Ma, Ning ; Yu, Angela J (2015)
Variability in Human Response Time Reflects Statistical Learning and Adaptive Decision-Making.
37th Annual Meeting of the Cognitive Science Society (Cogsci 2015). Pasadena, California (23-25 July 2015)
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Response time (RT) is an oft-used but ”noisy” behavioral measure in psychology. Here, we combine modeling and psychophysics to examine the hypothesis that RT variability may reflect ongoing statistical learning and consequent adjustment of behavioral strategy. We utilize the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, rare stop signal. We model across-trial learning of stop signal frequency (P(stop)) and stop-signal onset time (SSD) with a Bayesian hidden Markov model, and within-trial decision-making as optimal stochastic control. The model predicts that RT should increase with expected P(stop) and SSD, a prediction borne out by our human data. Thus, it appears that humans continuously monitor environmental statistics and adjust behavioral strategy accordingly. More broadly, our approach exemplifies the use of ”noisy” RT measures for extracting insights about cognitive and neural processing.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2015 |
| Autor(en): | Ma, Ning ; Yu, Angela J |
| Art des Eintrags: | Bibliographie |
| Titel: | Variability in Human Response Time Reflects Statistical Learning and Adaptive Decision-Making |
| Sprache: | Englisch |
| Publikationsjahr: | 2015 |
| Ort: | Seattle |
| Verlag: | Cognitive Science Society |
| Buchtitel: | Proceedings of the Annual Meeting of the Cognitive Science Society |
| Band einer Reihe: | 37 |
| Veranstaltungstitel: | 37th Annual Meeting of the Cognitive Science Society (Cogsci 2015) |
| Veranstaltungsort: | Pasadena, California |
| Veranstaltungsdatum: | 23-25 July 2015 |
| URL / URN: | https://cognitivesciencesociety.org/wp-content/uploads/2019/... |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Response time (RT) is an oft-used but ”noisy” behavioral measure in psychology. Here, we combine modeling and psychophysics to examine the hypothesis that RT variability may reflect ongoing statistical learning and consequent adjustment of behavioral strategy. We utilize the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, rare stop signal. We model across-trial learning of stop signal frequency (P(stop)) and stop-signal onset time (SSD) with a Bayesian hidden Markov model, and within-trial decision-making as optimal stochastic control. The model predicts that RT should increase with expected P(stop) and SSD, a prediction borne out by our human data. Thus, it appears that humans continuously monitor environmental statistics and adjust behavioral strategy accordingly. More broadly, our approach exemplifies the use of ”noisy” RT measures for extracting insights about cognitive and neural processing. |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie |
| Hinterlegungsdatum: | 01 Nov 2023 08:38 |
| Letzte Änderung: | 17 Nov 2023 07:39 |
| PPN: | 513263349 |
| Zugehörige Links: | |
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