Does temporal discounting explain unhealthy behavior?

Front Behav Neurosci. 2014 Mar 12;8:76. eCollection 2014.

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

Story GW1, Vlaev I2, Seymour B3, Darzi A2, Dolan RJ4.

Author information

• 1Department of Surgery and Cancer, Centre for Health Policy, Institute of Global Health Innovation, Imperial College London London, UK ; Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK.
• 2Department of Surgery and Cancer, Centre for Health Policy, Institute of Global Health Innovation, Imperial College London London, UK.
• 3Center for Information and Neural Networks, National Institute for Information and Communications Technology Tokyo, Japan.
• 4Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK.

Abstract

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.