Fig. 3 | Nature Communications

Fig. 3

From: Feature-based learning improves adaptability without compromising precision

Fig. 3

Transition from feature-based to object-based learning in static, non-generalizable environments. a The time course of performance during Experiment 3. The running average over time is computed using a moving box with the length of 20 trials. Shaded areas indicate s.e.m., and the dashed line shows chance performance. The red and blue solid lines show the maximum performance using the feature-based and object-based approaches, respectively, assuming that the decision maker selects the more rewarding option based on a given approach in every trial. Arrows mark the locations of estimation blocks throughout a session. For some subjects, there were only five estimation blocks indicated by black arrows. b The time course of model adoption measured by fitting subjects’ estimates of reward probabilities. Plotted is the relative weight of object-based to the sum of the object-based and feature-based approaches, and explained variance in estimates (R 2) over time. Dotted lines show the fit of data based on an exponential function. c Plotted is the fraction of subjects who showed a stronger correlation between their reward estimates and actual reward probabilities than the probabilities estimated using the reward values of features. The dotted line shows the fit of data based on an exponential function. d Transition from feature-based to object-based learning revealed by the average goodness-of-fit over time. Plotted are the average negative log likelihood based on the best feature-based model, best object-based RL model, and the difference between object-based and feature-based models during Experiment 3. Shaded areas indicate s.e.m., and the dashed line shows the measure for chance prediction. eh The same as in ad, but during Experiment 4.

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