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Related Experiment Video

Updated: May 21, 2026

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

Published on: March 18, 2019

Perceptions as hypotheses: saccades as experiments.

Karl Friston¹, Rick A Adams, Laurent Perrinet

¹The Wellcome Trust Centre for Neuroimaging, University College London London, UK.

Frontiers in Psychology

|June 2, 2012

Summary

This summary is machine-generated.

Visual search is modeled as optimal experiments where saccadic eye movements gather data to test hypotheses. This active inference approach minimizes uncertainty and explains how sensory evidence shapes our beliefs about the world.

Keywords:

Bayesian inference active inference exploration free energy perception salience surprise visual search

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Area of Science:

Cognitive Science
Computational Neuroscience
Perception

Background:

Perception can be viewed as hypothesis testing, with visual searches generating sensory data.
Saccadic eye movements are crucial for gathering information during visual exploration.

Purpose of the Study:

To model visual search as optimal experiments using active inference.
To explore how saccadic eye movements efficiently sample information to test hypotheses about the world.

Main Methods:

Formulated visual search as an active inference process based on minimizing uncertainty (entropy).
Utilized approximate Bayesian inference and variational free-energy minimization principles.
Simulated the active inference scheme to model eye movements.

Main Results:

The model reproduces sequential eye movements similar to empirically observed saccades.
Demonstrated that efficient sampling of salient information is key to hypothesis testing in visual search.
Provided insights into the assimilation of sensory evidence into beliefs.

Conclusions:

Saccadic eye movements can be understood as optimal experiments for hypothesis testing.
Active inference offers a principled framework for understanding visual search and information gathering.
This approach sheds light on the dynamic interplay between sensory data and belief updating.