Motivating Machines: The Potential of Modeling Motivation as MoA for Behavior Change Systems

Fawad Taj*, Michel C.A. Klein, Aart Van Halteren

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

The pathway through which behavior change techniques have an effect on the behavior of an individual is referred to as the Mechanism of Action (MoA). Digitally enabled behavior change interventions could potentially benefit from explicitly modelling the MoA to achieve more effective, adaptive, and personalized interventions. For example, if ‘motivation’ is proposed as the targeted construct in any behavior change intervention, how can a model of this construct be used to act as a mechanism of action, mediating the intervention effect using various behavior change techniques? This article discusses a computational model for motivation based on the neural reward pathway with the aim to make it act as a mediator between behavior change techniques and target behavior. This model’s formal description and parametrization are described from a neurocomputational sciences prospect and elaborated with the help of a sub-question, i.e., what parameters/processes of the model are crucial for the generation and maintenance of motivation. An intervention scenario is simulated to show how an explicit model of ‘motivation’ and its parameters can be used to achieve personalization and adaptivity. A computational representation of motivation as a mechanism of action may also further advance the design, evaluation, and effectiveness of personalized and adaptive digital behavior change interventions.

Original languageEnglish
Article number258
Pages (from-to)1-14
Number of pages14
JournalInformation (Switzerland)
Volume13
Issue number5
Early online date17 May 2022
DOIs
Publication statusPublished - May 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • AI in health
  • AI-powered behavioral change support systems
  • behavior change techniques
  • computational modeling
  • motivation
  • pervasive health system

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