Cognitive Innovation, Cumulative Cultural Evolution, and Enculturation

In: Journal of Cognition and Culture
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  • 1 Justus Liebig University of Giessen, Department of Psychology, Experimental Psychology and Cognitive Science

Abstract

Cognitive innovation has shaped and transformed our cognitive capacities throughout history. Until recently, cognitive innovation has not received much attention by empirical and conceptual research in the cognitive sciences. This paper is a first attempt to help close this gap. It will be argued that cognitive innovation is best understood in connection with cumulative cultural evolution and enculturation. Cumulative cultural evolution plays a vital role for the inter-generational transmission of the products of cognitive innovation. Furthermore, there are at least two important functions of enculturation for cognitive innovation. First, enculturation is responsible for the ontogenetic acquisition of cognitive practices governing the interaction with innovative products. Second, successful processes of enculturation provide opportunities for subsequent innovative processes. The trans-generational trajectory of calculation from mathematical symbol systems to the first digital computers will serve as a paradigm example of the delicate interplay of cognitive innovation, cumulative cultural evolution, and enculturation.

Abstract

Cognitive innovation has shaped and transformed our cognitive capacities throughout history. Until recently, cognitive innovation has not received much attention by empirical and conceptual research in the cognitive sciences. This paper is a first attempt to help close this gap. It will be argued that cognitive innovation is best understood in connection with cumulative cultural evolution and enculturation. Cumulative cultural evolution plays a vital role for the inter-generational transmission of the products of cognitive innovation. Furthermore, there are at least two important functions of enculturation for cognitive innovation. First, enculturation is responsible for the ontogenetic acquisition of cognitive practices governing the interaction with innovative products. Second, successful processes of enculturation provide opportunities for subsequent innovative processes. The trans-generational trajectory of calculation from mathematical symbol systems to the first digital computers will serve as a paradigm example of the delicate interplay of cognitive innovation, cumulative cultural evolution, and enculturation.

1 Introduction

Throughout history, innovative processes have had an enormous impact on our cognitive lives.* Most of the time, we take the products of innovative cognitive processes for granted and frequently integrate them into our cognitive processing routines, from number systems to scientific instruments and technical devices. Cognitive innovations have certainly contributed to our cognitive success as a species and to the many advances in technology and in the arts and sciences (Henrich, 2016; Laland, 2017). In turn, these advances have opened up new opportunities and possibilities for subsequent innovative cognitive processes. The cumulative process of cognitive innovation, which spans both phylogenetic and ontogenetic time scales, is an important precondition for many cognitive processes such as mathematical cognition, reading and writing, scientific reasoning, and problem solving. This suggests that cognitive innovation plays a double role in our cognitive endeavours. First, innovation is a complex cognitive process in its own right that often involves the exploratory interaction with the cognitive niche. Second, the products of innovative processes are integrated into multiple cognitive processes. Phylogenetically, they are modified and refined in the course of cumulative cultural evolution. Ontogenetically, the skilful interaction with innovative products is the result of enculturation (Menary, 2013, 2015).

Until recently, both roles of cognitive innovation have not received much attention by systematic research in the cognitive sciences (Carr, Kendal, & Flynn, 2016) and in empirically informed philosophical research (but see Sterelny, 2016a). The purpose of this paper is to pave the way towards a theoretical account of cognitive innovation and its relation to cumulative cultural evolution and enculturation. In the next section, I will clarify the notion of cognitive innovation and introduce the conceptual tools and background assumptions for the considerations developed in this paper. Section 3 will explore the relationship of cognitive innovation to cumulative cultural evolution and to the evolution of cultural learning. For the remainder of this paper, these considerations of the phylogenetic conditions of cognitive innovations will be complemented by an assessment of two functions of the ontogenetic process of enculturation for cognitive innovation. In Section 4 I will consider the most important components of enculturation. I will then suggest in Sections 5 and 6 that enculturation is responsible for the inter-generational transmission of innovative products and provides the foundation for new innovative processes. Along the way, I will apply these considerations and the conceptual tools on offer to an example. In particular, I will show how the invention and refinement of digital computers is a direct result of a temporally extended innovative process spanning from the innovation of Indian-Arabic numerals 5000 years ago to the present day in virtue of cumulative cultural evolution and enculturation.

2 Aspects of Cognitive Innovation

In his recent book, Laland (2017) suggests that cognitive innovation is “the devising of a novel solution to a problem, or a new way of exploiting the environment” (Laland, 2017, p. 100). The notion of cognitive innovation as I understand it refers to innovative processes, in contrast to innovative products (Carr et al., 2016; Chappell et al., 2015; Lane, 2016). Innovative cognitive processes are characterized by the realization of cognitive procedures that complement, augment, or transform the overall cognitive potential of a certain social group of organisms. Innovative cognitive products are the result of innovative processes and can take various forms — from objects and artefacts (e.g., symbol systems, tools, and technological devices) to new patterns of behaviour and belief systems (Lane, 2016). Laland’s (2017) working definition of cognitive innovation suggests that innovative products are rendered possible by a new and original way to interact with the local environment, either in the context of a concrete problem-solving task, or in the context of less goal-directed cognitive interactions with resources in the local environment.

