Proceedings of the Annual Meeting of the Cognitive Science Society

When making category decisions, humans sample features following their dynamic informativeness. Attention optimization models successfully predict these categorization behaviors, but optimization is not the only solution. Alternatively, categorization can be viewed as a Reinforcement Learning (RL) task in which learners sample information based on its expected utility. However, RL models of information sampling have high computational load, even though human learners solve this problem on the millisecond timescale. Therefore, we propose ATHENA-RSS, a model that implements reward-based information search in a more computationally efficient way, via the rapid sequential storage of memories and recurrent retrieval. To test the model, we conducted an experiment where participants (N = 99) learned hierarchically structured categories by uncovering stimulus features. We then conducted a simulation study, where ATHENA-RSS successfully reproduced all search patterns exhibited by participants. We conclude that rapid sequential storage and recurrent memory retrieval were sufficient to achieve human-like information sampling in this task.