Supplementary Figure 3: Simulations with a context-aware sampling model.

We ran simulations using a context-aware sampling model. The model reinstates a first episode with a probability related to its temporal recency, then some number of other episodes in turn. Each new reinstated episode is, with some probability, drawn from the same context as the previous reinstated episode. (See Methods for details of the model’s implementation.) In this task, when more than a few samples are drawn, the context-aware sampling model predicts that the effect of reminded context should be greater than the effect of reminded trials. a. Shown here is the regression model from Figure 2, fit to a simulated population of 32 subjects that sampled 12 episodes between each probe and the ensuing choice. Error bars are +/- 1 SEM. b. Simulations show that the influence of context reward should increase with greater numbers of episodic samples. We simulated the context-aware sampling model to generate populations of 1,600 subjects (50 groups of 32), holding fixed all parameters except the number of samples drawn in support of each decision. We then performed the regression analysis shown in Figure 2 of the main text on this simulated data, and plot here the regression weights for single-trial (item) rewards and context rewards. As the number of samples increased, so did the ratio of the effect of context reward to that of the reminded trial (correlation between context and single-trial effects: R(13)=−0.9436, P=1.3125e−07). Error bars are +/- 1 SEM.