Humans are resource-rational predictors in a sequence learning task

Organisms can solve complex tasks despite having limited cognitive resources when those resources are used optimally. Doing so optimally makes an organism "resource-rational". In this paper, we show for the first time that humans are resource-rational at prediction. In a novel sequence learning experiment, participants predict data generated from hidden Markov models (HMMs) and receive online feedback via clicker training. We compute the predictive rateaccuracy (PRA) curve for each HMM to solve for the highest accuracy achievable for a given cognitive capacity, or "rate", quantified as the mutual information between participants predictions and the underlying causal state of the sequence being predicted. We found that the majority of participants achieve near-optimal prediction with various cognitive capacities, despite performing well below the maximum predictive accuracy on the task overall. We also show that this information-theoretic approach to measuring cognitive capacity can be grounded in the established psychological science concept of working memory: participants who extracted higher quantities of mutual information in the sequence learning task showed significantly higher working memory in a complex digit span test. This research provides new avenues for assessing smart behavior in difficult prediction tasks, provides a new methodology for assessing resource-rationality, and provides evidence that humans are resource-rational predictors.