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17
A Hierarchical Model of the Evolution
of Human Brain Specializations
H. CLARK BARRETT
The study of information-processing adaptations in the brain is contro-
versial, in part because of disputes about the form such adaptations might
take. Many psychologists assume that adaptations come in two kinds,
specialized and general-purpose. Specialized mechanisms are typically
thought of as innate, domain-specific, and isolated from other brain
systems, whereas generalized mechanisms are developmentally plastic,
domain-general, and interactive. However, if brain mechanisms evolve
through processes of descent with modification, they are likely to be het-
erogeneous, rather than coming in just two kinds. They are likely to be
hierarchically organized, with some design features widely shared across
brain systems and others specific to particular processes. Also, they are
likely to be largely developmentally plastic and interactive with other
brain systems, rather than canalized and isolated. This chapter presents
a hierarchical model of brain specialization, reviewing evidence for the
model from evolutionary developmental biology, genetics, brain map-
ping, and comparative studies. Implications for the search for uniquely
human traits are discussed, along with ways in which conventional views
of modularity in psychology may need to be revised.
Center for Behavior, Evolution, and Culture and FPR-UCLA Center for Culture, Brain and
Development, Department of Anthropology, University of California, Los Angeles, CA
90095. E-mail: barrett@anthro.ucla.edu.
313
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W
hat is the nature of the brain mechanisms that give rise to human
cognition, and how do these mechanisms evolve? Although it
is clear that human cognition, like all organismal traits, must
be accounted for by some combination of ancestral and derived brain
processes, attempts to decompose human mental processes into functional
components whose features have been shaped by the process of natural
selection—that is, adaptations—have been highly contested and contro-
versial (Buller, 2005). The controversy centers on the difficulty of establish-
ing whether a particular aspect of cognition or behavior is the result of an
adaptation or adaptations, and in what way. Is a given cognitive ability
in humans or any other species—for example, the ability to discriminate
between different quantities of objects, to navigate spatially, or to learn
to speak a language—the product of an adaptation specifically for that
ability? Or is it just a specific instantiation of a more general ability, such
as associative learning, or the general computational properties of neural
networks? Or is it not the result of adaptations at all?
Proposals about functional specialization have long been a source of
debate in psychology and the brain sciences. In particular, there is little
agreement over whether cognitive processes other than perceptual and
motor processes—that is, so-called higher-level processes—are special-
ized, and if so, how (Mahon and Cantlon, 2011). At stake are both theoreti-
cal and empirical issues. Theoretically, although it is clear that the brain
is the product of evolutionary processes, including natural selection,
we cannot move past this simple truism if we are unable to answer the
question of what adaptations it contains, or to distinguish the results
of natural selection from the results of other processes. Empirically, a
variety of methods have been developed for studying brain specializa-
tions, including studies of developmental disorders and brain lesions,
brain mapping techniques, experimental psychology tasks, comparative
studies of brain anatomy and development, and more recently, studies of
gene expression and gene regulation in the brain. However, controversy
surrounds virtually all these methods and how they can be used to make
inferences about functional specialization (Uttal, 2001). Even when brain
researchers agree that specialization in the mature adult brain exists,
they often cannot agree whether it is a result of selection specifically for
that outcome, or is produced by more general developmental processes
(Elman et al., 1996). As a result, there is little or no consensus about the
nature of adaptations in the brain or even how to study them, especially
for “higher-level” cognitive abilities such as language and reasoning
(Mahon and Cantlon, 2011).
Although some of the reasons for this slow progress may be meth-
odological, the impasse may also stem from a lack of biologically plau-
sible models of what adaptations in the brain might be like (Barrett and
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Hierarchical Model of Evolution of Human Brain Specializations / 315
Kurzban, 2006). In psychology and neuroscience, it is common to think of
brain mechanisms as falling into two categories: specialized and general-
purpose. Specialized mechanisms are frequently associated with the idea
of cognitive “modules,” which are in turn associated with several kinds of
property (Fodor, 1983). Modules are often held to be “innate” in the sense
that they develop similarly or identically across individuals, regardless of
environmental input (i.e., they are canalized). They are “domain-specific,”
that is, tailored to specific tasks or types of information. In addition, they
operate autonomously or “automatically,” that is, independently of other
systems and processes, including consciousness, and therefore produce
the same outcomes regardless of context. Nonmodular processes, on the
contrary, are held to be domain-general, developmentally plastic instead
of innate, and interactive rather than autonomous. Many psychologists
believe that human cognition can be accounted for by some mix of these
two types of mechanism. This is sometimes called a “dual systems” view
(Stanovich, 2004).
This view, derived from models of perception, equates specialization
only with highly local, narrow, and stereotyped processes, and defines
general-purpose processes as whatever “modular” processes are not
(Fodor, 1983). Empirically, this means that evidence for developmental
plasticity, interactivity, or capacity to respond to evolutionarily novel
stimuli is typically taken as evidence that a brain region or process is not
evolutionarily specialized. Moreover, proximal factors such as plasticity
and developmental constraint are sometimes seen as alternatives to expla-
nations invoking selection for particular outcomes (Elman et al., 1996).
Biologically speaking, however, these distinctions may be based on false
dichotomies. There is no reason why adaptations in the brain (or else-
where) need to be developmentally canalized as opposed to plastic, iso-
lated from other systems rather than interactive, or tightly locked to spe-
cific categories of information regardless of developmental circumstance.
