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

## TL;DR

This study shows that combining search organization and intake rates best predicts when people should stop searching in visual tasks like detecting cancer nodules or threats.

## Contribution

Combining search organization and intake rates into a single factor improves quitting behavior prediction in visual foraging.

## Key findings

- Organization measures outperform traditional intake rates in predicting optimal quitting behavior.
- A unitary factor combining organization and intake rates is the best predictor of when to stop searching.
- Results were consistent across different foraging tasks and age groups.

## Abstract

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.

The online version contains supplementary material available at 10.1186/s41235-025-00624-7.

Predicting quitting rules in search is critical to most visual search tasks. Did I search enough for a cancer nodule in a breast X-ray or a threat in a baggage scanner at the airport? Did I look hard enough for my friend in the crowd? Making optimal decisions on when to leave a search is a crucial problem in psychological science. In this study, we examined the predictive power of search organization as an optimal criterion to leave a search, comparing it with classic intake rate measures commonly used in foraging. Using ROC curves, our results show that organization measures outperform intake rates to predict optimal quitting behavior, but importantly, a combination of organization and classic intake rates within a unitary factor seems the best predictor of leaving decisions. Our findings open a new research line for studying optimal decisions in visual search tasks based on search organization knowledge.

The online version contains supplementary material available at 10.1186/s41235-025-00624-7.