across mammals, showing strikingly extensive dendritic branching of Lugaro cells in elephants (Jacobs et al., 2014). The functional impact of such species differences in microstructure is yet to be fully understood, but should be considered alongside volumetric reorganization in a comprehensive model of cerebellar evolution.

The brain data and the phylogeny that were used in the analyses are available as source data files (Figure 2—source data 1, and Figure 3—source data 1). Behavioral data for primates is available from Figure 2, Deaner RO, Van Schaik CP, Johnson V. 2006. Do some taxa have better domain-general cognition than others? A meta-analysis of nonhuman primate studies. Evolutionary Psychology 4: 149-196.

1. 1. Adams DC
   2. Otárola-Castillo E

   (2013) geomorph: an R package for the collection and analysis of geometric morphometric shape data

   Methods in Ecology and Evolution 4:393–399.

   https://doi.org/10.1111/2041-210X.12035

   • Google Scholar
2. 1. Adams DC

   (2014) Quantifying and comparing phylogenetic evolutionary rates for shape and other high-dimensional phenotypic data

   Systematic Biology 63:166–177.

   https://doi.org/10.1093/sysbio/syt105

   • PubMed
   • Google Scholar
3. Book

   1. Altman J
   2. Bayer SA

   (1997)

   Development of the Cerebellar System: In Relation to Its Evolution, Structure, and Functions

   CRC Press.

   • Google Scholar
4. 1. Balsters JH
   2. Cussans E
   3. Diedrichsen J
   4. Phillips KA
   5. Preuss TM
   6. Rilling JK
   7. Ramnani N

   (2010) Evolution of the cerebellar cortex: the selective expansion of prefrontal-projecting cerebellar lobules

   NeuroImage 49:2045–2052.

   https://doi.org/10.1016/j.neuroimage.2009.10.045

   • PubMed
   • Google Scholar
5. Book

   1. Barkow JH
   2. Cosmides L
   3. Tooby J

   (1995)

   The Adapted Mind: Evolutionary Psychology and the Generation of Culture

   Oxford University Press.

   • Google Scholar
6. 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
7. 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
8. 1. Barton RA

   (2001) The coordinated structure of mosaic brain evolution

   Behavioral and Brain Sciences 24:281–282.

   https://doi.org/10.1017/S0140525X01253953

   • Google Scholar
9. 1. Barton RA

   (2006) Primate brain evolution: integrating comparative, neurophysiological, and ethological data

   Evolutionary Anthropology: Issues, News, and Reviews 15:224–236.

   https://doi.org/10.1002/evan.20105

   • Google Scholar
10. 1. Benson-Amram S
    2. Dantzer B
    3. Stricker G
    4. Swanson EM
    5. Holekamp KE

    (2016) Brain size predicts problem-solving ability in mammalian carnivores

    PNAS 113:2532–2537.

    https://doi.org/10.1073/pnas.1505913113

    • PubMed
    • Google Scholar
11. 1. Boesch C
    2. Boesch H

    (1990) Tool use and tool making in wild chimpanzees

    Folia Primatologica 54:86–99.

    https://doi.org/10.1159/000156428

    • Google Scholar
12. 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
13. 1. Cavalli-Sforza LL
    2. Edwards AW

    (1967)

    Phylogenetic analysis. models and estimation procedures

    American Journal of Human Genetics 19:233.

    • PubMed
    • Google Scholar
14. 1. Cosmides L
    2. Barrett HC
    3. Tooby J

    (2010) Colloquium paper: adaptive specializations, social exchange, and the evolution of human intelligence

    PNAS 107:9007–9014.

    https://doi.org/10.1073/pnas.0914623107

    • PubMed
    • Google Scholar
15. Book

    1. Cosmides L
    2. Tooby J

    (1992)

    Cognitive adaptations for social exchange

    In: Barkow J. H, Cosmides L, Tooby J, editors. The Adapted Mind: Evolutionary Psychology and the Generation of Culture. New York, NY: Oxford University Press. pp. 163–228.

