Towards a neural measure of perceptual distance-classification of electroencephalographic responses to synthetic vowels

How vowels are organized cortically has previously been studied using auditory evoked potentials (AEPs), one focus of which is to determine whether perceptual distance could be inferred using AEP components. The present study extends this line of research by adopting a machine-learning framework to classify evoked responses to four synthetic mid-vowels differing only in second formant frequency (F2 = 840, 1200, 1680, and 2280 Hz). 6 subjects attended 4 EEG sessions each on separate days. Classifiers were trained using time-domain data in successive time-windows of various sizes. Results were the most accurate when a window of about 80 ms was used. By integrating the scores from individual classifiers, the maximum mean binary classification rates improved to 70% (10 trials) and 77% (20 trials). To assess how well perceptual distances among the vowels were reflected in our results, discriminability indices (d') were computed using both the behavioral results in a screening test and the classification results. It was found that the two set of indices were significantly correlated. The pair that was the most (least) discriminable behaviorally was also the most (least) classifiable neurally. Our results support the use of classification methodology for developing a neural measure of perceptual distance.