C H A P T E R
38
Language Evolution
C. Boeckx
ICREA, Universitat de Barcelona, Barcelona, Spain
38.1 An Evolving Landscape
There has never been a better time, in my opinion, to
work on the topic of language evolution. The ﬁeld of
“evolutionary linguistics,” as one may call it, used to
be plagued by lack of relevant data and therefore constituted
a forum for all kinds of speculation. Such just-so
stories were denounced most forcefully in a famous
essay by Lewontin (1998). Unfortunately, they continue
to this day. However, next to, and slowly supplanting
this literature, a body of knowledge is emerging that
holds great promise. It is a body of knowledge that is
the epitome of interdisciplinarity, bringing together
scholars from evolutionary biology, genetics, linguistics,
cognitive neuroscience, and developmental psychology.
This growing body of knowledge, which is the focus of
this chapter, rests on several foundational assumptions:
1. Any evolutionary account of the human language
phenotype will have to take into account both the
species-speciﬁc biological endowment and the
powerful structuring forces of culture and social
interactions. The human language phenotype is too
complex to capture by means of single-level,
unidimensional, one-magic-bullet explanations.
2. Any account intended to appeal to biological
mechanisms, including cultural accounts that of
course must recognize the human biological
commitment to culture, will have to center its efforts
on the brain. There is simply no alternative to linking
genotype and phenotype. Accordingly, cognitive
descriptions will have to be linked to neural circuit
mechanisms prior to being linked to genes.
3. Any approach aiming to characterize the
evolutionary trajectory of human language will have
to be Darwinian. This (hopefully) sounds like a nobrainer,
but it is worth stressing that for an account to
be properly Darwinian, it must at the very least take
the notion of “descent with modiﬁcation” seriously.
This means that it must strive to exploit as broad a
comparative database as possible (in the case of
humans, focus on the lessons one can derive from
other animals) and avoid resorting to “special”
explanations (be they in terms of special brain
regions, mechanisms, etc.). No matter how modestly
“linguistic” nonhuman creatures appear to be, the
Darwinian logic of descent requires us to be able to
discern an evolutionary path from there (them) to
here (us). That is, ultimately, the human language
phenotype must be rooted in nonstrictly human and
nonstrictly linguistic cognition. “Only us”
approaches just will not do. (To be clear: this is not to
say that there is no specialization at the brain level.
There is. But specialized regions follow principles of
the surrounding brain circuits and work in an
integrated fashion with less-specialized circuits.)
It seems to me that the ﬁeld of evolutionary linguistics
has matured enough now to demand that points
(1)e(3) be present in any study falling under its scope.
In particular, it simply will not do to state (as has all
too frequently been the case until recently) that “we
know so little about the brain,” or that “some cognitive
process (ultimately implemented in neural hardware)
is encoded in our genes,” or that “animal studies are
not useful to capture the core human language phenotype
because language is something unique to humans.”
Of the three guidelines just listed, I take (2) to be the
most important one: evolutionary linguistics is a brain
initiative. As Marcus (2004) put it, genes do not build
behavior or cognition. They code for and regulate the
expression patterns of proteins that build neural building
blocks that assemble into circuits, whose
(environment-dependent) activities amount to processing
informationd that is to say, computingdthat we
can describe in cognitive terms and take to underlie
behavior. There is no way around this long chain of
inferential steps from genotypes to phenotypes. Each
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and every one of these steps is necessary and equally
important, and moving from one level of description
to the next requires a speciﬁc linking hypothesis. (For
a very clear discussion of this issue with respect to the
ﬁrst gene causally related to some aspects of language,
FOXP2, see Fisher, 2015.)
For several decades, the ﬁeld of linguistics has been
dominated by debates concerning “nature” or
“nurture,” until at some point advocates of the “rich
and domain-speciﬁc biological endowment” camp (typically
associated with work from the generative grammar
tradition most closely connected to Noam Chomsky)
realized that they were probably making too many demands
on said biological endowment (demands that it
could not possibly meet, especially given the assumption
that human language evolved recently). They then
began cutting down on the cognitive apparatus they
relied on. This in turn led them to realize that this
trimmed-down apparatus was incapable of capturing
all the subtleties of linguistic knowledge. As a result,
they began to delegate, explicitly or more often implicitly,
some (indeed, much) of the mechanisms supporting
adult linguistic knowledge onto the learning task and on
the context in which learning takes place. In other
words, the learning component came to play a much
more important role. In so doing, they converged with
advocates of the strong cultural component school (for
more detailed discussion, see Boeckx, 2014). Interestingly,
some of the strongest results of the latter school
emerged in the context of evolutionary studies (see
Kirby, 2013). Such studies, which formed the bulk of
works presented at all the Evolang meetings (the major
forum for studies on language evolution), demonstrate
that generations of language learners, equipped with
generic learning biases, shape the grammatical proﬁle
of the languages they learned. If this is true, then it
would be mistaken to insist on building these aspects
of grammatical knowledge into the initial substrate/biological
endowment. Rather, these attractors populating
the grammar space emerge from learning and communicative
pressures. As a result, the focus, for the biologically
inclined, should not only be on tracing down the
origins of these generic biases in other animals, but
also on our inclination to engaging in learning and
communicating (verbally).
Following authors such as Michael Arbib (2002), I like
to distinguish between the evolution of language and
the evolution of the language-ready brain. The term
“language-ready brain” not only draws attention to the
centrality of the brain (cf. point (2) mentioned earlier),
but also stresses the distance we ought to expect between
what we will ﬁnd at the neural level and what
we can observe in the linguistic phenotype (adult linguistic
knowledge).
The distinction between language and languageready
brain should not, I think, be conﬂated with the
distinction between either of these terms and the notion
of “protolanguage.” The latter, exhaustively reviewed in
Fitch (2010), refers to a stage (or multiple stages) in language
evolution that is intermediate between what we
can reasonably infer about the linguistic capacity of
the last ancestor we shared with great apes from the
behavior of living great apes and our species-speciﬁc linguistic
capacity. Some scholars, such as Arbib (2002),
take whatever degree of grammatical complexity that
may exist between modern language and protolanguage
to be a matter of culture. Others, such as myself, while
recognizing the importance of cultural learning and
transmission, still allow for signiﬁcant changes at the
level of the brain between a protolanguage user and a
full-ﬂedged, modern-language user. In other words,
just as one distinguishes between language and protolanguage,
it may be worth distinguishing between a
protolanguage-ready brain and a language-ready brain.
The difference between the two may just be a matter of
degree of commitment to culture, but it would still be reﬂected
in our biology, and need not be regarded as less
signiﬁcant than the step that led to the emergence of
protolanguage.
The position just outlined appears to match the model
insightfully sketched by Scott-Phillips and Kirby (2010).
They observe that “[w]e can characterise the study of
language evolution as being concerned with the emergence
of language out of non-language. This involves
two main processes of information transmission and
change: a biological one (.) and cultural one (.). Prior
to the existence of a culturally transmitted communication
system, we can consider only the various preadaptations
for language (e.g. vocal learning, conceptual
structure [etc.]). Once cultural transmission is in place,
then it might operate simultaneously with biological
evolution in a co-evolutionary process and/or there
might be cultural evolution alone [reference omitted].
In either case, we urgently need a better general understanding
of how cultural transmission and social coordination
shape language if we are to achieve a complete
picture of the evolution of language. Once language
has emerged, further changes can and do occur. This is
the domain of language change and historical linguistics.”
What this passage makes clear is that the distinction
between “phylogeny” (evolution of language;
genetic evolution) and “glossogeny” (evolution of languages;
cultural evolution), introduced by Hurford
(1990), is not as clear-cut as it may be, given the recognized
role of culture in adding complexity and structuring
the linguistic phenotype.
The passage quoted from Scott-Phillips and Kirby
(2010) also allows me to address another, related point.