    • Google Scholar
16. 1. Cressler CE
    2. Butler MA
    3. King AA

    (2015) Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model

    Systematic Biology 64:953–968.

    https://doi.org/10.1093/sysbio/syv043

    • PubMed
    • Google Scholar
17. 1. Deaner RO
    2. van Schaik CP
    3. Johnson V

    (2006) Do some taxa have better Domain-General cognition than others? A Meta-Analysis of nonhuman primate studies

    Evolutionary Psychology 4:147470490600400.

    https://doi.org/10.1177/147470490600400114

    • Google Scholar
18. 1. Dehnhardt G
    2. Mauck B
    3. Bleckmann H

    (1998) Seal whiskers detect water movements

    Nature 394:235–236.

    https://doi.org/10.1038/28303

    • Google Scholar
19. 1. Denton JSS
    2. Adams DC

    (2015) A new phylogenetic test for comparing multiple high-dimensional evolutionary rates suggests interplay of evolutionary rates and modularity in lanternfishes (Myctophiformes; Myctophidae)

    Evolution 69:2425–2440.

    https://doi.org/10.1111/evo.12743

    • Google Scholar
20. 1. Drummond AJ
    2. Suchard MA
    3. Xie D
    4. Rambaut A

    (2012) Bayesian phylogenetics with BEAUti and the BEAST 1.7

    Molecular Biology and Evolution 29:1969–1973.

    https://doi.org/10.1093/molbev/mss075

    • PubMed
    • Google Scholar
21. 1. Duncan J
    2. Seitz RJ
    3. Kolodny J
    4. Bor D
    5. Herzog H
    6. Ahmed A
    7. Newell FN
    8. Emslie H

    (2000) A neural basis for general intelligence

    Science 289:457–460.

    https://doi.org/10.1126/science.289.5478.457

    • PubMed
    • Google Scholar
22. 1. Faurby S
    2. Svenning JC

    (2015) A species-level phylogeny of all extant and late quaternary extinct mammals using a novel heuristic-hierarchical bayesian approach

    Molecular Phylogenetics and Evolution 84:14–26.

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

    • PubMed
    • Google Scholar
23. 1. Felleman DJ
    2. Van Essen DC

    (1991) Distributed hierarchical processing in the primate cerebral cortex

    Cerebral Cortex 1:1–47.

    https://doi.org/10.1093/cercor/1.1.1

    • PubMed
    • Google Scholar
24. 1. Felsenstein J

    (1973)

    Maximum-likelihood estimation of evolutionary trees from continuous characters

    American Journal of Human Genetics 25:471.

    • PubMed
    • Google Scholar
25. 1. Felsenstein J

    (1985) Phylogenies and the comparative method

    The American Naturalist 125:1–15.

    https://doi.org/10.1086/284325

    • Google Scholar
26. 1. Finlay BL
    2. Darlington RB
    3. Nicastro N

    (2001) Developmental structure in brain evolution

    Behavioral and Brain Sciences 24:263–278.

    https://doi.org/10.1017/S0140525X01003958

    • PubMed
    • Google Scholar
27. 1. Finlay BL
    2. Darlington RB

    (1995) Linked regularities in the development and evolution of mammalian brains

    Science 268:1578–1584.

    https://doi.org/10.1126/science.7777856

    • PubMed
    • Google Scholar
28. 1. Foote AD
    2. Griffin RM
    3. Howitt D
    4. Larsson L
    5. Miller PJ
    6. Hoelzel AR

    (2006) Killer whales are capable of vocal learning

    Biology Letters 2:509–512.

    https://doi.org/10.1098/rsbl.2006.0525

    • PubMed
    • Google Scholar
29. 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

    • PubMed
    • Google Scholar
30. 1. Glickstein M
    2. Sultan F
    3. Voogd J

    (2011) Functional localization in the cerebellum

    Cortex 47:59–80.

