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The Routledge Handbook of Philosophy of the Social Mind
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Embodying Culture

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4
EMBODYING CULTURE
Integrated cognitive systems
and cultural evolution
Richard Menary and Alexander James Gillett1

Introduction
The Cognitive Integration (henceforth CI) framework posits the existence of integrated cognitive systems (henceforth ICS). In this chapter we outline the nature of ICS and their phylogenetic
history. We shall argue that phylogenetically earlier forms of cognition are built upon by more
recent cultural innovations. Many of the phylogenetically earlier components are forms of sensorimotor interactions with the environment (Menary 2007a, 2010a, 2016). These sensorimotor
interactions are redeployed (or retrained) to service more recent cultural innovations (Dehaene &
Cohen 2007). Take, for example, a rudimentary ability for tool use, that is refined and then built
upon by innovations over many generations.The same refined sensorimotor skills for manipulating tools can be redeployed to recent cultural innovations for writing with stylus, brush or pencil
(Menary 2015). Redeployment happens after a process of learning or training and the cultural
innovations are inherited and spread out across groups.2 This process depends upon both high
fidelity cultural inheritance and a high degree of plasticity (Sterelny 2012), which in humans is a
specialised form of learning driven plasticity (Menary 2014). Learning driven plasticity (henceforth LDP) is the capacity for functional changes that are acquired from (usually) scaffolded
learning in a highly structured social niche. This results in a multi-layered system with heterogeneous components, dynamically interwoven into a complex arrangement of processes and states
in an integrated cognitive system. The coordination dynamics of the system are, at least in part,
understood in terms of the physical dynamics of brain–body–niche interactions in real-time.
One of the key ingredients of ICS is the social/cultural practices, which we call normative patterned practices (henceforth NPP), that govern the dynamics of brain–body–niche
interactions. NPPs operate at both social levels and individual, even sub-personal, levels. They
originate as patterns of activity spread out over a population of agents (Roepstorff et al. 2010);
consequently they should be understood primarily as public systems of activity and/or representation that are susceptible to innovative alteration, expansion and even contraction over
time. They are transmitted horizontally across generational groups and vertically from one
generation to the next. At the individual level they are acquired most often by learning and

training (hence the importance of LDP), and they manifest themselves as changes in the ways
in which individuals think, but also the ways in which they act (intentionally) and the ways in
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Embodying culture

which they interact with other members of their social group(s) and the local environment.
NPPs, therefore, operate at different levels (groups and individuals) and over different timescales
(intergenerationally and in the here-and-now).
The main aim of this chapter is to give an overview of the CI framework in terms of phylogenetically ancient embodied interactions with the environment and the more recent culturally evolved practices that redeploy our primitive capacities for sensorimotor interactions and
manipulations of tools, objects and, in a very recent innovation, public systems of representation. In doing so, we provide a case for the enculturation of our bodies and brains.
In the first section we outline the role of brain–body–niche interactions in ICS. In the
second section we place these interactions into the context of an inherited cognitive niche. In
the third section we lay out the fundamentals of the process of enculturation, and in the final
section we outline the enculturation of our basic abilities for mathematical cognition as an
example of the enculturation process.

ICS and embodied engagements
The CI framework explains how we learn to be active cognitive agents who think by manipulating their environments and by interacting with one another in social groups. One of the
key theses of CI is that body and environment coordinate, such that the environment is a
resource available to the organism for acting, thinking and communicating. In particular we
look at the role of body–environment coordination in the assembly of ICS. The coordination
dynamics of the system are understood in terms of the physical dynamics of brain–body–
niche interactions in real-time.3 However, the interactions that matter are those that are
governed by NPPs.
The primary form of NPPs that we shall consider are cognitive practices (CPs) (Menary
2007a, 2010a). Cognitive practices are enacted by creating and manipulating informational

structures in public space. For example, by creating shared linguistic content and developing it
through dialogue, inference and narrative; or it can be by actively creating and manipulating
environmental structures, which might take the form of tools of public and shared representations (or a combination of both).
How do individuals embody CPs? They do so by a process of transformation of body schemas or motor programmes (Menary 2007a, 2010b; Farne et al. 2007). Motor programmes are
acquired through learning and training, but existing programmes may also be extended during
training. Learning to catch, write, type, or flake a hand axe are examples of acquired motor
programmes. Cognition or thought is accomplished through the coordination of body and
environment and is, therefore, governed both by body schemas and by biological and cultural
norms. The latter will draw on many learned skills.
A clear way to understand the nature of the CPs at work is the manipulation thesis. The
manipulation thesis (Rowlands 1999, 2010; Menary 2007a, 2010a) concerns our embodied
engagements with the world, but it is not simply a causal relation. Bodily manipulations are
also normative – they are embodied practices developed through learning and training (in
ontogeny).We outline six different classes of bodily manipulation of the environment, with the
general label of Cognitive Practices.4 They are:
1
2
3

