One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curves

被引：0

作者：

Marcos Bella-Fernández ^{[1
]}

Manuel Suero Suñé ^{[2
]}

Alicia Ferrer-Mendieta ^{[3
]}

Beatriz Gil-Gómez de Liaño ^{[1
]}

机构：

[1] Universidad Autónoma de Madrid,

[2] UNIE Universidad,undefined

[3] Universidad Pontificia de Comillas,undefined

来源：

Cognitive Research: Principles and Implications | / 10卷 / 1期

关键词：

Visual foraging; Optimal foraging theory; Organization; ROC curves; Composite variables;

D O I：

10.1186/s41235-025-00624-7

中图分类号：

学科分类号：

摘要：

Predicting quitting rules is critical in visual search: Did I search enough for a cancer nodule in a breast X-ray or a threat in a baggage airport scanner? This study examines the predictive power of search organization indexes like best-r, mean ITD, PAO, or intersection rates as optimal criteria to leave a search in foraging (looking for several targets among distractors). In a sample of 29 adults, we compared static and dynamic foraging. Also, we reanalyze data from diverse foraging tasks in the lifespan already published to replicate results. Using ROC curves, all results consistently show that organization measures outperform classic intake rates commonly used in animal models to predict optimal human quitting behavior. Importantly, a combination of organization and traditional intake rates within a unitary factor is the best predictor. Our findings open a new research line for studying optimal decisions in visual search tasks based on search organization.

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