    https://doi.org/10.1016/j.cortex.2009.09.001

    • PubMed
    • Google Scholar
31. 1. Gómez-Robles A
    2. Smaers JB
    3. Holloway RL
    4. Polly PD
    5. Wood BA

    (2017) Brain enlargement and dental reduction were not linked in hominin evolution

    PNAS 114:468–473.

    https://doi.org/10.1073/pnas.1608798114

    • PubMed
    • Google Scholar
32. 1. Hanke FD
    2. Hanke W
    3. Scholtyssek C
    4. Dehnhardt G

    (2009) Basic mechanisms in pinniped vision

    Experimental Brain Research 199:299–311.

    https://doi.org/10.1007/s00221-009-1793-6

    • PubMed
    • Google Scholar
33. 1. Hansen TF

    (1997)

    Stabilizing selection and the comparative analysis of adaptation

    Evolution 51:1341–1351.

    • Google Scholar
34. 1. Herculano-Houzel S
    2. Catania K
    3. Manger PR
    4. Kaas JH

    (2015) Mammalian brains are made of these: a dataset of the numbers and densities of neuronal and nonneuronal cells in the brain of glires, primates, Scandentia, eulipotyphlans, afrotherians and artiodactyls, and their relationship with body mass

    Brain, Behavior and Evolution 86:145–163.

    https://doi.org/10.1159/000437413

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

    (2010) Coordinated scaling of cortical and cerebellar numbers of neurons

    Frontiers in Neuroanatomy 4:1–8.

    https://doi.org/10.3389/fnana.2010.00012

    • PubMed
    • Google Scholar
36. 1. Hoenig JM
    2. Heisey DM

    (2001)

    The abuse of power: the pervasive fallacy of power calculations for data analysis

    The American Statistician 55:19–24.

    • Google Scholar
37. 1. Hof PR
    2. Chanis R
    3. Marino L

    (2005) Cortical complexity in cetacean brains

    The Anatomical Record Part A: Discoveries in Molecular, Cellular, and Evolutionary Biology 287A:1142–1152.

    https://doi.org/10.1002/ar.a.20258

    • Google Scholar
38. 1. Hof PR
    2. Van Der Gucht E

    (2007) Structure of the cerebral cortex of the humpback whale,Megaptera novaeangliae (Cetacea, mysticeti, Balaenopteridae)

    The Anatomical Record: Advances in Integrative Anatomy and Evolutionary Biology 290:1–31.

    https://doi.org/10.1002/ar.20407

    • Google Scholar
39. 1. Ito M

    (2008) Control of mental activities by internal models in the cerebellum

    Nature Reviews Neuroscience 9:304–313.

    https://doi.org/10.1038/nrn2332

    • PubMed
    • Google Scholar
40. 1. Jacobs B
    2. Johnson NL
    3. Wahl D
    4. Schall M
    5. Maseko BC
    6. Lewandowski A
    7. Raghanti MA
    8. Wicinski B
    9. Butti C
    10. Hopkins WD
    11. Bertelsen MF
    12. Walsh T
    13. Roberts JR
    14. Reep RL
    15. Hof PR
    16. Sherwood CC
    17. Manger PR

    (2014) Comparative neuronal morphology of the cerebellar cortex in Afrotherians, carnivores, cetartiodactyls, and primates

    Frontiers in Neuroanatomy 8:24.

    https://doi.org/10.3389/fnana.2014.00024

    • PubMed
    • Google Scholar
41. 1. Janik VM
    2. Slater PJ

    (2000) The different roles of social learning in vocal communication

    Animal Behaviour 60:1–11.

    https://doi.org/10.1006/anbe.2000.1410

    • PubMed
    • Google Scholar
42. 1. Janik VM

    (2013) Cognitive skills in Bottlenose dolphin communication

    Trends in Cognitive Sciences 17:157–159.

    https://doi.org/10.1016/j.tics.2013.02.005

    • PubMed
    • Google Scholar
43. 1. Jansen J
    2. Jansen J

    (1969)

    The nervous system of cetacea

    The Biology of Marine Mammals pp. 175–252.