Mesoudi et al. (2013) and Laland (2017) mention two general procedures contributing to the possibility of generating innovative products. First, the refinement of already existing innovative products and the practices governing their manipulation might lead to the generation of innovative products. Second, the probability of new innovative products is increased by the recombination of already existing innovative products and the corresponding socio-culturally structured interaction patterns. The disposition for refinement and recombination relies on the ability to overcome functional fixedness (Carr, Kendal, & Flynn, 2015; Carr et al., 2016; Chappell et al., 2015; Legare & Nielsen, 2015). Functional fixedness refers to the tendency to attribute a particular, well-defined, and exclusive function to a certain object and to associate this object with a restricted and confined set of affordances (Duncker, 1945). Although functional fixedness might be useful in certain contexts where automatic, fast, and efficient actions are advantageous, it will become an obstacle if novel solutions to concrete or abstract problems need to be devised. Importantly, chance can also heavily influence the manifestation of cognitive innovation (Muthukrishna & Henrich, 2016). This suggests that it is not a goal-directed, fully determined process in all cases, but often involves the more or less contingent combination and refinement of innovative products over time.

The concrete manifestations of innovative processes are always constrained by the overall possibilities and limitations of the brain and the rest of the body. It has been suggested that cognitive innovation is embodied in the sense that it “depends on potential motor repertoire” (Sterelny, 2016a, p. 5). On this view, innovation is partly dependent upon the overall morphological properties of the entire body, especially of the hands and arms (Tebbich et al., 2016). For example, the morphology of hands in humans and great apes determines the degrees of freedom of joints and muscles, which in turn confine the development of dexterous movement co-ordination (Furuya & Altenmüller, 2013). This is in line with embodied, embedded, extended, and enactive (4E) accounts of cognition that subscribe to a strong embodiment thesis. According to this thesis, the embodied interaction with the local environment plays an indispensable functional role in at least some cognitive processes (Menary, 2015).

The overall potential of embodied cognition and the development of motor repertoires is always relative to the cognitive niche in which the organism is situated. I define the cognitive niche as the structured, trans-generationally shaped and modified environment contributing to cognitive processes. Over multiple generations, it has facilitated and amplified the development of evermore fine-grained and sophisticated problem solving routines of humans and many other animals (Clark, 2008; Kendal, 2011; Laland & O’Brien, 2011; Odling-Smee & Laland, 2011; Sterelny, 2003, 2012; Stotz, 2010). In the human case, an important consequence of cognitive niche construction is that innovations and other cognitive phenomena cannot be understood independently from considerations of the embodied interaction with the structured environment (MacKinnon & Fuentes, 2012). Thus, we need to take the interaction and the mutual dependence of organisms and their niche into account. This is consistent with 4E accounts of cognition that are informed by a strong embeddedness thesis. This thesis states that at least some cognitive processes are realized by the integration of cerebral, extra-cerebral bodily, and environmental components (Menary, 2015).

Strongly embodied and embedded innovative processes are not uniquely human. They are present in many other animals and have a long evolutionary history. Human innovation is thus evolutionarily continuous (Menary, 2007) with types of innovation being found in many other animals, including chimpanzees and New Caledonian crows (Tebbich et al., 2016). This gives rise to the theoretical possibility that cognitive innovation has been an adaptive response to the challenge to find — sometimes life-saving — solutions to various problems (Laland, 2017; Sterelny, 2016a). In what follows, I will focus on cognitive innovation in human organisms. However, it is important to keep in mind that many other animals are also capable of generating new innovative products in their niche.

Innovation is usually a group-level phenomenon that is realized by the collaboration and interaction of several organisms. In some cases, individuals might generate innovative ideas on their own, but even in these cases the individuals are always embedded in their cognitive niche, which is comprised of multiple representational systems, artefacts, tools, as well as other cognitive agents. For this reason, cognitive innovations are always distributed across a socio-cultural group that generates new innovative products (Muthukrishna & Henrich, 2016; Tennie, Call, & Tomasello, 2009).

This view, taken together with the strong embodiment and embeddedness theses, has important methodological consequences. In particular, the present account of cognitive innovation is opposed to methodological solipsism (Fodor, 1980; Putnam, 1975). Methodological solipsism consists of two claims. First, each psychological state can be ascribed to one and only one individual. Second, explanations of psychological states are confined to a single individual. Furthermore, against the view held by proponents of 4E cognition (Rowlands, 1991, 1995), methodological solipsists assume that the embodied and embedded dimensions of cognitive phenomena are not relevant for explanations in the cognitive sciences (Burge, 1986). The opposition to methodological solipsism leads to the commitment that cognitive innovations are embodied and embedded in a strong sense. Furthermore, cognitive innovations are assumed to be distributed across several individuals in a large number of cases, such that their explanations require units of analysis that go beyond the skin and skull of a single individual.

Given this social dimension of innovative processes, there are at least two different kinds of temporal resolution. First, innovative processes can be realized horizontally by collaborating individuals within the same generation. Second, they can be realized vertically by collaborating individuals across generations. It is also possible that innovative processes are realized both horizontally and vertically. This will be the case if a certain innovative product is refined or recombined both within and across generations. We will see in Sections 5 and 6 that the cultural evolution of numerical systems and the invention and refinement of digital computers are examples of both horizontal and vertical innovation.

3 On the Phylogenesis of Cognitive Innovation: Cumulative Cultural Evolution and Cultural Learning

With the conceptual clarifications of cognitive innovation in place, I will now explore the phylogenetic conditions of innovation. The claim of this section will be that the interaction of genetic and cumulative cultural evolution is the driving force and the result of cognitive innovation. Cultural and genetic evolution mutually constrain each other and jointly give rise to new, yet not open-ended ways to interact with the world. Genetically, human organisms are equipped with brains that are highly plastic and with bodies that have the potential to adapt to new movement patterns and motor programs (Menary, 2013, 2015).

