Language Evolution and Computation Bibliography

Neurocognitive and neurolinguistics theories make explicit statements relating specialized cognitive and linguistic processes to specific brain loci. These linking hypotheses are in need of neurobiological justification and explanation. Recent mathematical models of human language mechanisms constrained by fundamental neuroscience principles and established knowledge about comparative neuroanatomy offer explanations for where, when and how language is processed in the human brain. In these models, network structure and connectivity along with action- and perception-induced correlation of neuronal activity co-determine neurocognitive mechanisms. Language learning leads to the formation of action perception circuits (APCs) with specific distributions across cortical areas. Cognitive and linguistic processes such as speech production, comprehension, verbal working memory and prediction are modelled by activity dynamics in these APCs, and combinatorial and communicative-interactive knowledge is organized in the dynamics within, and connections between APCs. The network models and, in particular, the concept of distributionally-specific circuits, can account for some previously not well understood facts about the cortical 'hubs' for semantic processing and the motor system's role in language understanding and speech sound recognition. A review of experimental data evaluates predictions of the APC model and alternative theories, also providing detailed discussion of some seemingly contradictory findings. Throughout, recent disputes about the role of mirror neurons and grounded cognition in language and communication are assessed critically.

In neural network research on language, the existence of discrete combinatorial rule representations is commonly denied. Combinatorial capacity of networks and brains is rather attributed to probability mapping and pattern overlay. Here, we demonstrate that networks incorporating relevant features of neuroanatomical connectivity and neuronal function give rise to discrete neuronal circuits that store combinatorial information and exhibit a function similar to elementary rules of grammar. Key properties of these networks are rich auto- and hetero-associative connectivity, availability of sequence detectors similar to those found in a range of animals, and unsupervised Hebbian learning. Input of specific word sequences establishes sequence detectors in the network, and substitutions of words and larger string segments from one syntactic category, occurring in the context of elements of a second syntactic class, lead to binding between them into neuronal assemblies. Critically, these newly formed aggregates of sequence detectors now respond in a discrete generalizing fashion when members of specific substitution classes of string elements are combined with each other. The discrete combinatorial neuronal assemblies (DCNAs) even respond in the same way to learned strings and to word sequences that never appeared in the input but conform to a rule. We also show how combinatorial information interacts with information about functional and anatomical properties of the brain in the emergence of discrete neuronal circuits that may implement rules and discuss the model in the wider context of brain mechanism for syntax and grammar. Implications for the evolution of human language are discussed in closing.