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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
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Published on: January 23, 2017

Selective Bayes: attentional load and crowding.

Peter Dayan1, Joshua A Solomon

  • 1Gatsby Computational Neuroscience Unit, UCL, 17 Queen Square, London WC1N 3AR, United Kingdom. dayan@gatsby.ucl.ac.uk

Vision Research
|May 4, 2010
PubMed
Summary
This summary is machine-generated.

Visual receptive fields explain attention anomalies. Spatially extended neurons and Bayesian inference resolve issues in selective attention, distractor influence, and crowding, offering a unified explanation for these perceptual phenomena.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Vision
  • Psychophysics

Background:

  • The spatial extent of visual neuron receptive fields is fundamental to understanding spatial selection.
  • Existing models struggle to explain certain anomalies in selective attention and visual perception.

Purpose of the Study:

  • To explain three key anomalies in selective attention and visual perception.
  • To demonstrate how normative Bayesian inference can account for these phenomena.
  • To link neural receptive field properties to perceptual anomalies.

Main Methods:

  • Computational modeling using normative Bayesian inference.
  • Analysis of psychophysical phenomena including the Eriksen flanker task, attentional load effects, and crowding.
  • Integration of neural receptive field properties into the inference model.

Main Results:

  • The model successfully explains the undue influence of distractors under time pressure.
  • It accounts for the attentional load effect where distal distractors have more impact with lower attention demands.
  • It resolves the asymmetry observed in crowding phenomena.

Conclusions:

  • Spatially extended receptive fields, when combined with Bayesian inference, provide a unified framework for understanding attention and perception.
  • These findings suggest a normative account for complex visual processing anomalies.
  • The study bridges neural mechanisms with psychophysical observations in selective attention.