Extended Data Fig. 1. Data fitting with a reinforcement learning model that allows for a shift between model-based (MB) and model-free (MF) learning.

(A) Model fit results for our MB vs MF reinforcement learning model. Note that it can also replicate our behavioral results well. (B) Schematic of the critical aspect of the model and the expected result: the observation rate for both the MB and MF systems, as well as the potential contribution of each to behavior, were free parameters, and we expected that the contribution of the MB system would be diminished, either by a reduced MB observation rate or an increase in the MF contribution. (C) Values of the critical observation rate-related parameters, namely the proportion of contribution of the MF (w^mf) system, the MF observation rate (η^mf), and the MB observation rate (η^mb) for both control and hM4d model fits (two-tailed unpaired t-test; P=0.007**). Note that instead of a reduction in MB learning or proportional contribution, only the MF observation rate was significantly higher in the hM4d group. See Supplementary Table 2 for detailed parameter comparisons. (D) Correlations between estimated and original parameters for the MB vs MF model. Note that parameter recovery of all critical observation rate-related parameters was not very faithful (linear regression; r < 0.7). Data are represented as mean ± SEM. CTRL n= 13 and hM4d n=15 fits of data from biologically independent animals. **P<0.01.