Fig. 1 | Nature Communications

Fig. 1

From: Feature-based learning improves adaptability without compromising precision

Fig. 1

A framework for understanding model adoption during learning in dynamic, multi-dimensional environments. a Cross-over point is plotted as a function of the generalizability index of the environment for different values of the learning rate. The cross-over point increases with generalizability and decreases with the learning rate. The larger learning rate, however, comes at the cost of more noise in estimation (lower precision). The arrow shows zero cross-over point indicating that the object-based learning is always superior for certain environments. b Cross-over point is plotted as a function of generalizability separately for environments with different values of dimensionality (for α = 0.05). The advantage of feature-based over object-based learning increases with larger dimensionality. The inset shows the distribution of the generalizability index in randomly generated environments for three different dimensionalities. c The object-based approach for learning multi-dimensional options/objects requires learning n m values, where there are m possible features and n instances per feature in the environment, whereas the feature-based approach requires learning only n×m values resulting in a dimensionality reduction equal to (n mn×m). A feature-based approach, however, is beneficial if there are generalizable rules for estimating the reward values of options based on the combination of features’ values. A lack of generalizability should encourage using the object-based approach. Finally, frequent changes in reward contingencies (dynamic environment) should increase the use of feature-based learning because it allows update of multiple features based on a single feedback and thus increases adaptability without compromising precision

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