Abstract
Memory is essential to many cognitive tasks including language. Apart from empirical studies of memory effects on language acquisition and use, there lack sufficient evolutionary explorations on whether a high level of memory capacity is prerequisite for language and whether language origin could influence memory capacity. In line with evolutionary theories that natural selection refined language-related cognitive abilities, we advocated a coevolution scenario between language and memory capacity, which incorporated the genetic transmission of individual memory capacity, cultural transmission of idiolects, and natural and cultural selections on individual reproduction and language teaching. To illustrate the coevolution dynamics, we adopted a multi-agent computational model simulating the emergence of lexical items and simple syntax through iterated communications. Simulations showed that: along with the origin of a communal language, an initially-low memory capacity for acquired linguistic knowledge was boosted; and such coherent increase in linguistic understandability and memory capacities reflected a language-memory coevolution; and such coevolution stopped till memory capacities became sufficient for language communications. Statistical analyses revealed that the coevolution was realized mainly by natural selection based on individual communicative success in cultural transmissions. This work elaborated the biology-culture parallelism of language evolution, demonstrated the driving force of culturally-constituted factors for natural selection of individual cognitive abilities, and suggested that the degree difference in language-related cognitive abilities between humans and nonhuman animals could result from a coevolution with language.
Highlights
Perspectives and approaches on language evolution and human cognitionOrigins and evolution of human language have recently obtained a wide scope of academic interests [1,2,3,4]; in particular, origins and evolution of language faculty have attracted linguists andPLOS ONE | DOI:10.1371/journal.pone.0142281 November 6, 2015Modeling Language-Memory Coevolution scholars from other relevant disciplines [6,7,8,9,10]
Others advocate that along with language evolution, communicative success in the human cultural niche serves as the driving force for selecting languagerelated cognitive abilities in humans [6,17,21,23,28]; that is to say, the domain-general abilities for language might not have been fully-fledged in humans at the time of language origin, and the distinctive degree differences in those abilities between humans and other species could result from subsequent selections on those abilities along with language evolution
One may wonder why the threshold long-term memory (LTM) capacity is around 30. Answers to this question lie in the simulated semantic structures and correlations between semantic constituents across expressions, which are beyond the scope of the current study. These simulations confirm the correlation between LTM capacities and the understandability of the emergent language
Summary
Origins and evolution of human language have recently obtained a wide scope of academic interests [1,2,3,4]; in particular, origins and evolution of language faculty (the set of competencies for grasping and using any natural language [5]) have attracted linguists and. The speaker first selects randomly an integrated meaning from the semantic space It chooses some of its lexical, syntactic, and category rules in its LTM to form one or several candidate sets for production, each offering an utterance to encode the chosen meaning. With more compositional rules being shared by agents, a communal language comprising a set of common lexical rules and consistent word orders originates in the population The framework involves both biological transmissions (offspring copy parents’ LTM capacities with occasional mutation) and cultural transmissions (adults talk to each other or to offspring) (see Fig 3). For a particular agent i, its CS is measured as the mean percentage of meanings that agent i can accurately understand (based on acquired linguistic knowledge) when others talk to agent i:
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