38. Language Evolution900
5. Evolution of Human Brains
They talk about “the emergence of language out of
nonlanguage,” but just to be clear: the Darwinian logic
of descent forces us to recognize a continuum, at some
level, between these two states. If, as is generally recognized,
we must ground aspects of our linguistic capacity
into the brains of nonlinguistic creatures, calling these
creatures nonlinguistic is a bit misleading. This is exactly
the issue that Hauser et al. (2002) addressed in a widely
cited article. In an attempt to capture both the speciesspeciﬁc
character of language and the roots of aspects
of our language faculty in other species, they distinguished
between the faculty of language in the narrow
sense (FLN, corresponding to what makes us unique)
and the faculty of language in the broad sense (FLB).
In this way, nonhuman animals could be called broadly
linguistic creatures, but not narrowly linguistic creatures,
a term that should be reserved to us.
Unfortunately, instead of focusing on the fact that this
distinction drew attention to the roots of aspects of language
in other species and called for renewed appreciation
for the promises of comparative cognition, many
scholars, especially linguists, used the FLN-FLB distinction
to stress what made humans different. In other
words, instead of focusing on FLB, they focused on
FLN. Speciﬁcally, they tried to provide content to FLN
(something which, incidentally, Hauser, Chomsky, and
Fitch invited by hypothesizing that FLN consisted of
“recursion and its mapping to the sensorimotor and conceptual
interfaces”) and looked for some unique traits.
In so doing, they not only departed from the Darwinian
logic of descent but also departed from the consensus in
current biology that “phenotypic novelty is largely reorganizational
rather than a product of innovative genes”
(West-Eberhard, 2003). It seems to me that statements
such as West-Eberhard’s demand that the idea that
what makes human language unique is the way it organizes
or collects attributes that individually can be
rooted in nonhuman cognition be taken as the default
hypothesis. Of course, once collected under a single
roof (“language-ready brain”), these traits may give
rise to nonlinear, “emergent” effects. Likewise, as Fujita
(2016) has stressed, when placed in the context of the human
brain, “old” pieces may acquire new roles that
transform their nature (the sort of feedback loop familiar
in biology). So, in the end, as I already expressed in
Boeckx (2013), the FLN-FLB distinction does not strike
me as the most felicitous way of capturing the continuum
that the Darwinian logic of descent demands between
our linguistic capacity and nonhuman
cognition. I prefer the perspective advocated in Petkov
and Jarvis (2012) and Arriaga and Jarvis (2013).
As a ﬁnal note on this continuum, let me point out
that continuum need not mean “scale.” Rather,
following Franc¸ois Jacob’s well-known tinkering metaphor,
we should think of our language-ready brain as
a mosaic or patchwork, composed of parts (possibly,
multiple protolanguage components) that do not ﬁt
neatly a cognitive scala naturae but rather display deep
homologies with capacities found in a variety of species.
Of course, our language-ready brain evolved from a primate
brain, but the latent potential of subtle tinkering
events, as I will show later, need not always be best understood
by examining primate behavior and studying
primate cognition (for more on this way of understanding
cognitive phylogenies, see Theofanopoulou and
Boeckx, 2015).
38.2 Deep Homology
Until recently most language-oriented books on
comparative cognition just had one message: animals
do not have the cognitive capacities necessary to acquire
language (see Anderson, 2004 for a particularly clear
example of this). But the tides have changed. De Waal
and Ferrari (2010) put it best: “Over the last few decades,
comparative cognitive research has focused on the pinnacles
of mental evolution, asking all-or-nothing questions
such as which animals (if any) possess a theory
of mind, culture, linguistic abilities, future planning,
and so on. Research programs adopting this top-down
perspective have often pitted one taxon against another,
resulting in sharp dividing lines. Insight into the underlying
mechanisms has lagged behind . A dramatic
change in focus now seems to be under way, however,
with increased appreciation that the basic building
blocks of cognition might be shared across a wide range
of species. We argue that this bottom-up perspective,
which focuses on the constituent capacities underlying
larger cognitive phenomena, is more in line with both
neuroscience and evolutionary biology.”
In the domain of language this bottom-up perspective
is best illustrated in the context of birdsong studies.
There is now an extensive literature documenting parallels
between birdsong and aspects of human language
(not just speech, as I will discuss later) at various levels
of description: development, neural basis, and evolution
(for a recent state-of-the-art survey, see Bolhuis and
Everaert, 2013). Both birdsong and human language
are vivid illustrations of what Peter Marler (2004) called
the “instinct to learn.” Both show strong evidence for the
need to have a brain conﬁgured in a particular way to
engage in vocal learning (Jarvis, 2004) and at the same
time highlight the importance of the environment in
shaping the structure of the songs. Thus, like humans,
songbirds raised in isolation, without any conspeciﬁc
adult models during the critical period for song
learning, never recover. They never move much beyond
the babbling stage called “subsong.” But Feher et al.
(2009) showed that in a colony of songbirds founded
5. Evolution of Human Brains
38.2 Deep Homology 901
by an isolate, normal song structure reemerges over the
course of a few generations, in a way strongly reminiscent
of what happens with creole languages.
Like language users, then, birds demonstrate that
possessing a song-ready brain is not enough. Rather,
learning, taking place over several generations, is
required for the song to develop completely. The birdsong
literature also demonstrates that speciﬁc environmental
circumstances, such as domestication (crucially,
not targeting song structure per se), can lead to a complexiﬁcation
of the song phenotype (Okanoya, 2012),
raising the possibility that self-domestication processes
often mentioned in the context of recent human evolution
(see Thomas, 2014; Benitez-Burraco et al., 2016)
may have also played a role in structuring the human
language phenotype, an issue I will return to later.
Arguably the biggest advance coming from the birdsong
literature concerns the neurobiological basis of
vocal learning. The literature of the past 20 years has
accumulated robust ﬁndings concerning the brain pathways
necessary for vocal learning and the molecular underpinnings
of said pathways. Vocal learning abilities in
birds depend on two main pathways: a nidopallialstriatal-thalamic
loop responsible for the acquisition of
new vocalizations, known as the anterior pathway, and
a posterior pathway directly connecting the arcopallium
and the syrinx, which controls intentional vocal production.
Translating this to humans, a corticobasal gangliathalamic
loop, along with a direct connection between
the motor cortex and the larynx, appears to be the
required neurological substrate for vocal learning (see
Jarvis, 2004 and much subsequent work since).
Analyzing the rudimentary substrates for vocal learning
found in a nonevocal-learning suboscine, the eastern
phoebe, Liu et al. (2013) suggest that the posterior
pathway is probably the ﬁrst step in achieving a vocallearning
readiness.
More remarkably, in addition to uncovering these
neural pathways, birdsong scholars were capable of
canalizing the genomic revolution to uncover deep homologies
at the molecular level, so much so that comparisons
of brain transcriptomes of song-learning birds and
humans relative to vocal nonlearners identiﬁed convergent
gene expression specializations in speciﬁc song and
speech brain regions of avian vocal learners and humans
(Pfenning et al., 2014). In particular, the forebrain part of
the vocal-learning circuit that makes a robust direct
connection to brain stem vocal motor neurons in independent
lineages of vocal-learning birds (songbird, parrot,
and hummingbird) was shown to have specialized
regulation of axon guidance genes belonging to the
SLIT-ROBO molecular pathway (Wang et al., 2015).
Wang et al. showed that, unlike in mice and none
vocal-learning birds, in vocal-learning birds, SLIT1
was differentially downregulated in the motor song
output nucleus that provides a direct link between the
bird equivalent of the motor cortex and the syrinx,
whereas SLIT1’s receptor ROBO1 was developmentally
upregulated during critical periods for vocal learning.
Interestingly, the SLIT-ROBO pathway has been associated
with a range of disorders in humans where language
is affected (see references in Boeckx and
Benı´tez-Burraco, 2014b). In addition, SLIT1 is a direct
target of FOXP2 (Vernes et al., 2007; Konopka et al.,
2009). Thus, Wang et al.’s ﬁnding is directly related to
the remarkable literature on FOXP2 and its role in language
that was built on Lai et al.’s (2001) landmark association
between a mutation affecting this transcription
factor and a development language disorder.
