Figure 7.
The reduced Bayesian model qualitatively reproduces belief-updating behavior. All plots in this figure depict simulated data using the reduced Bayesian model. One model parameter, the hazard rate, was fit for each block to minimize the difference between model and subject predictions. A, Learning rate as a function of absolute error magnitude for different SDs of the generative distributions, as shown (compare Fig. 2B). B, Learning rate as a function of z-scored error, plotted as in A (compare Fig. 2C). C, Across-subject variability in the relationship between learning rate and z-scored error, simulated by fitting data from different subjects with different hazard rates (gray lines). The black line is the cumulative Weibull fit (compare Fig. 2D). D, Z-scored error (gray, left ordinate) and learning rate (black, right ordinate) plotted as a function of trials after a change point. The solid and dashed lines indicate mean ± SEM (compare Fig. 3A). E, Learning rate residuals plotted as a function of trials after a change point. Residuals were computed by subtracting the learning rates predicted by the cumulative Weibull fit shown in C from the actual learning rates, and thus reflect the portion of learning rate that was not explained by relative error magnitude. Points and error bars are mean ± SEM across all simulated data (compare Fig. 3B). F, Relative model uncertainty (computed as the minimal window containing at least 85% of the probability density in the predictive distribution specified by the model divided by the 85% width of the true generative distribution) plotted as a function of trials after a change point. The grayscale reflects the SD of the given task block, as indicated (compare Fig. 4C).