Ide, Jaime S. ; Hu, Sien ; Zhang, Sheng ; Yu, Angela J. ; Li, Chiang-shan R. (2015)
Impaired Bayesian learning for cognitive control in cocaine dependence.
In: Drug and Alcohol Dependence, 151
doi: 10.1016/j.drugalcdep.2015.03.021
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Background: Cocaine dependence is associated with cognitive control deficits. Here, we apply a Bayesian model of stop-signal task (SST) performance to further characterize these deficits in a theory-driven framework. Methods: A “sequential effect” is commonly observed in SST: encounters with a stop trial tend to prolong reaction time (RT) on subsequent go trials. The Bayesian model accounts for this by assuming that each stop/go trial increases/decreases the subject’s belief about the likelihood of encountering a subsequent stop trial, P(stop), and that P(stop) strategically modulates RT accordingly. Parameters of the model were individually fit, and compared between cocaine-dependent (CD, n = 51) and healthy control (HC, n = 57) groups, matched in age and gender and both demonstrating a significant sequential effect (p \textless 0.05). Model-free measures of sequential effect, post-error slowing (PES) and post-stop slowing (PSS), were also compared across groups. Results: By comparing individually fit Bayesian model parameters, CD were found to utilize a smaller time window of past experiences to anticipate P(stop) (p \textless 0.003), as well as showing less behavioral adjustment in response to P(stop) (p \textless 0.015). PES (p = 0.19) and PSS (p = 0.14) did not show group differences and were less correlated with the Bayesian account of sequential effect in CD than in HC. Conclusions: Cocaine dependence is associated with the utilization of less contextual information to anticipate future events and decreased behavioral adaptation in response to changes in such anticipation. These findings constitute a novel contribution by providing a computationally more refined and statistically more sensitive account of altered cognitive control in cocaine addiction.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2015 |
| Autor(en): | Ide, Jaime S. ; Hu, Sien ; Zhang, Sheng ; Yu, Angela J. ; Li, Chiang-shan R. |
| Art des Eintrags: | Bibliographie |
| Titel: | Impaired Bayesian learning for cognitive control in cocaine dependence |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2015 |
| Ort: | Amsterdam |
| Verlag: | Elsevier Science |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Drug and Alcohol Dependence |
| Jahrgang/Volume einer Zeitschrift: | 151 |
| DOI: | 10.1016/j.drugalcdep.2015.03.021 |
| URL / URN: | https://linkinghub.elsevier.com/retrieve/pii/S03768716150017... |
| Kurzbeschreibung (Abstract): | Background: Cocaine dependence is associated with cognitive control deficits. Here, we apply a Bayesian model of stop-signal task (SST) performance to further characterize these deficits in a theory-driven framework. Methods: A “sequential effect” is commonly observed in SST: encounters with a stop trial tend to prolong reaction time (RT) on subsequent go trials. The Bayesian model accounts for this by assuming that each stop/go trial increases/decreases the subject’s belief about the likelihood of encountering a subsequent stop trial, P(stop), and that P(stop) strategically modulates RT accordingly. Parameters of the model were individually fit, and compared between cocaine-dependent (CD, n = 51) and healthy control (HC, n = 57) groups, matched in age and gender and both demonstrating a significant sequential effect (p \textless 0.05). Model-free measures of sequential effect, post-error slowing (PES) and post-stop slowing (PSS), were also compared across groups. Results: By comparing individually fit Bayesian model parameters, CD were found to utilize a smaller time window of past experiences to anticipate P(stop) (p \textless 0.003), as well as showing less behavioral adjustment in response to P(stop) (p \textless 0.015). PES (p = 0.19) and PSS (p = 0.14) did not show group differences and were less correlated with the Bayesian account of sequential effect in CD than in HC. Conclusions: Cocaine dependence is associated with the utilization of less contextual information to anticipate future events and decreased behavioral adaptation in response to changes in such anticipation. These findings constitute a novel contribution by providing a computationally more refined and statistically more sensitive account of altered cognitive control in cocaine addiction. |
| Zusätzliche Informationen: | 19 citations (Crossref) 2023-10-13 |
| Fachbereich(e)/-gebiet(e): | 03 Fachbereich Humanwissenschaften 03 Fachbereich Humanwissenschaften > Institut für Psychologie |
| Hinterlegungsdatum: | 01 Nov 2023 14:09 |
| Letzte Änderung: | 02 Nov 2023 07:39 |
| PPN: | 512837481 |
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