How to model the neurocognitive dynamics of decision making: A methodological primer with ACT-R

Détails

Ressource 1Demande d'une copie Sous embargo jusqu'au 08/08/2020.
Etat: Public
Version: Author's accepted manuscript
Licence: Non spécifiée
ID Serval
serval:BIB_C9264A1EAF40
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
How to model the neurocognitive dynamics of decision making: A methodological primer with ACT-R
Périodique
Behavior Research Methods.
Auteur(s)
Dimov C., Khader P.H., Marewski J.N., Pachur T.
Statut éditorial
In Press
Peer-reviewed
Oui
Langue
anglais
Résumé
Higher cognitive functions are the product of a dynamic interplay of perceptual, mnemonic, and other cognitive processes. Modeling the interplay of these processes and generating predictions about both behavioral and neural data can be achieved with cognitive architectures. However, such architectures are still used relatively rarely, likely because working with them comes with high entry-level barriers. To lower these barriers, we provide a methodological primer for modeling higher cognitive functions and their constituent cognitive subprocesses with arguably the most developed cognitive architecture today—ACT-R. We showcase a principled method of generating individual response time predictions, and demonstrate how neural data can be used to refine ACT-R models. To illustrate our approach, we develop a fully specified neurocognitive model of a prominent strategy for memory-based decisions—the take-the-best heuristic—modeling decision making as a dynamic interplay of perceptual, motor, and memory processes. This implementation allows us to predict the dynamics of behavior and the temporal and spatial patterns of brain activity. Moreover, we show that comparing the predictions for brain activity to empirical BOLD data allows us to differentiate competing ACT-R implementations of take the best.
Création de la notice
29/08/2019 11:25
Dernière modification de la notice
18/09/2019 6:08
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