    • Google Scholar
44. 1. Jones KE
    2. Bielby J
    3. Cardillo M
    4. Fritz SA
    5. O'Dell J
    6. Orme CDL
    7. Safi K
    8. Sechrest W
    9. Boakes EH
    10. Carbone C
    11. Connolly C
    12. Cutts MJ
    13. Foster JK
    14. Grenyer R
    15. Habib M
    16. Plaster CA
    17. Price SA
    18. Rigby EA
    19. Rist J
    20. Teacher A
    21. Bininda-Emonds ORP
    22. Gittleman JL
    23. Mace GM
    24. Purvis A

    (2009) PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals

    Ecology 90:2648.

    https://doi.org/10.1890/08-1494.1

    • Google Scholar
45. 1. Khabbazian M
    2. Kriebel R
    3. Rohe K
    4. Ané C

    (2016) Fast and accurate detection of evolutionary shifts in Ornstein-Uhlenbeck models

    Methods in Ecology and Evolution 7:811–824.

    https://doi.org/10.1111/2041-210X.12534

    • Google Scholar
46. 1. Koziol LF
    2. Budding D
    3. Andreasen N
    4. D’Arrigo S
    5. Bulgheroni S
    6. Imamizu H
    7. Ito M
    8. Manto M
    9. Marvel C
    10. Parker K
    11. Pezzulo G
    12. Ramnani N
    13. Riva D
    14. Schmahmann J
    15. Vandervert L
    16. Yamazaki T

    (2014) Consensus paper: the cerebellum's Role in Movement and Cognition

    The Cerebellum 13:151–177.

    https://doi.org/10.1007/s12311-013-0511-x

    • Google Scholar
47. 1. Krützen M
    2. Mann J
    3. Heithaus MR
    4. Connor RC
    5. Bejder L
    6. Sherwin WB

    (2005) Cultural transmission of tool use in Bottlenose dolphins

    PNAS 102:8939–8943.

    https://doi.org/10.1073/pnas.0500232102

    • PubMed
    • Google Scholar
48. 1. Lange W

    (1975) Cell number and cell density in the cerebellar cortex of man and some other mammals

    Cell and Tissue Research 157:115–124.

    https://doi.org/10.1007/BF00223234

    • PubMed
    • Google Scholar
49. 1. Larsell O
    2. Dow RS

    (1935) The development of the cerebellum in the bat (Corynorhinus SP.) and certain other mammals

    The Journal of Comparative Neurology 62:443–468.

    https://doi.org/10.1002/cne.900620210

    • Google Scholar
50. Book

    1. Larsell O

    (1970)

    Comparative Anatomy and Histology of the Cerebellum From Monotremes Through Apes

    Minneapolis: University of Minnesota Press.

    • Google Scholar
51. 1. Lst H
    2. Ané C

    (2014) Intrinsic inference difficulties for trait evolution with ornstein‐uhlenbeck models

    Methods in Ecology and Evolution 5:1133–1146.

    https://doi.org/10.1111/2041-210X.12285

    • Google Scholar
52. 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
53. 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
54. 1. Martins EP

    (1994) Estimating the rate of phenotypic evolution from comparative data

    The American Naturalist 144:193–209.

    https://doi.org/10.1086/285670

    • Google Scholar
55. 1. Maseko BC
    2. Spocter MA
    3. Haagensen M
    4. Manger PR

    (2012) Elephants have relatively the largest cerebellum size of mammals

    The Anatomical Record: Advances in Integrative Anatomy and Evolutionary Biology 295:661–672.