Biological Interactions
Corrective Practices
Epistemic Practices
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4

Epistemic Tools and Representational Systems
a Epistemic Tools
b Representational Systems

5

Blended Practices

1. Biological interactions are direct sensorimotor interactions with the environment. An obvious example are sensorimotor contingencies (O’Regan and Noë 2001), a direct example of
low-level, embodied interactions with the environment. One might think of simple perceptionaction cycles, where direct perceptual input from the environment reciprocally causes action,
which then directly feeds into further behaviour. For example, Ballard and colleagues’ (1995)
study details how participants in a memory-taxing pattern-copying task offload these cognitive
demands through exploratory saccadic eye movements. Dewey anticipated such a model in his
discussion of the reflex arc (see Menary 2016).5
2. Corrective practices are a form of exploratory inference and are clearly present early in early
cognitive development. The main feature of this form of practice is action looping through
the environment to correct future action (e.g. instructional nudges (Sutton 2007)). This might
be done verbally, or it might be done by a form of epistemic updating, testing a hypothesis
through action. A classic example from Vygotsky helps to illustrate: A four-and-a-half-year-old
girl was asked to get candy from a cupboard with a stool and a stick as tools. The experiment
was described by Levina in the following way (his descriptions are in parentheses, the girl’s
speech is in quotation marks):
(Stands on a stool, quietly looking, feeling along a shelf with stick). “On the stool.”
(Glances at experimenter. Puts stick in other hand) “Is that really the candy?” (Hesitates) “I can get it from that other stool, stand and get it.” (Gets second stool) “No
that doesn’t get it. I could use the stick.” (Takes stick, knocks at the candy) “It will
move now.” (Knocks candy) “It moved, I couldn’t get it with the stool, but the, but
the stick worked.”
(Vygotsky 1978, p. 25)
The child uses speech as a corrective tool: “That didn’t work, so I’ll try this.” Speech as a

corrective tool is a medium through which the child can correct her activity in the process
of achieving the desired result. It may be that hypothesis formulation and test through action
is developing early in children. Indeed, there is good developmental evidence for exploratory
behaviour in neonates (Menary 2016). However, the dialogical nature of the self-corrective
practice in this example is likely to have been developed via verbal interactions with caregivers
(and possibly peers).6
3. Epistemic practices: A classic example is Kirsch and Maglio’s (1994) example of epistemic
action in expert Tetris players. Experts would often perform actions that did not directly result
in a pragmatic goal.7 The actions were designed to simplify cognitive processing. Other examples include the epistemic probing of an environment and epistemic diligence – maintaining
the quality of information stored in the environment (Menary 2012). Epistemic diligence can
take quite sophisticated forms: a simple form would be keeping the physical environment
organised in such a way that it simplifies visual search (Kirsh 1995, Heersmink 2013). However, more complicated forms of epistemic diligence include updating written information
in a notebook or computer file, organising it and adding information as it becomes available.
4. Epistemic tools and representational systems
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4a. Epistemic tools: Many tools aid in the completion of cognitive tasks, from rulers to calculators, from pen and paper to computers. Manipulating the tools as part of our completion
of cognitive tasks is something that we learn, often as part of a problem-solving task. So, more
complicated forms of tool use are built upon simpler forms of sensorimotor interactions with
the environment, and innovations allow for continual improvement of technique. Some tools
are more obviously designed to produce physical ends; however, other tools are designed to
measure, observe, record and extend our senses (Humphreys 2004). These are more obviously
epistemic tools and the way that we manipulate these tools is distinct from how we deploy,
for example, the hammer.Yet, the same sensorimotor programmes for physical tool use can be
redeployed as the biological basis for epistemic tool use. However, without sophisticated cognitive practices and public systems of representation, epistemic tools would be as useless to us as

they are to cats.
4b. Representational systems: Behaviourally modern humans display an incredible facility
for innovating new forms of representational systems. They also display a general capacity for
learning how to create, maintain and deploy representations. Alphabets, numerals, diagrams
and many other forms of representation are often deployed as part of the processing cycle that
leads directly to the completion of a cognitive task (Menary 2015). Without public systems
of representation, cognitive practices of the most sophisticated kind would be impossible.
Therefore, it is important to have an account of what the nature of these public systems of
representations are.8
5. Blended interactions: Complex cognitive tasks may involve combinations of practices in
cycles of cognitive processing. This seems likely given the hierarchical nature of ICS, where
more recent practices are built upon the more ancient. All levels of processing can be deployed
at once depending upon the nature of the task. As we shall see in the third section, mathematical cognition may call upon the manipulation of tools in conjunction with mastery of public
numeral systems and algorithms for manipulating those numerals.
Learning driven plasticity and cognitive practices
The acquisition of CPs depends upon our capacity to learn, and a capacity to learn is in turn
dependent upon neural plasticity (Menary 2014). We can think of neural plasticity in three
broad ways: the first is structural plasticity – actual changes to the structure of the brain; the
second is functional plasticity – actual changes to the function of the brain; and the third is
learning driven plasticity (Menary 2014, pp. 293–294).The important thing to note about LDP
is that it is not a matter of competitive learning in a neural network with randomised initial
weights. Whilst the brain may be constrained or biased to producing certain kinds of functions
in ontogeny, the learning environment of humans is highly structured and controlled and not
simply the location of undifferentiated input. Even when learning is exploratory it still takes
place in a highly structured and informationally rich environment. The scaffolding of culture
and education makes an important contribution to the way that the brain develops in children.
Learning is a situated activity immersed within a suite of patterned practices. It results in transformational effects on developmentally plastic brains, in the sense that our brains get sculpted
by the patterns of practices in our niche. The niche in question is the cultural niche and it
contains practices, representations, tools, artefacts, experts, teaching methods and so on. As we
shall see in the third section, neural circuitry can be redeployed via LDP such that phylogenetically older circuitry can be redeployed for new cultural functions (such as learning to read,

