Fig. 2 | Nature Communications

Fig. 2

From: Feature-based learning improves adaptability without compromising precision

Fig. 2

Dynamic reward schedules promote feature-based learning whereas a lack of generalizability promotes object-based learning. a Performance or the average reward harvested by subjects during Experiments 1 (generalizable environment) and 2 (non-generalizable environment). Dashed lines show the mean performance and solid lines show the threshold used for excluding subjects whose performance was not distinguishable from chance (0.5). b Plotted is the Bayesian information criterion (BIC) based on the best feature-based or object-based models, separately for each environment. The insets show histograms of the difference in BIC from the two models for the generalizable (blue) and non-generalizable (red) environments. The dashed lines show the medians and the stars indicate significant difference from zero (two-sided rank-sum, P < 0.05). Subjects were more likely to adopt a feature-based approach in the generalizable environment and an object-based approach in the non-generalizable environment. c, d Time course of learning during each block of trials in Experiments 1 and 2. Plotted are the average harvested reward  (c) and probability of selecting the better option (d) in a given trial within a block across all subjects (the shaded areas indicate s.e.m.). The dashed line shows chance performance. The solid blue and red lines show the maximum performance based on the feature-based approach in the generalizable and non-generalizable environments, respectively, assuming that the decision maker selects the more rewarding option based on this approach on every trial. The maximum performance for the object-based approach was similar in the two environments, and equal to that of the feature-based approach in the generalizable environment

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