Proceedings of the Annual Meeting of the Cognitive Science Society

The ability to form relational categories for objects that share few features in common is a hallmark of humancognition. However until recently, neuroimaging research largely focused solely on how people acquire categories defined byfeatures. In the current electroencephalography (EEG) study, we examine how relational and feature-based category learningcompare in well-matched learning tasks. Building on a previous functional magnetic resonance imaging study by our labo-ratory, we capitalise on the rich temporal information offered by EEG. Focusing on the neural dynamics of how people learncategory memberships over individual trials in an experimental task, we investigate how these single trial dynamics modulatecomputational estimates from decision-making modelling frameworks. Specifically, by sorting participants’ individual trialsby their position in the experimental sequence we observe striking relationships between EEG dynamics (e.g., frontal thetaoscillations and P300 component) and feature-based and relational categorisation behaviour.