    https://doi.org/10.1002/ar.22425

    • Google Scholar
56. 1. Moberget T
    2. Gullesen EH
    3. Andersson S
    4. Ivry RB
    5. Endestad T

    (2014) Generalized role for the cerebellum in encoding internal models: evidence from semantic processing

    Journal of Neuroscience 34:2871–2878.

    https://doi.org/10.1523/JNEUROSCI.2264-13.2014

    • PubMed
    • Google Scholar
57. 1. Moberget T
    2. Ivry RB

    (2016) Cerebellar contributions to motor control and language comprehension: searching for common computational principles

    Annals of the New York Academy of Sciences 1369:154–171.

    https://doi.org/10.1111/nyas.13094

    • PubMed
    • Google Scholar
58. 1. O'Reilly JX
    2. Beckmann CF
    3. Tomassini V
    4. Ramnani N
    5. Johansen-Berg H

    (2010) Distinct and overlapping functional zones in the cerebellum defined by resting state functional connectivity

    Cerebral Cortex 20:953–965.

    https://doi.org/10.1093/cercor/bhp157

    • PubMed
    • Google Scholar
59. 1. Osvath M
    2. Osvath H

    (2008) Chimpanzee (pan Troglodytes) and orangutan (Pongo abelii) forethought: self-control and pre-experience in the face of future tool use

    Animal Cognition 11:661–674.

    https://doi.org/10.1007/s10071-008-0157-0

    • PubMed
    • Google Scholar
60. Software

    1. Pagel M
    2. Meade A

    (2013)

    BayesTraits, version 2.0

    Reading: University of Reading.
61. 1. Pagel M

    (1997) Inferring evolutionary processes from phylogenies

    Zoologica Scripta 26:331–348.

    https://doi.org/10.1111/j.1463-6409.1997.tb00423.x

    • Google Scholar
62. 1. Passingham RE
    2. Smaers JB

    (2014) Is the prefrontal cortex especially enlarged in the human brain allometric relations and remapping factors

    Brain, Behavior and Evolution 84:156–166.

    https://doi.org/10.1159/000365183

    • PubMed
    • Google Scholar
63. 1. Passingham RE

    (1973) Anatomical differences between the neocortex of man and other primates

    Brain, Behavior and Evolution 7:337–359.

    https://doi.org/10.1159/000124422

    • PubMed
    • Google Scholar
64. 1. Polly PD

    (2004)

    On the simulation of the evolution of morphological shape: multivariate shape under selection and drift

    Palaeontologia Electronica 7:1–28.

    • Google Scholar
65. Software

    1. Polly PD

    (2017) Phylogenetics for Mathematica

    Department of Geological Sciences, Indiana University, Bloomington.

    http://mypage.iu.edu/~pdpolly/Software.html
66. 1. Preuss TM
    2. Goldman-Rakic PS

    (1991a) Ipsilateral cortical connections of granular frontal cortex in the strepsirhine primate Galago, with comparative comments on anthropoid primates

    The Journal of Comparative Neurology 310:507–549.

    https://doi.org/10.1002/cne.903100404

    • PubMed
    • Google Scholar
67. 1. Preuss TM
    2. Goldman-Rakic PS

    (1991b) Myelo- and cytoarchitecture of the granular frontal cortex and surrounding regions in the strepsirhine primate Galago and the anthropoid primate Macaca

    The Journal of Comparative Neurology 310:429–474.

    https://doi.org/10.1002/cne.903100402

    • PubMed
    • Google Scholar
68. 1. Preuss TM

    (1995) Do rats have prefrontal cortex? the rose-woolsey-akert program reconsidered

    Journal of Cognitive Neuroscience 7:1–24.

    https://doi.org/10.1162/jocn.1995.7.1.1

    • PubMed
    • Google Scholar
69. 1. Preuss TM

    (2007)

    Evolutionary Specializations of Primate Brain Systems

    625–675, Primate origins: adaptations and evolution, Evolutionary Specializations of Primate Brain Systems.

