Using artificial neural networks to reveal the human confidence computation
Fig 5
Stimulus category-wise model predictions of individual confidence for correct choices.
(A) We computed average confidence for correct trials within each stimulus category separately for each individual subject and plotted these against the corresponding quantities predicted by each model. All models generated strong and significant correlations with observed confidence, suggesting that they were able to capture stimulus-related variations in confidence. (B) The Top2Diff strategy generated the numerically highest correlations with human confidence. However, these correlations were not significantly higher than those generated by ProbAvgRes, ProbTop2Diff, Entropy, and PE 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 derived from the linear mixed models showed that the Top2Diff strategy generated the best fits to category-specific confidence for correct trials with an AIC difference of at least 10 points with all other strategies.