Literature DB >> 12199206

Computational analyses in cognitive neuroscience: in defense of biological implausibility.

I E Dror1, D P Gallogly.   

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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Year:  1999        PMID: 12199206     DOI: 10.3758/bf03212325

Source DB:  PubMed          Journal:  Psychon Bull Rev        ISSN: 1069-9384


  20 in total

1.  Decision making under time pressure: an independent test of sequential sampling models.

Authors:  I E Dror; J R Busemeyer; B Basola
Journal:  Mem Cognit       Date:  1999-07

2.  TARGET DISCRIMINATION BY THE ECHOLOCATION OF BATS.

Authors:  D R GRIFFIN; J H FRIEND; F A WEBSTER
Journal:  J Exp Zool       Date:  1965-03

3.  A model of the learning of arm trajectories from spatial deviations.

Authors:  M I Jordan; T Flash; Y Arnon
Journal:  J Cogn Neurosci       Date:  1994       Impact factor: 3.225

4.  Why are "What" and "Where" Processed by Separate Cortical Visual Systems? A Computational Investigation.

Authors:  J G Rueckl; K R Cave; S M Kosslyn
Journal:  J Cogn Neurosci       Date:  1989       Impact factor: 3.225

5.  Using artificial bat sonar neural networks for complex pattern recognition: recognizing faces and the speed of a moving target.

Authors:  I E Dror; F L Florer; D Rios; M Zagaeski
Journal:  Biol Cybern       Date:  1996-04       Impact factor: 2.086

6.  Feature analysis in early vision: evidence from search asymmetries.

Authors:  A Treisman; S Gormican
Journal:  Psychol Rev       Date:  1988-01       Impact factor: 8.934

7.  Network model of shape-from-shading: neural function arises from both receptive and projective fields.

Authors:  S R Lehky; T J Sejnowski
Journal:  Nature       Date:  1988-06-02       Impact factor: 49.962

8.  Target structure and echo spectral discrimination by echolocating bats.

Authors:  J A Simmons; W A Lavender; B A Lavender; C A Doroshow; S W Kiefer; R Livingston; A C Scallet; D E Crowley
Journal:  Science       Date:  1974-12-20       Impact factor: 47.728

9.  Modeling the neural substrates of associative learning and memory: a computational approach.

Authors:  M A Gluck; R F Thompson
Journal:  Psychol Rev       Date:  1987-04       Impact factor: 8.934

10.  The recent excitement about neural networks.

Authors:  F Crick
Journal:  Nature       Date:  1989-01-12       Impact factor: 49.962

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  3 in total

1.  Decision making under time pressure: an independent test of sequential sampling models.

Authors:  I E Dror; J R Busemeyer; B Basola
Journal:  Mem Cognit       Date:  1999-07

2.  The role of meaning and familiarity in mental transformations.

Authors:  W Smith; I E Dror
Journal:  Psychon Bull Rev       Date:  2001-12

3.  Neural networks for perceptual processing: from simulation tools to theories.

Authors:  Kevin Gurney
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2007-03-29       Impact factor: 6.237

  3 in total

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