Proceedings of the Annual Meeting of the Cognitive Science Society

Human decision-making deviates from the optimal solution,i.e. the one maximizing cumulative rewards, in many sit-uations. Here we approach this discrepancy from the per-spective of computational rationality and our goal is to pro-vide justification for such seemingly sub-optimal strategies.More specifically we investigate the hypothesis, that humansdo not know optimal decision-making algorithms in advance,but instead employ a learned, resource-constrained approxima-tion. The idea is formalized through combining a recently pro-posed meta-learning model based on Recurrent Neural Net-works with a resource-rational objective. The resulting ap-proach is closely connected to variational inference and theMinimum Description Length principle. Empirical evidenceis obtained from a two-armed bandit task. Here we observepatterns in our family of models that resemble differences be-tween individual human participants.