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.
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 (
Cultural learning, realized by
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,
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 (
The ontogenetic realization of the genetically inherited
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
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
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
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.
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Many thanks to Julian Kiverstein for his constructive feedback on an earlier version of this paper.
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.