If adaptations in the brain resemble other organismal adaptations—
for example, tissue types, limbs, organs, and the molecular machinery of
cells—they are likely to be both heterogeneous and hierarchical. Hetero-
geneity arises from the fact of form-function fit: adaptations have differ-
ent histories and have evolved to do different things, so they are likely to
have diverse properties rather than coming in just two kinds. Hierarchical
organization, in turn, is characteristic of systems that evolve via descent
with modification. Because new structures evolve from older structures,
adaptations frequently share a mix of ancestral and derived features, with
relatively ancient features (e.g., properties of neurons in general) shared
more widely across organismal structures, and relatively recent ones (e.g.,
properties of specialized brain regions) more narrowly distributed, in a
hierarchically organized fashion (Carroll et al., 2005).
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If this is true of adaptations in the brain, it has important implications
for current debates about them. Here I outline features of a hierarchical
specialization model of brain evolution and show how it may require
rethinking some commonly held assumptions about brain adaptations in
psychology and the social sciences.
EVOLUTION AND DEVELOPMENT OF BRAIN ARCHITECTURE
If adaptations in the brain exist, they are likely to be built during
ontogeny by developmental systems that orchestrate interactions between
external inputs (e.g., sensory information), internal inputs (e.g., interac-
tions within and between brain regions), and genetic regulatory machin-
ery to shape phenotypic structure, including the computational properties
of developed brain networks. Natural selection acts on these systems
based on the phenotypes they produce, and newer developmental systems
and mechanisms evolve from older ones via descent with modification.
These points have several implications for what specialization means in
the context of the brain.
Type and Token Outcomes of Developmental Processes
Because natural selection acts on phenotypes, developmental processes
are selected based on the phenotypic outcomes they produce. However, the
plastic nature of mammalian brain development means that actual pheno-
typic outcomes may vary substantially between individuals along some
dimensions, while exhibiting similarities along others. For example, the
brains of some mammals and birds may contain adaptations for develop-
ing cognitive maps of their local environments, but presumably the actual
content of those maps varies widely across individuals (Jacobs and Schenk,
2003). Similarly, if human brains contain adaptations for learning language
(still a controversial proposal), then the content of the developed pheno-
types of linguistic knowledge must vary across individuals in all the ways
that human languages vary (Evans and Levinson, 2009). Thus, developmen-
tal processes can be described functionally in terms of the type of outcomes
they produce (e.g., cognitive maps, linguistic knowledge), although the
instantiated tokens in individually developed brains vary in their phenotypic
details (e.g., French, Quechua). This is presumably the norm rather than the
exception for much of the brain (Buonomano and Merzenich, 1998).
Reaction Norms in the Brain
Plastic developmental systems can produce different phenotypes
when placed into different environments. The mapping functions between
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Hierarchical Model of Evolution of Human Brain Specializations / 317
FIGURE 17.1 A reaction norm describes the mapping relationship between
genotype, developmental inputs, and phenotypic outcomes (A). Brain regions
may exhibit different reaction norms as a result of differential patterns of gene
expression (B).
genotype, environment, and phenotype are known as reaction norms
(Schlichting and Pigliucci, 1998) (Fig. 17.1). Because human brains contain
multiple developmental processes, they are likely to contain different reac-
tion norms for different functional regions and processes. For example,
the reaction norms of motor cortex, which is partly organized around
coordinated motor routines such as grasping and defense, may differ from
those of somatosensory cortex, which is typically organized around body
topology (Stepniewska et al., 2011). Moreover, tissue may be induced to
adopt different reaction norms depending on the kinds of input it receives,
both external (e.g., sensory) and internal (e.g., from other brain regions)
(Sur and Rubenstein, 2005). Because reaction norms are the products of
inherited developmental machinery, and because that machinery can be
modified by selection based on the phenotypic outcomes it produces—that
is, the brain structure that develops in response to external and internal
inputs during development—reaction norms themselves evolve through
processes of descent with modification. Thus, the developmental compo-
nents of brain specializations may be thought of as a set of reaction norms,
and their phenotypic components as the developed neural structures that
they produce.
Ontogenetic Tuning and Module Spawning
As is the case for morphological development more generally, brain
development is likely to proceed through processes of serial differen-
tiation, subdividing into progressively finer elements whose neural and
computational properties are fine-tuned based on the inputs they receive,
interacting with whatever developmental processes are locally active (Sur
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318 / H. Clark Barrett
and Rubenstein, 2005; Rash and Grove, 2006). As development proceeds,
brain tissue can become increasingly dedicated to the function that it will
serve, with its computational properties becoming progressively tuned
to carry out that function. This process is sometimes known as modular-
ization (Karmiloff-Smith, 1992; Meunier et al., 2009). At least two factors
must play a role in this modularization process: the inputs that the tis-
sue receives (including patterns of neural firing) and the developmental
procedures (i.e., reaction norms) that shape development as a function
of inputs. These developmental procedures may include processes that
fine-tune the computational properties of tissue based on inputs, such
as long-term potentiation, pruning, and cell–cell signaling (Quartz and
Sejnowski, 1997; Redies and Puelles, 2001; Hua and Smith, 2004; O’Leary
et al., 2007). They may also include “module spawning” processes that
give rise to new modules under certain developmental circumstances. For
example, an initially undifferentiated region receiving two heterogeneous
types of input might bifurcate into two new modules, each becoming
progressively tuned to handle one of the two input types (Jacobs, 1997).
