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Chunk #13 — RESULTS — Relationship between neural SPE signal and behavior

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States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning.
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We next considered whether this neural correlate of an SPE is also behaviorally relevant for making better choices at the beginning of the free-choice session. To address this question, we correlated in each participant the parameter estimate for the SPE in those regions possessing a significant SPE representation in session 1 (bilateral latPFC and right pIPS, extracted and averaged from a 10 mm spherical volume centered on the group peak voxel) with the percent correct choices. The latter is a behavioral measure defined as the choice of the action with the highest expected value (reward magnitude × true transition probability) (see Figure S1), and is independent of the computational models employed for the imaging analysis. We observed a significant correlation between the neural and the behavioral data of r = 0.57 (p = 0.013) in the right pIPS, but not in lateral PFC (left: r = 0.28, p = 0.27; right; r = 0.38, p = 0.12). This suggests that the degree to which pIPS encodes an SPE representation across subjects, correlates significantly with the extent to which subjects deploy a forward model in guiding their choices at the beginning of session 2 (see Figure 5).