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

PLoS Comput Biol. 2015 Dec 11;11(12):e1004648. doi: 10.1371/journal.pcbi.1004648. eCollection 2015 Dec.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Choice Behavior / physiology*
  • Computer Simulation
  • Habits*
  • Humans
  • Models, Neurological*
  • Models, Statistical*
  • Reinforcement, Psychology*
  • Reversal Learning / physiology*
  • Task Performance and Analysis*