Autonomic Nervous System Correlates of Speech Categorization Revealed Through Pupillometry

Abstract

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-tonoise 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.

Auditory cortex is susceptible to lexical influence as revealed by informational vs. energetic masking of speech categorization

“…4B). This is consistent with the notion that phonetic features (e.g., speech categories) are more resilient to acoustic interference than their non-phonetic acoustic counterparts Bidelman et al, 2020a;Lewis and Bidelman, 2020). The leftward bias and temporal dynamics (IFG preceding AC) of this effect further imply that while categorical representations are evident in both higher-order (linguistic) and lower-level (auditory) cortex (e.g., Bidelman and Lee, 2015;Binder et al, 2004;Chang et al, 2010;Feng et al, 2018;Mankel et al, 2020;Myers et al, 2009), categories might be first decoded in IFG prior to their emergence in AC.…”

Effects of Noise on the Behavioral and Neural Categorization of Speech

“…In contrast, steady-state features like the F1 contrast of our static vowels lack an intrinsic reference so categorical hearing of these stimuli necessarily requires acoustic features be matched to the best exemplar in long-term memory (Pisoni, 1975;Xu et al, 2006). Thus, we explicitly chose vowels because they are likely to better reflect categorical brain processing as indexed via physiological measures (e.g., Lewis and Bidelman, 2020). Utilizing vowels also ensured the entire stimulus contributed to the categorical percept rather than only the initial transient onset (cf.…”

Comparing methods of analysis in pupillometry: application to the assessment of listening effort in hearing-impaired patients

“…Therefore, it appears that using a baseline period well before cue onset allowed us to measure both anticipatory processes and stimulus processing. While a few pupil-dilation studies have explicitly limited or controlled for the influence of anticipatory processes on pupil dilation ( Lewis and Bidelman, 2020 ; Winn et al., 2015 ), most studies of listening effort using implicit or explicit cues have focused exclusively on pupil dilation during stimulus processing. Anticipation has also been observed with pupillometry, when participants were engaged in a difficult attention task ( McCloy et al., 2016 , 2017 ) or preparing to answer questions in a task involving linguistic challenge ( Vogelzang et al., 2016 ).…”

“…(2015) Baseline correction Blocked Beginning of trial 2000 ms Active No Kramer et al. (1997) Baseline correction Blocked Prior to noise onset 1000 ms Active A Lewis and Bidelman (2020) Baseline correction Blocked Prior to stimulus onset 100 ms Active No Zellin et al. (2011) Baseline correction Event-related Prior to stimulus onset 200 ms Active V Wetzel et al.…”