Evolutionary Trajectories of Cognitive Abilities and of their Putative Neuroanatomical and Allometric Correlates: Testing Novel Hypotheses of Cognitive Evolution and Cognitive Integration with Phylogenetic Comparative Methods

The study of primate intelligence, and specifically of general intelligence, has progressed rapidly in the last two decades, however several issues remain unexplored. While neuroanatomical volume measures (NVMs) such as brain size, neocortex size, and absolute or relative size of other brain regions have been frequently framed as substrates for general intelligence, such claims are largely based on simple correlative analyses. Furthermore, while factor analytical techniques have identified a general factor among cognitive abilities when using datasets of species’ mean performances, there have been no examinations of whether the common factor is predictably more strongly present in some primate lineages than in others, and whether relations among brain regions are similarly stronger in some primate lineages than in others. Here, such issues in the comparative literature on primate intelligence are addressed in a new set of studies that present two main novel contributions to the scientific understanding of primate intelligence: First, the evolutionary patterns of the history of changes in general intelligence in primates is examined and compared to those behind the history of changes in brain size and the size of brain regions most commonly used in comparative cognition studies. Studying evolutionary patterns behind a trait permits examining how conserved it is across evolutionary time, how fast it has evolved, and the degree to which it has evolved in a particular direction (i.e., if natural selection regimes have been consistent). Phylogenetic comparative methods employed on datasets of primate species reveal that general intelligence has evolved at a faster pace than NVMs and it has evolved more consistently towards a selection optimum. In contrast to the overall emphasis given in the literature to brain size and neocortex ratio as substrates for intelligence, the NVM with results that most strongly approached the patterns identified for general intelligence is residual cerebellar size (relative to body size). Secondly, a hypothesis is advanced that species higher on general intelligence exhibit a stronger manifold (i.e., higher factor loadings) as general intelligence has previously been empirically associated with ecological generalism, rather than with specialism. As such, cognitive specialization and independence among abilities should be a hallmark of species that have not evolved strong general intelligence. The Continuous Parameter Estimation Model (CPEM) is used in a dataset of cognitive abilities in primate species, and largely confirms the hypothesis forwarded. However, when the same analytical approach is replicated using data on sizes of brain regions, it is found that brain size fails to predict or coevolve with factor loadings of brain regions. Similarly, telencephalon size (a brain structure that holds several of the regions theoretically proposed to serve as substrates for intelligence) fails to predict the strength of factor loadings of telencephalic regions. This set of studies supports the notion that the comparability between the evolution of general intelligence and the evolution of volumes of neuroanatomical structures is more limited than previously thought. Alternative substrates for general intelligence are discussed

Son to Father Reciprocity and Encephalization in Early Humans

Humans exhibit much more sharing of food harvested by prime-age hunter-gatherers with dependents relative to such sharing by lower-order primates. We investigate this behavior in a model in which a father provides generously to his dependent child-son in period t in the hope that this gesture will inspire his son to reciprocate in the next period when the father is in "retirement". In our formulation fathers provide better when (a) they are smarter hunters (b) they have a higher probability of living to experience a "retirement" and (c) when they are more con�dent that their child-sons will indeed provide generously for them in their "retirement". Better food provision by prime-age fathers is associated with brain-size expansion in our model.reciprocity, encephalization, intertemporal division of labor

Embodied cognitive evolution and the cerebellum

Much attention has focused on the dramatic expansion of the forebrain, particularly the neocortex, as the neural substrate of cognitive evolution. However, though relatively small, the cerebellum contains about four times more neurons than the neocortex. I show that commonly used comparative measures such as neocortex ratio underestimate the contribution of the cerebellum to brain evolution. Once differences in the scaling of connectivity in neocortex and cerebellum are accounted for, a marked and general pattern of correlated evolution of the two structures is apparent. One deviation from this general pattern is a relative expansion of the cerebellum in apes and other extractive foragers. The confluence of these comparative patterns, studies of ape foraging skills and social learning, and recent evidence on the cognitive neuroscience of the cerebellum, suggest an important role for the cerebellum in the evolution of the capacity for planning, execution and understanding of complex behavioural sequences—including tool use and language. There is no clear separation between sensory–motor and cognitive specializations underpinning such skills, undermining the notion of executive control as a distinct process. Instead, I argue that cognitive evolution is most effectively understood as the elaboration of specialized systems for embodied adaptive control

Beyond brain size: Uncovering the neural correlates of behavioral and cognitive specialization

© Comparative Cognition Society. Despite prolonged interest in comparing brain size and behavioral proxies of "intelligence" across taxa, the adaptive and cognitive significance of brain size variation remains elusive. Central to this problem is the continued focus on hominid cognition as a benchmark and the assumption that behavioral complexity has a simple relationship with brain size. Although comparative studies of brain size have been criticized for not reflecting how evolution actually operates, and for producing spurious, inconsistent results, the causes of these limitations have received little discussion. We show how these issues arise from implicit assumptions about what brain size measures and how it correlates with behavioral and cognitive traits. We explore how inconsistencies can arise through heterogeneity in evolutionary trajectories and selection pressures on neuroanatomy or neurophysiology across taxa. We examine how interference from ecological and life history variables complicates interpretations of brain-behavior correlations and point out how this problem is exacerbated by the limitations of brain and cognitive measures. These considerations, and the diversity of brain morphologies and behavioral capacities, suggest that comparative brain-behavior research can make greater progress by focusing on specific neuroanatomical and behavioral traits within relevant ecological and evolutionary contexts. We suggest that a synergistic combination of the "bottom-up" approach of classical neuroethology and the "top-down" approach of comparative biology/psychology within closely related but behaviorally diverse clades can limit the effects of heterogeneity, interference, and noise. We argue that this shift away from broad-scale analyses of superficial phenotypes will provide deeper, more robust insights into brain evolution

Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain