On the relative importance of the hot stove effect and the tendency to rely on small samples

Experiments have suggested that decisions from \textit{experience} differ from decisions from \textit{description}. In experience-based decisions, the decision makers often fail to maximise their payoffs. Previous authors have ascribed the effect of underweighting of rare outcomes to this deviation from maximisation. In this paper, I re-examine and provide further analysis on the effect with an experiment that involves a series of simple binary choice gambles. In the current experiment, decisions that bear small consequences are repeated hundreds of times, feedback on the consequence of each decision is provided immediately, and decision outcomes are accumulated. The participants have to learn about the outcome distributions through sampling, as they are not explicitly provided with prior information on the payoff structure. The current results suggest that the ``hot stove effect'' is stronger than suggested by previous research and is as important as the payoff variability effect and the effect of underweighting of rare outcomes in analysing decisions from experience in which the features of gambles must be learned through a sampling process.