Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares

Kamila M. Jozwik, Nikolaus Kriegeskorte, Marieke Mur
doi: https://doi.org/10.1101/029314
Kamila M. Jozwik
aMedical Research Council, Cognition and Brain Sciences Unit, 15 Chaucer Road Cambridge, CB2 7EF, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nikolaus Kriegeskorte
aMedical Research Council, Cognition and Brain Sciences Unit, 15 Chaucer Road Cambridge, CB2 7EF, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marieke Mur
aMedical Research Council, Cognition and Brain Sciences Unit, 15 Chaucer Road Cambridge, CB2 7EF, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Object similarity, in brain representations and conscious perception, must reflect a combination of the visual appearance of the objects on the one hand and the categories the objects belong to on the other. Indeed, visual object features and category membership have each been shown to contribute to the object representation in human inferior temporal (IT) cortex, as well as to object-similarity judgments. However, the explanatory power of features and categories has not been directly compared. Here, we investigate whether the IT object representation and similarity judgments are best explained by a categorical or a feature-based model. We use rich models (> 100 dimensions) generated by human observers for a set of 96 real-world object images. The categorical model consists of a hierarchically nested set of category labels (such as “human”, “mammal”, “animal”). The feature model includes both object parts (such as “eye”, “tail”, “handle”) and other descriptive features (such as “circular”, “green”, “stubbly”). We used nonnegative least squares to fit the models to the brain representations (estimated from functional magnetic resonance imaging data) and to similarity judgments. Model performance was estimated on held-out images not used in fitting. Both models explained significant variance in IT and the amounts explained were not significantly different. The combined model did not explain significant additional IT variance, suggesting that it is the shared model variance (features correlated with categories, categories correlated with features) that best explains IT. The similarity judgments were almost fully explained by the categorical model, which explained significantly more variance than the feature-based model. The combined model did not explain significant additional variance in the similarity judgments. Our findings suggest that IT uses features that help to distinguish categories as stepping stones toward a semantic representation. Similarity judgments contain additional categorical variance that is not explained by visual features, reflecting a higher-level more purely semantic representation.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted October 16, 2015.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares
Kamila M. Jozwik, Nikolaus Kriegeskorte, Marieke Mur
bioRxiv 029314; doi: https://doi.org/10.1101/029314
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Visual features as stepping stones toward semantics: Explaining object similarity in IT and perception with non-negative least squares
Kamila M. Jozwik, Nikolaus Kriegeskorte, Marieke Mur
bioRxiv 029314; doi: https://doi.org/10.1101/029314

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4228)
  • Biochemistry (9107)
  • Bioengineering (6751)
  • Bioinformatics (23944)
  • Biophysics (12089)
  • Cancer Biology (9495)
  • Cell Biology (13741)
  • Clinical Trials (138)
  • Developmental Biology (7616)
  • Ecology (11661)
  • Epidemiology (2066)
  • Evolutionary Biology (15479)
  • Genetics (10618)
  • Genomics (14296)
  • Immunology (9463)
  • Microbiology (22792)
  • Molecular Biology (9078)
  • Neuroscience (48889)
  • Paleontology (355)
  • Pathology (1479)
  • Pharmacology and Toxicology (2565)
  • Physiology (3823)
  • Plant Biology (8308)
  • Scientific Communication and Education (1467)
  • Synthetic Biology (2290)
  • Systems Biology (6172)
  • Zoology (1297)