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Psychology - Cognitive Psychology | Psychonomic Bulletin and Review

Psychonomic Bulletin & Review

Psychonomic Bulletin & Review

Editor-in-Chief: Gregory Hickok

ISSN: 1069-9384 (print version)
ISSN: 1531-5320 (electronic version)

Journal no. 13423

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Empirical Approaches to the Study of Language Evolution

Guest Editor: W. Tecumseh Fitch, Department of Cognitive Biology, University of Vienna

The articles in this special issue, guest-edited by W. Tecumseh Fitch, Department of Cognitive Biology, University of Vienna, provide a concise overview of current models of language evolution, emphasizing the testable predictions that they make, along with overviews of the many sources of data available to test them (emphasizing comparative, neural, and genetic data). The evolution of language has been termed "the most challenging scientific problem of our time", and from a sociological point of view, the inter-disciplinary collaboration that it requires is indeed a challenge. But it is also one of the most interesting problems in modern science, and concerns an issue absolutely central to understanding our own species: how we rose from being an ecologically peripheral African ape, a few million years ago, to the most important (and dangerous) species alive on our planet today. The articles in this special issue suggest that a deep understanding of this ancient problem may be attainable in the next few decades.

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    The journal provides coverage spanning a broad spectrum of topics in all areas of experimental psychology. The journal is primarily dedicated to the publication of theory and review articles and brief reports of outstanding experimental work. Areas of coverage include cognitive psychology broadly construed, including but not limited to action, perception, & attention, language, learning & memory, reasoning & decision making, and social cognition. We welcome submissions that approach these issues from a variety of perspectives such as behavioral measurements, comparative psychology, development, evolutionary psychology, genetics, neuroscience, and quantitative/computational modeling. We particularly encourage integrative research that crosses traditional content and methodological boundaries.

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