| Popis: |
Category learning helps us process the influx of information we experience daily. A commonly encountered category structure is “rule-plus-exceptions,” in which most items follow a general rule, but exceptions violate this rule. People are worse at learning to categorize exceptions than rule-following items, but improved exception categorization has been positively associated with hippocampal function. In light of model-based predictions that the nature of existing memories of related experiences should impact memory formation, here we use behavioural and computational modelling data to explore the impact of learning sequence on performance in a rule-plus-exception categorization task. Our 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 computational model of hippocampus replicate these behavioural findings. Representational similarity analysis of the model’s hidden layers, which correspond to hippocampal subfields, suggests that model representations are impacted by trial sequence: delaying the introduction of an exception shifts its representation closer to those of its own category members; this finding corroborates the superior categorization behaviour observed for delayed exceptions. Our results provide novel computational evidence of HC’s sensitivity to learning sequence and further support HC’s proposed role in category learning. |
