Literature DB >> 23964758

Visual Object Representation: Interpreting Neurophysiological Data within a Computational Framework.

D C Plaut1, M J Farah.   

Abstract

Significant progress has been made in understanding vision by combining computational and neuroscientific constraints. However, for the most part these integrative approaches have been limited to low-level visual processing. Recent advances in our understanding of high-level vision in the two separate disciplines warrant an attempt to relate and integrate these results to extend our understanding of vision through object representation and recognition. This paper is an attempt to contribute to this goal, by using a computational framework arising out of computer vision research to organize and interpret human and primate neurophysiology and neuropsychology.

Entities:  

Year:  1990        PMID: 23964758     DOI: 10.1162/jocn.1990.2.4.320

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  5 in total

1.  Size and reflection effects in priming: a test of transfer-appropriate processing.

Authors:  K Srinivas
Journal:  Mem Cognit       Date:  1996-07

2.  Bridging Levels of Understanding in Schizophrenia Through Computational Modeling.

Authors:  Alan Anticevic; John D Murray; Deanna M Barch
Journal:  Clin Psychol Sci       Date:  2015-05

3.  Nonverbal priming in amnesia.

Authors:  G Musen; L R Squire
Journal:  Mem Cognit       Date:  1992-07

4.  Use of superordinate labels yields more robust and human-like visual representations in convolutional neural networks.

Authors:  Seoyoung Ahn; Gregory J Zelinsky; Gary Lupyan
Journal:  J Vis       Date:  2021-12-01       Impact factor: 2.240

5.  Object responses are highly malleable, rather than invariant, with changes in object appearance.

Authors:  Desiree E Holler; Sara Fabbri; Jacqueline C Snow
Journal:  Sci Rep       Date:  2020-03-13       Impact factor: 4.379

  5 in total

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