Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9212
Main Title: A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making
Author(s): Prezenski, Sabine
Brechmann, André
Wolff, Susann
Russwinkel, Nele
Type: Article
Is Part Of: 10.14279/depositonce-7951
Language Code: en
Abstract: Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from behavioral, psychobiological and neurophysiological data may help to optimize future applications of this model such that it can be transferred to other domains of comparable dynamic decision tasks.
URI: https://depositonce.tu-berlin.de/handle/11303/10250
http://dx.doi.org/10.14279/depositonce-9212
Issue Date: 4-Aug-2017
Date Available: 6-Nov-2019
DDC Class: 150 Psychologie
Subject(s): dynamic decision making
category learning
ACT-R
strategy formation
reversal learning
cognitive modeling
auditory cognition
Sponsor/Funder: DFG, SFB/TRR 62, Eine Companion-Technologie für kognitive technische Systeme
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Frontiers in Psychology
Publisher: Frontiers Media S.A.
Publisher Place: Lausanne
Volume: 8
Article Number: 1335
Publisher DOI: 10.3389/fpsyg.2017.01335
EISSN: 1664-1078
Appears in Collections:FG Kognitive Modellierung in dynamischen Mensch-Maschine Systemen » Publications

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