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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Probabilistic, positional averaging predicts object-level crowding effects with letter-like stimuli.

Steven C Dakin1, John Cass, John A Greenwood

  • 1Institute of Ophthalmology, University College London, London, UK. s.dakin@ucl.ac.uk

Journal of Vision
|October 2, 2010
PubMed
Summary

Crowding, a visual object recognition issue, shows object-centered patterns even with simple shapes. Low-level feature interactions explain these high-level crowding effects in visual perception.

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Area of Science:

  • Visual perception
  • Cognitive psychology
  • Computational neuroscience

Background:

  • Crowding is a breakdown in object recognition caused by surrounding clutter.
  • Understanding crowding is crucial for explaining visual object recognition limits.

Purpose of the Study:

  • To investigate object-centered anisotropies in crowding for complex letter-like stimuli.
  • To determine if low-level feature interactions can explain high-level crowding effects.

Main Methods:

  • Participants identified the orientation of a central 'T' target flanked by bar patterns.
  • Analysis focused on error patterns, including target rotation and flanker intrusion.
  • A computational model based on feature averaging was used for comparison.

Main Results:

  • Object-centered anisotropies were observed, including fewer 180° inversions than ±90° rotations.
  • End-flankers (above/below) were more intrusive than side-flankers (left/right).
  • End-flankers produced structured errors resembling the flanker, while side-flankers produced random errors.

Conclusions:

  • Crowding effects exhibit object-centered characteristics beyond simple retinotopic influences.
  • These high-level perceptual effects can emerge from low-level interactions among visual features.
  • A feature-based probabilistic averaging model successfully accounts for the observed crowding phenomena.