Literature DB >> 26338324

Comparison of Object Recognition Behavior in Human and Monkey.

Rishi Rajalingham1, Kailyn Schmidt2, James J DiCarlo3.   

Abstract

Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize "pooled human" object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT: To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to further the goal of the field of translating knowledge gained from animal models to humans. To the best of our knowledge, this study is the first systematic attempt at comparing a high-level visual behavior of humans and macaque monkeys.
Copyright © 2015 the authors 0270-6474/15/3512127-10$15.00/0.

Entities:  

Keywords:  human; monkey; object recognition; vision

Mesh:

Year:  2015        PMID: 26338324      PMCID: PMC4556783          DOI: 10.1523/JNEUROSCI.0573-15.2015

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  32 in total

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2.  Robust object recognition with cortex-like mechanisms.

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3.  A rodent model for the study of invariant visual object recognition.

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5.  The inversion effect reveals species differences in face processing.

Authors:  Lisa A Parr
Journal:  Acta Psychol (Amst)       Date:  2011-07-23

6.  How does the brain solve visual object recognition?

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Review 7.  Comparative mapping of higher visual areas in monkeys and humans.

Authors:  Guy A Orban; David Van Essen; Wim Vanduffel
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8.  Matching categorical object representations in inferior temporal cortex of man and monkey.

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Review 9.  How good is the macaque monkey model of the human brain?

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Journal:  Curr Opin Neurobiol       Date:  2009-03-02       Impact factor: 6.627

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Authors:  Nicolas Pinto; David D Cox; James J DiCarlo
Journal:  PLoS Comput Biol       Date:  2008-01       Impact factor: 4.475

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  28 in total

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2.  Occipital White Matter Tracts in Human and Macaque.

Authors:  Hiromasa Takemura; Franco Pestilli; Kevin S Weiner; Georgios A Keliris; Sofia M Landi; Julia Sliwa; Frank Q Ye; Michael A Barnett; David A Leopold; Winrich A Freiwald; Nikos K Logothetis; Brian A Wandell
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3.  Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

Authors:  Najib J Majaj; Ha Hong; Ethan A Solomon; James J DiCarlo
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Review 4.  The Organization and Operation of Inferior Temporal Cortex.

Authors:  Bevil R Conway
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5.  Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex.

Authors:  Xiaoxuan Jia; Ha Hong; James J DiCarlo
Journal:  Elife       Date:  2021-06-11       Impact factor: 8.140

6.  Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks.

Authors:  Rishi Rajalingham; Elias B Issa; Pouya Bashivan; Kohitij Kar; Kailyn Schmidt; James J DiCarlo
Journal:  J Neurosci       Date:  2018-07-13       Impact factor: 6.167

Review 7.  Beyond the feedforward sweep: feedback computations in the visual cortex.

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8.  Decoding the processing stages of mental arithmetic with magnetoencephalography.

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Review 9.  Understanding Image Memorability.

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10.  Linear Integration of Sensory Evidence over Space and Time Underlies Face Categorization.

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Journal:  J Neurosci       Date:  2021-07-29       Impact factor: 6.167

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