Literature DB >> 27470193

The simplicity principle in perception and cognition.

Jacob Feldman1.   

Abstract

The simplicity principle, traditionally referred to as Occam's razor, is the idea that simpler explanations of observations should be preferred to more complex ones. In recent decades the principle has been clarified via the incorporation of modern notions of computation and probability, allowing a more precise understanding of how exactly complexity minimization facilitates inference. The simplicity principle has found many applications in modern cognitive science, in contexts as diverse as perception, categorization, reasoning, and neuroscience. In all these areas, the common idea is that the mind seeks the simplest available interpretation of observations- or, more precisely, that it balances a bias toward simplicity with a somewhat opposed constraint to choose models consistent with perceptual or cognitive observations. This brief tutorial surveys some of the uses of the simplicity principle across cognitive science, emphasizing how complexity minimization in a number of forms has been incorporated into probabilistic models of inference. WIREs Cogn Sci 2016, 7:330-340. doi: 10.1002/wcs.1406 For further resources related to this article, please visit the WIREs website.
© 2016 Wiley Periodicals, Inc.

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Year:  2016        PMID: 27470193      PMCID: PMC5125387          DOI: 10.1002/wcs.1406

Source DB:  PubMed          Journal:  Wiley Interdiscip Rev Cogn Sci        ISSN: 1939-5078


  46 in total

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6.  Occam's rattle: children's use of simplicity and probability to constrain inference.

Authors:  Elizabeth Baraff Bonawitz; Tania Lombrozo
Journal:  Dev Psychol       Date:  2011-12-26

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Journal:  Vision Res       Date:  2010-01       Impact factor: 1.886

8.  Algorithmic complexity for psychology: a user-friendly implementation of the coding theorem method.

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Journal:  Behav Res Methods       Date:  2016-03

9.  Relations between the statistics of natural images and the response properties of cortical cells.

Authors:  D J Field
Journal:  J Opt Soc Am A       Date:  1987-12       Impact factor: 2.129

10.  Rule-plus-exception model of classification learning.

Authors:  R M Nosofsky; T J Palmeri; S C McKinley
Journal:  Psychol Rev       Date:  1994-01       Impact factor: 8.934

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