Since the emergence of our species at least, natural selection based on genetic variation has been replaced by culture as the major driving force in human evolution. It has made us what we are today, by ratcheting up cultural innovations, promoting new cognitive skills, rewiring brain networks, and even shifting gene distributions. Adopting an evolutionary perspective can therefore be highly informative for cognitive science in several ways: It encourages us to ask grand questions about the origins and ramifications of our (...) cognitive abilities; it equips us with the means to investigate, explain, and understand key dimensions of cognition; and it allows us to recognize the continued and ubiquitous workings of culture and evolution in everyday instances of cognitive behavior. Taking advantage of this reorientation presupposes a shift in focus, though, from human cognition as a general, homogenous phenomenon to the appreciation of cultural diversity in cognition as an invaluable source of data. (shrink)

In his profound discussion, Sterelny draws out common themes in the contributions to this topic: selective drivers in the coevolution of cognition and culture, the role of language in it, characteristics of cumulative cultural evolution, and issues of testability. He highlights the growing body of evidence for positive feedback mechanisms in cultural evolution, but also notes that progress is piecemeal, calling for more cross‐border work between cognitive science and research on cultural evolution.

On the basis of recent advancements in both neuroscience and archaeology, we propose a plausible biocultural mechanism at the basis of cultural evolution. The proposed mechanism, which relies on the notions of cultural exaptation and cultural neural reuse, may account for the asynchronous, discontinuous, and patchy emergence of innovations around the globe. Cultural exaptation refers to the reuse of previously devised cultural features for new purposes. Cultural neural reuse refers to cases in which exposure to cultural practices induces the formation, (...) activation, and stabilization of new functional and/or structural brain networks during the individual lifespan. The invention of writing is interpreted as a case of cultural exaptation of previous devices to record information, in use since at least the Early Later Stone Age and the beginning of the Upper Paleolithic. The measurable changes in brain structure and functioning caused by learning to read are proposed as an exemplar case of cultural neural reuse. It is argued that repeated cycles of cultural exaptation, development of appropriate strategies of cultural transmission, and ensuing cultural neural reuse represent the fundamental mechanism that has regulated the cultural evolution of our lineage. A general predictive model of when and under which circumstances the proposed mechanism should be expected to occur is proposed, and the relationship of our mechanism with gene-culture coevolutionary models is discussed. (shrink)

How linguistic structures evolve so as to become easier to process is addressed by Isbilen and Christiansen for the Now‐or‐Never bottleneck. The authors suggest that this fundamental challenge in language processing is coped with by rapid compression of the transient linguistic input into chunks then to be passed on. As linguistic structures that can be chunked more easily tend to stabilize and proliferate, language evolves to fit learners’ cognitive capabilities.

In this paper I argue, first, that human lifeways depend on cognitive capital that has typically been built over many generations. This process of gradual accumulation produces an adaptive fit between human agents and their environments; an adaptive fit that is the result of hidden‐hand, evolutionary mechanisms. To explain distinctive features of human life, we need to understand how cultures evolve. Second, I distinguish a range of different evolutionary models of culture. Third, I argue that none of meme‐based models, dual (...) inheritance models, nor Boyd and Richerson's models fully succeed in explaining this adaptive fit between agent and the world. I then briefly develop an alternative. Finally, I explore (in a preliminary way) constraints on cultural adaptation. The processes of cultural evolution sometimes built a fit between agents and their environment, but they do not always do so. Why is folk medicine, for example, so much less reliable than folk natural history? (shrink)

Memory is fleeting. New material rapidly obliterates previous material. How, then, can the brain deal successfully with the continual deluge of linguistic input? We argue that, to deal with this “Now-or-Never” bottleneck, the brain must compress and recode linguistic input as rapidly as possible. This observation has strong implications for the nature of language processing: (1) the language system must “eagerly” recode and compress linguistic input; (2) as the bottleneck recurs at each new representational level, the language system must build (...) a multilevel linguistic representation; and (3) the language system must deploy all available information predictively to ensure that local linguistic ambiguities are dealt with “Right-First-Time”; once the original input is lost, there is no way for the language system to recover. This is “Chunk-and-Pass” processing. Similarly, language learning must also occur in the here and now, which implies that language acquisition is learning to process, rather than inducing, a grammar. Moreover, this perspective provides a cognitive foundation for grammaticalization and other aspects of language change. Chunk-and-Pass processing also helps explain a variety of core properties of language, including its multilevel representational structure and duality of patterning. This approach promises to create a direct relationship between psycholinguistics and linguistic theory. More generally, we outline a framework within which to integrate often disconnected inquiries into language processing, language acquisition, and language change and evolution. (shrink)