learning to recognise Arabic numerals and so on (see Dehaene & Cohen 2007). We turn next
to the evolution of plasticity and the cultural inheritance of structured developmental niches.
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Richard Menary and Alexander James Gillett

ICS and niche construction
We are all familiar with the idea of natural selection, derived from the modern synthesis, of
environmental selection pressures that influence populations of phenotypes and the inheritance of genetic material from the previous generation. The relationship between environment
and organism is asymmetric in the modern synthesis. An extension of the modern synthesis
(it should be noted that this is not a replacement) involves not seeing evolution as an asymmetric relationship of selective pressures from environments to organisms, but as a symmetrical
relationship (Godfrey-Smith 1996) where organisms (and phenotypic traits) and environments
co-evolve.
The traditional model of evolution only recognises one line of inheritance of traits from
genes. More recently, biologists interested in niche construction (Odling-Smee, Laland & Feldman 2003) have proposed that there is another line of inheritance: ecological inheritance. Niche
construction involves modifications to the ancestral environment that are bequeathed to the
next generation. This encompasses physical alterations, such as living in mounds or constructing hives, as well as cultural artefacts, practices and institutions. Niche construction is a process by which organisms modify the selective environment such that there are new selection
pressures acting on generations over long periods of time. The modifications change selective
pressures which in turn modify traits. This occurs over long periods of evolutionary time
(potentially millions of years).9
Humans are cultural “niche constructors par excellence”; however, they don’t just physically alter the environment, they also epistemically or cognitively engineer the environment
(Sterelny 2003, 2012). Humans are born into a highly structured cognitive niche that contains
not only physical artefacts, but also representational systems that embody knowledge (writing systems, number systems, etc.), and skills and methods for training and teaching new skills
(Menary and Kirchhoff 2014). Following Sterelny (2012) we term this “cognitive capital”.
These highly structured socio-cultural niches have had profound evolutionary consequences in
the hominin lineage. The primary consequence is phenotypic and developmental plasticity. We
have evolved to be a very behaviourally plastic species (Sterelny 2012). Rather than thinking

of humans as adapted for Pleistocene hunting and gathering environments, we should think
of human behavioural and developmental plasticity as an adaptive response to the variability
and contingency of the local environment (Finlayson 2009; Potts 2012; Sterelny 2003, 2012).
Modern humans are capable of developing a wide range of skills that allow them to cope with
a wide variety of environments.This cognitive flexibility requires an extended period of cognitive development, much more so even than that of our nearest relatives, such as the different
species of great apes.
What’s the importance of the cognitive niche? The main innovations are to add an extra line
of inheritance to the single genetic line of inheritance whereby an ecological niche, as well as
genetic material, are inherited by the next generation (Odling-Smee, Laland & Feldman 2003).
Organisms are born into niches that they inherit from the previous generation. These niches
have been acted upon by previous generations often structuring and organising it in ways that
would not otherwise occur. The constructed niche places selective pressure onto phenotypes,
which in turn results in further modifications of the niche, leading to a reciprocal relationship
between organism and niche. Over time the reciprocal relationship can result in evolutionary
cascades, which can have profound effects on phenotypes, including morphological and behavioural changes (Sterelny 2005).
“Humans are niche constructors par excellence” (Sterelny 2012, p. 145). To understand the
nature of human niche construction, we must introduce a third line of inheritance: cultural
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inheritance.10 Cultural inheritance includes tools, artefacts and so on, but also more intangible
products of human cultures such as knowledge, narratives, skills and representational systems,
systems of pedagogy and a large variety of practices.The cultural niche is a rich milieu in which
human children learn and develop.The crucial change for behaviourally modern humans is the
capacity for cumulative cultural inheritance, “which was ultimately to transform Homo sapiens
into the richly cultural species we are today” (Whiten et al. 2011, p. 942).

