Information uncertainty influences learning strategy from sequentially delayed rewards
Fig 4
Model prediction of trial-by-trial choice.
(A) Average participants’ propensity to choose left on three example pairs from one of the reward random walks, selected to show a clean reversal moving from left-to-right choice. The title identifies the reward contingency (I = Immediate, D = Delayed) with the initial value enclosed in parentheses (4 = initial start 4, -1 = initial start -1). The y-axis is the probability of selecting the left choice from the title, such as I(4)-D(1) illustrating the left choice to be the immediate option with starting value 4. The grey dotted line tracks when the optimality of the reward shifts from left (1.00) to equivalent (0.50) and then to right (0.00). Thus, participants in the different conditions (disjoint, conjoint) and different stages (1st, 2nd) should follow the optimality of reward with participant data on top. Further, lighter colors belong to the same group as darker colors. (B) The same analysis was performed on choice data generated by the hybrid model, using the best-fitting participant parameters. (C) Predictive accuracy (i.e., percentage of model-predicted choice matching participant choice) for each model and each pair type with delayed vs. delayed (Del v Del), immediate vs. immediate (Imm v Imm), and immediate vs. delayed (Mixed), shown as a boxplot across participants’ best-fitting parameters. (D) Fixed effects from a logistic regression predicting choice on each trial from tabular predictions, eligibility predictions, and their interaction with condition (equation 2), aligning with our hypothesis that tabular predicts behavior better in disjoint than in the conjoint condition (significant disjoint * tabular interaction), while eligibility only showed the opposite direction (non-significant disjoint * eligibility interaction). Dots and associated numbers represent the odds ratio for each effect; horizontal error bars represent 95% confidence intervals. *** p < .001.