This is not the place to provide an extensive review of
the FOXP2 literature (for excellent recent overviews, see
Fisher, 2016; Fisher and Vernes, 2015; Graham and
Fisher, 2015). Sufﬁce it to say that this literature has provided
a unique point of entry into the molecular basis of
the neural implementation of aspects of the human language
capacity. Especially worthy of note are (1) discoveries
pertaining to the neural function of FOXP2; (2) the
strong expression proﬁle of FOXP2 in subcorticular circuits;
and (3) the recent evolutionary changes affecting
FOXP2. Let me touch on each of these brieﬂy.
Following Lai et al.’s discovery that heterozygous
mutations of the human FOXP2 gene cause a monogenic
speech and language disorder (reviewed in the overviews
cited earlier), intensive work has uncovered that
reduced functional dosage of the mouse version
(Foxp2) causes deﬁcient corticostriatal synaptic plasticity
and impairs motor-skill learning and that the songbird
orthologue appears to be critically important for
vocal learning. Signiﬁcant research has been made in
delineating the gene’s interactome, which has led to results
such as Vernes et al. (2011), where Foxp2 was
shown to regulate gene networks implicated in neurite
outgrowth in the developing brain by directly and indirectly
regulating mRNAs involved in the development
and plasticity of neuronal connections. In another study,
Tsui et al. (2013) concluded that FOXP2 regulates genesis
of some intermediate progenitors and neurons in the
mammalian cortex and suggested that the evolution of
the gene may be associated with the expansion of the human
cortex.
One of the most robust ﬁndings in the FOXP2 literature
concerns the gene’s strong expression in subcortical
structures such as the basal ganglia, the thalamus, and
the cerebellum (Vargha-Khadem et al., 2005; Haesler
et al., 2004; Teramitsu et al., 2004). This has led to the
renewed interest in these structures for language and
has led to the reappraisal of models that attribute a
key role, both ontogenetically and phylogenetically, to
5. Evolution of Human Brains
38. Language Evolution902
subcortical structures (eg, Jarvis, 2004; Lieberman, 2006;
but see also Boeckx, 2013; Boeckx and Benı´tez-Burraco,
2014a,b; Barton, 2012; among others).
In terms of impact, the evolutionary history of FOXP2
cannot be matched. FOXP2 is a highly conserved gene,
with only two amino acids separating the human
version from the chimpanzee version, and only three
amino acids separating the human version from the
mouse version. Needless to say, the discovery of two
recent changes in the otherwise remarkable stable history
of the gene has led to a lot of speculation concerning
language evolution (speculation unfortunately rarely
accompanied by detailed linking hypotheses between
these mutations and neurobiology). The discovery that
the Neanderthal genome contained the two key mutations
found in modern humans (Krause et al., 2007)
led to the claim that language has a deeper history
than most scholars claimed (for the most compelling
case in favor of this position, see Dediu and Levinson,
2013). Although hard to prove at this point, independent
data suggest that this position is worth taking very seriously
(although it is also important to bear in mind the
evidence pointing to species-speciﬁc regulation of the
gene; cf. Maricic et al., 2013). The SLIT-ROBO GTPase
activating protein 2 (SRGAP2) is duplicated three times
in the human genome compared to the chimpanzee
genome, and one of the partial duplicated copies
partially suppresses SLIT protein activity, thereby
causing slower forebrain dendritic pruning, leaving
more and longer dendrites (Charrier et al., 2012; Dennis
et al., 2012). Crucially, the relevant duplications are not
only speciﬁc to our species but are also part of the Neanderthal
genome. Given that the SLIT-ROBO pathway appears
to be signiﬁcant in the context of vocal learning,
and that the SRGAP2 duplication is not unique to the
genome of anatomically modern humans, it is tempting
to claim that some of our extinct ancestors were, if not
equipped with all aspects of the modern language faculty,
at least capable of complex vocal learning.
Independent evidence for this position may come
once again from the birdsong literature. Liu et al.
(2013) point out in their analysis that “vocal learners
share relatively small body size, which may allow these
birds (and their ancestors) to better manoeuvre ﬂight
and create more ecological niches for foraging (nectar
feeding, ﬂying-insect catching) and aerial vocal display.
Such elaborate ﬂight manoeuvreing may require a better
coordination or reconﬁguration of respiratory control
from the forebrain. The forebrain respiratory control
may subsequently integrate pre-existing motor pathway
in the arcopallium for the control of ﬂight, jaw, and vocal
movement, and/or auditory relays, and lead to evolution
of vocal learning (see a similar view proposed by
Janik and Slater (1997) for vocal learning in mammals).”
Although, as Liu et al. point out, this “respiratory
control” hypothesis is highly speculative, it is worth
bearing in mind, in line with Levinson (2016), that the
African variety of Homo erectus (c.1.6 My) appears to
have lacked the breathing control necessary for modern
speech. By contrast, the last common ancestor between
anatomically modern humans and Neanderthals appears
to have had all the physiological prerequisites.
Levinson suggests, reasonably, that by that time communication
was predominantly vocal, as opposed to
gestural.
This reasoning is to be placed in the context of the motor
theory of vocal learning put forth by Feenders et al.
(2008). This theory states that the “cerebral brain pathways
for vocal learning in distantly related animals
evolved independently as specializations of a preexisting
motor system inherited from their common ancestors”
and is based on gene expression in very closely
located brain circuits responsible for activities such as
hopping and signing. Plausibly, as discussed by Chakraborty
and Jarvis (2015), the pathway critical for vocal
learning may have evolved by duplication and then
divergence from the motor pathways regulating
nonvocal motor behaviors.
Chakraborty and Jarvis (2015) have also argued for
another duplication event, responsible for the type of
advanced vocal imitation found in parrots. By means
of baseline gene expression, singing-driven gene expression,
and neural connectivity tracing experiments,
Chakraborty et al. (2015) demonstrated that the parrot
brain contains what looks like a song system within
another song system. The inner song system, which
they call “core,” appears similar to other vocallearning
birds, whereas the outer system, called “shell,”
appears unique. Chakraborty et al. propose that it is this
shell pathway that is responsible for their imitative abilities
compared to other vocal-learning bird lineages. As
Chakraborty et al. observe, the connectivity of the shells
had some signiﬁcant differences to the core nuclei. Also,
the size of some of the shells, contrary to what happens
in the case of cores, seems to scale allometrically with
brain size and positively correlates with both vocal
and cognitive complexity in different parrot species. I
ﬁnd the latter ﬁnding of great interest, as Deacon
(1989, 1997) suggested that one of the factors allowing
for the establishment of the vocal learning circuit is
encephalization. Deacon points out that when space is
sparse, the innate, noncortically controlled vocalization
circuit outcompetes the cortical connections making
intentional vocalization possible. But as brains expand,
space is created for both systems to be maintained.
The vocal-learning literature in birds does not seem to
support a strong version of this hypothesis, as vocal
learning does not seem to require big brains, although
there are two caveats to be made in this context. First,
encephalization appears to be a factor for shell systems,
5. Evolution of Human Brains
38.2 Deep Homology 903
and it is precisely in those birds with shell systems that
we see a richer semantics associated with vocal output.
Second, although absolute size may not be a factor,
neuron number may be. That is, it has recently been
shown by Olkowicz et al. (2016) that bird brains pack
more neurons in the same brain space as mammals,
and vocal-learning birds have an even higher packing
density than other bird species and twice as many neurons
as some nonhuman primate brains bigger than
theirs.
In this context, it is also worth pointing out, following
Hillert (2015), that the timing of the duplication events
(Dennis et al., 2012) of the SRGAP2 gene mentioned
earlier coincides with landmarks of neocortical expansion
in the transition from Australopithecus to Homo.
This suggests to me that a better understanding of the
functional consequences of the peculiar evolution of
SRGAP2 may bear directly on the vocal-learning system
we possess(ed).
Be that as it may, Chakraborty et al.’s work highlights
the relevance of complexity in vocal learning that complements
the notion of complexity that emerged from,
eg, the work of Okanoya (2012) on the complexiﬁcation
of songs in the context of domestication. [I should note
that there may be different kinds of complexity, as discussed
in Deacon (2006), just like there are different degrees
of novelty/complexiﬁcation in an organism; cf.
