How virtual agents can learn to synchronize: An adaptive joint decision-making model of psychotherapy
Cognitive Systems Research, vol. 79, pp. 138–155
Abstract
Joint decision-making can be seen as the synchronization of actions and emotions, usually via nonverbal interaction between people while they show empathy. The aim of the current paper was (1) to develop an adaptive computational model for the type of synchrony that can occur in joint decision-making for two persons modeled as agents, and (2) to visualize the two persons by avatars as virtual agents during their decision-making. How to model joint decision-making computationally while taking into account adaptivity is rarely addressed, although such models based on psychological literature have a lot of future applications like online coaching and therapeutics. We used an adaptive network-oriented modelling approach to build an adaptive joint decision-making model in an agent-based manner and simulated multiple scenarios of such joint decision-making processes using a dedicated software environment that was implemented in MATLAB. Programming in the Unity 3D engine was done to virtualize this process as nonverbal interaction between virtual agents, their internal and external states, and the scenario. Although our adaptive joint decision model has general application areas, we have selected a therapeutic session as example scenario to visualize and interpret the example simulations.
Authors 6
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Sophie C. F. Hendrikse corresponding Aachen Methodology and Statistics Research Unit Institute of Psychology Faculty of Social and Behavioural Sciences
Leiden University · Vrije Universiteit Amsterdam
Affiliation as printed
Amsterdam Emotion Regulation Lab, Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Methodology and Statistics Research Unit, Institute of Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden, The Netherlands
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Affiliation as printed
Amsterdam Emotion Regulation Lab, Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Jan Treur corresponding
Affiliation as printed
Social AI Group, Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Tom F. Wilderjans Aachen Methodology and Statistics Research Unit Institute of Psychology Faculty of Social and Behavioural Sciences
Leiden University · Vrije Universiteit Amsterdam · KU Leuven
Affiliation as printed
Amsterdam Emotion Regulation Lab, Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
Methodology and Statistics Research Unit, Institute of Psychology, Faculty of Social and Behavioural Sciences, Leiden University, Leiden, The Netherlands
Research Group of Quantitative Psychology and Individual Differences, Faculty of Psychology and Educational Sciences, Katholieke Universiteit (KU) Leuven, Leuven, Belgium
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New York University · Vrije Universiteit Amsterdam
Affiliation as printed
Amsterdam Emotion Regulation Lab, Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
NYU – Max Planck Center for Language, Music and Emotion, New York University, United States
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Sander Leon Koole corresponding
Affiliation as printed
Amsterdam Emotion Regulation Lab, Department of Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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