Wagers for work: Decomposing the costs of cognitive effort
Fig 3
Computational modeling results.
A. The number of subjects best fit by each model with a non-zero model frequency. Of the 84 computational models fit to subjects’ fair wages, the winning models were alpha cost-learning models containing update costs (cupdate), interference costs (cinterference), and maintenance costs (cmaintenance), and false alarm costs (cfa), in various combinations. The model with the highest model frequency was the model including update costs alone. B. The mean of the posterior distribution of each cost parameter from the models that best fit at least 1 subject’s fair wages. These posterior distributions were calculated by combining inferred parameter distributions across subjects and across models. Inference was performed over joint 4D distributions to capture co-variance between update, interference, maintenance, and false alarm costs. For plotting purposes we summed over the three irrelevant dimensions for each parameter to construct its marginal distribution, and then calculated the means and variances of the marginals. Error bars reflect the hierarchical standard error of the mean; they were calculated not with the square root of the total number of subjects in the denominator, but with the square root of the number of subjects’ data explained by models containing that parameter. Note that the error bars describe the spread of the marginal parameter distributions, not variance in the fitting process, and thus are not suitable for estimating the statistical significance of the effects plotted. C. Real (solid lines) versus simulated (dashed lines) fair wage ratings on each rating iteration for each task. Data simulated using each subjects’ best model faithfully reproduces real subject data (r2 = 0.52).