Often it is assumed that neural inputs alone play the most important
role in ontogenetic differentiation of this kind: that is, that adult cortical
organization is largely a function of where inputs are sent, what neural
firing patterns they contain, and other properties such as granularity of
receptive fields (Quartz and Sejnowski, 1997). However, analogy with
morphological specialization elsewhere in animal bodies suggests that
contingently activated developmental procedures, themselves potentially
stimulated by inputs, may also play a role. Increasing evidence suggests
that local patterns of gene expression may influence the developmental
reaction norm that an area of tissue adopts, that is, how it self-organizes
in response to inputs (Rash and Grove, 2006; O’Leary et al., 2007). If the
topological organization of inputs to different brain regions is consistent
across generations, then locally contingent developmental procedures can
begin to evolve via descent with modification.
Descent with Modification of Reaction Norms
During evolution of the brain, the developmental properties of brain
tissue are subject to evolutionary modification based on the effects they
have on brain phenotypes. This can be initiated by initial changes in
developmental systems (e.g., via mutation), changes in the environment
in which they develop, or both. For example, as organisms’ environments
change (including changes in the social environment), developmental out-
comes that were theoretically part of the brain’s reaction norm, but rarely
or never produced, can become more strongly acted upon by selection
(Price et al., 2003). For example, if a previously non–language-speaking
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Hierarchical Model of Evolution of Human Brain Specializations / 319
species begins to evolve language capacities, developmental processes
that were not previously involved in language may come under selection
specifically because of their effects in language acquisition, resulting in
modification of older adaptations.
Descent with modification results in patterns of specialization that
have a hierarchical character (Fig. 17.2). As brain specializations evolve
through descent with modification, they inherit ancestral design features—
including underlying genomic building blocks and regulatory machin-
ery—that were present before recently derived changes. This means that
adaptations usually exhibit a mix of ancestral and derived features, which
interact in their contribution to the adaptation’s function. Ancestral fea-
tures may in turn be shared across homologous specializations within
or between taxa, meaning that derived specializations may be tokens of
homologous types within the same organism, and across organisms.
For example, the evolution of limb specialization in animals exhibits
this hierarchical character. Within taxa, diverse limb types evolve through
processes of serial homology, descending from ancestral limb types via
processes of diverging specialization. The result is that the distinct limb
types of a given species of animal, such as a crustacean, an insect, or a
mammal, exhibit many shared design features and shared developmen-
tal machinery, but nested within this shared specialization at the level of
limbs are divergent specializations for each limb type (Carroll et al., 2005).
In this sense, limb specialization is hierarchical. It exhibits substantial
evolutionary conservation of developmental machinery, meaning that
“new” specializations are composed largely of “old” design features,
rearranged and modified. Brain specializations are likely to exhibit these
properties as well.
FIGURE 17.2 Descent with modification of organismal traits can lead to hierarchi-
cally organized design features. In this example, descendent versions (Lower) of an
ancestral design (Upper) exhibit a mix of ancestral and derived features.
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Implications for Conventional Models in Psychology
Psychologists typically assume a choice between domain-specific and
domain-general mechanisms: a given psychological process must be han-
dled by one or the other, or perhaps a mix of the two. However, if brain
specializations contain a mix of general and specialized features within the
same adaptation, this has important implications for efforts to empirically
test between domain-specific and domain-general accounts in psychology,
because the assumption that domain-specific and domain-general aspects
of processing represent distinct mechanisms may be false.
As an example, consider the debate over face recognition in cognitive
neuroscience. Studies with functional MRI and single-unit recording have
shown that humans and other primates possess brain regions that are dif-
ferentially sensitive to faces, particularly the so-called facial fusiform area
(FFA) in the fusiform gyrus (Kanwisher and Yovel, 2006). Impairments to
this area can produce deficits in face recognition while leaving other object
recognition abilities relatively intact, a condition known as prosopagnosia
(Duchaine et al., 2006). Debates have ensued over whether the FFA is an
evolved adaptation for face recognition. The domain-specific view holds
that it is (Kanwisher and Yovel, 2006). The domain-general or “expertise”
view holds that the relevant adaptation is for developing expertise about
objects in the local environment, and that faces are simply a type of object
that is frequently encountered, leading to ontogenetic specialization of an
area highly sensitive to faces without any evolved adaptation for recogniz-
ing faces per se (Gauthier and Nelson, 2001). Evidence in favor of this view
includes training studies that show that exposure to repeated instances of
novel objects can produce cognitive processing signatures similar to those
seen with faces (e.g., inversion effects), and activation of the FFA for those
stimuli (Gauthier et al., 1999).
Although both positions are cogent, a hierarchical specialization view
suggests that they might not be as distinct as the debate suggests. Given
the location of the FFA within a larger region known to be active in object
recognition more generally, it is likely that face recognition abilities are a
specific token within a type category of object recognition procedures, akin
to claws as a token of crustacean limbs more generally. Thus, processing
signatures characteristic of objects in general are of limited use in testing
between the domain-specific and domain-general hypotheses because,
like limbs, specialized brain structures are likely to exhibit a combination
of specialized and general properties. Moreover, observations suggesting
that the FFA becomes progressively tuned to faces during development
(Scherf et al., 2007) do not rule out the domain-specific hypothesis, because
one would expect module-spawning procedures to use input as part of
their ontogenetic differentiation process. The question is whether the
ontogenetic specialization of the FFA is something that has been specifi-
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Hierarchical Model of Evolution of Human Brain Specializations / 321
cally selected for as a result of its consequences for fitness in ancestral
environments—a much more difficult question to answer.
These problems have been viewed as weighing against domain-
specific hypotheses, on the assumption that domain-general hypotheses
are more parsimonious (i.e., simpler) and therefore more likely to be true.
