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Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity
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Causal Inference for Spatial Constancy across Saccades.

Jeroen Atsma1, Femke Maij1, Mathieu Koppen1

  • 1Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.

Plos Computational Biology
|March 12, 2016
PubMed
Summary
This summary is machine-generated.

The brain uses Bayesian inference to maintain visual stability, distinguishing eye movements from scene shifts. This process, called saccadic suppression of displacement, optimally combines visual memory and sensory feedback.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Visual Perception

Background:

  • Visual stability is crucial for environmental interaction, requiring the brain to differentiate retinal image shifts from eye movements.
  • Saccadic suppression of displacement (SSD) demonstrates that visual stability is not always perfect, particularly during eye movements (saccades).

Purpose of the Study:

  • To investigate how the brain evaluates presaccadic memory and postsaccadic visual feedback for visual stability.
  • To understand the computational mechanisms underlying saccadic suppression of displacement (SSD).

Main Methods:

  • A saccadic suppression of displacement (SSD) task was employed.
  • Participants localized targets after a horizontal saccade, with targets displaced parallel or orthogonal and viewed for varying durations.

Main Results:

  • Localization errors varied based on target type, postsaccadic viewing time, and spatial separation from the presaccadic location.
  • A Bayesian causal inference model, mixing integration and separation strategies, accurately predicted the observed data.

Conclusions:

  • Human visual stability relies on a Bayesian inference process employing two causal structures.
  • The brain optimally combines memorized presaccadic information with postsaccadic sensory signals to achieve visual stability.