Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Bayesian contour integration.

J Feldman1

  • 1Department of Psychology, Center for Cognitive Science, Rutgers University, New Brunswick, New Jersey 08903, USA. jacob@ruccs.rutgers.edu

Perception & Psychophysics
|January 5, 2002
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks.

Nature communications·2026
Same author

Combining structural modeling and deep learning to calculate the <i>E. coli</i> protein interactome and functional networks.

bioRxiv : the preprint server for biology·2025
Same author

Study of upright patient positioning reproducibility in image-guided proton therapy for head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2024
Same author

Impact of an automated report comparison tool on trainee report modification rate at a tertiary hospital.

Clinical radiology·2024
Same author

Searching for clinical trials in oncology: finding a path through the maze.

Annals of oncology : official journal of the European Society for Medical Oncology·2023
Same author

ESMO expert consensus statements on the management of EGFR mutant non-small-cell lung cancer.

Annals of oncology : official journal of the European Society for Medical Oncology·2022
Same journal

Response organization in selective adaptation to speech sounds.

Perception & psychophysics·2014
Same journal

Reaction times to comparisons within and across phonetic categories.

Perception & psychophysics·2012
Same journal

Auditory and phonetic memory codes in the discrimination of consonants and vowels.

Perception & psychophysics·2012
Same journal

Simple and contingent adaptation effects for place of articulation in stop consonants.

Perception & psychophysics·2012
Same journal

Auditory property detectors and processing place features in stop consonants.

Perception & psychophysics·2012
Same journal

Visual working memory for line orientations and face identities.

Perception & psychophysics·2008
See all related articles

Bayesian probability theory accurately models human visual grouping, specifically contour integration. This suggests evolutionary principles may underlie perceptual grouping rules.

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Computer Vision

Background:

  • Perceptual grouping, the visual system's parsing of images into objects, lacks a rigorous theory.
  • Bayesian probability theory offers optimal data interpretation under uncertainty, a potential framework for perceptual grouping.
  • Previous research lacked methods to probabilistically test Bayesian theory against human grouping judgments.

Purpose of the Study:

  • To develop probabilistic methods for testing Bayesian theory in perceptual grouping.
  • To assess the fit of Bayesian theory to human contour integration judgments.
  • To provide a quantitative account of contour integration and its potential evolutionary basis.

Main Methods:

  • Developed probabilistic methods for Bayesian modeling of perceptual grouping.

Related Experiment Videos

  • Conducted two experiments on human contour integration using ambiguous dot configurations.
  • Experiment 1: Judged 'corner' presence in five-dot sequences.
  • Experiment 2: Differentiated between two disjoint contours versus one smooth contour in six-dot arrangements.

    • Main Results:

    • Bayesian theory accounted for over 75% of the variance in human judgments for both contour integration tasks.
    • The developed methods allow precise calculation of the subjective 'goodness' of perceived contours.
    • Demonstrated a strong quantitative fit between Bayesian predictions and human perceptual grouping.

    Conclusions:

    • Bayesian probability theory provides a highly accurate and quantitative model for human contour integration.
    • The findings suggest that human perceptual grouping rules may have a rational, potentially evolutionary, justification.
    • This work advances the understanding of visual perception and probabilistic modeling in cognitive science.