Supplementary Fig. 6: Use of a graded learning curve for behavioral metric confirms findings.

As an alternative measure of the learning rate for each subject, we use the slope of response accuracy across all three sessions of the learning phase in Day 1. We observe that this learning rate has a positive correlation with the dimension of representation across subjects, with a non-parametric permutation test yielding p < 0.006 (n = 1000 bootstrapped samples, n = 19 subjects). Specifically, we consider the slope of response accuracy across all three sessions of the learning phase on Day 1; this metric provides an estimate of the rate at which individuals learned to associate the assigned values to the presented shapes. To unpack this metric a bit further, we note that as each session consisted of 132 trials, where responses to each trial were binary (right or wrong), we examine the number of correct responses within a given window to give an average accuracy for that window. Windows of 22 trials were chosen in order to create 6 equally sized windows for each session. Hence, the three learning sessions on Day 1 yield 18 windows, and we calculate the slope of response accuracy across those windows for each individual. Next, we calculate the correlation between this slope (or learning rate) and the dimension of the stimuli representation from day 4. We found that the two variables were positively correlated with one another (Pearson’s r = 0.34, one-sided, n = 19 subjects), confirming the findings that we report in the main text.