However, analogy with morphological development suggests that this is
a problematic assumption. It would be hard to argue that morphological
differentiation in animals, for example, proceeds via the simplest possible
set of processes, or that parsimony considerations alone would lead us to
correctly infer their design. Moreover, the phylogeny and natural history
of taxa can shift the burden regarding which account is more parsimoni-
ous. Many primates are highly social and can identify individuals in the
wild, an ability that likely has fitness benefits (Cheney and Seyfarth, 2007),
and social species such as macaques appear to have face recognition areas
homologous to those in humans (Rolls, 2000). Thus, the hypothesis that
there has been no selection for face recognition in our lineage may be less
likely than the hypothesis that there has been.
An implication of the hierarchical specialization view is that sig-
natures of general processing, such as Bayesian updating or statistical
learning, may be shared by specialized mechanisms as well. Thus, the
common assumption that such signatures weigh against more domain-
specific accounts (Elman et al., 1996) should be taken with caution, and
other factors should be weighed in mediating between domain-general
and domain-specific hypotheses, including phylogeny, natural history,
and cognitive form-function analyses akin to those used in functional
morphology (Tooby and Cosmides, 1992).
ORIGIN OF NEW BRAIN SPECIALIZATIONS
How do “new” brain specializations—that is, specializations that are
derived rather than ancestral in a particular lineage—evolve? If derived
brain specializations evolve from ancestral ones via processes of descent
with modification, and if these historical processes leave a signature in
the design and organization of brain mechanisms, this has implications
for the study of human brain architecture and the evolution of so-called
uniquely human traits such as language and complex culture.
Varieties of Homology
Homologous traits are traits that descended from a single ancestral
trait. Homologies therefore exhibit nested hierarchical relationships that
are the signature of phylogenetic processes of descent with modification.
Complex brains in humans and other vertebrates likely evolved from
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simpler nervous systems through processes of divergent specialization of
brain regions and structures, so many (but not all) human brain mecha-
nisms and processes are likely to exhibit relationships of homology (Kaas,
1989; Striedter, 2005).
Several types of homology can be distinguished based on how and
when they originate (Fig. 17.3). Orthologous traits are traits in two species
that originate from a single ancestral trait in the last shared common ances-
tor of those species. Paralogous traits, also known as serial homologs, are
homologous traits within a single species that have originated through a
process of duplication and divergence (Fitch, 1970; Ohno, 1970; Hall, 1995;
Koonin, 2005). Outparalogs are traits that arose via duplication and diver-
gence before a speciation event that split two taxa; the descendent taxa will
therefore all possess versions of the multiple, paralogous traits. Inparalogs
evolved via duplication and divergence within a specific lineage (note that
these terms were originally proposed to refer to gene homologies, but are
extended here to phenotypic and developmental traits).
Many traits of organisms appear to have arisen through processes of
duplication and divergence. Examples include the specialized limb types
of vertebrates and invertebrates (Carroll et al., 2005), protein families such
as opsins (Dulai et al., 1999), and regulatory gene families such as the Hox
cluster (Lemons and McGinnis, 2006). Brain scientists believe that processes
FIGURE 17.3 Varieties of homology. Region A is orthologous across taxa 1 to 4 as
a result of shared descent from the ancestral taxon. Regions A and B are paralogs,
originating through a duplication event (as are the two copies of A in taxon 3).
Regions A and B are outparalogs in taxa 1 and 2, originating through duplication
before divergence of the two taxa. Regions B and C are inparalogs in taxon 1.
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Hierarchical Model of Evolution of Human Brain Specializations / 323
of duplication and divergence may account for the origin of new brain
areas and processes as well (Kaas, 1984, 1989; Striedter, 2005; Marcus, 2006).
Duplication and Divergence in the Brain
There are several possibilities for how new brain structures might
evolve through duplication and divergence. One is that an initial change
in development, for example, caused by a mutation, duplicates an existing
brain area, producing two structures where there had been one. These can
then diverge if, for example, one structure retains its initial function while
selection then modifies the function of the other (Ohno, 1970; Kaas, 1989).
Duplication may also alter selection on both structures, allowing them to
carve up what was previously a single functional space, in a process akin
to adaptive radiation (Hughes, 1994). Such a process may have driven
functional divergence following gene duplication in the evolution of pri-
mate digestive enzymes (Zhang et al., 2002b) and color vision (Dulai et
al., 1999). It is also possible that divergence could begin without an initial
mutation, with an environmental change producing novel phenotypic
outcomes, which are then exposed to selection (Price et al., 2003). For
example, module-spawning reaction norms might initially bifurcate an
area into two as a function of new inputs in the environment (e.g., tools,
language), setting the stage for selection to act independently on the two
new areas (Krubitzer and Huffman, 2000).
Specialized, category-specific object recognition capacities may have
evolved via duplication and divergence from a previously undifferenti-
ated object recognition system. There is evidence for such category-specific
capacities in humans and other primates: for example, areas possibly
specialized for recognition of faces (Kanwisher and Yovel, 2006), bodies
(Downing et al., 2001), places (Epstein and Kanwisher, 1998), and tools
(Johnson-Frey, 2004). Such areas could have evolved from a single, primi-
tive, object recognition system in an ancestral mammal, which had not
yet been parcellated into specialized regions. In such a scenario, an initial
change (i.e., a mutation or environmental change) could have caused
developmental subdivision or duplication of this region, allowing selec-
tion to favor divergence of the new areas.
