Fig. 7 | Nature Communications

Fig. 7

From: Feature-based learning improves adaptability without compromising precision

Fig. 7

Replicating the pattern of experimental data using the PDML and HDML models. a Comparison of the goodness-of-fit in for the data generated by the PDML model in Experiments 1 (generalizable) and 2 (non-generalizable) using the object-based and feature-based RL models with decays. The insets show histograms of the difference in the negative log likelihood (-LL) based on the fits of the two models. In contrast to the experimental data, choice behavior of the PDML model in Experiment 1 was equally fit by the object-based and feature-based models. b The time course of model adoption in the PDML model. Plotted is the relative weight of object-based to the sum of the object-based and feature-based weights, and explained variance in estimates (R 2) over time in Experiment 3. Dotted lines show the fit of data based on an exponential function. c Transition from feature-based to object-based learning in the PDML model. 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 in Experiment 3. Shaded areas indicate s.e.m., and the dashed line shows the measure for chance prediction. d, e The same as in b, c, but for simulations of Experiment 4. fj The same as in ae, but for the HDML model. Although both models qualitatively replicated the pattern of experimental data in Experiments 2–4, only the behavior of HDML model was consistent with data in Experiment 1

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