Using artificial neural networks to reveal the human confidence computation
Fig 6
Stimulus category-wise model predictions of individual confidence for error trials.
(A) We computed average confidence for error trials within each stimulus category separately for each individual subject and plotted these against the corresponding quantities predicted by each model. As with correct trials, models generated strong and significant correlations with observed confidence, suggesting that they are generally able to capture stimulus-related variations in confidence. (B) The Top2Diff strategy generated the numerically highest correlations with human confidence, but these correlations were not significantly higher than those generated by ProbAvgRes, ProbTop2Diff, PE and BCH models. Error bars show 95% confidence intervals. (C) We fit a linear-mixed model that quantified how closely each model captured observed category-wise variations in confidence, while controlling for the repeated measurement across individuals. AIC scores from these regression models showed that the Top2Diff strategy generated the substantially better fits to category-specific confidence for error trials with an AIC difference of at least 62 points with all other strategies.