Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons
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Baldassi-2013-Shape Similarity, Be.pdf
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Author(s)
Baldassi, Carlo
Alemi-Neissi, Alireza
Pagan, Marino
Zecchina, Riccardo
Zoccolan, Davide
DiCarlo, James
Date Issued
August 2013
Journal
PLoS Computational Biology
Publisher
Public Library of Science
Citation
Baldassi, Carlo, Alireza Alemi-Neissi, Marino Pagan, James J. DiCarlo, Riccardo Zecchina, and Davide Zoccolan. “Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons.” Edited by Wolfgang Einhäuser. PLoS Computational Biology 9, no. 8 (August 8, 2013): e1003167.
Version
Final published version
Abstract
The anterior inferotemporal cortex (IT) is the highest stage along the hierarchy of visual areas that, in primates, processes visual objects. Although several lines of evidence suggest that IT primarily represents visual shape information, some recent studies have argued that neuronal ensembles in IT code the semantic membership of visual objects (i.e., represent conceptual classes such as animate and inanimate objects). In this study, we investigated to what extent semantic, rather than purely visual information, is represented in IT by performing a multivariate analysis of IT responses to a set of visual objects. By relying on a variety of machine-learning approaches (including a cutting-edge clustering algorithm that has been recently developed in the domain of statistical physics), we found that, in most instances, IT representation of visual objects is accounted for by their similarity at the level of shape or, more surprisingly, low-level visual properties. Only in a few cases we observed IT representations of semantic classes that were not explainable by the visual similarity of their members. Overall, these findings reassert the primary function of IT as a conveyor of explicit visual shape information, and reveal that low-level visual properties are represented in IT to a greater extent than previously appreciated. In addition, our work demonstrates how combining a variety of state-of-the-art multivariate approaches, and carefully estimating the contribution of shape similarity to the representation of object categories, can substantially advance our understanding of neuronal coding of visual objects in cortex.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
McGovern Institute for Brain Research at MIT
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DOI of Published Version
https://doi.org/10.1371/journal.pcbi.1003167
