Fig. 3: Computational modeling reveals characteristic patterns of maladaptive learning in subgroups of participants.

a–c Learning rate coefficients from the model-agnostic analysis are plotted for two groups of participants with positive (n = 158) and negative (n = 65) sensitivity to stochasticity quantified using the Kalman model. Negative sensitivity to stochasticity does not merely abolish the corresponding effects on the learning rate but reverses them. Moreover, maladaptive stochasticity learners show adaptive behavior with respect to the true volatility factor. d–f. Learning rate coefficients are plotted for two groups of participants with positive (n = 159) and negative (n = 64) sensitivity to volatility. Similarly, the learning rate coefficients are plotted for two groups of participants categorized by their sensitivity to volatility: one group with positive sensitivity and the other with negative sensitivity to volatility. Negative sensitivity to volatility also goes beyond nullifying the corresponding effects on the learning rate and actually flips them. Moreover, maladaptive volatility learners show adaptive behavior with respect to the true stochasticity factor. Note that the two maladaptive groups do not show substantial overlap, with only 8% of participants exhibiting both types of maladaptively. Mean and standard error of the mean are plotted.