The standard interpretation of the archaeological record indicates that there was a revolution approximately 60,000–40,000 years ago – the Upper Palaeolithic revolution – in which
there was a real explosion of novelty and the advent of behaviourally modern humans. However, there is evidence that many of these traits, including symbolic activity, could precede the
Upper Palaeolithic revolution and could have appeared and vanished irregularly over the last
150,000 years or so (see Sterelny 2012 for an overview). For instance d’Errico and colleagues
(2001) propose that there is evidence of symbolic activity on bone fragments 70,000 years ago.
Sterelny argues that this transient appearance of precursors of behavioural modernity implies
that behavioural modernity is a cultural achievement premised on multiple factors rather than
a single genetic change or cultural innovation. This suggests that establishing the successful
retention of cultural innovations is difficult, but once it can be transmitted in a stable manner
that cultural niche construction escalates – what Tomasello (1999) calls the “cultural ratchet
effect”.11
This fits nicely with the emphasis on cognitive niche construction proposed by the CI
position. The explosion of cultural and behavioural diversity that accelerates from the Upper
Palaeolithic is dependent on a range of factors coming together: inherited cultural capital, phenotypic and learning driven plasticity, complex social relations and language. In this period we
see increasing genuine novelty in tool production and use; art, including jewellery, paintings,
sculpture and musical activity; fishing and a wider range of cooperative hunting and foraging;
burial practices; cultural diversification; and the first signs of proto-numerical and writing systems as novel representational innovations such as tally notch systems (see Conard 2006 for an
overview).These could have been for keeping track of economic exchanges, lunar calendars or
hunting tallies (d’Errico & Caucho 1994).
The tools themselves, but also the skills necessary to make, maintain and deploy the tools,
must be inherited from the previous generation. Tool creation and use requires very refined
sensorimotor skills (Stout et al. 2008),12 which must be learned. Basic sensorimotor skills are
being retrained and extended during the acquisition process. Here is where LDP really makes
a difference; without LDP the acquisition of the skills required for creating, maintaining and
manipulating tools would be very difficult.
Social learning in highly scaffolded niches and LDP are co-constraining. Without a sufficient degree of neural plasticity social learning is attenuated, but without structured and stable
learning environments functional redeployment of neural circuitry cannot happen through
learning.This construction accounts for the structuring of the environment and its inheritance
by future generations. LDP accounts for how our brains can acquire novel culturally derived
cognitive functions. Putting the two together explains how we have evolved to be the cultural

creatures that we are. The next section explores the process of enculturation.

Enculturation
Tomasello (1999, 2009) has pointed out that although other animals have culture, in humans
it is both quantitatively and qualitatively unique. Human culture is quantitatively unique due
to the extraordinary amount of techniques and tools and accompanying NPPs which novices
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must necessarily learn in order to survive. But Tomasello also identifies two senses in which
human culture is qualitatively unique: cultural ratcheting (accumulative downstream niche
construction), and social institutions (“sets of behavioural practices governed by various kinds
of mutually recognised norms and rules” (2009, p. xi)) – what we have termed NPPs. Both of
these profoundly change the nature of human cognition. Learning NPPs in a developmental
niche transforms a human agent’s cognitive capacities so that they can tackle cognitive tasks
that were previously impossible or inconceivable.
A broad range of theorists have advanced enculturated cognitive positions (see Hutchins
2011; Lende & Downey 2012; Nisbet et al. 2001; Roepstorff et al. 2010; Tomasello 1999;
Vygotsky 1978). Here we develop the position advanced by Menary (2007a, b, 2010a, b, 2012,
2013, 2014, 2015) which argues that humans construct and inhabit cognitive niches in which
our minds become enculturated and transformed through the learning and mastering of NPPs
that govern the manipulation of environmental resources and interactions of social groups.
The key factors of the enculturated cognitive position of CI can be summarised as follows:
[1] NPPs governing the embodied manipulations of physical tools; which operate in [2] highly
structured and cooperative shared cognitive niches, importantly including a developmental
component with implicit and explicit teaching through which NPPs are acquired; and this

