Table 1.

Overview of the learning models. The initial value of the risky option $\left({\text{Q}}_1\right)$ was a free parameter in all models as well (not included in the table).

	Parameters
	Reinforcement learning (1)	Bayesian ideal-observer (2)
Basic models (A)	Learning rate α	Update rate π
Asymmetric learning (B): Stronger weighting of win outcomes promotes risk seeking	Learning rates for win and no-win outcomes: α+ and α−	Update rates for win and no-win outcomes: π+ and π−
Nonlinear utility function (C): Overvaluation of higher outcomes promotes risk seeking	Utility parameter $\mathrm{\kappa }$ $\mathrm{\kappa }$ > 1 and $\mathrm{\kappa }$ < 1 cause over- and undervaluation of higher outcomes, respectively	Utility parameter $\mathrm{\kappa }$
Uncertainty affects value (D): Uncertainty bonus promotes risk seeking	Not applicable	Uncertainty parameter $\phi$ $\phi$ > 0 and $\phi$ < 0 cause uncertainty bonus and penalty, respectively