ArticleAttention, perception & psychophysics2024
Activation thresholds, not quitting thresholds, account for the low prevalence effect in dynamic search.
Article in Attention, perception & psychophysics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Intrinsic motivation and false feedback reduce the low prevalence effect.Cognitive research: principles and implications · 2025Article
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5 authors.
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Abstract
The low-prevalence effect (LPE) is the finding that target detection rates decline as targets become less frequent in a visual search task. A major source of this effect is thought to be that fewer targets result in lower quitting thresholds, i.e., observers respond target-absent after looking at fewer items compared to searches with a higher prevalence of targets. However, a lower quitting threshold does not directly account for an LPE in searches where observers continuously monitor a dynamic display for targets. In these tasks there are no discrete "trials" to which a quitting threshold could be applied. This study examines whether the LPE persists in this type of dynamic search context. Experiment 1 was a 2 (dynamic/static) x 2 (10%/40% prevalence targets) design. Although overall performance was worse in the dynamic task, both tasks showed a similar magnitude LPE. In Experiment 2, we replicated this effect using a task where subjects searched for either of two targets (Ts and Ls). One target appeared infrequently (10%) and the other moderately (40%). Given this method of manipulating prevalence rate, the quitting threshold explanation does not account for the LPE even for static displays. However, replicating Experiment 1, we found an LPE of similar magnitude for both search scenarios, and lower target detection rates with the dynamic displays, demonstrating the LPE is a potential concern for both static and dynamic searches. These findings suggest an activation threshold explanation of the LPE may better account for our observations than the traditional quitting threshold model.
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