Proceedings of the Annual Meeting of the Cognitive Science Society

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.