Mu¨ller (2010).] There appear to be (possibly not unrelated)
environmental and neural factors contributing to
the range of vocal-learning capacities. In fact, Petkov
and Jarvis (2012) and Arriaga and Jarvis (2013) propose
that vocal-learning abilities are distributed along a continuum.
Thus, instead of thinking of vocal learning as a
relative rare trait found only in three distantly related
groups of mammals (humans, bats, and cetaceans) and
three groups of birds (parrots, hummingbirds, and songbirds),
we may conceive of vocal learning as more widespread.
According to this view, the degree of voluntary
control that an animal has over its vocalizations is primarily
determined by the robustness of the corticale
laryngeal (or equivalent) pathway, very weak in mice
and nonhuman primates, but very strong in canonical
vocal learners. This continuum hypothesis makes it
possible to study a wider range of species to reveal the
neurobiological substrates of vocal learning and parallels
attempts to decompose the human language faculty
into more basic building blocks that can be studied in a
wide range of species (see, eg, Samuels, 2011 for a
detailed attempt in the context of phonology).
The ultimate lesson I want to highlight from the literature
on vocal learning in birds (which I suspect will
soon be enriched by work on bats; see RodenasCuadrado
et al., 2015) is that solid progress and formulation
of testable hypotheses in understanding the
evolution of aspects of language must go hand in hand
with a very precise characterization of the neural basis
of these cognitive/behavioral capacities.
38.3 Primate Ancestry
Whereas the previous section provided a survey of
substantial results building on more than a decade of
sustained work on the neurobiology of vocal learning,
this section will focus on an equally important, but
less well understood, topic in the domain of language
evolution: the cognitive abilities we inherited from our
primate ancestry.
Until recently, researchers disregarded nonhuman
primates in the context of evolutionary linguistics
because unlike vocal-learning birds, the imitative abilities
of our closest living relatives, especially their vocal
imitative abilities, were far from remarkable. In addition,
experimenting with nonhuman primates is far
more challenging when it comes to neurobiological issues
than it is with birds or mice. But as I hope to convey
in this section, we can still gather valuable information
concerning our primate ancestry; in fact, in the few cases
where investigating the neural basis of their behavior,
primates have been shown to shed light on the evolution
of human language, so much so that I anticipate signiﬁcant
progress in this area in the near future.
There is very good evidence of a rich cognitive life
among our primate relatives. Works such as Cheney
and Seyfarth (1992, 2008), Hauser (2001), and, especially
Hurford (2007) have documented a wide range of behaviors
that strike me as providing solid grounds for
trusting in Darwinian descent when it comes to “semantics.”
This is not to say, of course, that there was no innovation
in this domain when full-blown language
emerged, but in the domain of semantics, and in fact language
as a whole, the gap between “them” and “us” has
not infrequently been exaggerated by the fact that our
own behavior was described in extremely rich,
domain-speciﬁc computational terms, but not so for
our relatives. When less “intellectualized” descriptions
of our behavior are provided, the gap is considerably
reduced, and the plausibility of descent reinforced.
To my mind the clearest demonstration of the statement
just made comes from the work of Moore (2016a,b)
on intentional communication and its cognitive prerequisites.
Often described in uniquely human terms (semantics/pragmatics),
it turns out that more
“minimalist/elementary” descriptions can be offered
and shown to be adequate.
The reason I will not discuss semantics/pragmatics
much here is because even for humans, the neurobiological
basis of these abilities is not well understood. It is
often said that semantics remains the “last frontier” for
both neurolinguistics and evolutionary linguistics, but
5. Evolution of Human Brains
38. Language Evolution904
I think that it is especially in the context of neurolinguistics
that insight is lacking. Evolutionary speaking, one
can be fairly conﬁdent that a rich conceptual basis,
“waiting” to be modiﬁed, was in place millions of years
ago. Hopefully, the growing appreciation for the cognitive
sophistication of parrots and corvid songbirds (Emery
and Clayton, 2004) will also provide additional
insights into this area, following the logic of deep homology
discussed in the previous section.
The question I will focus on in the rest of this section
is, how did this rich conceptual structure we have every
reason to believe was present in our last common
ancestor with great apes, got to be paired with a vocallearning
ability? Put another way, how did we come to
take volitional control of our vocalizations?
It is not the case that the auditory processing system
of primates is poor. In fact, Bornkessel-Schlesewsky
et al. (2015) and Wilson et al. (2015) have revealed substantial
homology in this domain, with both a ventral
and dorsal pathway linking up the frontal and temporal
cortex to organize auditory perception at various scales.
Rather, the issue pertains to vocal imitation.
To address these questions, recent work has focused
on several areas and primate behaviors. One has been
to show that primate calls, though not learned, have a
richer internal structure than one may have suspected,
with some scholars seeing the seeds of compositional semantics
in them (Arnold and Zuberbu¨hler, 2008 et seq.;
work which incidentally led to a reexamination of
combinatorial meaning in birds’ vocal output; Engesser
et al., 2016).
Another line of research has focused on the ways in
which primates get around the poor control of their
vocal apparatus. Consider, for example, the way in
which orangutans achieve the desired modiﬁcation of
their calls by manipulating leaves (Hardus et al., 2009;
Lameira et al., 2012): by positioning a hand or holding
leaves in front of their lips, orangutans lower the
maximum frequency but maintain other parameters of
the call similar. This is a bit like playing music with a
wind instrument. More interestingly still, when orangutans
modify their calls in this way, they sound as if their
body size is bigger than it actually is, which has obvious
functional consequences. Since size exaggeration has
also been claimed to be at the heart of the evolution of
a descended larynx (Fitch, 2000; Fitch and Reby, 2001),
it is tempting to see these modiﬁed calls by orangutans
as a solution forced upon them by the lack of laryngeal
control.
Yet another line of research has identiﬁed sophisticated
vocal behaviors in primates such as the elaborate
songs in gibbons (Koda et al., 2012 and references
therein). These have been shown to engage in duets
and perform sophisticated movements of the vocal
apparatus (very reminiscent of human soprano singers)
to propagate their long-distance vocalizations.
These cases aside, studies on primate communication
have focused on the fact that primates may be poor vocal
learners but far better gesture users. There is a rich literature
showing the complexity of primate gestures (see,
eg, Roberts et al., 2014), with very clear evidence of intentionality
in them (Genty and Zuberbu¨hler, 2014, 2015).
This has often been used as evidence for the primacy of
a gestural protolanguage (see, eg, Arbib, 2002) and claims
that human languages are not always a matter of sounds,
but signs (sign languages). In fact, the very term “protolanguage”
was ﬁrst used by Hewes (1973) with a gestural
protolanguage in mind. But it seems to me that the question
is not so much whether sounds or signs were ﬁrst,
but how sounds and signs were paired to achieve a multimodal
signal like human language. This is so because (1)
if we set aside sign language for a moment, it is clear that
sounds and signs are paired very early in ontogeny in
humans (Esteve-Gibert and Prieto, 2014) and are always
coupled in adults (speakers gesturing and deaf signers
vocalizing) and (2) the motor theory of vocal learning
put forth by Jarvis et al. for birds, which I discussed in
the previous section, suggests that gestures may well
have provided a ﬁrst evolutionary step, but the crucial
leaning step came afterward [inspired by the work on
vocal-learning birds, Fitch (2011) suggests that the direct
corticolaryngeal connection key to vocal learning exapted
from the corticospinal pathway for nonvocal motor production].
One has to bear in mind that speech does not
go away as a linguistic medium, except in extreme
situations.
It is for these reasons that I ﬁnd particularly illuminating
studies that identify multimodal signals in
nonhuman primates. I am certainly not the ﬁrst to call
for a multimodal study of nonhuman primate communication
(see Waller et al., 2013; Zuberbu¨hler, 2015), but it
is quite likely that many previous studies missed valuable
information by focusing on one or the other modality.
Recent work (Liebal et al., 2004; Micheletta et al.,
2013; Genty et al., 2015; Taglialatela et al., 2015) has tried
to correct this bias and has suggested that the activation
of the homologous Broca area in chimpanzees in both
attention calls and gestures points to a multimodal
origin of language (Taglialatela et al., 2011).
