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Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized

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  • Amir Dezfouli
  • Bernard W Balleine

Abstract

Behavioral evidence suggests that instrumental conditioning is governed by two forms of action control: a goal-directed and a habit learning process. Model-based reinforcement learning (RL) has been argued to underlie the goal-directed process; however, the way in which it interacts with habits and the structure of the habitual process has remained unclear. According to a flat architecture, the habitual process corresponds to model-free RL, and its interaction with the goal-directed process is coordinated by an external arbitration mechanism. Alternatively, the interaction between these systems has recently been argued to be hierarchical, such that the formation of action sequences underlies habit learning and a goal-directed process selects between goal-directed actions and habitual sequences of actions to reach the goal. Here we used a two-stage decision-making task to test predictions from these accounts. The hierarchical account predicts that, because they are tied to each other as an action sequence, selecting a habitual action in the first stage will be followed by a habitual action in the second stage, whereas the flat account predicts that the statuses of the first and second stage actions are independent of each other. We found, based on subjects' choices and reaction times, that human subjects combined single actions to build action sequences and that the formation of such action sequences was sufficient to explain habitual actions. Furthermore, based on Bayesian model comparison, a family of hierarchical RL models, assuming a hierarchical interaction between habit and goal-directed processes, provided a better fit of the subjects' behavior than a family of flat models. Although these findings do not rule out all possible model-free accounts of instrumental conditioning, they do show such accounts are not necessary to explain habitual actions and provide a new basis for understanding how goal-directed and habitual action control interact.Author Summary: In order to make choices that lead to desirable outcomes, individuals tend to deliberate over the consequences of various alternatives. This goal-directed deliberation is, however, slow and cognitively demanding. As a consequence, under appropriate conditions decision-making can become habitual and automatic. The nature of these habitual actions, how they are learned, expressed, and interact with the goal-directed process is not clearly understood. Here we report that (1) habits interact with the goal-directed process in a hierarchical manner (i.e., the goal-directed system selects a goal, and then determines which habit should be executed to reach that goal), and (2) habits are learned sequences of actions that, once triggered by the goal-directed process, can be expressed quickly and in an efficient manner. The findings provide critical new experimental and computational information on the nature of habits and how they interact with the goal-directed decision-making.

Suggested Citation

  • Amir Dezfouli & Bernard W Balleine, 2013. "Actions, Action Sequences and Habits: Evidence That Goal-Directed and Habitual Action Control Are Hierarchically Organized," PLOS Computational Biology, Public Library of Science, vol. 9(12), pages 1-14, December.
  • Handle: RePEc:plo:pcbi00:1003364
    DOI: 10.1371/journal.pcbi.1003364
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    References listed on IDEAS

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    1. Will D Penny & Klaas E Stephan & Jean Daunizeau & Maria J Rosa & Karl J Friston & Thomas M Schofield & Alex P Leff, 2010. "Comparing Families of Dynamic Causal Models," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-14, March.
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    1. Carolina Feher da Silva & Todd A Hare, 2018. "A note on the analysis of two-stage task results: How changes in task structure affect what model-free and model-based strategies predict about the effects of reward and transition on the stay probabi," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-13, April.
    2. Wouter Kool & Fiery A Cushman & Samuel J Gershman, 2016. "When Does Model-Based Control Pay Off?," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-34, August.
    3. Amir Dezfouli & Bernard W Balleine, 2019. "Learning the structure of the world: The adaptive nature of state-space and action representations in multi-stage decision-making," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-22, September.
    4. Nitzan Shahar & Tobias U Hauser & Michael Moutoussis & Rani Moran & Mehdi Keramati & NSPN consortium & Raymond J Dolan, 2019. "Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-25, February.
    5. Proctor, K. Ryan & Niemeyer, Richard E., 2020. "Retrofitting social learning theory with contemporary understandings of learning and memory derived from cognitive psychology and neuroscience," Journal of Criminal Justice, Elsevier, vol. 66(C).
    6. Bruno Miranda & W M Nishantha Malalasekera & Timothy E Behrens & Peter Dayan & Steven W Kennerley, 2020. "Combined model-free and model-sensitive reinforcement learning in non-human primates," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-25, June.
    7. Thomas Akam & Rui Costa & Peter Dayan, 2015. "Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-25, December.

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