Consider a hypothetical scenario for the evolution of a specialized
capacity to distinguish between individual conspecifics based on their
facial features. In some social species, there may be significant benefits
to being able to recognize and distinguish between individual conspe-
cifics (e.g., distinguishing between kin and nonkin, remembering prior
cooperative partners), setting the stage for selection to act on variants
that might enhance this ability (Cheney and Seyfarth, 2007). One can
imagine an ancestral state in which no face-specific ability existed, only
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more general object recognition systems that develop expertise through
exposure to large samples of within- and between-category variation
in objects repeatedly attended to (e.g., predators, conspecifics). Against
such a background, any initial change that caused individuals to attend
specifically to faces would begin to drive the development of face exper-
tise within the object recognition area, a change that could be favored by
selection if it yielded fitness advantages. For example, a mutation or set
of mutations that altered perceptual and/or attentional systems to draw
more attention to eyes or other facial features (themselves potentially
favored for additional reasons, for example, emotion processing) would
lead to longer bouts of face input to object systems and in turn greater face
expertise. Additionally, any event leading to duplication or bifurcation
of the object recognition area—including, perhaps, a module-spawning
process triggered by increased face input—could set the stage for further
specialization of a dedicated face area via duplication and divergence. In
such a scenario, one would expect development of the resulting region
to be reliant both on external inputs (i.e., exposure to faces) and mecha-
nisms causing preferential attention to faces during development. There
is evidence for attention-orienting mechanisms of this kind in newborn
human infants (Johnson et al., 1991) and in other primates (Sugita, 2008).
Similar scenarios could account for the evolution of other specialized
capacities from more generalized precursors, including other types of spe-
cialized object recognition (e.g., tools, places, body parts) and higher-level
skills of language and reasoning as specialized versions of more general
primate brain processes. We might expect many new abilities to exhibit
relationships of homology to more general-purpose abilities, and relation-
ships of paralogy to their relatives in the duplication and divergence pro-
cess. If so, this could be evidenced by, among other things, shared network
connectivity in the brain, adjacent localization, and shared processing
signatures. For example, features of object processing such as inversion
effects (i.e., difficulties with recognizing individual objects upside-down)
and “holistic” processing effects (i.e., processing of relationships between
parts) could be shared partly or fully across distinct object-processing
systems (Bukach et al., 2006). More generally, other signatures of neural
processing might be widely duplicated across brain mechanisms and
regions, for example, Bayesian updating procedures, statistical learning,
effects of magnitude such as those described in Weber’s law, and others
(Kirkham et al., 2002; Nieder and Miller, 2003; Chater et al., 2006).
Role of Evolutionary Feedback
Over evolutionary time, changes in the brain can beget further evo-
lutionary changes through processes of evolutionary feedback, includ-
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Hierarchical Model of Evolution of Human Brain Specializations / 325
ing “runaway” or self-catalyzing evolutionary processes (Lehtonen and
Kokko, 2012). Changes in one part of the brain can alter how information
is routed to or processed by other parts of the brain, potentially altering
how natural selection acts on those areas, as in the scenario described
earlier in which increased attention to faces might alter selection on object
recognition areas. Changes in the brain can also alter the environment
itself, setting the stage for further evolutionary change as the new envi-
ronmental properties in turn alter selection on those same brain regions
or others, a process sometimes known as niche construction (Laland et al.,
2000). For example, an initial change in the brain that enables slightly more
complex communicative abilities—for example, the ability to combine
words into more complex utterances in an early protolanguage—changes
what is possible for individuals to communicate to each other, potentially
leading to further selection when new variants on these communicative
skills arise (Jackendoff, 1999). This can lead to a runaway process as brain
mechanisms and their behavioral products increase in complexity over
evolutionary time.
Similar effects may have obtained throughout human evolution as
ancestral hominins developed more sophisticated cultural transmission
abilities, leading to environments filled with the products of culture, such
as complex languages, tools, and built environments (Richerson and Boyd,
2006). In addition, increasing social complexity may have favored the evo-
lution of new or modified brain mechanisms for social cognition, such as
increasingly sophisticated abilities to make inferences about the intentions
and mental states of others, known as “mindreading” or “theory of mind”
(Saxe, 2006), as well as improved abilities of cooperation and an associated
moral sense (Richerson and Boyd, 2006). In all these cases, evolutionary
feedback effects could have occurred between brain mechanisms (i.e.,
evolutionary change of one brain mechanism alters selection on others)
and between brain and world (i.e., evolutionary change in the brain alters
the species’ environment, and vice-versa).
Word Perception as an Example
A useful example of how such evolutionary change might occur comes
from studies of how reading occurs in the brain. Converging evidence from
brain mapping, behavioral studies, and cases of brain damage point to the
existence of an area in the left fusiform gyrus of the visual cortex that is
specialized for the processing of written words. This area, called the visual
word form area (VFWA), occupies a similar location across individuals
literate in different languages and exhibits processing signatures consistent
with specialization for identifying whole written words, such as insensitiv-
ity to font and word length (Cohen and Dehaene, 2004; Dehaene, 2009).
