Fig. 3: Behavior of the lesioned model.

a Stochasticity and volatility module inside the model compete to explain experienced noise. b–c Two characteristic lesioned models produce seemingly contradictory behaviors, because if the stochasticity module is lesioned, noise due to stochasticity is misattributed to volatility (b), and vice versa (c). d–f Mean learning rate is plotted for the 2 × 2 design of Fig. 2 for the healthy and lesioned models. For both the lesion models, lesioning does not merely abolish the corresponding effects on learning rate, but reverses them. Thus, the stochasticity lesion model shows elevated learning rate with increases in stochasticity (e), and the volatility lesion model shows reduced learning rate with increases in volatility (f). This is due to misattribution of the noise due to the lesioned factor to the existing module. g The stochasticity lesion model makes erroneous inference about volatility and increases its volatility estimate in higher stochastic environments. h The volatility lesion model makes erroneous inference about stochasticity and increases its stochasticity estimate for higher volatile environments. In fact, both the lesion models are not able to distinguish between the volatility and stochasticity and therefore show similar pattern for the remaining module. For the healthy model, volatility and stochasticity estimates are the same as Figs. 2b and 2c, respectively. Simulation and model parameters were the same as those used in Fig. 2. Errorbars reflect standard error of the mean computed over 10,000 simulations and are too small to be visible. Source data are provided as Source Data file.