process is in turn dependent on [3] general phenotypic plasticity – especially neural plasticity – that allows for the transformative effects of the learning and enculturating processes to
take place.This transformation relies on the recycling or redeploying of older cortical structures
to newer cultural acquisitions (Anderson 2010, Dehaene & Cohen 2007). As Tomasello (1999,
p. 7) puts it:
enculturation processes do not . . . create new cognitive skills out of nothing, but
rather they took existing individually based cognitive skills – such as those possessed
by most primates for dealing with space, objects, tools, quantities, categories, social
relationships, communication, and social learning – and transformed them into new,
culturally based cognitive skills with a social-collective dimension.
(emphasis added)
Importantly, this quote highlights that enculturation is the exaptation or redeployment of
pre-existing cortical structures to newer culturally generated functions. But Tomasello also
points out that enculturation is both an ancient and ongoing process occurring at three distinct
timescales (Tomasello 1999). Firstly, over phylogenetic timescales – the evolution of the human
primate; Laland et al. (2010) have collected a wide range of evidence that cultural practices
have affected the human genome. Secondly, over historical timescales – this is the accumulation of
cognitive capital with the high fidelity transmission of skilled practices and cultural knowledge
both horizontally and vertically and downstream epistemic engineering in a specific cognitivecultural niche (Sterelny 2003, 2012). The veridicality of communication and learning channels within the niche allows for the retention of improvements – what Tomasello (1999) calls
“cultural ratcheting”. Hutchins (2001) refers to this process as the distribution of cognition
across time, whereby cognitive tasks are successfully tackled intergenerationally through the
collaborative and distributed effort of multiple agents building and refining shared mediums
and tools that are accumulated and refined to manage recurring everyday cognitive tasks. This
changes the informational profile of the epistemic niche over time and alters the nature of the
cognitive tasks as well.
Lastly, enculturation takes place over ontogenetic timescales – this is the inculcation of specific agents in developmental niches (Stotz 2010). Humans have an incredibly high propensity
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for teaching and learning (Dean et al. 2012; Keil 2011). A key element of human learning is
the functionally correct deployment of tools and perceiving of task-salient affordances of the
environment (see Vaesen 2012, p. 206). By learning to master NPPs that govern the cognitive
resources that have been accumulated by previous generations, agents are able to engage in
cognitive tasks that would otherwise be incredibly difficult, impossible or potentially inconceivable.This is the transformative aspect of enculturation (Menary 2007a).13 LDP and the high
degree of plasticity make humans highly susceptible to enculturation processes and acquiring
cultural practices and skills. Older cortical structures are redeployed into newer diverse cultural
functions which have transformative effects on both neuronal architecture and physiological
structure of the body. It also enhances the functional performance of cognitive tasks, enabling
agents to tackle novel cognitive tasks. This is supported by an abundance of empirical evidence
in a range of experimental paradigms to support enculturation: in cognitive domains such as
attention (Ketay et al. 2009), perception and motor processes (Nisbet et al. 2001; Draganski
et al. 2004); music (Gaser & Schlaug 2003); literacy and language (Castro-Caldas et al. 1999);
moral reasoning, social cognition and emotions (Henrich et al. 2010); categorisation, judgment,
reasoning, problem solving and decision making (Henrich et al. 2005, 2010; Nisbet et al. 2001);
memory and navigation (Maguire et al. 2000); and tool use (Farne et al. 2007). Downey and
Lende (2012) provide a very useful overview of this evidence (and for more critical assessments
of some of this research, see Roepstorff et al. 2010; Reynolds Losin et al. 2010). In the next
section we will outline the practice of mathematics as a case of the transformative effects of
enculturation, and also as partially constitutive of cognitive processes in hybrid ICS encompassing brain–body–niche interactions.
Before we do so, it is important to clarify a few key aspects of the transformation thesis.
Firstly, to recap: Menary (2014) argues that the convergent evidence of a late-developing cortex; an extended developmental stage in humans; evidence of continuing plasticity in adults;
diverse and hostile environments in our hominin evolutionary history; and complex social
situations all drive the need for LDP. In developmental niches this allows for the transformation
of the agent’s functional capacities through the redeployment of neural circuits to enable the
bodily manipulation of external representational vehicles and thus the acquisition of new skills
(Menary 2015, p. 9). In turn, this allows the scaffolded agent to both [a] tackle cognitive tasks
in new ways and [b] tackle cognitive tasks that could have been previously inconceivable (also

see De Cruz & De Smedt 2013; Kirsh 2010; and Nieder & Dehaene 2009).
Menary (2015) goes further in clarifying this. He postulates that external material symbols
and tools provide “novel” functions (p. 10) – i.e. functional aspects that could not be done
merely in the head – and it was these novel factors that lead to their proliferation. As such,
Menary argues that a wide range of human cognitive abilities are partially constituted by
the learnt NPPs that agents must master in order to tackle novel cognitive problems using
shared public symbols and other cognitive resources (also see Dutilh Novaes 2012, 2013).These
environmental resources and the NPPs that govern their usage are part of particular culturalcognitive niches that are definitive of human cognition as ICS. As Nersessian puts it: culture is
not something additional to human cognition, “culture is what makes human cognition what
it is” (2005, pp. 31–32).
It is also important to clarify that the transformative effects of deploying cognitive artefacts
is often misconstrued as simply “amplifying” or “augmenting” the cognitive capacities of the
agent (for example, see Bruner et al. 1966). Cole and Griffin (1980) have rightly observed that
the use of epistemic tools does not straightforwardly amplify cognition in the way that a physical tool amplifies our physical prowess. For instance, a spade may improve an agent’s digging
abilities and a loudhailer amplifies the volume of someone’s voice, but it is not strictly true
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that the manipulation of physical public symbol systems on a page or in a calculator amplify
an agent’s capacities. Instead, it is more accurate to see their manipulation as the alteration of
the cognitive task or functional capacities to form a cognitive system that has different and
“unique” sets of cognitive properties that are not present in the agent considered in isolation
(Hutchins 2006; Norman 1991). This shows that when we consider the transformative effects
of enculturation processes we must be careful to discern the level of analysis (Norman 1991).
Additionally, the fact that hybrid integrated cognitive systems have properties not reducible to
the individual indicates the need for a shift in the unit of analysis to necessarily incorporate the

