Guided Search Theory
Updated 2026-08-08
INTRODUCTION
English translation pending.
CORE DEFINITION
Guided search theory was developed by Jeremy Wolfe and colleagues as an extension of feature integration theory. It proposes that preattentive feature maps are weighted by top-down task settings and summed into an activation map, which then guides attention to the most likely target locations in order of activation. This preserves the parallel-then-serial structure while explaining why conjunction searches can be much faster than pure serial search. The qualification is that guidance is probabilistic: the target is likely but not guaranteed to be in the highest-ranked locations.
SCAFFOLDING EFFECT
Reduce cognitive load
- Guidance audit: check whether the searcher already knows the target features before starting. - Priority design: strengthen the target's distinctive features and keep the distractors as homogeneous as possible. - Efficiency prediction: expect progressively slower search as distractor variety increases.
Anchor fast decisions
Unguided search would require checking every location, which is slow and scales badly with display size. Guided search replaces random sampling with a ranking: preattentive feature maps already encode how target-like each location is, and the observer's goal adjusts the weight each dimension receives. Attention then visits locations in descending order of that score, so the target is usually reached early. Because the ranking is probabilistic, some wrong locations are still checked, which explains the residual set-size effect. Efficiency therefore depends on how well guidance separates the target.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Visual_searchverified
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