We propose that the crucial difference between human cognition and that of other species is the ability to participate with others in collaborative activities with shared goals and intentions: shared intentionality. Participation in such activities requires not only especially powerful forms of intention reading and cultural learning, but also a unique motivation to share psychological states with others and unique forms of cognitive representation for doing so. The result of participating in these activities is species-unique forms of cultural cognition and (...) evolution, enabling everything from the creation and use of linguistic symbols to the construction of social norms and individual beliefs to the establishment of social institutions. In support of this proposal we argue and present evidence that great apes understand the basics of intentional action, but they still do not participate in activities involving joint intentions and attention. Human children's skills of shared intentionality develop gradually during the first 14 months of life as two ontogenetic pathways intertwine: the general ape line of understanding others as animate, goal-directed, and intentional agents; and a species-unique motivation to share emotions, experience, and activities with other persons. The developmental outcome is children's ability to construct dialogic cognitive representations, which enable them to participate in earnest in the collectivity that is human cognition. Key Words: collaboration; cooperation; cultural learning; culture; evolutionary psychology; intentions; shared intentionality; social cognition; social learning; theory of mind; joint attention. (shrink)

It is widely assumed that human learning and the structure of human languages are intimately related. This relationship is frequently suggested to derive from a language-specific biological endowment, which encodes universal, but communicatively arbitrary, principles of language structure (a Universal Grammar or UG). How might such a UG have evolved? We argue that UG could not have arisen either by biological adaptation or non-adaptationist genetic processes, resulting in a logical problem of language evolution. Specifically, as the processes of language change (...) are much more rapid than processes of genetic change, language constitutes a both over time and across different human populations, and, hence, cannot provide a stable environment to which language genes could have adapted. We conclude that a biologically determined UG is not evolutionarily viable. Instead, the original motivation for UG arises because language has been shaped to fit the human brain, rather than vice versa. Following Darwin, we view language itself as a complex and interdependent which evolves under selectional pressures from human learning and processing mechanisms. That is, languages themselves are shaped by severe selectional pressure from each generation of language users and learners. This suggests that apparently arbitrary aspects of linguistic structure may result from general learning and processing biases deriving from the structure of thought processes, perceptuo-motor factors, cognitive limitations, and pragmatics. (shrink)

We present a theoretical framework bearing on the evolution of written communication. We analyze writing as a special kind of graphic code. Like languages, graphic codes consist of stable, conventional mappings between symbols and meanings, but (unlike spoken or signed languages) their symbols consist of enduring images. This gives them the unique capacity to transmit information in one go across time and space. Yet this capacity usually remains quite unexploited, because most graphic codes are insufficiently informative. They may only be (...) used for mnemonic purposes or as props for oral communication in real-time encounters. Writing systems, unlike other graphic codes, work by encoding a natural language. This allows them to support asynchronous communication in a more powerful and versatile way than any other graphic code. Yet, writing systems will not automatically unlock the capacity to communicate asynchronously. We argue that this capacity is a rarity in non-literate societies, and not so frequent even in literate ones. Asynchronous communication is intrinsically inefficient because asynchrony constrains the amount of information that the interlocutors share and limits possibilities for repair. This would explain why synchronous, face-to-face communication always fosters the development of sophisticated codes (natural languages), but similar codes for asynchronous communication evolve with more difficulties. It also implies that writing cannot have evolved, at first, for supporting asynchronous communication. (shrink)

Culture drives cognitive evolution by supporting the transmission and intergenerational accumulation of skills and knowledge, based on social learning and teaching: Later generations benefit from what their predecessors acquired. Taking a metaperspective on those experimental studies that explore the mechanisms underlying cultural transmission, Caldwell discusses their potential for generating valuable insights, their possible limitations, and their generalizability to other species.

An overview on archaeological evidence, provided by Colagè and d’Errico, reveals that the timing, location, and pace of cultural innovations are more consistent with scenarios that take culture, rather than genetic evolutionary processes, as the key driving force for human cognition. The authors elaborate on those mechanisms by which cultural evolution operates, with a specific focus on cultural exaptation and cultural neural reuse.

Smith gives an excellent overview on research in language evolution, in which he discusses several recent models of how linguistic systems and the cognitive capacities involved in language learning may have co‐evolved. He illustrates how combined pressures on language learning and communication/use produce compositionally structured languages. Once in place, a (culturally transmitted) communication system creates new selection pressures on the capacity for acquiring these systems.

Kinship terminologies are basic cognitive semantic systems that all human societies use for organizing kin relations. Diversity in kinship systems and their categories is substantial, but constrained. Rácz, Passmore, and Jordan explore hypotheses about such constraints from learning theories and social pressures, testing the impact of a community‐size driven learning bottleneck against the social coordination demands of different kinds of marriage and resource systems.