Machine learning to detect schedules using spatiotemporal data of behavior: A proof of concept.
Traditionally, the experimental analysis of behavior has relied on the single discrete response paradigm (e.g., key pecks, lever presses, screen clicks) to identify behavioral patterns. However, the development and availability of new technology allow researchers to move beyond this paradigm and use...
| Publicado en: | Journal of the Experimental Analysis of Behavior Vol. 124; no. 1; pp. 1 - 13 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
Jul2025
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=186997776&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 186997776 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00225002 JOE jtl: Journal of the Experimental Analysis of Behavior issn: 00225002 maglogo: Y pubinfo: dt: Jul2025 vid: 124 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 186997776 10.1002/jeab.70029 ppf: 1 ppct: 12 formats: tig: atl: Machine learning to detect schedules using spatiotemporal data of behavior: A proof of concept. aug: au: Lanovaz, Marc J. Hernandez, Varsovia León, Alejandro affil: École de psychoéducation, Université de Montréal,, Canada Centre de recherche de l'Institut universitaire en santé mentale de Montréal,, Canada Centro de Investigaciones Biomédicas, Universidad Veracruzana,, Mexico su: Behavioral sciences Machine learning Artificial neural networks Support vector machines Geospatial data Random forest algorithms Logistic regression analysis Pattern perception sug: subj: Behavioral sciences Machine learning Artificial neural networks Support vector machines Geospatial data Random forest algorithms Logistic regression analysis Pattern perception keyword: machine learning neural network spatiotemporal data time‐based schedule machine learning neural network spatiotemporal data time‐based schedule ab: Traditionally, the experimental analysis of behavior has relied on the single discrete response paradigm (e.g., key pecks, lever presses, screen clicks) to identify behavioral patterns. However, the development and availability of new technology allow researchers to move beyond this paradigm and use other features to detect schedules. Thus, our study used spatiotemporal data to compare the accuracy of four machine learning algorithms (i.e., logistic regression, support vector classifiers, random forests, and artificial neural networks) in detecting the presence and the components of time‐based schedules in 12 rats involved in a behavioral experiment. Using spatiotemporal data, the algorithms accurately identified the presence or absence of programmed schedules and correctly differentiated between fixed‐ and variable‐space schedules. That said, our analyses failed to identify an algorithm to discriminate fixed‐time from variable‐time schedules. Furthermore, none of the algorithms performed systematically better than the others. Our findings provide preliminary support for the utility of using spatiotemporal data with machine learning to detect stimulus schedules. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|