Computational model fitting
We used a reinforcement learning model to further assess the effects of hunger on experience‐based risk‐taking. The model itself is described in the results section. We used a hierarchical model‐fitting strategy that takes into account the likelihood of individual participant choices given the individual participant parameters and also the likelihood of the individual participant parameters given the parameter distribution in the overall population across conditions. This two‐stage hierarchical procedure is an estimation strategy of the iterative expectation–maximization algorithm (EM) (Guitart‐Masip et al., In the fitting procedure, all context trials were used to estimate all parameters. Forced sampling trials were only included for the estimation of learning rates for the mean and variance of a stimulus using Equations (