Learning driven plasticity (ldp) is an evolved principle governing brain development that gives rise to the acquisition of multiple cognitive skills during ontogeny. ldp is associated with structural changes to the organization and connectivity of brain areas that lead to new neuronal functions (Ansari, 2012; Menary, 2015). ldp is constrained by the functional biases of the cerebral areas contributing to the development of a certain new neuronal circuit. This is suggested by empirical and conceptual work on neural reuse (Anderson, 2010, 2015) and neuronal recycling (Dehaene, 2005, 2010).1 The idea is that especially evolutionarily recent cognitive processes such as reading, writing, and symbol-based mathematical practices need to allocate and re-exploit already existing structural and functional connections of brain areas and integrate them into new neuronal circuitry. The scope of neural reuse in each particular case depends on the evolved functional and structural biases of specific brain areas and on the functional proximity of uses to which these areas can be put (Anderson, 2015).

ldp is complemented by the evolved principle of learning driven bodily adaptability (ldba). According to this principle, new bodily ways to interact with the cognitive niche emerge in the course of enculturation. ldba guides the ontogenetic trajectory of skilled embodied action. ldp and ldba, understood as genetically evolved principles that are partly dependent upon the affordances in the cognitive niche, jointly give rise to the possibility of cumulative cultural evolution. The upshot is that contemporary human cognition has been brought about by the autocatalytic co-ordination of genetic and cultural evolution (Downey & Lende, 2012; Henrich, 2016; Wilson, 1985).

ldp and ldba are sub-personal mechanisms that realize cumulative cultural evolution in virtue of cultural learning in the cognitive niche (Boyd, Richerson, & Henrich, 2011; Sterelny, 2016b). Cultural learning is responsible for the vertical, inter-generational transmission of innovative products, e.g., of artefacts and representational systems. Cultural learning is a specific variant of social learning. Social learning is an ubiquitous mechanism of skill transmission in the animal kingdom, one that manifests itself in imitation, direct instruction, and social opportunity provision (Laland, 2017; Mesoudi et al., 2013). Cultural learning is special, because it “refers to types of social learning that support cultural evolution” (Heyes, 2016, p. 280). It is thus characterized by the inter-generational transmission of culturally evolved skills and knowledge in human groups and societies (Derex & Boyd, 2015).

Cultural learning, realized by ldp and ldba, gives rise to an intricate relationship of cognitive innovation and cumulative cultural evolution. First, cultural learning transmits innovative products both within and across generations (Carr et al., 2016; Laland, 2017; Muthukrishna & Henrich, 2016). This is the main reason why cultural learning gives rise to cumulative cultural evolution (Laland, 2017). Second, it provides an important condition for the continuous construction and modification of the cognitive niche (Chappell et al., 2015; Laland, 2017). This double role of cognitive innovation for cumulative cultural evolution gives rise to the ratchet effect (Tennie et al., 2009). This effect refers to a cumulative process in the course of which “modifications and improvements stay in the population fairly readily […] until further changes ratchet things up again” (Tennie et al., 2009, p. 2405). On this construal, ratcheting up is nothing but a process that is characterized by the reciprocal interaction of cumulative cultural evolution and cognitive innovation.

4 Key Components of Enculturation

Enculturation is the temporally extended acquisition of cognitive practices in the cognitive niche during ontogeny (Menary, 2015). Cognitive practices are defined as evolutionarily recent, embodied interactions with innovative products such as writing systems and numerical systems (Menary, 2007, 2013a, 2015, 2016). Examples include reading, writing, and symbol-based mathematical practices. Given the relative recency of cognitive practices, there was not sufficient time for the development of dedicated brain circuits, motor programs, and domain-specific learning mechanisms (Dehaene, 2010, 2011, Menary, 2014, 2015). However, there are at least three evolved principles governing human cognition that jointly give rise to the possibility of enculturation.

First, as already mentioned in the last section, ldp — understood as a manifestation of neural reuse — is not an open-ended process of resource allocation in the brain. Rather, it is constrained by the structural properties and functional biases that define the possibility space for plastic changes in the brain (Anderson, 2015; Anderson & Finlay, 2014). Second, in close interaction with ldp, the development and refinement of embodied action routines and movement patterns also play an indispensable functional role in the acquisition of cognitive practices. The emergence of these new embodied ways to interact with the cognitive niche is rendered possible by ldba. In humans and other animals, it avails itself as a principle that guides the ontogenetic trajectory of skilled motor action. The specific realization of the embodied interaction with the cognitive niche is dependent upon the functional biases displayed by the anatomical and mechanical configuration of the human body. It is the employment and allocation of embodied interaction patterns that bring about the bodily adaptation to new cognitive functions.

Finally, human organisms are enculturated by their active, temporally extended participation in organized forms of knowledge and skill transmission. During ontogeny, human organisms are enculturated by a specific variant of ontogenetically relevant cultural learning, namely scaffolded learning (Menary, 2010). Scaffolded learning is both structured and explicit. It allows the novice to acquire new cognitive capacities at a rate that accommodates her current cognitive capacities in the course of ontogenetic development (Clark, 1997; Estany & Martínez, 2014; Sterelny, 2012; Wood, Bruner, & Ross, 1976). Lev Vygotsky’s zone of proximal development (Vygotsky, 1978) and John Dewey’s (Dewey, 1997) educational principle of continuity are theoretical precursors of this idea. The zone of proximal development is defined as “[…] the distance between the actual developmental level as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance or in collaboration with more capable peers” (Vygotsky, 1978, p. 86; italics removed). The zone of proximal development defines the properties and the temporal organization of scaffolded learning. It represents a continuum of increasingly robust problem solving capacities throughout ontogenetic development. Component skills learned early during the acquisition of a certain cognitive practice are continuous with component skills that will be learned later on. This is in line with Dewey’s principle of continuity: “[…] by acquiring certain skills and by learning certain subjects which would be needed later […] pupils are as a matter of course made ready for the needs and circumstances of the future” (Dewey, 1997, p. 47). The upshot is that cognitive capacities are augmented and transformed by cultural scaffolded learning routines that depend on the novice’s ongoing social interaction with teachers and other caregivers in the cognitive niche. This interaction often includes the concerted embodied engagement with innovative products.

