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Computational analyses in cognitive neuroscience: nn defense of biological implausibility

Computational analyses in cognitive neuroscience: nn defense of biological implausibility
Computational analyses in cognitive neuroscience: nn defense of biological implausibility
Because cognitive neuroscience researchers attempt to understand the human mind by bridging behavior and brain, they expect computational analyses to be biologically plausible. In this paper, biologically implausible computational analyses are shown to have critical and essential roles in the various stages and domains of cognitive neuroscience research. Specifically, biologically implausible computational analyses can contribute to (1) understanding and characterizing the problem that is being studied, (2) examining the availability of information and its representation, and (3) evaluating and understanding the neuronal solution. In the context of the distinct types of contributions made by certain computational analyses, the biological plausibility of those analyses is altogether irrelevant. These biologically implausible models are nevertheless relevant and important for biologically driven research.
173-182
Dror, Itiel E.
4d907da2-0a2e-41ed-b927-770a70a35c71
Gallogly, Donald P.
4aa01c4d-5b77-48eb-8a9d-fa768f0bb6a7
Dror, Itiel E.
4d907da2-0a2e-41ed-b927-770a70a35c71
Gallogly, Donald P.
4aa01c4d-5b77-48eb-8a9d-fa768f0bb6a7

Dror, Itiel E. and Gallogly, Donald P. (1999) Computational analyses in cognitive neuroscience: nn defense of biological implausibility. Psychonomic Bulletin & Review, 6 (2), 173-182.

Record type: Article

Abstract

Because cognitive neuroscience researchers attempt to understand the human mind by bridging behavior and brain, they expect computational analyses to be biologically plausible. In this paper, biologically implausible computational analyses are shown to have critical and essential roles in the various stages and domains of cognitive neuroscience research. Specifically, biologically implausible computational analyses can contribute to (1) understanding and characterizing the problem that is being studied, (2) examining the availability of information and its representation, and (3) evaluating and understanding the neuronal solution. In the context of the distinct types of contributions made by certain computational analyses, the biological plausibility of those analyses is altogether irrelevant. These biologically implausible models are nevertheless relevant and important for biologically driven research.

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Published date: 1999

Identifiers

Local EPrints ID: 18353
URI: http://eprints.soton.ac.uk/id/eprint/18353
PURE UUID: 9bfe1648-7b6a-4587-a376-690e404fa592

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Date deposited: 10 Jan 2006
Last modified: 22 Jul 2020 16:36

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