Ma, Ning ; Yu, Angela (2015)
Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.
In: Frontiers in Psychology, 6
doi: 10.3389/fpsyg.2015.01046
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2015 |
| Autor(en): | Ma, Ning ; Yu, Angela |
| Art des Eintrags: | Bibliographie |
| Titel: | Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control |
| Sprache: | Englisch |
| Publikationsjahr: | 2015 |
| Ort: | Lausanne |
| Verlag: | Frontiers Research Foundation |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Frontiers in Psychology |
| Jahrgang/Volume einer Zeitschrift: | 6 |
| DOI: | 10.3389/fpsyg.2015.01046 |
| URL / URN: | https://www.frontiersin.org/articles/10.3389/fpsyg.2015.0104... |
| Kurzbeschreibung (Abstract): | Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making. |
| Zusätzliche Informationen: | Article 1046 |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie |
| Hinterlegungsdatum: | 30 Okt 2023 07:30 |
| Letzte Änderung: | 31 Okt 2023 06:53 |
| PPN: | 512760020 |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
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