cognitive niche in order to properly understand human cognition as essentially enculturated
(Hutchins 2011; Menary 2012, 2013, 2015).

Mathematical cognition as a process of enculturation
Experiments with animals (Ansari 2008), young children (Dehaene 1997), bilingual adults
(Dehaene et al. 1999) and adults from cultures without discrete number words (Dehaene 2007)
in a range of experimental paradigms are highly suggestive of an “ancient number system”
(ANS).This system is proposed to be amodal14 (Cantlon et al. 2009; Dehaene et al. 1998, 2004)
and displays characteristics which render it approximate and fuzzy – distance effects (whereby
the error rates in quantity comparison tasks increase as the distance separating the two quantities decreases) and magnitude effects (error rates increase as the absolute totals of the quantities
involved in the tasks increase) (see Dehaene 1997 for an overview). On the basis of a large body
of evidence, the ANS is postulated to be evolutionarily ancient. The notion being that a basic
capacity for discerning and discriminating quantity is evolutionarily advantageous: whether
one can detect larger benefits and avoid larger dangers is something that improves the survival
of an organism (Ansari 2008; Dehaene 1997).
In humans, numerous neuroimaging studies and neuropathology studies indicate that the
neural basis for the ANS is in the intraparietal sulcus and surrounding regions (Dehaene 1997,
2007 and colleagues 1999). But in addition to making approximate judgments about quantities, humans can also perform discrete computations with a “discrete number system” (DNS).
A wide range of neuroimaging and behavioural studies indicates that the DNS and ANS share
neural correlates (see Lyons et al. 2012 for an extensive list of corroborating studies). The neural basis of a mental number line and ANS involves number-detecting neurons. These neurons
were postulated to fire approximately with fat tails: e.g. a number detector that fires for 6 will
also partially fire for 5 and 7. This then explains the distance effect because for any value, multiple neurons will fire at differing degrees and this causes a degree of fuzziness for judgments of
largest or smallest, etc. Neural net models have been made of the distance and magnitude effects
(Dehaene 2007;Verguts & Fias 2004) and these were supported by evidence of single-neuron
studies on rhesus monkeys (Nieder et al. 2006; see Ansari 2008 and Nieder & Dehaene 2009
for discussion). The tuning curves of these number-detecting cells overlap in a manner that is
consistent with what one would expect with the distance effects.
Importantly, rather than undermining the enculturated cognitive position as some have
argued (see Zahidi & Myin forthcoming), the transition from the ANS to the DNS is perhaps
one of the best examples of enculturation. The two effects and approximate nature of the

ANS combine to give the mental number line a logarithmic structure. Dehaene (2007) has
argued that the acquisition of symbolic representations in development alters the structure of
the mental number line to a more precise linear format. Learning how to manipulate public
symbolic notation – cultural practices – has a transformative effect on both cognitive functional
performance and also on neural architecture. Numerous sources of evidence lead to this view:
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[1] longitudinal studies of brain activity in 8–19-year-olds show decreasing activity in the PFC
during mathematical tasks suggestive of automatisation, but also shows increasing activity in
the left parietal cortex (the postulated neural substrate of the mental number line); [2] young
children asked to space the numbers 1 to a 100 evenly on a page place “10” at the halfway point
and bunch all the larger numbers up at one end; this behaviour is absent in adults but is present in some illiterate adults of traditional communities (e.g. the Munduruku of the Amazon)
that do not have discrete number words; and [3] there is a mixed response to number tasks by
bilinguals that is indicative of the participants switching into their native tongue to carry out
the calculation of symbolic tasks (see Dehaene et al. 1999; Lyons et al. 2012; Piazza et al. 2013;
Viarouge et al. 2010).
This is clear evidence not just of enculturation, but also of the truly transformative effects
that learning cultural practices can have on human cognition. Culturally new capacities exapt
and recycle older phylogenetic functional regions to newer culturally generated purposes
(Dehaene & Cohen 2007). Dehaene further argues that the older phylogenetic functional basis
constrains the extent to which it can be recycled/redeployed and shifted into a new function.
In this particular case the evidence suggests that an ancient primate or core neural system integrates symbolic numerical representations and that this both transforms mathematical cognitive
functional capacities and alters neurological architecture. Additionally, Cantlon and colleagues
(2009) present evidence that young children use the same network of brain regions to tackle
both symbolic and non-symbolic notations and that this is therefore an abstract, notationindependent appreciation of number.