Scaffolded learning structures the engagement with these products, partly by instructing novices in the cognitive norms that constrain the performance of cognitive practices: “During the learning and training of a skill, […] we are guided by the norms for the correct actions that make up the skilled practice” (Menary, 2015, p. 8). Sets of cognitive norms acquired in the course of scaffolded learning constrain the manipulation and interpretation of innovative products (Menary, 2007, 2010).

In sum, scaffolded learning is a variant of cultural learning. It is likely to be the result of autocatalytic gene-culture co-evolution that has been an important and indispensable condition for enculturation. This is in agreement with the idea that “[h]uman minds are not just built for culture; they are also built by culture” (Laland, 2017, p. 30). After having considered the most important components of enculturation, the next two sections will be dedicated to two functions of enculturation for cognitive innovation.

5 Enculturation and the Transmission of Innovative Products

The main purpose of enculturation is to augment and transform the cognitive capacities of human organisms. This is realized by providing novices with knowledge and skills that allow them to interact with innovative products through cultural scaffolded learning in the cognitive niche. Enculturation is thus responsible for the transmission of the innovative products and the cognitive norms that govern the interaction with them. In what follows, I will use the Indian-Arabic numerical system as an example of a broad class of innovative products. I assume that the relationship of enculturation and cognitive innovation under consideration extends to other innovative products, such as writing systems, notational systems, artefacts, and tools.

Human and non-human animals are evolutionarily endowed with the capacity to subitize and to approximate the number of the members of a collection of objects (Everett, 2017). Subitizing is the ability to intuitively estimate the quantity of collections of up to three visually presented items (Dehaene & Cohen, 1994; Menary, 2015). It is likely that this capacity relies on ancient mechanisms for the effective detection of environmental affordances (Dehaene, 2011). Subitizing is complemented by the capacity to approximate the quantity of collections of four items and more (Everett, 2017), which is defined as “the rapid perception of approximate numbers of objects” (Dehaene, 2011, p. 238). It is widely assumed that number approximation is an evolved capacity realized by a language-independent ancient or approximate number system (ans). On a neuronal level, converging evidence suggests that the bilateral intraparietal sulci (ips) play a crucial functional role in the realization of the ans (Ansari, 2008; Dehaene, 2005, 2011; Lyons, Ansari, & Beilock, 2015). We will see in a bit that the quantity estimations realized by the ans are functionally related and both phylogenetically and ontogenetically antecedent to other, genuinely human mathematical capacities. Subitizing and number approximation are qualitatively and functionally different from counting, which can be defined as exact enumeration (Menary, 2015). Moving beyond the capacity to enumerate small collections of up to three items, counting is probably the oldest genuinely human practice that makes an important contribution to the subsequent phylogenetic development of mathematical cognition. Counting is often, but not always accompanied by linguistic utterances referring to number concepts. It requires explicit instruction in terms of scaffolded learning and presupposes the application of cognitive norms governing the ordinality and the cardinality of natural numbers (De Cruz, 2008; Everett, 2017; Merkley & Ansari, 2016). These norms will become especially important in the subsequent acquisition of symbol-based mathematical practices, where ordinality, numeral-number-collection correspondences, and cardinality govern multiple mathematical operations.

The ontogenetic realization of the genetically inherited ans is the foundation for the development of the discrete numerical system (dns) in the course of the exposure to numerical innovative products (Menary, 2015). The postulation of the dns is supported by recent neuroscientific research on the relationship between numerosity and what I call numerality (Dehaene, 2011). For example, there is converging evidence suggesting that activations in the bilaterial ips are reliably associated with both number approximation and calculation (Amalric & Dehaene, 2016; Ansari, 2008; Hannagan, Amedi, Cohen, Dehaene-Lambertz, & Dehaene, 2015; Lyons et al., 2015). Despite this overlap, however, there are non-trivial functional and structural differences between the realization of the two systems taken as a whole (Lyons et al., 2015). The contribution of the bilateral ips to the dns is a good example of neural reuse in the service of ldp (Dehaene, 2005, 2011). Another example is the reliable neuronal activation in the bilateral inferior temporal gyri, which is associated with the visual processing of numerals. Activations in these areas are also associated with letter string or picture recognition, which suggests that the dns redeploys these areas, because they have functional biases that are suitable for the contribution to the visual recognition of numerals and other object classes. Both examples indicate that the “unique properties acquired from cultural innovation” ascribed to the dns are dependent upon the functional biases and the general organizational principles of the human brain (Menary, 2015, p. 11). The overall morphological properties and the functional capacities of the body are also important for the transition from number approximation to calculation and other genuinely mathematical operations. Phylogenetically, the properties of movements of the eyes, hands, and arms, and the dexterity of the fingers significantly contribute to the ontogenetic possibility to develop embodied patterns to interact with numerals, calculations, and increasingly complex equations.

