Abstract
Subordinate level category learning, which is thought to elicit perceptual expertise, affects allocation of attention to learned stimuli and creation of new perceptual and mnemonic representations. There is some controversy concerning whether effects of expertise are driven primarily by attention or formation of new perceptual representations (e.g. recruitment of the FFA). It is therefore desirable to study the neural correlates of category learning in the context of an attention manipulation. Interestingly, the N250, an ERP component associated with perceptual expertise, has a similar time course and postero-lateral scalp distribution to an attention-related ERP component, the selection negativity. Here we attempted to replicate and extend a previous study in which we found evidence dissociating the N250, which was sensitive to trained vs. untrained stimuli, from the selection negativity, which was sensitive to number of features shared with a rare target. Participants were trained over six sessions (an increase from our previous study) to categorize cartoon alien stimuli, followed by an EEG session during which participants detected single target alien and ignored non-targets that shared between zero and four features with the target. This task was performed on trained vs. untrained stimuli appearing at fast or slow presentations rates (an attempt to manipulate attentional load). The selection negativity scaled with the number of target features in the stimulus, but was insensitive presentation rate. The comparison of trained to untrained stimuli elicited an unexpected new component, which we call the N300p. This large negative component had a similar time course to the N250 but, unlike the N250, a clearly parietal scalp distribution. The N300p could be a new expertise component associated with novel aspects of our task, which included many-to-one mapping and a stimulus set in which participants were required to process disjunctions of highly interchangeable features.
Meeting abstract presented at VSS 2017