This large body of evidence lends credence to the notion that the evolutionarily new use
and manipulation of symbolic mediums recycles an evolutionarily older mechanism. And the
experiments by Lyons and colleagues (2012) also lend support to the claim that the number
line is altered by enculturation. These experiments reveal a disjunction between symbolic to
symbolic processing and symbolic to non-symbolic processing – this matches the “rupture”
noted by Radford (2003) in the development of mathematical abilities from pre-symbolic to
symbolic manipulations (also see Deacon 1997; Nieder 2009). And this also fits with the wider
body of evidence that shows that increased PFC activity in novices diminishes as they become
expert in modern mathematical cognitive practices. Finally, these learning driven neuroplastic
changes reach their peak in expert mathematicians who have macroscopically altered regions
that are involved in both arithmetic and also the visuospatial imagery necessary for the manipulation of complex objects required for advanced mathematics (Aydin et al. 2007).15
If modern mathematical abilities involve the redeployment of older cortical structures
to newer functions, we would expect to find both diversity and constraints in how humans
from different cognitive-cultural backgrounds perform in mathematical cognitive tasks. And
indeed this is what has been found. An experiment by Tang and colleagues (2006) demonstrates that the differing NPPs of different cultural-cognitive niches can have effects on
both neuronal architecture and function, and behavioural performance. Tang and colleagues
compared two groups of students – English speakers and Chinese speakers – and found that
the former had neural correlates in the perisylvian language regions whereas the latter had
correlates in the premotor cortex. Additionally, although of comparative intelligence, the
Chinese students outperformed their English counterparts. In a review, Cantlon and Brannon (2006) observed that there were many factors from the cognitive-cultural niche that
could account for such differences: abacus use; differences in writing styles; differing styles
of number words (Chinese number words are much less demanding on working memory);
preferred cognitive strategies; and overall education systems (also see Butterworth 1999; cf.
Reynolds Losin et al. 2010).
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This example shows the importance of the cognitive niche for how agents approach cognitive tasks. Differing sets of tools, techniques and NPPs alter how cognitive tasks are performed.
The importance of the brain–body–niche interactions in mathematical cognition is further
demonstrated by a range of behavioural studies. A series of experiments by Zhang (1997;
Zhang & Norman 1994) has shown that if the structure of the external representations used
for a cognitive task instantiate salient features of the abstract task properties, they facilitate cognitive offloading and reduce the information load on working memory and improve overall
task performance. Zhang and Norman (1995) supported these findings with an analytic comparison of various notational systems to show that the prevalence of Hindu-Arabic and general
cultural invasiveness is due to the formal structure of the material symbols which makes them
far superior to Roman numerals for calculations. The structure of the external representation
separates out the base and power dimensions in a perceptually convenient manner. For example, four-hundred and forty-seven in Arabic numerals is 447 = 4 × 10(2) × 4 × 10(1) × 7 ×
10(0), and the shape is the base and position is the power. In Roman numerals it is CDXLVII
and as such position does not correspond to power and the shape does not correspond to base.
In another set of experiments, Landy and Goldstone (2007) subtly modified seemingly
non-task specific perceptual groupings around algebraic equations in a series of experiments.
This included increasing or decreasing the size of gaps between terms in the equations; adding in shaded areas in the backgrounds of the equations that created perceptual groups; and
reordering terms to be either cognisant or contradictory to the FOIL order of operations (also
see Dutlih Novaes 2012 for discussion). As in Zhang and colleagues’ work, these modifications
of the structure of the external representations either aided or hindered task performance
dependent on whether they were congruent to the order of operations in the equations or
not. Crucially, these modifications had an effect even when participants knew they were being
influenced, indicating that “perceptual groupings” play a larger role in abstract mathematical
thinking than is normally acknowledged.
We can interpret the work of a wide range of theorists from different fields (Alibali &
DiRusso 1999; Landy et al. 2014; Nemirovsky et al. 2013; Radford 2009; Sato et al. 2007) as all
broadly arguing that embodied manipulations of cognitive tools – looping brain–body–niche
interactions – are incredibly important in mathematical cognition; not just for pedagogy and
learning, but also for high-level expert problem solving (Marghetis & Nunez 2013). Building
on this we can argue that accumulative downstream cognitive niches constrain and enable how
mathematical cognitive tasks are tackled. Along similar lines De Cruz and De Smedt (2013)
have argued that symbols (and other external representational vehicles such as body parts,