The acquisition of symbol-based mathematical practices is associated with the temporally extended process of scaffolded learning. Counting and the knowledge of number words are continuous with symbol-based mathematical capacities (Merkley & Ansari, 2016). The ontogenetic emergence of symbol-based mathematical competence is characterized by the acquisition of knowledge about the correspondence relations of “all three representations of number: words, numerals, and non-symbolic arrays” (Merkley & Ansari, 2016, p. 16). The continuous availability of these correspondence relations to the novice is the result of explicit instruction by teachers and other caregivers. According to Merkley and Ansari’s (2016) account of the acquisition of symbol-based mathematical practices, the comprehension and application of knowledge about these correspondence relations stand in a two-way relation to informal mathematics knowledge (e.g., symbol-based counting) and formal mathematics knowledge (e.g., symbol-based calculation).

For the Indian-Arabic numeral system, it seems reasonable to suppose that the place-value principle is crucial for the successful acquisition of mathematical knowledge. The place-value principle is the cognitive norm that the magnitude of a symbolically represented number (>9) is determined not only by the value of the composite digits, but also by their spatial arrangement. It is likely that knowledge about the correspondence relations of numbers, numerals, and collections of items is temporally antecedent to the acquisition of the place-value principle, provided that the former type of knowledge is required for the meaningful interaction with multiple-digits numerals. The transition from knowledge about number-numeral-collection correspondence to the understanding and application of the place-value principle is put centre stage in the course of scaffolded learning. This transition is structured by the zone of proximal development.

In sum, the ontogenetic development of the capacity to perform symbol-based mathematical practices is a matter of enculturation. It relies on the scaffolded and temporally extended acquisition of skills for number approximation, counting, number-numeral-collection correspondences, and discrete mathematical operations realized by ldp and ldba. The Indian-Arabic numeral system is an innovative product with a long history of cumulative cultural evolution that spans approximately 5000 years (Donald, 1991; Everett, 2017). The continuous existence and recruitment of these symbols has been rendered possible by enculturation. In virtue of enculturation, this innovative product is transmitted from one generation to the next and thus augments and transforms the cognitive potential of human organisms in the domain of calculation.

6 Enculturation and Opportunity Provision for New Innovative Processes

As already mentioned in Section 2, recombination and refinement are powerful procedures that characterize innovative processes (Laland, 2017; Muthukrishna & Henrich, 2016). Given this, it seems reasonable to assume that enculturation is an important condition for the generation of new innovative products. The reason is that it builds upon already existing innovative products by recombining or refining their properties and the practices associated with their manipulation. This suggests that complex, socio-culturally structured groups and societies provide the background conditions for subsequent innovative processes, because they enable human organisms, in virtue of cumulative cultural evolution and enculturation, to have “access to a wider array of information, including physical, cognitive, and linguistic tools, which may be recombined in new ways, generating new innovations” (Muthukrishna & Henrich, 2016, p. 10). In this sense, the ontogenetic process of enculturation is directly relevant for cognitive innovation. This is because enculturation in the cognitive niche provides a plethora of opportunities for innovative processes (Tebbich et al., 2016).

This gives rise to the idea that expertise in a certain cognitive practice can causally contribute to the generation of innovative products. Expertise is the result of enculturation and is extended in two senses as suggested by Menary and Kirchhoff (2014). First, expertise extends the overall probability space of innovations and other cognitive processes. Second, expertise is also extended in the sense that it is spread across a group and is thus always a socio-culturally distributed phenomenon. I will provide an example below in support of the hypothesis that enculturated expertise contributes to innovative processes in certain domains.

We have seen in Section 2 that overcoming functional fixedness is also an important contributing factor to the manifestation of innovative processes. Functional fixedness is partly determined by cognitive norms. Usually, the norms regulating how to interact with a certain innovative product constrain the function to which it can be put. My hypothesis is that innovations require the extension and modification of already existing norms. The idea is that expertise — understood as the capacity to fluently and efficiently apply cognitive norms — enables the extension of these norms.

In what follows, I will consider the invention and refinement of the Indian-Arabic numerical system as an example of an important cognitive innovation that ultimately led to another innovation, namely the birth of digital computers as we know them today. We will see that this continuous history of materialized ideas is characterized by cumulative cultural evolution and enculturation and both horizontal and vertical collaboration.

The development of increasingly complex societies and the widespread importance of trade made it necessary to develop new ways of counting goods and keeping track of the debits and credits in transactions (Donald, 1991). The invention and refinement of numerals and calculations, first developed in Mesopotamia by the Sumerians, met this challenge (De Cruz, 2008; Everett, 2017). Dating back to approx. 3000 bc, “[…] number signs were among the first true visual symbols; they were totally arbitrary, bearing no perceptual relationship to what they represented” (Donald, 1991, p. 287). The arbitrariness of numerals and their independence of any structural isomorphism to their representanda leads to a genuinely new form of visual representation. It is possible that the initial invention and subsequent refinement of numerals is grounded in our body. It particular, it has been hypothesized that the crucial role of the numerals 5 and 10 in our contemporary forms of mathematical cognition can be explained by the number of our fingers at each hand (Everett, 2017). If this hypothesis is correct, this means that complex mathematical operations are a direct consequence of our phylogenetic propensity to use our fingers and hands as a counting device. There are three other properties of numerals that are important for understanding the cognitive role this innovative product has played throughout the history of mathematical cognition. First, as already mentioned, the place-value principle is important for the interaction with tokens of the Indian-Arabic numerical system. Second, mathematical symbols are primarily “nonphonetic structured systems for representing numbers” (De Cruz 2008, 484). This means that there is no direct correspondence of numerals and the phonemes of number words. Call this property the de-phonetization of numerals. Finally, numerals and the subsequently developed operational signs are characterized by their independence from the semantic content of the representandum (Krämer, 2015). The result of this process of de-semantification is the independence “of representation and operation” in the course of the history of calculation (Krämer, 2003, p. 531). The de-semantification of mathematical symbols is of vital importance for the subsequent development of complete mathematical notational systems, which give rise to entirely new forms of reasoning (Dutilh Novaes, 2013, 2014). For current purposes, it is important to note that the emergence of numerals, calculations, and the associated practices on the one hand and the overall cognitive potentials (and limitations) of human organisms on the other hand mutually influence and constrain each other over time. The phylogenetic history of calculation is characterized by the co-evolution of mathematical symbol systems and the cerebral and extra-cerebral bodily potential to interact with tokens of these systems (Dehaene, 2011). The development of number systems is constrained by the potentials and limitations of the human brain and the rest of the body.

