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

BioRxiv : the Preprint Server for Biology
Thomas AkamPeter Dayan

Abstract

The recently developed ‘two-step’ behavioural task promises to differentiate model-based or goal-directed from model-free or habitual reinforcement learning, while generating neurophysiologically-friendly decision datasets with parametric variation of decision variables. These desirable features have prompted widespread adoption of the task. However, the signatures of model-based control can be elusive – here, we investigate model-free learning methods that, depending on the analysis strategy, can masquerade as being model-based. We first show that unadorned model-free reinforcement learning 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 also suggest a correction to the analysis that can alleviate this problem. We then consider model-free reinforcement learning strategies based on different state representations from those envisioned by the experimenter, which generate behaviour that appears model-based under these, and also more sophisticated, analyses. The existence of such strategies is of particular relevance to the design and interpretation of animal studies using t...Continue Reading

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