    • Google Scholar
70. 1. Raby CR
    2. Alexis DM
    3. Dickinson A
    4. Clayton NS

    (2007) Planning for the future by western scrub-jays

    Nature 445:919–921.

    https://doi.org/10.1038/nature05575

    • PubMed
    • Google Scholar
71. 1. Ralls K
    2. Fiorelli P
    3. Gish S

    (1985) Vocalizations and vocal mimicry in captive harbor seals, Phoca vitulina

    Canadian Journal of Zoology 63:1050–1056.

    https://doi.org/10.1139/z85-157

    • Google Scholar
72. 1. Ramnani N
    2. Behrens TE
    3. Johansen-Berg H
    4. Richter MC
    5. Pinsk MA
    6. Andersson JL
    7. Rudebeck P
    8. Ciccarelli O
    9. Richter W
    10. Thompson AJ
    11. Gross CG
    12. Robson MD
    13. Kastner S
    14. Matthews PM

    (2006) The evolution of prefrontal inputs to the cortico-pontine system: diffusion imaging evidence from macaque monkeys and humans

    Cerebral Cortex 16:811–818.

    https://doi.org/10.1093/cercor/bhj024

    • PubMed
    • Google Scholar
73. 1. Ravignani A
    2. Fitch WT
    3. Hanke FD
    4. Heinrich T
    5. Hurgitsch B
    6. Kotz SA
    7. Scharff C
    8. Stoeger AS
    9. de Boer B

    (2016) What pinnipeds have to say about human speech, music, and the evolution of rhythm

    Frontiers in Neuroscience 10:274.

    https://doi.org/10.3389/fnins.2016.00274

    • PubMed
    • Google Scholar
74. 1. Reiss D
    2. McCowan B

    (1993) Spontaneous vocal mimicry and production by bottlenose dolphins (Tursiops truncatus): evidence for vocal learning

    Journal of Comparative Psychology 107:301.

    https://doi.org/10.1037/0735-7036.107.3.301

    • PubMed
    • Google Scholar
75. 1. Revell LJ

    (2012) Phytools: an R package for phylogenetic comparative biology (and other things)

    Methods in Ecology and Evolution 3:217–223.

    https://doi.org/10.1111/j.2041-210X.2011.00169.x

    • Google Scholar
76. 1. Rohlf FJ

    (2001) Comparative methods for the analysis of continuous variables: geometric interpretations

    Evolution 55:2143–2160.

    https://doi.org/10.1111/j.0014-3820.2001.tb00731.x

    • Google Scholar
77. 1. Roth G
    2. Dicke U

    (2005) Evolution of the brain and intelligence

    Trends in Cognitive Sciences 9:250–257.

    https://doi.org/10.1016/j.tics.2005.03.005

    • PubMed
    • Google Scholar
78. 1. Roth G

    (2015) Convergent evolution of complex brains and high intelligence

    Philosophical Transactions of the Royal Society B: Biological Sciences 370:20150049.

    https://doi.org/10.1098/rstb.2015.0049

    • Google Scholar
79. 1. Sawyer EK
    2. Turner EC
    3. Kaas JH

    (2016) Somatosensory brainstem, Thalamus, and cortex of the California sea lion (Zalophus californianus)

    Journal of Comparative Neurology 524:1957–1975.

    https://doi.org/10.1002/cne.23984

    • PubMed
    • Google Scholar
80. 1. Schmahmann JD
    2. Pandya DN

    (1991) Projections to the basis pontis from the superior temporal sulcus and superior temporal region in the rhesus monkey

    The Journal of Comparative Neurology 308:224–248.

    https://doi.org/10.1002/cne.903080209

    • PubMed
    • Google Scholar
81. Book

    1. Schmahmann JD

    (1997)

    The Cerebellum and Cognition, 41

    London: Academic Press.

