Assessing the fit between phonological feature theories and neural data

Abstract

Phonological features have been called the most important advance in linguistic theory of the 20th century, yet there is still no consensus regarding their epistemic basis. Many feature systems are based on articulatory properties of consonants and acoustic properties of vowels, others are solely articulatory, and perceptually-based feature systems are rare, despite the fact that phonemes (and by extension, features) allegedly reflect how speech sounds are represented in the brain. Here we examine the fit between several phonological feature systems and electroencephalographic (EEG) recordings made during speech perception. We label EEG data with phonological feature values from different systems, train classifiers to categorize the EEG signals, and assess the accuracy of classifying held-out unlabeled EEG data. By varying only the phonological feature system from which we draw the training labels, we can assess the extent to which neural data recapitulate the patterns inherent in the different feature systems.