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Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task

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  • Thomas Akam
  • Rui Costa
  • Peter Dayan

Abstract

The recently developed ‘two-step’ behavioural task promises to differentiate model-based from model-free reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted its widespread adoption. Here, we analyse the interactions between a range of different strategies and the structure of transitions and outcomes in order to examine constraints on what can be learned from behavioural performance. The task involves a trade-off between the need for stochasticity, to allow strategies to be discriminated, and a need for determinism, so that it is worth subjects’ investment of effort to exploit the contingencies optimally. We show through simulation that under certain conditions model-free strategies can masquerade as being model-based. We first show that seemingly innocuous modifications to the task structure can induce correlations between action values at the start of the trial and the subsequent trial events in such a way that analysis based on comparing successive trials can lead to erroneous conclusions. We confirm the power of a suggested correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies that exploit correlations between where rewards are obtained and which actions have high expected value. These generate behaviour that appears model-based under these, and also more sophisticated, analyses. Exploiting the full potential of the two-step task as a tool for behavioural neuroscience requires an understanding of these issues.Author Summary: Planning is the use of a predictive model of the consequences of actions to guide decision making. Planning plays a critical role in human behaviour, but isolating its contribution is challenging because it is complemented by control systems which learn values of actions directly from the history of reinforcement, resulting in automatized mappings from states to actions often termed habits. Our study examined a recently developed behavioural task which uses choices in a multi-step decision tree to differentiate planning from value-based control. We compared various strategies using simulations, showing a range that produce behaviour that resembles planning but in fact arises as a fixed mapping from particular sorts of states to action. These results show that when a planning problem is faced repeatedly, sophisticated automatization strategies may be developed which identify that there are in fact a limited number of relevant states of the world each with an appropriate fixed or habitual response. Understanding such strategies is important for the design and interpretation of tasks which aim to isolate the contribution of planning to behaviour. Such strategies are also of independent scientific interest as they may contribute to automatization of behaviour in complex environments.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1004648
    DOI: 10.1371/journal.pcbi.1004648
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    References listed on IDEAS

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    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. 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. Jaron T Colas & Wolfgang M Pauli & Tobias Larsen & J Michael Tyszka & John P O’Doherty, 2017. "Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-32, October.
    4. Zhewei Zhang & Yuji K. Takahashi & Marlian Montesinos-Cartegena & Thorsten Kahnt & Angela J. Langdon & Geoffrey Schoenbaum, 2024. "Expectancy-related changes in firing of dopamine neurons depend on hippocampus," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    5. 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.
    6. Julie J Lee & Mehdi Keramati, 2017. "Flexibility to contingency changes distinguishes habitual and goal-directed strategies in humans," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-15, September.
    7. Evan M Russek & Ida Momennejad & Matthew M Botvinick & Samuel J Gershman & Nathaniel D Daw, 2017. "Predictive representations can link model-based reinforcement learning to model-free mechanisms," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-35, September.
    8. He A Xu & Alireza Modirshanechi & Marco P Lehmann & Wulfram Gerstner & Michael H Herzog, 2021. "Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-32, June.

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