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Marr’s levels and the minimalist program

Overview of attention for article published in Psychonomic Bulletin & Review, July 2016
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Title
Marr’s levels and the minimalist program
Published in
Psychonomic Bulletin & Review, July 2016
DOI 10.3758/s13423-016-1062-1
Pubmed ID
Authors

Mark Johnson

Abstract

A simple change to a cognitive system at Marr's computational level may entail complex changes at the other levels of description of the system. The implementational level complexity of a change, rather than its computational level complexity, may be more closely related to the plausibility of a discrete evolutionary event causing that change. Thus the formal complexity of a change at the computational level may not be a good guide to the plausibility of an evolutionary event introducing that change. For example, while the Minimalist Program's Merge is a simple formal operation (Berwick & Chomsky, 2016), the computational mechanisms required to implement the language it generates (e.g., to parse the language) may be considerably more complex. This has implications for the theory of grammar: theories of grammar which involve several kinds of syntactic operations may be no less evolutionarily plausible than a theory of grammar that involves only one. A deeper understanding of human language at the algorithmic and implementational levels could strengthen Minimalist Program's account of the evolution of language.

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The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Germany 1 2%
Unknown 45 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 23%
Student > Master 6 13%
Researcher 6 13%
Student > Bachelor 5 10%
Professor 5 10%
Other 8 17%
Unknown 7 15%
Readers by discipline Count As %
Linguistics 13 27%
Psychology 7 15%
Computer Science 4 8%
Agricultural and Biological Sciences 3 6%
Neuroscience 3 6%
Other 8 17%
Unknown 10 21%