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What does it mean to say that this area is “specialized” for word rec-
ognition? That natural selection has shaped this region specifically because
of the fitness benefits of reading seems unlikely, as the oldest human
writing systems are no more than a few thousand years old. Instead, it
seems more likely that this area becomes ontogenetically specialized for
words through a process of increasing expertise (Dehaene and Cohen,
2007; Dehaene, 2009; Anderson, 2010). Indeed, the development of this
area shares similarities with development of perceptual expertise more
generally, including correlations between practice and developmental
speed and experience-specific sensitivity to properties of the stimulus
class. However, this is not to say that the region in which the VWFA area
develops is evolutionarily general-purpose, nor that the VWFA could
develop anywhere in the brain. Instead, the location of the VWFA is
remarkably similar across individuals literate in diverse languages, and it
develops within an area of the visual cortex, the fusiform gyrus, in which
other specialized object recognition capacities, such as face recognition,
develop (Dehaene, 2009). This is consistent with a hierarchical special-
ization view: word recognition is a token, albeit an evolutionarily novel
one, of an evolutionarily specialized type of brain mechanism, that is, a
category-specific object recognition module. It develops when and where
it does, in individuals exposed to written language, because written words
activate the reaction norm of a specialized developmental system that
spawns category-specific modules upon repeated exposure to a recurring
class of objects.
Interestingly, there is evidence that written languages themselves have
culturally evolved to satisfy the input conditions of object recognition
systems. A recent study (Changizi et al., 2006) found that the distribution
of junction types in the written letters of diverse world languages closely
overlaps the distribution of such junctions in natural scenes, suggesting
that processes of cultural evolution have favored retention of letters that
are easily processed by human object recognition systems. This appears
to be a case of evolutionary feedback in which the design of perceptual
systems influences the cultural evolution of written words, which in turn
ontogenetically shape a specialized brain area. It may also represent a case
of evolution in progress and could be a useful exemplar of how new brain
specializations evolve following an initial event such as the appearance
of writing.
Effects of Increasing Brain Size
Humans have much larger brains than our closest primate relatives,
even relative to body size (Striedter, 2005). When explaining unique
aspects of human intelligence and flexibility, increased brain size is some-
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Hierarchical Model of Evolution of Human Brain Specializations / 327
times presented as an alternative to the idea that humans possess species-
specific brain specializations. However, a hierarchical specialization view
suggests that these are not necessarily mutually exclusive alternatives.
Indeed, increasing brain size may lead to more specialization, not less, and
more specialization may be related to greater, not less, flexibility.
Modularity can be defined in multiple ways. In network theory,
modularity refers to the relative amount of within-region vs. between-
region connectivity in a network, such as a network of neurons: more
modularity means less relative connectivity between regions (Meunier
et al., 2009). As brains increase in size, there are simple architectural
reasons to expect that modularity, in this sense, will increase (Kaas, 1989;
Striedter, 2005). As the number of nodes in a network increases, keeping
them all connected to every other node becomes more and more difficult
for reasons of space, leading to greater modularity. Comparative brain
studies suggest that species with larger brains tend to have greater dif-
ferentiation of the expanded brain areas, for example, cortical regions
(Kaas, 1989, 2000; Striedter, 2005).
If increasing brain size and increasing modularity are linked, there are
interesting empirical questions about what selective factors have driven
the evolution of large brains in humans. One possibility is that the prime
mover in brain expansion was selection for increased neural processing
power per se. However, if increased brain size forces increased modular-
ity for architectural reasons, this may set the stage for natural selection
to favor further specialization of the resulting brain regions. Another
possibility is that selection for specialization itself was the prime mover.
If the best way to produce new specialized regions is to increase brain
size—including, perhaps, duplicating existing brain areas—then selection
for specialization could have favored mutations that increased overall
brain volume, thereby increasing modularity. These are not mutually
exclusive scenarios, and it may be difficult if not impossible to empirically
tease them apart.
In psychology, it is common to assume that increasing modularity is
associated with decreasing flexibility, and that undifferentiated, general-
purpose systems are more flexible than differentiated, modular ones.
However, there are reasons to think that the opposite may be true. In com-
puter science, for example, it is generally recognized that modular soft-
ware designs yield greater flexibility than nonmodular ones: adding a new
modular algorithm to an existing system increases the number of functions
it can perform while keeping previously existing functions intact, thereby
adding flexibility (as well as robustness, i.e., ability of the system to with-
stand partial loss of function) (Baldwin and Clark, 2000). Similarly, it may
be that the greater modularity seen in larger brains may yield greater
behavioral flexibility compared with smaller, less modular brains (Kaas,
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328 / H. Clark Barrett
1989). The reason is that increasing modularity allows a greater number
of interacting parts, yielding more and more complex combinatorial rep-
ertoires. If modularity and flexibility are positively rather than negatively
related, this may have important implications for understanding the evo-
lution of drastically larger brains in the human lineage.
EXPLAINING HUMAN COGNITION
One of psychology’s holy grails is to explain what makes us psycho-
logically unique: different from other apes, primates, mammals, and ani-
mals more generally. The facts of descent with modification mean that this
will mostly involve modifications to the brain machinery present in the
chimpanzee-human common ancestor (CHCA), along with the addition of
some truly new, or derived, mechanisms. These changes include modifica-
tions to the base pair sequences in our genome (Chimpanzee Sequencing
and Analysis Consortium, 2005), modifications to the regulatory machin-
ery that shapes how genes are expressed during development (Khaitovich
et al., 2004; Preuss et al., 2004), and changes in the physical and cultural
environments in which humans develop, which differ substantially from
those of chimpanzees (Richerson and Boyd, 2006).
