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Does temporal discounting explain unhealthy behavior? A systematic review and reinforcement learning perspective

期刊

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fnbeh.2014.00076

关键词

discounting; health; addiction; model-based; model-free; habit; preference reversal; hyperbolic

资金

  1. Wellcome Trust Senior Investigator Award [098362/Z/12/Z]
  2. Wellcome Trust [091593/Z/10/Z]
  3. National Institute for Health Research [NF-SI-0510-10186] Funding Source: researchfish

向作者/读者索取更多资源

The tendency to make unhealthy choices is hypothesized to be related to an individual's temporal discount rate, the theoretical rate at which they devalue delayed rewards. Furthermore, a particular form of temporal discounting, hyperbolic discounting, has been proposed to explain why unhealthy behavior can occur despite healthy intentions. We examine these two hypotheses in turn. We first systematically review studies which investigate whether discount rates can predict unhealthy behavior. These studies reveal that high discount rates for money (and in some instances food or drug rewards) are associated with several unhealthy behaviors and markers of health status, establishing discounting as a promising predictive measure. We secondly examine whether intention-incongruent unhealthy actions are consistent with hyperbolic discounting. We conclude that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting. We propose a framework for understanding these state-based effects in terms of the interplay of two distinct reinforcement learning mechanisms: a model-based (or goal-directed) system and a model-free (or habitual) system. Under this framework, while discounting of delayed health may contribute to the initiation of unhealthy behavior, with repetition, many unhealthy behaviors become habitual; if health goals then change, habitual behavior can still arise in response to environmental cues. We propose that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes.

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