Once numerals and their manipulation are in place, it becomes possible to augment their functions by inventing operators that signify the relation of these symbols (Menary, 2015). This possibility is at the core of the history of mathematics, which can be understood as a “material-social feedback loop” (Everett, 2017, p. 224). The invention and refinement of numerals and operators provides the epistemic resources to perform qualitatively and quantitatively new mathematical operations. For example, the invention of the numeral 0 was the solution to the need to determine the place value of each digit constituting multiple-digit numerals (Dutilh Novaes, 2013). Being invented in Babylonia around 300 bc, it took until the Renaissance for it to become a generally accepted mathematical, de-semantified symbol in European cultures (Seife, 2000).The normative eligibility to operate with 0 has led to remarkable scientific and technological progress (Everett, 2017). It was an important precondition for the innovation of digital computers in the 19th and 20th century (see below).

Other examples of refinements of the Indian-Arabic mathematical symbol system include the introduction of negative numbers, square roots, or variables (De Cruz & De Smedt, 2013; Menary, 2015). Taken together, these developmental steps in the history of cognitive innovation gave rise to a genuinely new type of symbol system that is indispensable for mathematical practices. They were a direct result of the enculturatedness of the innovators collaborating on the refinement of the Indian-Arabic mathematical symbol system. The resulting “numerical technologies enable certain types of reasoning that, in turn, yield new kinds of innovations” (Everett, 2017, p. 284). I will now continue to consider the vertically and horizontally collaborative cognitive innovation of digital computers as a further example of the historical trajectory of cognitive innovation.

Originally, computation was classified as a specific type of symbol-based calculation before it became the standard operation performed by digital computers (Krämer, 2003). It required expertise in symbol-based mathematical practices. With the industrialization in England and other European countries, the relation of humans and machines changed. The mathematization of technologies had led to the innovation of increasingly sophisticated machines. An important example is the innovation of the mechanical weaving machine (Isaacson, 2015; Krämer, 2015; Schröter, 2015). It became increasingly clear that machines could substitute for human workers in certain parts of the production chain (Gigerenzer, 2000). In addition to the important work by mathematicians such as Leibniz and Pascal (Krämer, 2015), this development was an important factor for the innovation, at least on paper, of the first mechanical computing machine: Charles Babbage’s and Ada Lovelace’s Analytical Engine. In her annotated translation of Menabrea’s description of the Analytical Engine (Menabrea, 1989), originally published in 1843, Lovelace added multiple considerations to the original material (Dotzler, 2015). A thorough analysis of the historical sources suggests that it is unlikely that Babbage was the only person deserving all the credit for the innovation of the Analytical Engine, as Gigerenzer (2000) and others have assumed. Rather, it was the collaborative effort of Babbage and Lovelace that led to the recombination of mathematical and technological ideas (Isaacson, 2015). I submit that Menabrea and Lovelace were in fact co-authors, meeting the criteria of having a “joint commitment” to describe the properties of the Analytical Engine and to consider its wider ramifications (Bacharach & Tollefsen, 2010).

There are at least three aspects of the Analytical Engine as described by Menabrea and Lovelace that suggest that it was an intriguing innovation that helped pave the way towards digital computers of our day. First, the considerations on the Analytical Engine are the starting point for the distinction of hardware and software, that is of “the physically real and the symbolic-virtual machine” (Krämer, 2015, p. 88; my translation). Second, the Analytical Engine is the first machine whose workings are constituted by data, addresses, and instructions (Dotzler, 2015). Finally, it was the first universal machine carrying out combinatorial operations (Isaacson, 2015; Krämer, 2015), a machine that was later refined and recombined by Turing (1936) in his seminal description of computing machines. These machines, which are now known as Turing machines, were at the core of the subsequent innovation of digital computers by Shannon, Atanasoff, and others. Again, the enculturatedness of these innovators, and of the many others not mentioned here, provided the opportunity to manipulate mathematical symbols and to draw connections to recent developments in the design and refinement of machines such as the mechanical weaving machine. This example also lends support to the idea that innovations are, in many cases at least, collaborative processes that are realized both horizontally and vertically to the point where histories of ideas become histories of collaborative, enculturated innovation.

7 Concluding Remarks

The purpose of this paper was to explore the concept of cognitive innovation and to show how cognitive innovation relates to cumulative cultural evolution and enculturation. We have seen that cognitive innovation is a complex process that spans both phylogenetic and ontogenetic time scales and both vertical and horizontal collaboration in the cognitive niche. We have also seen that there are multiple factors that contribute to innovation. Symbol-based mathematical practices and their influence on the innovation of digital computers served as an example for the more general idea that enculturation is an important process that is responsible for the transmission and generation of innovative products. In sum, both cumulative cultural evolution and enculturation are likely to play a crucial role in future research on the intricate processes that make us a remarkably innovative species.