As a matter of fact, multimodal communication appears
deeply rooted in evolutionary history. Bass and
Chagnaud (2012) identiﬁed shared developmental and
evolutionary origins of neural basis of vocal-acoustic
and pectoral-gestural signaling present in vocalizing
ﬁshes. In a similar vein, Lawton et al. (2014) has revealed
a conserved role of Drosophila melanogaster FoxP
5. Evolution of Human Brains
38.3 Primate Ancestry 905
(FOXP2’s homolog) in motor coordination and courtship
song. Male ﬂies with reduced FoxP expression exhibit
decreased levels of courtship behavior, altered pulsesong
structure, and sex-speciﬁc motor impairments in
walking and ﬂight. Multimodal communication has
also been found in birds (Pika and Bugnyar, 2011; Bostwick
and Prum, 2005; Hoepfner and Goller, 2013; Williams,
2001; Soma and Mori, 2015), where wing
movement, beak movement, and even feet movement
accompany song in a synchronized fashion.
All in all, it seems to me that the study of multimodal
communication holds great promise for the study of human
language evolving, breaking free of the somewhat
sterile debate between musical and gestural protolanguage
(incidentally, song and dance might be just
another instance of multimodal communication whose
evolutionary history may be worth elucidating).
Among the instances of multimodal communication
in primates, there is one that I wish to highlight: lip
smacking. Lip smacking is an afﬁliative facial expression
observed in many primates, and it has been the focus of
intense and very productive research recently (Ghazanfar
et al., 2012). The reason researchers were drawn to it
is because although it is not “vocal” (with at least one
exception to which I return later), it constitutes a case
of “silent” vocalization and provides one of the best precursors
for aspects of human speech. Lip smacking is
made up of regular cycles of vertical jaw movement
and is clearly directed toward a conspeciﬁc. During
the lip-smacking act, the lips, tongue, and hyoid have
been shown to move at 4e5 Hz (Ghazanfar et al.,
2012), which is exactly within the range of the universal
rhythm of speech and distinct from the rhythm of chewing
movements in both monkeys and humans. In addition,
(aspects of) speech and lip smacking appear to be
dependent on homologous cortical circuits (unlike
innate calls, they are cortically controlled) (Ghazanfar
et al., 2010) and display a remarkably similar developmental
trajectory (Ghazanfar et al., 2013; Ghazanfar
and Takahashi, 2014). All these parallelisms suggest
that aspects of human speech, most speciﬁcally the syllabic
envelope, evolved from the rhythmic facial expressions
of a common ancestor to both humans and
macaques (Ghazanfar and Poeppel, 2014a; MacNeilage,
2010). In my view, the lip-smacking studies by Ghazanfar
and colleagues offer us the strongest parallelism with
human speech in the nonhuman primate domain, only
surpassed by work on vocal-learning birds.
Remarkably, gelada baboons have been shown to pair
lip smacks and vocalizations, in a way even more similar
to human syllables (Bergman, 2013; Richman, 1976).
This is not to say, of course that these “syllables” uttered
by geladas are identical to syllables produced by
humans (on these differences, see Martins and Boeckx,
2014), but it reinforces the idea that some aspects of human
vocal behavior can be found in nonhuman primates,
exactly as the Darwinian logic of descent leads
us to expect.
Pursuing this hypothesis, Lameira et al. (2015) have
looked at other possible precursors of aspects of human
speech in great apes, beyond lip smacks, and have documented
orangutan calls at a speechlike rhythm, coined
“clicks” and “faux speech.” They suggest that “like
voiceless consonants, clicks required no vocal fold action,
but did involve independent manoeuvring over
lips and tongue.” And “in parallel to vowels, faux
speech showed harmonic and formant modulations,
implying vocal fold and supralaryngeal action.” They
conclude that great apes may be less respiratorily, articulatorily,
or neurologically constrained for the production
of consonant- and vowel-like calls at speech
rhythm than previously thought. Certainly, as Lameira
et al. (2014) point out, there seems to be growing evidence
for evolutionary continuity within the great apes
as far the control of the supralaryngeal vocal tract.
Thus, they argue, control of the vocal folds (that key
aspect of vocal learning we understand well from
vocal-learning birds) evolved as a subsequent step (see
Levinson, 2016 for a similar historical reconstruction
based on a comparative study of turn taking).
In a related line of research, Pisanski et al. (2016) offer
an impressive review arguing that “the ability of
humans to ﬂexibly control the size-related sourceeﬁlter
dimensions of our vocal signals (i.e., vocal control) is
likely to predate our ability to articulate the verbal dimensions
of speech and therefore may provide an evolutionary
pathway from nonhuman primate vocal
communication to human speech.” Pisanski et al. present
a range of evidence, most of it from the past 5 years
or so, that nonhuman primates may share our capacity
to modulate F0 and formants to perhaps a greater extent
than previously thought. As an example, they mention
cases of chimpanzees producing “novel and apparently
ﬂexible attention-seeking grunts toward humans.” They
point out that although it does not occur in the wild, this
type of vocal behavior demonstrates “a latent capacity to
control vocal fold adduction and airﬂow that is required
to produce sustained laryngeal vibration.” Thus, among
nonhuman primates, manipulation of the larynx and
vocal tract may be more ﬂexible than once thought. I
personally anticipate more evidence in favor of this position
in the near future, especially from bonobos, where
evidence for functional ﬂexibility is accumulating
rapidly (Clay et al., 2015). Pisanski et al. conclude their
review by pointing out that “vocal ﬂexibility in
nonhuman primates suggests that other species have
5. Evolution of Human Brains
38. Language Evolution906
greater neuroanatomical elaboration of the direct lateral
motor cortical route than previously thought or, alternatively,
may be achieving ﬂexibility with older neural
structures.” This I take to be a crucial issue in the domain
of the evolution of the neurobiology of language that
hopefully will be the focus of intense research in the
years to come.
To conclude this brief survey of our primate ancestry
when it comes to vocal behavior, I would like to highlight
current work on marmoset and the evolution of
turn taking. Levinson (2016) has made a strong case
for it being a crucial trait of human verbal communication,
one that can be found among our closest living relatives,
the bonobos and chimpanzees (Fro¨hlich et al.,
2016). As a highly vocal primate, marmosets offer a
rich source of data concerning turn taking. In fact, Takahashi
et al. (2013) argue that the turn-taking behavior of
marmosets is so similar to what is found in our behavior
that this is yet another piece of evidence that our primate
ancestry may have been rich enough to provide a foundation
from which human cooperative vocal (and not
only gestural) communication could have evolved.
Takahashi et al. (2016) investigated the ontogeny of
this behavior in marmosets and were able to show
how engaging in vocal turn taking with parents at a
very early age shapes vocal acoustics in infant marmoset
monkeys. Reviewing a related study by Zhang and Ghazanfar
(2016), Tchernichovski and Oller (2016) offer a
very interesting scenario for the evolution of vocal
communication. They suggest that, at ﬁrst, slow ﬂuctuations
in respiratory rate are mirrored in the rate of vocalization.
As the channel evolves, according to them,
“additional physiological variables start affecting acoustic
features until, eventually, ﬁne forebrain control
drives differentiation of syllable type, producing vocal
sequences, which may carry rich information about
behavioral state.”
I ﬁnd this hypothesis interesting, not because it suggests
that the cortex may have taken control over
emotional vocalizations, but because it means that
emotional vocalizations controlled by the limbic system
in nonvocal learners, including mice, could provide a
rich source of aspects of human linguistic behavior
(see Theofanopoulou, 2016 for interesting suggestions
in this direction).
As should be obvious, the neurobiology of nonhuman
primates (with the possible exception of lip smacking) is
nowhere nearly as precisely understood as the neural
(and molecular) underpinning of vocal learning in birds,
but, as I hope to have shown, recent work has taken the
crucial step of showing that nonhuman primates can
provide a rich evolutionary substrate for human language,
which is an important departure from what
was received wisdom only a few years ago.
