IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003364.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003364
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003364&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003364?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dimitrije Marković & Jan Gläscher & Peter Bossaerts & John O’Doherty & Stefan J Kiebel, 2015. "Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-34, October.
    2. Moe Okayasu & Tensei Inukai & Daiki Tanaka & Kaho Tsumura & Reiko Shintaki & Masaki Takeda & Kiyoshi Nakahara & Koji Jimura, 2023. "The Stroop effect involves an excitatory–inhibitory fronto-cerebellar loop," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. Andreea O Diaconescu & Christoph Mathys & Lilian A E Weber & Jean Daunizeau & Lars Kasper & Ekaterina I Lomakina & Ernst Fehr & Klaas E Stephan, 2014. "Inferring on the Intentions of Others by Hierarchical Bayesian Learning," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-19, September.
    4. Falk Lieder & Klaas E Stephan & Jean Daunizeau & Marta I Garrido & Karl J Friston, 2013. "A Neurocomputational Model of the Mismatch Negativity," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-14, November.
    5. Ahmed A Moustafa & Jony Sheynin & Catherine E Myers, 2015. "The Role of Informative and Ambiguous Feedback in Avoidance Behavior: Empirical and Computational Findings," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-21, December.
    6. Sam Gijsen & Miro Grundei & Robert T Lange & Dirk Ostwald & Felix Blankenburg, 2021. "Neural surprise in somatosensory Bayesian learning," PLOS Computational Biology, Public Library of Science, vol. 17(2), pages 1-36, February.
    7. repec:dau:papers:123456789/9572 is not listed on IDEAS
    8. Giovanni Leone & Charlotte Postel & Alison Mary & Florence Fraisse & Thomas Vallée & Fausto Viader & Vincent Sayette & Denis Peschanski & Jaques Dayan & Francis Eustache & Pierre Gagnepain, 2022. "Altered predictive control during memory suppression in PTSD," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    9. Jean Daunizeau & Kerstin Preuschoff & Karl Friston & Klaas Stephan, 2011. "Optimizing Experimental Design for Comparing Models of Brain Function," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-18, November.
    10. Fabien Vinckier & Lionel Rigoux & Irma T Kurniawan & Chen Hu & Sacha Bourgeois-Gironde & Jean Daunizeau & Mathias Pessiglione, 2019. "Sour grapes and sweet victories: How actions shape preferences," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-24, January.
    11. Eduardo A Aponte & Dario Schöbi & Klaas E Stephan & Jakob Heinzle, 2017. "The Stochastic Early Reaction, Inhibition, and late Action (SERIA) model for antisaccades," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-36, August.
    12. Jean Daunizeau & Vincent Adam & Lionel Rigoux, 2014. "VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data," PLOS Computational Biology, Public Library of Science, vol. 10(1), pages 1-16, January.
    13. Melody K Morris & Julio Saez-Rodriguez & David C Clarke & Peter K Sorger & Douglas A Lauffenburger, 2011. "Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-20, March.
    14. Alizée Lopez-Persem & Lionel Rigoux & Sacha Bourgeois-Gironde & Jean Daunizeau & Mathias Pessiglione, 2017. "Choose, rate or squeeze: Comparison of economic value functions elicited by different behavioral tasks," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-18, November.
    15. Li Liu & Amit Vira & Emma Friedman & Jennifer Minas & Donald Bolger & Tali Bitan & James Booth, 2010. "Children with Reading Disability Show Brain Differences in Effective Connectivity for Visual, but Not Auditory Word Comprehension," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-11, October.
    16. Richard P Mann & Andrea Perna & Daniel Strömbom & Roman Garnett & James E Herbert-Read & David J T Sumpter & Ashley J W Ward, 2013. "Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection," PLOS Computational Biology, Public Library of Science, vol. 9(3), pages 1-13, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003364. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.