References

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*

Many thanks to Julian Kiverstein for his constructive feedback on an earlier version of this paper.

1

I take it that neuronal recycling is a particular kind of neural reuse, where the latter is a general principle of brain organization across multiple domains. By contrast, neuronal recycling governs ontogenetic brain development associated with the acquisition of evolutionarily recent, culturally shaped cognitive processes such as reading, writing, and symbol-based mathematical practices. All cases of neuronal recycling are cases of neural reuse, but not all cases of neural reuse are cases of neuronal recycling.

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  • Amalric, M., & Dehaene, S. (2016). Origins of the brain networks for advanced mathematics in expert mathematicians. Proceedings of the National Academy of Sciences, 113(18), 49094917.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. L. (2010). Neural reuse: A fundamental organizational principle of the brain. Behavioral and Brain Sciences, 33(4), 245266. https://doi.org/http://doi.org/10.1017/S0140525X10000853.

    • Search Google Scholar
    • Export Citation
  • Anderson, M. L. (2015). After phrenology: Neural reuse and the interactive brain. Cambridge, Mass: MIT Press.

  • Anderson, M. L., & Finlay, B. L. (2014). Allocating structure to function: The strong links between neuroplasticity and natural selection. Frontiers in Human Neuroscience, 116. https://doi.org/10.3389/fnhum.2013.00918.

    • Search Google Scholar
    • Export Citation
  • Ansari, D. (2008). Effects of development and enculturation on number representation in the brain. Nature Reviews Neuroscience, 9(4), 278291.

    • Search Google Scholar
    • Export Citation
  • Ansari, D. (2012). Culture and education: New frontiers in brain plasticity. Trends in Cognitive Sciences, 16(2), 9395. https://doi.org/10.1016/j.tics.2011.11.016.

    • Search Google Scholar
    • Export Citation
  • Bacharach, S., & Tollefsen, D. (2010). We did it: From mere contributors to coauthors. The Journal of Aesthetics and Art Criticism, 68(1), 2332.

    • Search Google Scholar
    • Export Citation
  • Boyd, R., Richerson, P. J., & Henrich, J. (2011). The cultural niche: Why social learning is essential for human adaptation. Proceedings of the National Academy of Sciences, 108(Supplement 2), 1091810925.

    • Search Google Scholar
    • Export Citation
  • Burge, T. (1986). Individualism and psychology. The Philosophical Review, 95(1), 345. https://doi.org/10.2307/2185131.

  • Carr, K., Kendal, R. L., & Flynn, E. G. (2015). Imitate or innovate? Children’s innovation is influenced by the efficacy of observed behaviour. Cognition, 142, 322332.

    • Search Google Scholar
    • Export Citation
  • Carr, K., Kendal, R. L., & Flynn, E. G. (2016). Eureka!: What is innovation, how does it develop, and who does it? Child Development, 87(5), 15051519.

    • Search Google Scholar
    • Export Citation
  • Chappell, J., Cutting, N., Tecwyn, E. C., Apperly, I. A., Beck, S. R., & Thorpe, S. K. S. (2015). Minding the gap: A comparative approach to studying the development of innovation. In A. B. Kaufman & J. C. Kaufman (Eds.), Animal creativity and innovation (pp. 287316). London: Academic Press.

    • Search Google Scholar
    • Export Citation
  • Clark, A. (1997). Being there: Putting brain, body, and world together again. Cambridge, Mass.: MIT Press.

  • Clark, A. (2008). Supersizing the mind: Embodiment, action, and cognitive extension. New York: Oxford University Press.

  • De Cruz, H. (2008). An extended mind perspective on natural number representation. Philosophical Psychology, 21(4), 475490.

  • De Cruz, H., & De Smedt, J. (2013). Mathematical symbols as epistemic actions. Synthese, 190(1), 319.

  • Dehaene, S. (2005). Evolution of human cortical circuits for reading and arithmetic: The “neuronal recycling” hypothesis. In S. Dehaene, J.-R. Duhamel, M. D. Hauser, & G. Rizzolatti (Eds.), From monkey brain to human brain: A Fyssen Foundation Symposium (pp. 133157). Cambridge, Mass: MIT Press.

    • Search Google Scholar
    • Export Citation
  • Dehaene, S. (2010). Reading in the brain: The new science of how we read. New York: Penguin Books.

  • Dehaene, S. (2011). The number sense: How the mind creates mathematics (2nd ed.). Oxford: Oxford University Press.

  • Dehaene, S., & Cohen, L. (1994). Dissociable mechanisms of subitizing and counting: Neuropsychological evidence from simultanagnosic patients. Journal of Experimental Psychology: Human Perception and Performance, 20(5), 958.

    • Search Google Scholar
    • Export Citation
  • Derex, M., & Boyd, R. (2015). The foundations of the human cultural niche. Nature Communications, 6, 17.

  • Dewey, J. (1997). Experience and education. New York: Simon & Schuster.

  • Donald, M. (1991). Origins of the modern mind: Three stages in the evolution of culture and cognition. Cambridge, Mass: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Dotzler, B. J. (2015). Notes by the translator: Charles Babbage and Ada Augusta Lovelace in cooperation [Anmerkung der Übersetzerin: Charles Babbage und Ada Augusta Lovelace in Kooperation]. In S. Krämer (Ed.), Ada Lovelace: The pioneer of computer technology and her successors [Ada Lovelace: Die Pionierin der Computertechnik und ihre Nachfolgerinnen] (pp. 5367). Paderborn: Wilhelm Fink.

