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

Interpolating sampled contours in 3D: perturbation analyses.

Paul A Warren1, Laurence T Maloney, Michael S Landy

  • 1Department of Psychology, Cardiff University, Tower Building, Park Place, PO Box 901, Wales, UK. warrenpa@cardiff.ac.uk

Vision Research
|February 18, 2004
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

Dissociating the behavioral and computational features of implicit motor learning and explicit perturbation detection.

bioRxiv : the preprint server for biology·2026
Same author

The Robustness of Anchoring in a Naturalistic VR-Based Task.

Cognitive science·2026
Same author

Normalization accounts for temporal dynamics in human somatosensory cortex.

bioRxiv : the preprint server for biology·2026
Same author

Visual confidence accurately tracks increasing internal noise with eccentricity in peripheral vision.

bioRxiv : the preprint server for biology·2026
Same author

Implicit adaptation's effect on sensorimotor and motor confidence.

bioRxiv : the preprint server for biology·2025
Same author

Sensorimotor confidence during explicit motor adaptation.

bioRxiv : the preprint server for biology·2025
Same journal

Impact of crowding on visual appearance and performance in amblyopia.

Vision research·2026
Same journal

Editorial for VSI Amblyopia: Advances in Amblyopia Research.

Vision research·2026
Same journal

Computational and mathematical models in vision: Quantitative approaches to understanding visual perception.

Vision research·2026
Same journal

Complex interactions between lightness, chroma, and hue in color ensemble perception.

Vision research·2026
Same journal

Driving with autism spectrum disorder: Exploring the impact of tactile hazard warnings on gaze behavior and hazard responses.

Vision research·2026
Same journal

Early visual processing in adults with ADHD: evidence from contrast sensitivity, spatial integration, and external noise.

Vision research·2026
See all related articles

Human visual interpolation of parabolic contours is local, with influence decreasing rapidly with distance from visible points. This finding aligns best with algorithms minimizing angular variance between contour segments.

Area of Science:

  • Perception and Cognition
  • Computational Neuroscience
  • Computer Vision

Background:

  • Human observers perceive and interpolate complex shapes from limited visual information.
  • Understanding the mechanisms of visual interpolation is crucial for fields like computer vision and human-computer interaction.

Purpose of the Study:

  • To investigate the locality of human visual interpolation for parabolic contours.
  • To compare human performance with standard interpolation algorithms.

Main Methods:

  • Four experiments involved observers interpolating parabolic contours defined by eight irregularly spaced points in 3D space.
  • Observer performance was measured by perturbing visible point locations and assessing their influence on interpolation.
  • Human data was compared against quadratic fitting, cubic spline, and angle-minimizing algorithms.

Related Experiment Videos

Main Results:

  • The influence of visible points on interpolation decreased rapidly with increasing distance.
  • Human interpolation was inconsistent with a simple quadratic fit.
  • Results showed reasonable consistency with cubic splines and strongest consistency with angle-minimizing algorithms.

Conclusions:

  • Human visual interpolation of parabolic contours is a local process.
  • Algorithms minimizing the variance of angles between contour segments best model human performance in this task.