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

Statistical learning of distractor co-occurrences facilitates visual search

Sushrut Thorat, Genevieve Quek, View ORCID ProfileMarius V. Peelen
doi: https://doi.org/10.1101/2022.04.20.488921
Sushrut Thorat
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Genevieve Quek
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
2The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marius V. Peelen
1Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Marius V. Peelen
  • For correspondence: marius.peelen@donders.ru.nl
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Visual search is facilitated by knowledge of the relationship between the target and the distractors, including both where the target is likely to be amongst the distractors and how it differs from the distractors. Whether the statistical structure amongst distractors themselves, unrelated to target properties, facilitates search is less well understood. Here, we assessed the benefit of distractor structure using novel shapes whose relationship to each other was learned implicitly during visual search. Participants searched for target items in arrays of shapes that comprised either four pairs of co-occurring distractor shapes (structured scenes) or eight distractor shapes randomly partitioned into four pairs on each trial (unstructured scenes). Across five online experiments (N=1140), we found that after a period of search training, participants were more efficient when searching for targets in structured than unstructured scenes. This structure-benefit emerged independently of whether the position of the shapes within each pair was fixed or variable, and despite participants having no explicit knowledge of the structured pairs they had seen. These results show that implicitly learned co-occurrence statistics between distractor shapes increases search efficiency. Increased efficiency in the rejection of regularly co-occurring distractors may contribute to the efficiency of visual search in natural scenes, where such regularities are abundant.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Conflict of interest statement: The authors declare no competing financial interests.

  • -New Figure 1 with additional examples -Bayes Factors added for null results -Updated discussion section

  • https://doi.org/10.17605/OSF.IO/EM2XF

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 4.0 International license.
Back to top
PreviousNext
Posted July 27, 2022.
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.
Statistical learning of distractor co-occurrences facilitates visual search
(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
Statistical learning of distractor co-occurrences facilitates visual search
Sushrut Thorat, Genevieve Quek, Marius V. Peelen
bioRxiv 2022.04.20.488921; doi: https://doi.org/10.1101/2022.04.20.488921
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Statistical learning of distractor co-occurrences facilitates visual search
Sushrut Thorat, Genevieve Quek, Marius V. Peelen
bioRxiv 2022.04.20.488921; doi: https://doi.org/10.1101/2022.04.20.488921

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 (4369)
  • Biochemistry (9546)
  • Bioengineering (7068)
  • Bioinformatics (24768)
  • Biophysics (12560)
  • Cancer Biology (9924)
  • Cell Biology (14297)
  • Clinical Trials (138)
  • Developmental Biology (7930)
  • Ecology (12074)
  • Epidemiology (2067)
  • Evolutionary Biology (15954)
  • Genetics (10904)
  • Genomics (14706)
  • Immunology (9844)
  • Microbiology (23582)
  • Molecular Biology (9454)
  • Neuroscience (50691)
  • Paleontology (369)
  • Pathology (1535)
  • Pharmacology and Toxicology (2674)
  • Physiology (3997)
  • Plant Biology (8639)
  • Scientific Communication and Education (1505)
  • Synthetic Biology (2388)
  • Systems Biology (6415)
  • Zoology (1344)