Literature DB >> 34253629

Predicting Identity-Preserving Object Transformations across the Human Ventral Visual Stream.

Viola Mocz1, Maryam Vaziri-Pashkam2, Marvin M Chun3,4, Yaoda Xu1.   

Abstract

In everyday life, we have no trouble categorizing objects varying in position, size, and orientation. Previous fMRI research shows that higher-level object processing regions in the human lateral occipital cortex may link object responses from different affine states (i.e., size and viewpoint) through a general linear mapping function capable of predicting responses to novel objects. In this study, we extended this approach to examine the mapping for both Euclidean (e.g., position and size) and non-Euclidean (e.g., image statistics and spatial frequency) transformations across the human ventral visual processing hierarchy, including areas V1, V2, V3, V4, ventral occipitotemporal cortex, and lateral occipitotemporal cortex. The predicted pattern generated from a linear mapping function could capture a significant amount of the changes associated with the transformations throughout the ventral visual stream. The derived linear mapping functions were not category independent as performance was better for the categories included than those not included in training and better between two similar versus two dissimilar categories in both lower and higher visual regions. Consistent with object representations being stronger in higher than in lower visual regions, pattern selectivity and object category representational structure were somewhat better preserved in the predicted patterns in higher than in lower visual regions. There were no notable differences between Euclidean and non-Euclidean transformations. These findings demonstrate a near-orthogonal representation of object identity and these nonidentity features throughout the human ventral visual processing pathway with these nonidentity features largely untangled from the identity features early in visual processing.SIGNIFICANCE STATEMENT Presently we still do not fully understand how object identity and nonidentity (e.g., position, size) information are simultaneously represented in the primate ventral visual system to form invariant representations. Previous work suggests that the human lateral occipital cortex may be linking different affine states of object representations through general linear mapping functions. Here, we show that across the entire human ventral processing pathway, we could link object responses in different states of nonidentity transformations through linear mapping functions for both Euclidean and non-Euclidean transformations. These mapping functions are not identity independent, suggesting that object identity and nonidentity features are represented in a near rather than a completely orthogonal manner.
Copyright © 2021 the authors.

Entities:  

Keywords:  object invariance; object recognition; perception; transformation; visual representations

Mesh:

Year:  2021        PMID: 34253629      PMCID: PMC8412993          DOI: 10.1523/JNEUROSCI.2137-20.2021

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


  28 in total

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Journal:  J Neurosci       Date:  2000-05-01       Impact factor: 6.167

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Authors:  Maryam Vaziri-Pashkam; JohnMark Taylor; Yaoda Xu
Journal:  J Cogn Neurosci       Date:  2018-09-06       Impact factor: 3.225

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5.  Goal-Directed Visual Processing Differentially Impacts Human Ventral and Dorsal Visual Representations.

Authors:  Maryam Vaziri-Pashkam; Yaoda Xu
Journal:  J Neurosci       Date:  2017-08-14       Impact factor: 6.167

6.  General Transformations of Object Representations in Human Visual Cortex.

Authors:  Emily J Ward; Leyla Isik; Marvin M Chun
Journal:  J Neurosci       Date:  2018-08-20       Impact factor: 6.167

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Journal:  J Neurosci       Date:  2010-09-29       Impact factor: 6.167

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Journal:  Science       Date:  1995-05-12       Impact factor: 47.728

9.  Recurrence is required to capture the representational dynamics of the human visual system.

Authors:  Tim C Kietzmann; Courtney J Spoerer; Lynn K A Sörensen; Radoslaw M Cichy; Olaf Hauk; Nikolaus Kriegeskorte
Journal:  Proc Natl Acad Sci U S A       Date:  2019-10-07       Impact factor: 11.205

10.  Why is real-world visual object recognition hard?

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

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