gestures, number words and tally systems – see De Cruz 2008) act as “material anchors” and
are “epistemic actions” – whereby the physical manipulations of the environment are not just
physical movements but are themselves also movements in an abstract problem space towards a
cognitive task (Kirsh & Maglio 1994; also see Hutchins 2005). De Cruz and De Smedt demonstrate their position through a number of historical case studies: zero (0); imaginary (i) and
subsequent complex numbers (a+bi); negative numbers (‑n); and algebra (x, y, z, etc.). In each
case, they show that the material sign played a role in discovery by facilitating the cementation
(stability) of vague ideas which aids the creative effort. For example, in the case of negative
numbers, the minus sign was already used as an operator before the drive for closure enabled
the invention of numbers “below” or “beyond” zero. This allowed the possibility of conceiving
of a task that was previous inconceivable. De Cruz and De Smedt nicely demonstrate this by
juxtaposing the seemingly mundane nature of the task in the modern era with a quote from a
prominent mathematician Masères from the 17th century: “ ‘3 − 8 is an impossibility; it requires
you take from 3 more than there is in 3, which is absurd’ ” (2013, p. 13). As Menary (2010b,
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2015) has argued, it is the learning and deploying of NPPs which govern the embodied manipulations of these cognitive artefacts and which transforms the cognitive abilities of the wider
integrated cognitive system. As such, the agent trained in the manipulation of mathematical
notation is able to tackle cognitive tasks in a superior manner, and also able to comprehend
tasks that would otherwise be impossible.

Conclusion
Cognitive integration is a framework which allows us to explain how cognition is enculturated. It does so by providing a dual account of the cultural evolution of cognition and learning.
It uniquely provides an account of how we embody culture and how culture provides new
cognitive functions that redeploy our basic sensorimotor interactions with the environment.
These looping interactions and cognitive practices are a form of cognitive niche construction

whereby brain–body–niche interactions alter not only the physical environment but also the
inherited informational profile in which future generations are enculturated, and transforms
the nature of the cognitive tasks that they face.The process of enculturation hinges on the plasticity of our brains and our capacity for flexible redeployment of existing cognitive capacities
to innovative cultural functions. It also requires openness to learning in highly scaffolded and
social learning environments. The importance of enculturation lies in the acquisition of new
capacities, allowing us to perform tasks that we should otherwise be unable to.
Culture permeates our physical and mental lives, but it does so through our inherited cognitive capital and the plasticity of our existing cognitive circuitry.

Notes
1 The chapter is jointly authored. Both authors are based at the department of Philosophy, Macquarie
University Sydney. Research for this article was supported by the Australian Research Council, Future
Fellowship FT 130100960.
2 This is what Menary (2012, 2015) calls a process of enculturation.
3 Coordination dynamics are the interactions between the components of the system – both processes and
structures (see Menary 2013 for more details).
4 This refines Menary’s earlier analysis of cognitive practices in terms of Biological coupling, Epistemic
actions, Self-correcting actions and Cognitive practices. (Menary 2007a, 2010a, 2010b) The term cognitive practices is now more all-encompassing for all these other kinds of cognitive manipulations.
5 Although interactions of this kind aren’t obviously practice-like, they are often influenced by cultural
practice. Sensorimotor capacities that underlie our capacities for various skills, such as driving and writing, are good examples of how we embody cultural practices.
6 The exposition here aims for brevity. Menary (2007a, 2016) provides a detailed account of the developmental aspects of corrective practices.
7 These actions are direct manipulations of the task structure in the environment rather than internal representations. And although experts do perform more physical acts, their performance is faster and more
accurate than novices who rely more heavily on internal resources.
8 For such an account see Menary (2007a, Chapters 4–6).
9 See Turner (2000) for plentiful examples.
10 Or we might blend the ecological and cultural into a single line of inheritance. Odling-Smee (2007) has
expressed skepticism about the need for a third line of inheritance. He argues that separating the ecological and the cultural is ad hoc and complicated and outweighs the benefits of treating them separately.
Irrespectively, cultural inheritance matters for understanding human niche construction; and there does
seem to be a prima facie qualitative difference between cultural inheritance and physical engineering.
11 See section three for more discussion of Tomasello.
12 This is evident even in Homo habilis and the Erectines and is another example of a biological interaction.

13 This has been discussed in a number of places: Menary (2007b, 2010a, 2010b, 2012, 2013, 2014, 2015).

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1 4 Or perhaps multi-modal, since the ANS appears to be sensitive to multiple sensory modalities.
15 Potentially, the enculturated cognitive approach offers an interesting perspective on a perennial topic
in the psychology of mathematics: the prevalence of the folk-metaphysical belief in Platonism amongst
practicing mathematicians. A precursor to this was formulated by the mathematician Keith Devlin
(2008). We can rephrase his claim in the following manner: a possible explanation for why Platonism
is the default folk-belief system of mathematical practitioners is that by redeploying cortical circuits
whose original function was spatial navigation and patterns, these neural circuits bring “baggage” with
them – namely, that they are directed at real entities out there in the world. And the prevalence of spatial
language in discussions of mathematical entities may be indicative of this.

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