38.4 Tinkering With Our Inheritance
Up until now I have been keen to stress descent,
because it has been minimized for too long, and it offers
genuine results and promises for the ﬁeld as a whole.
But the Darwinian logic of descent comes jointly with
that of “modiﬁcation,” and this is the issue I want to
address in this section. What got modiﬁed in our recent
history that matters for the evolution of our linguistic capacity?
Even granting, as I think we must, the real possibility
that our ancestors became vocal learners long
ago, were there further modiﬁcation steps that took
place more recently in our lineage that may be cognitively
relevant?
Recall from the discussion in Section 38.2 that the
vocal learning literature, especially that focused on
birds, has identiﬁed at least two ways in which vocallearning
capacities can be enhanced. One is via domestication,
the other via the elaboration of shell structures on
top of the core vocal-learning circuit, which seems to go
hand in hand with brain growth.
I believe that both processes were critically involved
in the emergence of our species. In particular, the fossil
record, enriched by discoveries made possible by the
successful retrieval of ancient DNA (Pa¨a¨bo, 2014), suggests
that the brain growth trajectory in our species
departed signiﬁcantly from that our extinct ancestors,
early in life. There is every reason to believe that
although infants do not produce grammatically complex
expressions at that time, the ﬁrst months after birth
constitute a critical period for subsequent language
(and indeed, cognitive) development. Although much
work that adopted the concept of critical period for language
development in the wake of Lenneberg (1967)
usually refers to a period from birth to puberty, a closer
examination reveals that there are, in fact, several critical
periods for different aspects of language development,
and many of these periods are much shorter than anticipated.
As reviewed by Friedmann and Rusou (2015),
the acquisition of some of the most distinctive properties
of language such as complex syntax has a critical period
that ends during the ﬁrst year of life, and children who
missed this window of opportunity later show severe
syntactic impairments. Early postnatal life is also a critical
window during which many neurodevelopmental
disorders, such as autism, develop (though some of
them, such as schizophrenia, may only manifest themselves
later in life). As LeBlanc and Fagiolini (2011) write
in their review article on whether autism should be seen
as a “critical period disorder,” brain circuits that are key
to language and higher-order cognition are reﬁned by
experience during critical periods early in postnatal
life, and even slight dysregulations can have largescale
effects.
5. Evolution of Human Brains
38.4 Tinkering With Our Inheritance 907
It is for this reason that I ﬁnd it extremely interesting
that a series of work (Neubauer et al., 2010; Gunz et al.,
2010, 2012; Scott et al., 2014; Hublin et al., 2015) has
shown on the basis of data from living primates as
well as computer-assisted reconstructions of brain developments
from Neanderthal skulls that anatomically
modern humans, with their globularized braincases,
follow an early postnatal brain growth trajectory that
sets us apart from our closest extinct and extant relatives.
Although Neanderthal brains were signiﬁcantly
bigger than what we can observe from our closest living
primate relatives, their brain growth trajectory was
essentially a “blown-up” version of a chimpanzee brain
development. Ours, by contrast, follows a different
course that is reﬂected in our basketball-like head shape.
Interestingly, it is during this very same period (ﬁrst
months postnatally) that the growth pattern of our faces
also departs from that of our closest extinct and extant
relatives (Lacruz et al., 2015).
I have argued in a series of articles (Boeckx, 2013,
2017a; Boeckx and Benı´tez-Burraco, 2014a) that this
globularization phase must have been signiﬁcant for understanding
the evolution of our cognitive capacities,
including language. If the brain grows differently, it
wires differently, because growth and wiring are two
sides of the same coin. These two units coevolve. And
if we believe that different connectivity patterns give
rise to distinct circuits with functional consequences,
then globularizing a brain at such a critical period
must have been cognitively signiﬁcant.
I recognize that we cannot yet be sure about the precise
consequences of globularization, because we do not
yet understand fully how different brains can produce
different cognitive modes. But the long list of disorders
displaying both craniofacial and cognitive/linguistic
deﬁcits (Boeckx and Benı´tez-Burraco, 2014a) can help
in this regard. Furthermore, Gunz et al., as well as Bruner
(2010) et seq., have provided us with important information:
the globularization of the human brain
reconﬁgures the seat of cognition in several ways. It
has been claimed to affect most clearly the parietal
lobe, the cerebellum, and the frontal pole; plausibly as
well, it affects the temporal lobe and the olfactory bulbs.
This much can be inferred from fossilized crania. But
our knowledge of brain development leads us to suspect
that more deeply embedded structures that do not leave
obvious marks on bones must have also been affected,
be it in terms of volume or wiring diagrams (or both):
the thalamus, the hippocampus, and other subcortical
structures that strongly interact with regions of the brain
most visibly affected by globularization. Many of these
regions have not traditionally been associated with neural
circuits thought to be responsible for language but in
the next section, I will list a series of reasons to doubt the
classical neurolinguistic models.
Work on archaic genomes has provided us with lists
of genes that were modiﬁed in the course of evolution
of our species since the separation from the Neanderthal
lineage. Several of these changes appear to be related to
brain development and function (as well as to craniofacial
bone development; see Boeckx and Benı´tez-Burraco,
2015). In work in progress I am exploring the possibility
that quite of few of these changes were in fact connected
and caused the globularization of the brain (case) by
affecting neural processes that delayed neurogenesis
and inﬂuence brain growth (affecting most clearly the
subventricular zone), generating a brain that matured
more slowly (essentially a remarkably neotenous brain),
and as a result a brain that was more dependent on the
environment. Such a brain had to be a better “languagelearning”
brain. Accordingly, structures traditionally
responsible with learning and memory, both cortical
and subcorticular, came to play an even more salient
role in our species.
Apart from allowing us to detect a different brain
growth trajectory, the fossil record also leads us to suspect
that our species underwent an intensiﬁcation of
what amounts to a self-domestication process (as
Benitez-Burraco et al., 2016 discuss, globularization
and self-domestication may in fact be linked). As
reviewed in Thomas (2014), several scholars have long
entertained the idea that anatomically modern humans
were self-domesticated. Behavioral similarities (as well
as neurobiological similarities, see Rilling et al., 2012) between
us and bonobos, who have also claimed to be selfdomesticated
(Hare et al., 2012), reinforce this idea
(MacLean, 2016). The idea of self-domestication will be
obvious to anyone who recognizes the heavy dependence
of ours species on its sociocultural environment.
This is the cooperative spirit that seems to make us so
special among our closest relatives (Tomasello, 2009).
But behavior is not the only source of evidence, which
is a good thing given the notorious difﬁculty of reconstructing
behavior from the fossil record. As stressed
in Theofanopoulou and Boeckx (2016a), several anatomical
characteristics (as well as emerging molecular data)
of our species are reminiscent of what one ﬁnds in the
context of domestication, especially when contrasted
with what we know about Neanderthals (taken as a
representative example of archaic Homo). As reviewed
under the rubric of the “domestication syndrome” by
Wilkins et al. (2014), domesticated species display a
range of characteristics that set them apart from their
wild counterparts: the suite of characteristics that signals
domestication includes depigmentation, ﬂoppy,
reduced ears, shorter muzzles, curly tails, smaller teeth,
smaller brain/cranial capacity, neotenous (juvenile)
behavior, docility, and reproductive cycle changes
(more frequent estrous cycles). Of course, not all of these
characteristics are found in all domesticates, but most of
5. Evolution of Human Brains
38. Language Evolution908
them are indeed present in all domesticates. Theofanopoulou
and Boeckx (2016a) note that many of these
changes match fairly well some of the well-known
anatomical differences between anatomically modern
humans and Neanderthals. I will review some of their
observations here: ﬁrst, the distinct ontogenetic trajectories
discussed earlier under the rubric of globularization
(Hublin et al., 2015; Lacruz et al., 2015) result in
craniofacial differences that invariably lead to a more
“gracile,” “juvenile” proﬁle in anatomically modern
humans. This proﬁle is sometimes considered “feminized”
(Cieri et al., 2014) and is associated with an overall
reduction of sexual dimorphism, which also appears
to be a trait associated with domestication (Hare et al.,
2012). Incidentally, Theofanopoulou and Boeckx point
out this process of “feminization” (reduction of
androgen levels, rise of estrogen levels; see Cieri et al.,
2014 for references) is often associated with a reduced
reactivity of the hypothalamusepituitaryeadrenal axis
(Trut et al., 2009), a physiological trait also associated
with domestication (Kunzl and Sachser, 1999).