What We Will Need to Explain
The CHCA was a hominoid ape with a likely brain volume in the
range of 300 to 400 cm3 and the large and complex cortex characteristic
of ape brains (Kappelman, 1996). Comparisons with modern chimps and
bonobos suggest that the CHCA was likely to be a social species with a
relatively long lifespan and a sophisticated cognitive toolkit including
social learning of tool use, “Machiavellian” social intelligence, and some
elements of theory of mind, such as tracking others’ knowledge of food
in food competition and sensitivity to intentional communication in
contexts such as aggression and reconciliation (Call and Tomasello, 2008;
Whiten, 2011). However, although humans and chimps share versions of
all of these abilities, most appear to have been substantially elaborated
in our lineage, along with some genuinely new abilities not present in
chimps.
Modern humans differ from chimps, and probably from the CHCA,
in many ways. Humans have spoken languages with complex gram-
mars and arbitrary symbol-meaning mappings (Pinker, 1994). We live
and cooperate in larger and more diverse social groups, and are the only
species known to have cumulative or “ratcheting” cultural evolution in
which the products of culture (e.g., languages, tools, social practices)
increase in complexity over generations (Richerson and Boyd, 2006).
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Hierarchical Model of Evolution of Human Brain Specializations / 329
Humans are much more rapid social learners than chimps, probably at
least in part because of greater sensitivity to others’ goals and mental
states (Whiten, 2011). There are likely many other differences as well,
such as finer-grained motor capacities (Gibson, 2002) and improved
“executive” capacities of impulse control and deliberative weighing of
behavioral options (Striedter, 2005). Changes in human brains and human
lifeways likely coevolved via a feedback processes, involving multiple
changes in brain and behavior (Kaplan et al., 2004).
Changes in Genes, Gene Regulation, and Environments
Derived features of human cognition must eventually be accounted
for by changes in genes, gene regulation, and human environments (e.g.,
cultural, linguistic, and artifactual environments). Although we still have
a long way to go before understanding these changes, rapid technological
advances—including advances in genome sequencing, expression studies,
and the sequencing of archaic DNA—are beginning to yield the raw data
that can be used to make inferences about how hominin brain architecture
has been modified since the CHCA.
Several candidate genes thought to influence brain size (Evans et
al., 2005), brain differentiation (Pollard et al., 2006), and other aspects of
nervous system development (Dorus et al., 2004) show evidence of selec-
tion in the human lineage, although the functional significance of many
of these changes is still unknown and they are the subject of active debate
and research (Montgomery et al., 2011). In addition to changes in cortical
development, there appears to have been selection for increased white
matter in humans (Schoenemann et al., 2005), suggesting that modifica-
tions in how brain regions communicate with each other may have played
an important role in hominin brain evolution.
If the hierarchical specialization view is correct, we should expect to
see selection on genes with different patterns of expression or activity in
different parts of the brain. Evidence suggests that some brain areas
in humans have expanded differentially with respect to their orthologs
in other primates, for example, prefrontal cortex (Rilling and Insel, 1999;
Schoenemann et al., 2005; Balsters et al., 2010). Work with other species
suggests area-specific gene expression is likely to play an important role
in such differential development (Rash and Grove, 2006; O’Leary et al.,
2007). Studies of gene expression in the brain have shown substantial
differences between humans and chimps (Khaitovich et al., 2004; Preuss
et al., 2004), although these studies involve brainwide differences in
gene expression measured at the end of life, not ontogenesis. Although
detailed studies of regional gene expression in the brain during develop-
ment must await technological advances, the hierarchical model suggests
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330 / H. Clark Barrett
several types of changes we might expect to see in humans compared
with other primates.
Modified Orthologies
Many derived features of human cognition may be the result of modifi-
cations to older brain systems, in the form of modifying the design of those
systems per se or modifying how they interface with other and perhaps
newer systems. Such modifications are likely to be involved, for example,
in the evolution of human language abilities. Although there is debate
about exactly how to characterize the uniquely derived features of human
linguistic abilities, there is little doubt that these abilities, taken as a whole,
are unique among primates and animals more generally (Christiansen and
Kirby, 2003). Yet, human abilities to learn, produce, and understand speech
appear to mostly or entirely depend on brain regions and processes that
have homologs in other primates (Hickok and Poeppel, 2007; Rauschecker
and Scott, 2009). This implies that unique aspects of human language may
result from derived changes in one or more of these regions, along with,
perhaps, changes in how they interface with each other during develop-
ment and language processing. One example may be the planum tempo-
rale, a region associated with language processing that appears to have
undergone internal changes in the organization of minicolumns compared
with chimpanzees, and specifically in the left hemisphere (Buxhoeveden
et al., 2001). Some regions involved in human language processing exhibit
substantial laterality (Hickok and Poeppel, 2007), have greater connectiv-
ity between them via white matter pathways (Friederici, 2009), and have
greater connectivity to other brain areas than do orthologous regions in
nonhuman primates (Rilling et al., 2008). This suggests that modifica-
tions in how specialized structures interact may play an important role in
derived human abilities, in addition to modifications within specialized
structures themselves (Balsters et al., 2010). Human language capacities
may also rely heavily on interfaces between language areas and other sys-
tems, allowing us to, for example, refer to objects in our visual field, talk
about things we remember, and use metaphors in the service of reasoning
(Jackendoff, 1999; Boroditsky, 2000; Hickok and Poeppel, 2007). Thus, at
least some apparently unique aspects of human cognition may result from
novel synergies between phylogenetically older mechanisms, enabled by
changes in how these mechanisms interact.
