One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curves
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 cr...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-04-01
|
| Series: | Cognitive Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41235-025-00624-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850153887740198912 |
|---|---|
| author | Marcos Bella-Fernández Manuel Suero Suñé Alicia Ferrer-Mendieta Beatriz Gil-Gómez de Liaño |
| author_facet | Marcos Bella-Fernández Manuel Suero Suñé Alicia Ferrer-Mendieta Beatriz Gil-Gómez de Liaño |
| author_sort | Marcos Bella-Fernández |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9a2694edb6374ea694724257036352e5 |
| institution | OA Journals |
| issn | 2365-7464 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cognitive Research |
| spelling | doaj-art-9a2694edb6374ea694724257036352e52025-08-20T02:25:36ZengSpringerOpenCognitive Research2365-74642025-04-0110112210.1186/s41235-025-00624-7One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curvesMarcos Bella-Fernández0Manuel Suero Suñé1Alicia Ferrer-Mendieta2Beatriz Gil-Gómez de Liaño3Universidad Autónoma de MadridUniversidad Autónoma de MadridUniversidad Autónoma de MadridUniversidad Autónoma de MadridAbstract 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.https://doi.org/10.1186/s41235-025-00624-7Visual foragingOptimal foraging theoryOrganizationROC curvesComposite variables |
| spellingShingle | Marcos Bella-Fernández Manuel Suero Suñé Alicia Ferrer-Mendieta Beatriz Gil-Gómez de Liaño One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curves Cognitive Research Visual foraging Optimal foraging theory Organization ROC curves Composite variables |
| title | One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curves |
| title_full | One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curves |
| title_fullStr | One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curves |
| title_full_unstemmed | One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curves |
| title_short | One factor to bind them all: visual foraging organization to predict patch leaving behavior with ROC curves |
| title_sort | one factor to bind them all visual foraging organization to predict patch leaving behavior with roc curves |
| topic | Visual foraging Optimal foraging theory Organization ROC curves Composite variables |
| url | https://doi.org/10.1186/s41235-025-00624-7 |
| work_keys_str_mv | AT marcosbellafernandez onefactortobindthemallvisualforagingorganizationtopredictpatchleavingbehaviorwithroccurves AT manuelsuerosune onefactortobindthemallvisualforagingorganizationtopredictpatchleavingbehaviorwithroccurves AT aliciaferrermendieta onefactortobindthemallvisualforagingorganizationtopredictpatchleavingbehaviorwithroccurves AT beatrizgilgomezdeliano onefactortobindthemallvisualforagingorganizationtopredictpatchleavingbehaviorwithroccurves |