Autonomic Nervous System Correlates of Speech Categorization Revealed Through Pupillometry

Human perception requires the many-to-one mapping between continuous sensory elements and discrete categorical representations. This grouping operation underlies the phenomenon of categorical perception (CP)—the experience of perceiving discrete categories rather than gradual variations in signal input. Speech perception requires CP because acoustic cues do not share constant relations with perceptual-phonetic representations. Beyond facilitating perception of unmasked speech, we reasoned CP might also aid the extraction of target speech percepts from interfering sound sources (i.e., noise) by generating additional perceptual constancy and reducing listening effort. Specifically, we investigated how noise interference impacts cognitive load and perceptual identification of unambiguous (i.e., categorical) vs. ambiguous stimuli. Listeners classified a speech vowel continuum (/u/-/a/) at various signal-to-noise ratios (SNRs [unmasked, 0 and −5 dB]). Continuous recordings of pupil dilation measured processing effort, with larger, later dilations reflecting increased listening demand. Critical comparisons were between time-locked changes in eye data in response to unambiguous (i.e., continuum endpoints) tokens vs. ambiguous tokens (i.e., continuum midpoint). Unmasked speech elicited faster responses and sharper psychometric functions, which steadily declined in noise. Noise increased pupil dilation across stimulus conditions, but not straightforwardly. Noise-masked speech modulated peak pupil size (i.e., [0 and −5 dB] > unmasked). In contrast, peak dilation latency varied with both token and SNR. Interestingly, categorical tokens elicited earlier pupil dilation relative to ambiguous tokens. Our pupillary data suggest CP reconstructs auditory percepts under challenging listening conditions through interactions between stimulus salience and listeners’ internalized effort and/or arousal.