Philip J. Monahan (University of Toronto) will give a virtual colloquium on November 4th.
Title: Our Neurophysiology Supports Abstract Phonological Features: Before and During Speech
Abstract: The fundamental nature of speech sound representations remains intensely debated. This is despite over a half-century of detailed work. While generative theories have long posited a role for abstract phonological features, their support from psycholinguistic investigation has been sparse and sometimes equivocal. This talk presents the results from four experiments investigating how speech sounds are represented in the brain. All four experiments employ a many-to-one oddball paradigm. In such designs, participants habituate to a repeated standard stimulus which is intermittently interrupted by a deviant. Human brain responses are recorded and the mismatch negativity (MMN) is measured. First, I use magnetoencephalography (MEG) to investigate how mid vowels are represented. Across two experiments, I report asymmetric responses for when the standard is a low or high vowel compared to when the standard is a mid vowel. These results are consistent with an underspecified featural account for the place of articulation of mid vowels. Moreover, evidence from the time-frequency domain suggests that this abstract featural knowledge plays a role in predicting the upcoming stimulus. Next, I present results from two electroencephalography (EEG) experiments using a similar paradigm. In particular, I test consonant voicing in English and retroflex consonants in Mandarin Chinese. Both experiments again show asymmetric brain responses consistent with featural representations of voicing and retroflex. Time-frequency analyses of the EEG response highlight differences prior to the presentation of the stimulus, suggesting that the brain utilizes such featural representations to predict upcoming speech sounds. Taken together, abstract phonological features are not only supported by the brain but are used to help predict the incoming signal.
Zoom information:
- Meeting link: https://ubc.zoom.us/j/69269494816?pwd=NitDZWgxL1VLeW4vZzZOdHVJbVJRQT09
- Meeting ID: 692 6949 4816
- Passcode: 335547
