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

Unsupervised learning predicts human perception and misperception of gloss

View ORCID ProfileKatherine R. Storrs, View ORCID ProfileBarton L. Anderson, View ORCID ProfileRoland W. Fleming
doi: https://doi.org/10.1101/2020.04.07.026120
Katherine R. Storrs
1Department of Experimental Psychology, Justus Liebig University Giessen, Giessen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Katherine R. Storrs
  • For correspondence: katherine.storrs@gmail.com
Barton L. Anderson
2School of Psychology, University of Sydney, Sydney, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Barton L. Anderson
Roland W. Fleming
1Department of Experimental Psychology, Justus Liebig University Giessen, Giessen, Germany
3Centre for Mind, Brain and Behaviour (CMBB), University of Marburg and Justus Liebig University Giessen, Giessen, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Roland W. Fleming
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Reflectance, lighting, and geometry combine in complex ways to create images. How do we disentangle these to perceive individual properties, like surface glossiness? We suggest that brains disentangle properties by learning to model statistical structure in proximal images. To test this, we trained unsupervised generative neural networks on renderings of glossy surfaces and compared their representations with human gloss judgments. The networks spontaneously cluster images according to distal properties such as reflectance and illumination, despite receiving no explicit information about them. Intriguingly, the resulting representations also predict the specific patterns of ‘successes’ and ‘errors’ in human perception. Linearly decoding specular reflectance from the model’s internal code predicts human gloss perception better than ground truth, supervised networks, or control models, and predicts, on an image-by-image basis, illusions of gloss perception caused by interactions between material, shape, and lighting. Unsupervised learning may underlie many perceptual dimensions in vision, and beyond.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Substantially revised Introduction and Discussion, and extended Supplementary Information with new analyses including network hyperparameter evaluations.

  • http://doi.org/10.5281/zenodo.4495586

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 March 11, 2021.
Download PDF
Data/Code
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.
Unsupervised learning predicts human perception and misperception of gloss
(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
Unsupervised learning predicts human perception and misperception of gloss
Katherine R. Storrs, Barton L. Anderson, Roland W. Fleming
bioRxiv 2020.04.07.026120; doi: https://doi.org/10.1101/2020.04.07.026120
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools
Unsupervised learning predicts human perception and misperception of gloss
Katherine R. Storrs, Barton L. Anderson, Roland W. Fleming
bioRxiv 2020.04.07.026120; doi: https://doi.org/10.1101/2020.04.07.026120

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 (2646)
  • Biochemistry (5266)
  • Bioengineering (3678)
  • Bioinformatics (15796)
  • Biophysics (7253)
  • Cancer Biology (5627)
  • Cell Biology (8095)
  • Clinical Trials (138)
  • Developmental Biology (4765)
  • Ecology (7516)
  • Epidemiology (2059)
  • Evolutionary Biology (10576)
  • Genetics (7730)
  • Genomics (10131)
  • Immunology (5193)
  • Microbiology (13905)
  • Molecular Biology (5385)
  • Neuroscience (30779)
  • Paleontology (215)
  • Pathology (879)
  • Pharmacology and Toxicology (1524)
  • Physiology (2254)
  • Plant Biology (5022)
  • Scientific Communication and Education (1041)
  • Synthetic Biology (1385)
  • Systems Biology (4146)
  • Zoology (812)