An LDA Approach to the Neural Correlates of Configural Learning

Abstract

The purpose of our current study is to employ linear discriminant analysis (LDA; Philiastides & Sajda, 2006) to characterize the changes in ERPs over the entire course of a perceptual learning task. Configural learning is the perceptual learning process by which participants develop configural processing strategies or representations characterized by extremely efficient parallel information processing (Blaha & Townsend, Under Revision). Participants performed a perceptual unitization task in which they learned to categorize novel images. Correct categorization responses required exhaustive feature identification, which encouraged unitization of images into unified object percepts. Linear discriminator accuracy, measured by Az, increased each day of training, showing significant differences in neural signals between categories on and after training day 3 or 4 for all participants. Additionally, the LDA training window starting time resulting in discriminator performance of 65% accuracy or better shifted from 450-500ms to 300ms after stimulus onset at the completion of training. LDA results are consistent with our earlier report (Blaha & Busey, 2007} of peak ERP amplitude differences between categories after training at approximately 170ms and 250ms after stimulus onset. Our EEG results are consistent with the hypothesis that perceptual unitization results in configural perceptual processing mechanisms.


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