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Published on: April 11, 2025
220
Optimal visual search with highly heuristic decision rules
Anqi Zhang1,2,3,4, Wilson S Geisler1,5,6,7
1Center for Perceptual Systems, University of Texas at Austin, Austin, TX, USA.
Journal of Vision
|April 15, 2025
Summary
Humans surprisingly outperform Bayesian optimal decision-making in visual search tasks. Simple heuristics, limited foveal neglect, and correlated neural noise explain this enhanced performance in visual search.
Area of Science:
- Cognitive Psychology
- Neuroscience
- Computational Vision
Background:
- Visual search is a fundamental cognitive process for humans and animals.
- Understanding decision-making in visual search is crucial for various fields.
Purpose of the Study:
- To investigate human decision processes in covert visual search.
- To compare human performance against a Bayesian-optimal model.
Main Methods:
- Experimentally comparing human performance in brief visual search tasks with well-separated targets.
- Analyzing decision processes against Bayesian optimality under statistical independence assumptions.
Main Results:
- Humans performed slightly better than the Bayesian-optimal model.
- This paradoxical result was explained by heuristic decision rules, limited central foveal neglect, and spatially correlated neural noise.
Conclusions:
- Simple heuristic decision rules can achieve near-optimal visual search performance.
- Neural noise characteristics can enhance search efficiency beyond predictions based on independent noise.
- Findings offer insights into visual search and identification tasks across species.
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