Modeling of risk‐sensitive choice behavior
PEIRS
The previous analyses showed that hunger only altered decision‐making when risks had to be learned. However, the behavioral analyses do not provide insight into what computational process was altered by hunger. Therefore, we employed a computational modeling strategy to account for the integration of a specific reward history triggered by sampling. This strategy allowed us to attribute the effects of hunger to a specific computational process. We applied a reinforcement learning model to explain the behavioral data. We used an adapted version of standard reinforcement learning (RW) (Rescorla & Wagner, Reinforcement learning models describe the learning process in associative learning when subjects learn from the discrepancy between what is expected to happen and what actually happens. The expected mean value of the chosen stimulus The equations so far provide trial‐by‐trial estimates of the expected mean value of each stimulus, but do not consider the variability in outcomes. The PEIRS model extends standard RW learning by accounting for both the average outcome and the variability, or spread (The PEIRS model accounts for how participants differentiate matched mean stimuli based on the spread and captures individual risk propensities. For this model, the utility that was entered into the softmax function (Equation The third term captures the biasing effect of high‐ or low‐reward contexts on choice behavior. Context effects play an important modulatory role in risky decision‐making (De Martino et al., 