    • Google Scholar
82. 1. Scholtyssek C
    2. Kelber A
    3. Dehnhardt G

    (2008) Brightness discrimination in the harbor seal (Phoca vitulina)

    Vision Research 48:96–103.

    https://doi.org/10.1016/j.visres.2007.10.012

    • PubMed
    • Google Scholar
83. 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

    • PubMed
    • Google Scholar
84. 1. Smaers JB
    2. Mongle CS
    3. Kandler A

    (2016) A multiple variance brownian motion framework for estimating variable rates and inferring ancestral states

    Biological Journal of the Linnean Society 118:78–94.

    https://doi.org/10.1111/bij.12765

    • Google Scholar
85. 1. Smaers JB
    2. Mongle CS

    (2017) On the accuracy and theoretical underpinnings of the multiple variance brownian motion approach for estimating variable rates and inferring ancestral states

    Biological Journal of the Linnean Society 121:229–238.

    https://doi.org/10.1093/biolinnean/blx003

    • Google Scholar
86. 1. Smaers JB
    2. Rohlf FJ

    (2016) Testing species’ deviation from allometric predictions using the phylogenetic regression

    Evolution 70:1145–1149.

    https://doi.org/10.1111/evo.12910

    • Google Scholar
87. 1. Smaers JB
    2. Steele J
    3. Case CR
    4. Amunts K

    (2013) Laterality and the evolution of the prefronto-cerebellar system in anthropoids

    Annals of the New York Academy of Sciences 1288:59–69.

    https://doi.org/10.1111/nyas.12047

    • PubMed
    • Google Scholar
88. 1. Smaers JB
    2. Steele J
    3. Zilles K

    (2011) Modeling the evolution of cortico-cerebellar systems in primates

    Annals of the New York Academy of Sciences 1225:176–190.

    https://doi.org/10.1111/j.1749-6632.2011.06003.x

    • Google Scholar
89. 1. Smaers JB

    (2014a) Modeling the evolution of the cerebellum: from macroevolution to function

    Progress in Brain Research 210:193–216.

    https://doi.org/10.1016/B978-0-444-63356-9.00008-X

    • PubMed
    • Google Scholar
90. Website

    1. Smaers JB
    2. Mongle CS

    (2018) Evomap: r package for the evolutionary mapping of continuous traits

    Github.

    https://github.com/JeroenSmaers/evomap
91. 1. Sokolov AA
    2. Erb M
    3. Grodd W
    4. Pavlova MA

    (2014) Structural loop between the cerebellum and the superior temporal sulcus: evidence from diffusion tensor imaging

    Cerebral Cortex 24:626–632.

    https://doi.org/10.1093/cercor/bhs346

    • PubMed
    • Google Scholar
92. 1. Sokolov AA
    2. Miall RC
    3. Ivry RB

    (2017) The cerebellum: adaptive prediction for movement and cognition

    Trends in Cognitive Sciences 21:313–332.

    https://doi.org/10.1016/j.tics.2017.02.005

    • PubMed
    • Google Scholar
93. 1. Spearman C

    (1904) "General intelligence," objectively determined and measured

    The American Journal of Psychology 15:201–292.

    https://doi.org/10.2307/1412107

    • Google Scholar
94. 1. Stoeger AS
    2. Mietchen D
    3. Oh S
    4. de Silva S
    5. Herbst CT
    6. Kwon S
    7. Fitch WT

    (2012) An asian elephant imitates human speech

    Current Biology 22:2144–2148.

    https://doi.org/10.1016/j.cub.2012.09.022

    • PubMed
    • Google Scholar
95. 1. Strick PL
    2. Dum RP
    3. Fiez JA

    (2009) Cerebellum and nonmotor function

    Annual Review of Neuroscience 32:413–434.

    https://doi.org/10.1146/annurev.neuro.31.060407.125606

    • PubMed
    • Google Scholar
96. 1. Sultan F

    (2005) Why some bird brains are larger than others

    Current Biology 15:R649–R650.

