A Sequential Sampling Approach to the Integration of Habits and Goals

Chao Zhang (Corresponding author), Arlette van Wissen, Ron Dotsch, Daniël Lakens, Wijnand A. IJsselsteijn

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review


Habits often conflict with goal-directed behaviors and this phenomenon continues to attract interests from neuroscientists, experimental psychologists, and applied health psychologists. Recent computational models explain habit-goal conflicts as the competitions between two learning systems, arbitrated by a central unit. Based on recent research that combined reinforcement learning and sequential sampling, we show that habit-goal conflicts can be more parsimoniously explained by a dynamic integration of habit and goal values in a sequential sampling model, without any arbitration. A computational model was developed by extending the multialternative decision field theory with the assumptions that habits bias starting points of preference accumulation, and that goal importance and goal relevance determine sampling probabilities of goal-related attributes. Simulation studies demonstrated our approach’s ability to qualitatively reproduce important empirical findings from three paradigms – classic devaluation, devaluation with a concurrent schedule, and reversal learning, and to predict gradual changes in decision times. In addition, a parameter recovery exercise using approximate Bayesian computation showcased the possibility of fitting the model to empirical data in future research. Implications of our work for habit theories and applications are discussed.

Originele taal-2Engels
TijdschriftComputational Brain and Behavior
Nummer van het tijdschriftX
Vroegere onlinedatum5 mrt. 2024
StatusE-publicatie vóór gedrukte publicatie - 5 mrt. 2024


The research is supported by the Data Science Flagship collaboration between Eindhoven University of Technology and Philips Research.

Technische Universiteit Eindhoven


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