Returning to craniofacial considerations, it is well
established that prognathism is signiﬁcantly reduced
in our species (Maureille and Bar, 1999; Lacruz et al.,
2015). Browridges and nasal projections are smaller in
us than in our most closely related (extinct) relatives
(Cieri et al., 2014), as is our cranial capacity (Mellars
et al., 2007), and our tooth size (Zilberman and Smith,
1992). Differences in other traits associated with domestication
may also exist, but there are either confounding
factors (eg, geography for pigmentation) involved or the
data are subject to more controversial interpretation (eg,
Knight et al., 1995 in the case of reproductive cycle
changes) than the anatomical record we just reviewed
brieﬂy. What is clear is that on balance we appear to
bear more anatomical characteristics associated with
domestication than Neanderthals.
Surprisingly, proponents of the self-domestication
hypothesis have not always been explicit about what
species they were comparing anatomically modern
humans to in order to make their case. Comparison
with more distantly related species, such as chimpanzees
(taken as proxy for the last common ancestors
they shared with us), strikes me as inherently less
compelling, as there are just too many anatomical differences
(and thus, confounding factors) involved. In addition,
the domestication process is supposed to be rapid
and, as such, ﬁts better with a Neanderthal/anatomically
modern human comparison. It is also unlikely
that the self-domestication process took place after the
emergence of anatomically modern humans (contra
Cieri et al., 2014), because some of the traits associated
with a domestication process are already present in the
earliest specimens of our species, although of course it
is very likely the case that this self-domestication process
intensiﬁed after our species emerged.
What is most important for present purposes is that
much like changes at the level of brain growth, domestication
and concomitant-relaxed selection have been
claimed to lead to an enhancement of complexity in vocal
learning (Okanoya, 2012; Deacon, 2010), so that if the
anatomical record (as well as the emerging molecular evidence,
which I have not reviewed here) is anything to go
by, it is quite plausible that early anatomically modern
humans were biologically committed to a greater dependence
on culture and learning, which naturally led to a
more complex grammatical mind (and maybe a greater
dependence on activity-dependent genes, which appear
to be important in all vocal learners).
38.5 Updating the Neurobiological Model for
Human Language
Apart from the fact that many scholars have insisted
too much on the species uniqueness of our language capacity,
the lack of a comprehensive neurobiological
model for said capacity has certainly played a major
role in keeping the evolution of human language
shrouded in mystery. In the absence of an adequate
neurobiological target for evolutionary studies, the
temptation to bypass the brain and spin evolutionary
tales is just too great.
For over a century, researchers stuck to the classical
model derived from the work of Broca and Wernicke.
This is not, of course, to say that the classical frontotemporal
cortical circuit makes no contribution to high-level
linguistic processing. But it is equally clear that language
processing recruits a far more widely distributed
network (Fedorenko and Thompson-Schill, 2014; Blank
et al., 2016; Friederici and Singer, 2015; Hagoort and
Indefrey, 2014; Poeppel et al., 2012), drawing on multiple
brain rhythms to provide a rich enough oscillatory
regime for such a complex task (Theofanopoulou and
Boeckx, 2016c; Lam et al., 2016). At the same time, an
increasing number of papers on subcortical structures
such as the hippocampus or the cerebellum start with
statements such as “a growing body of work suggests
[x; a subcortical structure] contributes to a variety of
cognitive domains beyond its traditional role in [y; a
very basic function not part of any core description of
language]” (see Jarvis, 2004 on the forebrain broadly,
Marien and Manto, 2016 for the cerebellum; Theofanopoulou
and Boeckx, 2016b for the thalamus; Kurczek
et al., 2013 for the hippocampus).
In the remainder of this section, I would like to highlight
a few research paths and trends that strike me as
worth pursuing, since they provide a window of
5. Evolution of Human Brains
38.5 Updating the Neurobiological Model for Human Language 909
opportunity to tackle, productively, very difﬁcult questions
about the evolution of human language.
Most of these highlights bear on semantic cognition,
rather than vocal learning in the narrow sense (although
I repeat that I do not think one should draw a sharp
distinction between the two), because I think that this
is the domain that we understand the least, precisely
because comparative studies are so hard [in the domain
of vocal learning, Jarvis (2004) offers a compelling candidate
model]. Incidentally, what I call “semantic cognition”
likely falls under the rubric of syntax for most
researchers. This is because decades of work in theoretical
linguistics have made clear that syntax cannot be
dissociated from (structural) semantics.
The ﬁrst trend I would like to discuss is the importance
of the temporal lobe. In my experience, when people
think of higher-order (linguistic) cognition, they
immediately associate it with the frontal lobe and,
more speciﬁcally, Broca area. No doubt, Broca area is
an important node of the language network, but it is
certainly not the more central one, at least when the construction
of compositionally interpreted structures (ie,
syntax/semantics) is concerned. True, as revealed by
Schenker et al. (2010), Broca area has expanded sixfold
compared to what we ﬁnd in chimpanzees (Brodmann
areas 44 and 45 that make up most of Broca region
appear to be the most greatly expanded cortical areas
yet identiﬁed in humans), but a growing number of experiments
have revealed that the temporal lobe, traditionally
associated with “memory” and “the lexicon,”
plays an equally, if not more, important role in cognition
(on the possible role of Broca region, see Boeckx et al.,
2014; Bornkessel-Schlesewsky et al., 2015; Fitch and
Martins, 2014). For instance, work by Pylkka¨nen and colleagues
has focused on the contribution of the left anterior
temporal lobe to combinatorial processing. Together
with the ventromedial prefrontal cortex, the angular gyrus,
and the medial parietal cortex, the left anterior temporal
lobe plays a central function in the compositional
interpretation of hierarchically structured expressions
(Pylkka¨nen, 2015; Westerlund et al., 2015).
A recent study by Davey et al. (2016) points to the role
of another temporal region, the posterior middle temporal
gyrus in semantic cognition. The latter appears to
function as a hub within a large-scale network that allows
the integration of automatic retrieval in the default
mode network with demanding, goal-oriented cognition.
In terms of connectivity, this temporal region appears
to be uniquely placed to link two macrocircuits,
the default mode network, thought to be the seat of
“self-generated thoughts,” and a frontoparietal
“multiple-demand”/“executive control”/“dorsal attention
circuit.” As reviewed in Buckner and Krienen
(2013), cortical association regions spanning the frontal
and parietal cortices are disproportionately expanded
in humans compared with other primates. They mature
late in development and are often disrupted in mental
disorders (Sato et al., 2016). They appear to impose order
on the self-generated thoughts by the default mode
network.
A more deeply embedded temporal structure, the
hippocampus is a core member of the default mode
network. Recently, and largely thanks to the work of
Duff and colleagues, its potential role in linguistic cognition
has been reappreciated (see Alamri, 2016 for a more
extensive review and discussion). Kurczek et al. (2015)
have highlighted the signiﬁcance of the hippocampus
in the neural network that supports a range of abilities
including remembering the past, thinking about the
future, and introspecting about oneself and others, abilities
that are often said to be human speciﬁc and shown
to break down in a range of mental disorders where language
is also affected (eg, schizophrenia, see Wible,
2012). Studies involving individuals with hippocampal
amnesia, such as Duff et al. (2013), show that quintessentially
human behaviors, such as the creative use of language
as evidenced in verbal play, is severely
damaged, which serves to highlight the interdependence
of language and memory. Like evolution, language
appears to make new use of old parts, so
memorized units are as important as novel combinations,
and in recent years scholars have been calling
for a renewed appreciation of memory as an integral
component of online information processing (see Hasson
et al., 2015). Traditional divisions like declarative
versus procedural memory systems may have to make
way to a more integrated system that recognizes a strong
hippocampalestriatal axis (prototypical declarative vs.
procedural memory hubs) in learning, prediction, and
goal-directed behavior (Pennartz et al., 2011). In this
context, it is worth mentioning that mice endowed
with the humanized version of Foxp2 have been claimed
to accelerate learning by enhancing transitions from
declarative to procedural performance (Schreiweis
et al., 2014).
