Proceedings of the Annual Meeting of the Cognitive Science Society

How do people decide whether to try out novel options? We argue that they utilize contextual information toefficiently generalize from learned functional relations in order to decide between known or novel options. In a contextualmulti-armed bandit task, in which rewards are a noisy function of observable features, we assess participants’ preferences fornewly introduced options. We show that participants preferably choose a novel option if its features indicate high rewards,but shun the option if its features indicate low rewards, a behavior that can only be explained by functional generalization.Moreover, we assess people’s preferences for novel options that have medium rewards to test whether they prefer options lesssimilar to experienced options, consistent with choices guided by uncertainty. Given that novel options normally come withobservable features, we argue that contextual learning is a parsimonious yet powerful explanation of behavior in the face ofnovelty.