DISCUSSION
Using information about the current metabolic state to adapt to variable reward outcomes is critical for survival (Stephens, As postulated by the risk‐sensitive foraging theory (Stephens, “Optimal” choice scenarios for high‐ and low‐reward contexts. The circles denote the expected value of the high‐ and low‐risk option and the arrows denote the spread of the reward. Filled circle indicates the preferred choice. Panel (a) represents a scenario in a low‐mean/low‐reward context. When forced to choose between options of similar expected value but different risk (i.e., outcome variance), decision‐makers should prefer high‐risk options (filled circle) when hungry (because it is the only option that offers a chance of fulfilling their need), and prefer low‐risk options (open circle) when sated to ensure the goal state is achieved and avoid unnecessary downside costs that might be incurred if the high‐risk option is chosen. Panel (b) represents a scenario in a high‐mean/high‐reward context. The goal state can now be achieved with the low‐risk option so this should be chosen in a high‐reward context. The risky option should only be chosen if the needs cannot be met by choosing a safe option. Sated individuals can afford the costs (as this is still close to their goal state) and may therefore be more willing to gamble.While risk‐taking from description and experience are both modulated by the contextual value of the options presented, they are not equally susceptible to modulation by hunger. For experience‐based decisions, information about the availability of reward and the metabolic need is integrated (Abizaid et al., Importantly, the behavioral data showed that the decision context was important for choice behavior. For example, participants preferred the risky option in high‐reward contexts, but preferred the safer option when it was presented with a low‐mean stimulus in a mixed context. This contextual adaptability is beneficial for survival and recent work has provided a mechanistic explanation for these contextual effects in experiential risk‐taking (Moeller et al., What might the cognitive mechanisms of the hunger effect be? One possibility is that hunger might increase cognitive load or reduce memory capacity. These two domains might be more important for decisions from experience than decisions made from descriptions. Memory biases, particularly for big wins, could contribute to the asymmetrical effects we observe for experienced risk (Madan et al., Hunger did not affect description‐based risk‐taking, which is in line with the findings by Howard et al. (One potentially relevant difference between the two tasks in the present study is the proportion of mixed‐context (easy) trials; in the description task, half of trials were mixed context, whereas in the experience task, they were in a 4:1 ratio. This was primarily to keep the description‐based task matched to previous work. Further, it allowed us to look for any hunger‐related differences between loss, gain, and probability processing, which were absent in our data. We have not run this task without losses so we cannot be certain the effects would hold with rewards only.One might also argue that the differential effect of hunger on risk‐taking could be driven by the strong effect of task type. We observe that the overall risk‐taking behavior in both tasks is neutral, which is illustrated by an overall As in previous studies (e.g., Clark et al., We first opted for a design that was more similar in reward outcomes to the experience‐based task (similar to the design by Symmonds et al., Our data suggest that hunger does not impact risk‐taking for description‐based choices, at least when explicitly comparing two risky options with feedback provided, perhaps because the neural processes that drive explicit risk‐taking are not under the direct control of hunger.An important contribution of the current study is that we compared the effect of hunger on risk preferences for description‐ and experience‐based risks in the same individual following the same level of deprivation. It may be difficult to compare our results to those of existing studies due to varying levels of deprivation; some studies report 4 h of fasting (Levy et al., Our study demonstrates that the opposing risk patterns in the description–experience gap are driven by how risks are presented, rather than individual risk propensities. Previous studies suggested that these different risk patterns arise from memory biases (Madan et al., In conclusion, we found that hunger decreased risk‐taking for high‐reward context in decisions where outcome statistics had to be learned. This observation matches optimal foraging theory, which predicts a survival advantage when individuals consider the variability of resources in the environment according to the current level of energy reserves. For learned risks, hungry individuals considered their metabolic need and the availability of rewards when making choices, whereas sated individuals only considered the availability for rewards. Hunger did not alter explicit risk‐taking, suggesting that cognitive evaluation of risk may be unaffected. This is the first study that uses a within‐subject design to test the effects of hunger on risk attitudes for decisions involving learned and described risks in high‐ and low‐reward contexts. It provides new insights into the modulatory role of hunger in adaptive behavior. Further studies will need to address the neural processing involved in the effects of hunger on decision‐making under uncertainty.