As Rubin et al. (2014) write, the frontal lobes have
ﬁgured prominently in most studies of ﬂexible or goaldirected
behavior, but the hippocampus appears to
play an equally important role by “forming and reconstructing
relational memory representations that underlie
ﬂexible cognition and social behavior.” Recently,
Ellamil et al. (2016) showed how the hippocampus is
recruited in the spontaneous generation of thoughts.
As they point out, “[i]n contrast to the connectivity of
other cortical areas, where short-distance synapses to
nearby neurons predominate and long-distance connections
are rare, neurons within a large part of the hippocampus
are equally likely to connect to nearby or distant
neighbors (Buzsaki, 2006). This highly variable microcircuitry
may facilitate the creation of arbitrary or unlikely
5. Evolution of Human Brains
38. Language Evolution910
connections between groups of neurons that otherwise
encode distinct memories or experiences (Buzsaki,
2006).” This description ﬁts quite well with the core creativity
function often attributed to language (see, eg,
Boeckx, 2014). Such a generation of thought would
have to be regulated by circuits responsible for dorsal
attention, but it may be that the source of novel combinations
may not lie in the neocortex. If true, this would be
excellent news for evolutionary linguistics, for the hippocampus
is one of the most closely scrutinized brain
structures in neuroscience. Perhaps, some of our
higher-order cognitive functions were indeed exapted
from basic circuits associated with memory and spatial
cognition. At the very least, this possibility reinforces
the need to adopt a much broader conception of the neural
circuits responsible for linguistic cognition.
As a ﬁnal note on the possible hippocampal involvement
in language, I would like to comment in the fact
that it is usually the temporal cortex that is taken to provide
the basis for linguistic memory (“the lexicon”), not
the hippocampus. But the two structures clearly interact
and may play complementary roles. When Teyler and
DiScenna (1986) put forth their “hippocampal memory
indexing theory,” they suggested that the role of hippocampus
may be “to form and retain an index of neocortical
areas activated by experiential events,” serving to
“establish a cortically based memory trace.” This role
of the hippocampus as a “pointer” to more elaborate
conceptual structures ﬁts well with descriptions of
word meaning (Pietroski, 2007) and could explain how
hippocampal lesions need not completely abolish linguistic
output, as the matured cortex could supplement
it.
This raises an important issue, I think. Many brain
structures have often been discarded as peripheral for
language because of lesion studies showing how linguistic
cognition can be preserved after seriously
damaging them. But this sort of conclusion is often
arrived at by studying the language system in adults,
as opposed to asking how the system developed in the
individual. It may well be that some structures were
once critical, but their roles can be transient (and harder
to detect, like the Wittgensteinian ladder that got used to
climb and was then kicked off). Too often, the frontotemporal
cortical circuit is studied once it is fully in place,
but this may just reﬂect a specialization (modularization
or routinization) lying at the conﬂuence of broader, more
distributed corticosubcortical circuits engaged in more
generic functions. Instead of viewing the “core language
circuit” in isolation, ontogenetic as well as phylogenetic
considerations force us to view it as part and parcel of a
broader cognitive basis. (I think this is consistent with
the high degree of functional speciﬁcity for high-level
linguistic processing in the adult human brain described
in Fedorenko et al. (2011).)
As a ﬁnal topic for this section, I would like to touch
on the role of the parietal lobe in the context of language.
I do so because this is the most conspicuous brain structural
change that can be associated with the emergence
of anatomically modern humans from the fossil record
(Bruner, 2010). As I point out in Boeckx (2017b), there
is evidence of signiﬁcant “parietalization” of certain
neural circuits highly relevant for language. For
instance, Kumar et al. (2016) suggest that the enhanced
laryngeal motor cortexeparietal connections in humans
could provide the right neural substrate for more complex
synchrony of higher-order sensorimotor coordination,
proprioceptive and tactile feedback, and
modulation of learned voice for speech production.
I also argued that the expansion of the parietal region
played a role on the “semantic” side. Bruner (2010) reviews
studies that point to the hub status of the parietal
lobe for functions associated with the default mode
network, which I already alluded to in the context of
the hippocampus. Moreover, there is a range of studies
that have documented a human-speciﬁc modiﬁcation
of how Broca region and Wernicke region are connected
with one another through white matter pathways. In
addition to a direct connection that is shared with
nonhuman primates, there is also an indirect pathway,
through the parietal lobe in humans (Catani and Jones,
2005; Dick and Tremblay, 2012; Hecht et al., 2013; Mendoza
and Merchant, 2014; Catani and Bambini, 2014).
Although this remains to be conﬁrmed, it is plausible
to envisage that this indirect connection may provide
the basis for richer representational capacities. For
instance, it could be that the formation of a frontoparietotemporal
loop allows for the formation of recursive
representations, since both frontoparietal and frontotemporal
networks are routinely associated with processing
sequences, and recursive representations are,
at some level of description, sequences of sequences.
Several authors have independently stressed the relevance
(both ontogenetically and phylogenetically) of
an indirect, frontoparietotemporal pathway (Friederici,
2012; de Diego-Balaguer et al., 2016) for language tasks,
and so evolutionary studies stand to beneﬁt from future
investigations of the role of what is sometimes called
Geschwind territory, next to the more familiar Broca
and Wernicke regions.
38.6 Conclusion
To summarize the main message of this chapter, like
vocal learning in birds, human language is a highly complex,
polygenic trait that recruits numerous brain regions,
over and above the classical language regions,
to provide a computational regime supporting linguistic
cognition. Strong adherence to the Darwinian logic of
5. Evolution of Human Brains
38.6 Conclusion 911
descent, with its emphasis on a rich cognitive life for
nonhuman species, offers the hope to shed light on
what looks like a very human-speciﬁc, domain-speciﬁc
capacity like language. Such an approach requires
studies that embrace the multifactorial (not only
genes, not only environment), multidimensional
(genome, connectome, “dynome,” “cognome”) nature
of the capacity to master grammatical systems of the
kinds we do.
There is no doubt in my mind that a richer data set
thanks to the genomic revolution, and a broader
comparative basis adopting a bottom-up approach to
study primate cognition guarantees that in the very
near future a much more elaborate, and more precise
picture of language evolution will be available than
the one I have offered here. But even the present one
strikes me as a far cry from claims not so long ago that
we would never ﬁnd out.
Acknowledgments
For helping me understand some of the issues discussed here, I am
indebted to Erich Jarvis, Tecumseh Fitch, Simon Fisher, Sonja Vernes,
Simon Kirby, Marc Hauser, David Poeppel, Gary Marcus, Ev Fedorenko,
Dick Lewontin, Koji Fujita, Kazuo Okanoya, Bridget Samuels,
Saleh Alamri, Pedro Tiago Martins, and Constantina Theofanopoulou.
I am particularly grateful for the comments on the preﬁnal draft I
received from Todd Preuss and Erich Jarvis.
This chapter was written while I was a visitor at the UPF-CSIC Institute
for Evolutionary Biology. I thank all the members of that institute for
providing an environment conducive to research.
I also would like to acknowledge the ﬁnancial support from the Spanish
Ministry of Economy and Competitiveness (grants FFI2013-43823-P
and FFI2014-61888-EXP), a Marie Curie International Reintegration
Grant from the European Union (PIRG-GA-2009-256413), research
funds from the Fundacio´ Bosch i Gimpera, and from the Generalitat
de Catalunya (2014-SGR-200).
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Further reading
Bastir, M., Rosas, A., Gunz, P., Pen˜a-Melian, A., Manzi, G., Harvati, K.,
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Theofanopoulou, C., 2015. Brain asymmetry in the white matter making
and globularity. Front. Psychol. 6, 1355. https://doi.org/10.
3389/fpsyg.2015.01355.
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38. Language Evolution916