The Cognitive Neuroscience of Category Learning
Category learning is an adaptive skill that enables organisms to efficiently interpret and respond to their environment. Evidence supports the existence of a declarative learning system that applies explicit rules and test hypotheses, and a procedural learning system that gradually forms stimulus-response associations through reinforcement learning. Questions that remain underexplored in the category learning literature include the extent to which prior learning experience shapes learning ability, and how distinct cognitive characteristics and age impact an individuals’ ability to acquire categories. Understanding these influences is important because they explain why learning efficiency and flexibility vary between individuals and across the lifespan. This thesis aims to address this gap by examining how participants adapt to changing learning demands, how learning ability alters with age, and how specific cognitive domains contribute to successful category acquisition. The first study examined task order effects in category learning and found that early reinforcement of a simple rule-based approach impeded later engagement with complex declarative and procedural learning, whereas prior exposure to more complex procedural or declarative tasks enhanced subsequent performance across both systems. The second study assessed age-related differences by comparing younger and older adults’ performance on declarative and procedural tasks, revealing deficits in older adult performance across both systems. The final study examined relationships between performance in several novel cognitive domains and category learning. Results found that fluid reasoning ability and visual working memory predicted performance in simple declarative but not complex declarative rule-based or procedural tasks. Different components of visual-spatial processing skills were found to relate to different task types. For example, attention to detail supported performance on simple rule-based tasks, whereas abstract pattern recognition and visual reasoning were linked to success in more complex declarative and procedural conditions. Collectively, the results highlight how task complexity and category structure recruit distinct cognitive abilities and identify that prior learning history shapes learners’ expectations and subsequent performance. Additionally, findings raise the possibility that cognitive flexibility influences not only how efficiently learners apply explicit strategies in the declarative system, but also how readily they shift control to the procedural system when explicit strategies are unsuccessful. Taken together, these findings provide evidence to support the dual-system model of category learning, while emphasising that system engagement and system switching are constrained by individual age and cognitive profiles, task demands, and learning history.