Category learning is our ability to generalize across experiences and apply existing knowledge to new situations. Many real-world categories adhere to a “rule-plus-exceptions” structure, wherein most items are rule-followers, but a subset of “exceptions” violate category rules. Rule-plus-exception learning seems tightly coupled with hippocampal function. Though past work has demonstrated that prediction error drives hippocampus to form distinct representations of exceptions, limited work has investigated how this process impacts existing rule-follower representations. Here we use a neural network model of hippocampus to quantify how rule- follower representations are altered by the introduction of exceptions. By recording model representations of rule- followers before and after exceptions are introduced, we computed the shift in rule-follower representation elicited by exceptions. A rule-follower’s similarity to exceptions along category-relevant, but not irrelevant, dimensions predicted its degree of representational shift. This work furthers our understanding of how hippocampus supports the integration of surprising information in dynamic environments.

We explore the impact of learning sequence on performance in a rule-plus-exception categorization task. Behavioural results indicate that exception categorization accuracy improves when exceptions are introduced later in learning, after exposure to rule-following stimuli. Simulations of this task using a neural network model of hippocampus and its subfields replicate these behavioural findings. Representational similarity analysis of the model’s hidden layers suggests that model representations are also impacted by trial sequence. These results provide novel computational evidence of hippocampus’s sensitivity to learning sequence and further support this region’s proposed role in category learning.

Ways in which ovarian hormones affect cognition have been long overlooked in psychology and neuroscience research despite strong evidence of their effects on the brain. In order to address this gap, we study performance on a rule-plus-exception category learning task, a complex task that requires careful coordination of core cognitive mechanisms, across the menstrual cycle. Results show that the menstrual cycle distinctly affects learning of exceptions in a manner that matches the typical estradiol cycle. Furthermore, participants in their high estradiol phase outperform participants in their low estradiol phase, and show steeper learning slopes than men in exception-learning. These results provide novel evidence of the role of estradiol in category learning, underscore the importance of recruiting diverse samples in cognitive neuroscience research, and highlight the ways in which cognition varies throughout the fundamental biological cycles of the human experience.