We present a new road map for research on “How the Brain Got Language” that adopts an EvoDevoSocio perspective and highlights comparative neuroprimatology – the comparative study of brain, behavior and communication in extant monkeys and great apes – as providing a key grounding for hypotheses on the last common ancestor of humans and monkeys and chimpanzees and the processes which guided the evolution LCA-m → LCA-c → protohumans → H. sapiens. Such research constrains and is constrained by analysis of (...) the subsequent, primarily cultural, evolution of H. sapiens which yielded cultures involving the rich use of language. (shrink)

In this paper, I argue that an important component of the language-ready brain is the ability to recognize and conceptualize events. By ‘event’, I mean any situation or activity in the world or our mental life, that we find salient enough to individuate as a thought or word. While this may sound either trivial or non-unique to humans, I hope to show that abstracting away events and their participants from the embodied flow of experience is a characteristic unique to humans. (...) This ability is enabled, I will argue, by two critical competencies that act as scaffolds for language-ready thought in the prehuman brain. The first, as argued by Arbib (2006,2012,2016) and others, is a sophisticated system of gesture production and understanding in prehumans, which provided a template for schema-like sequencing and slot-filling of information units. The second involves the integration of multiple modalities of expression in the communicative act, in particular, the alignment of co-gestural speech and co-speech gesture. With such computational facilities, action-based gestures can be abstracted away from their associated objects and become full event representations. This view supports the MSH argument for the emergence of more complex linguistic expressions from initially holophrastic units. In particular, actions can be thought of as protoverbs, which through this process are abstracted to full event descriptions, i.e., verbs. (shrink)

This reader collects and introduces important work in linguistics, computer science, artificial intelligence, and computational linguistics on the use of linguistic devices in natural languages to situate events in time: whether they are past, present, or future; whether they are real or hypothetical; when an event might have occurred, and how long it could have lasted. In focussing on the treatment and retrieval of time-based information it seeks to lay the foundation for temporally-aware natural language computer processing systems, for example (...) those that process documents on the worldwide web to answer questions or produce summaries. The development of such systems requires the application of technical knowledge from many different disciplines. The book is the first to bring these disciplines together, by means of classic and contemporary papers in four areas: tense, aspect, and event structure; temporal reasoning; the temporal structure of natural language discourse; and temporal annotation. Clear, self-contained editorial introductions to each area provide the necessary technical background for the non-specialist, explaining the underlying connections across disciplines.A wide range of students and professionals in academia and industry will value this book as an introduction and guide to a new and vital technology. The former include researchers, students, and teachers of natural language processing, linguistics, artificial intelligence, computational linguistics, computer science, information retrieval, library sciences, human-computer interaction, and cognitive science. Those in industry include corporate managers and researchers, software product developers, and engineers in information-intensive companies, such as on-line database and web-service providers. (shrink)

Cognitive science has evolved since early disputes between radical empiricism and radical nativism. The authors are reacting to the revival of radical empiricism spurred by recent successes in deep neural network (NN) models. We agree that language-like mental representations (language-of-thoughts [LoTs]) are part of the best game in town, but they cannot be understood independent of the other players.

We present a new road map for research on “How the Brain Got Language” that adopts an EvoDevoSocio perspective and highlights comparative neuroprimatology – the comparative study of brain, behavior and communication in extant monkeys and great apes – as providing a key grounding for hypotheses on the last common ancestor of humans and monkeys and chimpanzees and the processes which guided the evolution LCA-m → LCA-c → protohumans → H. sapiens. Such research constrains and is constrained by analysis of (...) the subsequent, primarily cultural, evolution of H. sapiens which yielded cultures involving the rich use of language. (shrink)