    https://doi.org/10.1016/j.cub.2005.08.043

    • PubMed
    • Google Scholar
97. 1. Tiemeier H
    2. Lenroot RK
    3. Greenstein DK
    4. Tran L
    5. Pierson R
    6. Giedd JN

    (2010) Cerebellum development during childhood and adolescence: a longitudinal morphometric MRI study

    NeuroImage 49:63–70.

    https://doi.org/10.1016/j.neuroimage.2009.08.016

    • PubMed
    • Google Scholar
98. 1. Turner EC
    2. Sawyer EK
    3. Kaas JH

    (2017) Optic nerve, superior colliculus, visual thalamus, and primary visual cortex of the northern elephant seal (Mirounga angustirostris) and California sea lion (Zalophus californianus)

    Journal of Comparative Neurology 525:2109–2132.

    https://doi.org/10.1002/cne.24188

    • PubMed
    • Google Scholar
99. 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
100. Software

     1. Uyeda JF
     2. Eastman J

     (2014) Bayou: Bayesian Fitting of Ornstein-Uhlenbeck Models to Phylogenies, version R package version 1.0.1

     Bayou: Bayesian Fitting of Ornstein-Uhlenbeck Models to Phylogenies.

     http://CRAN.R-project.org/package=bayou
101. 1. van Kann E
     2. Cozzi B
     3. Hof PR
     4. Oelschläger HHA

     (2017) Qualitative and quantitative analysis of primary neocortical Areas in selected mammals

     Brain, Behavior and Evolution 90:193–210.

     https://doi.org/10.1159/000477431

     • PubMed
     • Google Scholar
102. 1. Van Overwalle F
     2. Baetens K
     3. Mariën P
     4. Vandekerckhove M

     (2014) Social cognition and the cerebellum: a meta-analysis of over 350 fMRI studies

     NeuroImage 86:554–572.

     https://doi.org/10.1016/j.neuroimage.2013.09.033

     • PubMed
     • Google Scholar
103. 1. Venditti C
     2. Meade A
     3. Pagel M

     (2011) Multiple routes to mammalian diversity

     Nature 479:393–396.

     https://doi.org/10.1038/nature10516

     • PubMed
     • Google Scholar
104. 1. Vernes SC

     (2017) What bats have to say about speech and language

     Psychonomic Bulletin & Review 24:111–117.

     https://doi.org/10.3758/s13423-016-1060-3

     • PubMed
     • Google Scholar
105. 1. Wise SP
     2. Boussaoud D
     3. Johnson PB
     4. Caminiti R

     (1997) Premotor and parietal cortex: corticocortical connectivity and combinatorial computations

     Annual Review of Neuroscience 20:25–42.

     https://doi.org/10.1146/annurev.neuro.20.1.25

     • PubMed
     • Google Scholar
106. 1. Zilles K
     2. Amunts K
     3. Smaers JB

     (2011) Three brain collections for comparative neuroanatomy and neuroimaging

     Annals of the New York Academy of Sciences 1225:E94–E104.

     https://doi.org/10.1111/j.1749-6632.2011.05978.x

     • PubMed
     • Google Scholar

Author details

1. Jeroen B Smaers

   1. Department of Anthropology, Stony Brook University, New York, United States
   2. Center for the Advanced Study of Human Paleobiology, Stony Brook University, New York, United States

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   jeroen.smaers@stonybrook.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1741-9839

2. Alan H Turner

   Department of Anatomical Sciences, Stony Brook University, New York, United States

   Contribution

   Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Aida Gómez-Robles

   1. Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
   2. Department of Anthropology, The George Washington University, Washington, United States

   Contribution

   Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Chet C Sherwood

   Department of Anthropology, The George Washington University, Washington, United States

   Contribution

   Conceptualization, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

• 3,346

  views

• 508

  downloads

• 47

  citations

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