One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curves
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...
| Authors: | , , , |
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| Format: | article |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Universidad Loyola Andalucía |
| Repository: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Language: | English |
| OAI Identifier: | oai:dnet:biblosearchi::d8f93bcf33dcb47f5904f5bc857c160e |
| Online Access: | https://hdl.handle.net/10486/767443 https://dx.doi.org/10.1186/s41235-025-00624-7 |
| Access Level: | Open access |
| Keyword: | Visual foraging Optimal foraging theory Organization ROC curves Composite variables Psicología |
| Summary: | 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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