    • Search Google Scholar
    • Export Citation
  • Downey, G., & Lende, D. H. (2012). Neuroanthropology and the encultured brain. In D. H. Lende & G. Downey (Eds.), The encultured brain: An introduction to neuroanthropology (pp. 2365). Cambridge, Mass: MIT Press.

    • Search Google Scholar
    • Export Citation
  • Duncker, K. (1945). On problem-solving. Psychological Monographs, 58(5), 1113.

  • Dutilh Novaes, C. (2013). Mathematical reasoning and external symbolic systems. Logique & Analyse, 221, 4565.

  • Dutilh Novaes, C. (2014). Formal languages in logic: A philosophical and cognitive analysis. Cambridge: Cambridge University Press.

  • Estany, A., & Martínez, S. (2014). “Scaffolding” and “affordance” as integrative concepts in the cognitive sciences. Philosophical Psychology, 27, 98111. https://doi.org/10.1080/09515089.2013.828569.

    • Search Google Scholar
    • Export Citation
  • Everett, C. (2017). Numbers and the making of us: Counting and the course of human cultures. Cambridge, Mass: Harvard University Press.

    • Search Google Scholar
    • Export Citation
  • Fodor, J. A. (1980). Methodological solipsism considered as a research strategy in cognitive psychology. Behavioral and Brain Sciences, 3(1), 6373.

    • Search Google Scholar
    • Export Citation
  • Furuya, S., & Altenmüller, E. (2013). Flexibility of movement organization in piano performance. Frontiers in Human Neuroscience, 110. https://doi.org/10.3389/fnhum.2013.00173.

    • Search Google Scholar
    • Export Citation
  • Gigerenzer, G. (2000). Adaptive thinking: Rationality in the real world. Oxford: Oxford University Press.

  • Hannagan, T., Amedi, A., Cohen, L., Dehaene-Lambertz, G., & Dehaene, S. (2015). Origins of the specialization for letters and numbers in ventral occipitotemporal cortex. Trends in Cognitive Sciences, 19(7), 374382.

    • Search Google Scholar
    • Export Citation
  • Henrich, J. P. (2016). The secret of our success: How culture is driving human evolution, domesticating our species, and making us smarter. Princeton: Princeton University Press.

    • Search Google Scholar
    • Export Citation
  • Heyes, C. (2016). Born pupils? Natural pedagogy and cultural pedagogy. Perspectives on Psychological Science, 11(2), 280295.

  • Isaacson, W. (2015). The innovators: How a group of hackers, geniuses and geeks created the digital revolution. London, New York: Simon & Schuster.

    • Search Google Scholar
    • Export Citation
  • Kendal, J. R. (2011). Cultural niche construction and human learning environments: Investigating sociocultural perspectives. Biological Theory, 6(3), 241250.

    • Search Google Scholar
    • Export Citation
  • Krämer, S. (2003). Writing, notational iconicity, calculus: On writing as a cultural technique. MLN, 118(3), 518537.

  • Krämer, S. (2015). Why is Ada Lovelace said to be the “first programmer” and what does “programming” mean after all? [Wieso gilt Ada Lovelace als die “erste Programmiererin” und was bedeutet überhaupt “programmieren”?]. In S. Krämer (Ed.), Ada Lovelace: The pioneer of computer technology and her successors [Ada Lovelace: Die Pionierin der Computertechnik und ihre Nachfolgerinnen] (pp. 7589). Paderborn: Wilhelm Fink.

    • Search Google Scholar
    • Export Citation
  • Laland, K. N. (2017). Darwin’s unfinished symphony: How culture made the human mind. New Jersey: Princeton University Press.

  • Laland, K. N., & O’Brien, M. J. (2011). Cultural niche construction: An introduction. Biological Theory, 6(3), 191202.

  • Lane, D. A. (2016). Innovation cascades: Artefacts, organization and attributions. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 371: 20150, 19.

    • Search Google Scholar
    • Export Citation
  • Legare, C. H., & Nielsen, M. (2015). Imitation and innovation: The dual engines of cultural learning. Trends in Cognitive Sciences, 19(11), 688699.

    • Search Google Scholar
    • Export Citation
  • Lyons, I. M., Ansari, D., & Beilock, S. L. (2015). Qualitatively different coding of symbolic and nonsymbolic numbers in the human brain. Human Brain Mapping, 36(2), 475488.

    • Search Google Scholar
    • Export Citation
  • MacKinnon, K. C., & Fuentes, A. (2012). Primate social cognition, human evolution, and niche construction: A core context for neuroanthropology. In D. H. Lende & G. Downey (Eds.), The encultured brain: An introduction to neuroanthropology (pp. 67102). Cambridge, Mass: MIT Press.

    • Search Google Scholar
    • Export Citation
  • Menabrea, L. F. (1989). Sketch of the analytical engine invented by Charles Babbage: With notes upon the memoir by the translator, Ada Augusta, Countess of Lovelace. In P. Morrison & E. Morrison (Eds.), Charles Babbage: On the principles and development of the calculator and other seminal writings (pp. 225295). Mineola: Dover Publications.

    • Search Google Scholar
    • Export Citation
  • Menary, R. (2007). Cognitive integration: Mind and cognition unbounded. Basingstoke, New York: Palgrave Macmillan.

  • Menary, R. (2010). Dimensions of mind. Phenomenology and the Cognitive Sciences, 9(4), 561578. https://doi.org/10.1007/S11097-010-9186-7.

    • Search Google Scholar
    • Export Citation
  • Menary, R. (2013). The enculturated hand. In Z. Radman (Ed.), The hand, an organ of the mind: What the manual tells the mental (pp. 349367). Cambridge, Mass: MIT Press.

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