Paralogies
Studies of language areas and other cortical regions showing anatomi-
cal evidence of microstructural changes within the areas of themselves—
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Hierarchical Model of Evolution of Human Brain Specializations / 331
for example, planum temporale (Friederici, 2009) and Brodmann area 10,
implicated in executive functioning (Semendeferi et al., 2001; Gilbert et
al., 2006)—suggest the possibility of duplication of subunits within those
regions. There may be other cases of paralogy at larger scales as well.
Some of these might be outparalogies, as in the case of specialized areas in
the visual cortex for recognizing faces, bodies, and other kinds of objects.
Others might be inparalogies: duplication and divergence events that
have occurred within the hominin lineage. One, the VWFA, might be an
example of a paralogy in progress. Others might be older.
Consider, for example, areas specialized for tool use. There is consid-
erable evidence for specialized processing of human-made tools in the
human brain, involving coordinated links among perceptual, conceptual,
and motor systems (Johnson-Frey, 2004). Although studies of tool use
in other primates show that homology is clearly involved (Obayashi et
al., 2001), specialized regions in temporal cortex and parietal cortex (for
tool identification and action knowledge, respectively) may have evolved
through processes of differentiation and specialization as use of complex
tools became a regular part of the human cognitive and behavioral rep-
ertoire from the origins of the genus Homo onward. Tool identification
regions in the temporal lobes may be paralogous with other specialized
object perception regions, for example, for faces, and tool areas in the
parietal lobes may represent tool-specific tokens of adaptations for sys-
tematizing gestural knowledge.
Of course, these examples are tentative and await further work. There
may be other mechanisms of higher-level cognition that have evolved
through duplication and divergence—a possibility suggested by expan-
sion of prefrontal cortex in humans—but there remains controversy over
how to characterize specializations in this area. Given the many appar-
ently unique aspects of human cognition, including ratcheting cultural
evolution, language, the ability to cooperate in large groups, morality,
and a unique elaborated theory of mind, there are likely to be many addi-
tional examples of derived specializations in humans that we have not yet
discovered. However, we should not necessarily expect all or even most
of these to have appeared entirely de novo in our lineage, but rather, to
have evolved from older precursors through descent with modification.
CONCLUSIONS
If the model presented here is correct, many widely held views in
psychology about the nature of brain specializations may need to be
rethought, along with the empirical implications of those views. In par-
ticular, many of the perceived tensions between specialized and general-
purpose mechanisms may not exist, or at least not in the form envisioned
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332 / H. Clark Barrett
by “dualist” accounts. In addition, many types of evidence widely thought
to adjudicate between domain-specific and domain-general accounts—for
example, plasticity, which is often held to weigh against specialization—
might not.
From a biological point of view, what makes an aspect of brain struc-
ture an adaptation is whether it has been selected for, not whether it has
a particular set of features such as canalization, narrow targeting of a
particular class of stimuli, or isolation from other systems. If the hierar-
chical specialization model is correct, some brain networks and processes
may be minimally different from others, highly plastic, and depend on
human-specific environmental factors to develop—and yet may still be
the products of selection. If so, the human brain contains adaptations
whose empirical signature is quite different from what many psycholo-
gists expect to see for a “module.”
For example, variation in developmental outcomes across individu-
als, environments, or cultures—typically interpreted by psychologists as
evidence against specialized adaptations—might be standard for many
brain adaptations, especially in our highly variable and cultural spe-
cies. Adaptations for language acquisition, if they exist, would be an
example: they must produce highly variable outcomes as part of their
evolved design, given the many ways in which the world’s languages
differ (Evans and Levinson, 2009). Moreover, if new brain specializations
evolve through divergent specialization from existing structures, “gene
shortage” arguments against the existence of multiple, derived brain spe-
cializations in humans—that is, that there are not sufficient genetic and
regulatory differences between humans and chimpanzees to account for
brain ifferences—may not hold water (Marcus, 2004).
d
Although specialist/generalist tradeoffs are likely to be important in
shaping brain evolution, they might not always take the form we envi-
sion. Many psychologists believe that evolutionary considerations imply a
tradeoff between a few generalized processes and many specialized ones,
and that the former is more likely because generalized processes yield
more flexibility. However, if it turns out that the way evolution creates
more flexible brains is by proliferating specialized brain regions that carve
up computational problems via specialized division of labor, this widely
held assumption may turn out to be wrong.
The hierarchical model presented here poses new challenges for devel-
oping and testing hypotheses about evolved specializations in the brain.
First, it suggests that the “checklist” of features widely associated with
modules does not constitute a checklist for adaptations. Second, it sug-
gests that proximate-level accounts invoking, for example, spatial and
temporal interactions between developing brain regions, should not be
treated as alternatives to ultimate-level accounts invoking selection; after
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Hierarchical Model of Evolution of Human Brain Specializations / 333
all, modification of those interactions is an important way in which devel-
opmental outcomes can be selected for. Third, it suggests that domain-
general processing signatures may be characteristic of more specialized
mechanisms as well.
If these conclusions are true, many current debates about how to inter-
pret data for or against specialization may represent arguments over apples
vs. oranges. For example, a particular phenotypic outcome in the brain may
be contingent on developmental input, and also the result of a reaction
norm selected to produce that phenotype given that input. To properly test
evolutionary hypotheses about brain specialization, then, it is important to
compare apples against apples and oranges against oranges: to compare
hypotheses posed at equivalent levels of the ultimate–proximate continuum
of evolutionary causation. Ideally, the most progress will be made when we
can compare hypotheses that specify both proximate mechanisms, such as
developmental constraints and neural wiring rules, and ultimate reasons
for how and why those mechanisms have evolved and been modified in
various species, including us, to produce the outcomes we see.
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