Adaptation to pitch-altered feedback is independent of one’s own voice pitch sensitivity

Abstract

Monitoring voice pitch is a fine-tuned process in daily conversations as conveying accurately the linguistic and affective cues in a given utterance depends on the precise control of phonation and intonation. This monitoring is thought to depend on whether the error is treated as self-generated or externally-generated, resulting in either a correction or inflation of errors. The present study reports on two separate paradigms of adaptation to altered feedback to explore whether participants could behave in a more cohesive manner once the error is of comparable size perceptually. The vocal behavior of normal-hearing and fluent speakers was recorded in response to a personalized size of pitch shift versus a non-specific size, one semitone. The personalized size of shift was determined based on the just-noticeable difference in fundamental frequency (F0) of each participant’s voice. Here we show that both tasks successfully demonstrated opposing responses to a constant and predictable F0 perturbation (on from the production onset) but these effects barely carried over once the feedback was back to normal, depicting a pattern that bears some resemblance to compensatory responses. Experiencing a F0 shift that is perceived as self-generated (because it was precisely just-noticeable) is not enough to force speakers to behave more consistently and more homogeneously in an opposing manner. On the contrary, our results suggest that the type of the response as well as the magnitude of the response do not depend in any trivial way on the sensitivity of participants to their own voice pitch. Based on this finding, we speculate that error correction could possibly occur even with a bionic ear, typically even when F0 cues are too subtle for cochlear implant users to detect accurately.

Identifying distinct latent classes of pitch-shift response consistency: Evidence from manipulating the predictability of shift direction

“…The latent class analyses identified three subgroups in our respondents: 57% switchers, 28% opposers, and 15% followers. The distribution of the three subgroups, to a certain degree, resembled the distribution in Alemi et al (2020) , who found 47% switchers, 41% opposers, and 12% followers. The methodological differences between our study and Alemi et al (2020) lie in three aspects.…”

“…The distribution of the three subgroups, to a certain degree, resembled the distribution in Alemi et al (2020) , who found 47% switchers, 41% opposers, and 12% followers. The methodological differences between our study and Alemi et al (2020) lie in three aspects. First, we examined vocal responses to sudden and short pitch shifts at a random point during vocalizations (compensation study), whereas Alemi et al (2020) examined adaptive responses to sustained pitch shifts that were applied from vocal onset to the end of vocalizations (adaptation study).…”

“…Identifying a reference point (internal vs. external) does not guarantee response consistency because a speaker may demonstrate both opposing and following responses across trials or across experiments. In a pitch-shift adaptation study, Alemi et al (2020) found that 41% of participants showed consistently opposing responses and 12% consistently exhibited following responses; however, 47% participants changed their response types from one experiment (adaptation to 100-cents pitch shift) to another (adaptation to personalized pitch shifts). The presence of a considerable proportion of following responses suggests that the two opposite mechanisms (opposing vs. following) could be activated simultaneously in the muscular control for vocal pitch ( Li et al, 2013 ; Franken et al, 2018 ).…”

“… Ning’s (2022) simulation results on a mix of opposing and following responses were obtained at the population level (i.e., pooling all participants together) and the grand average level (i.e., calculating the mean percentages). Alemi et al’s (2020) response inconsistency was discovered across experiments. In the present study, we would like to go into further detail on individual behaviors across trials within an experiment.…”

LaDIVA: A neurocomputational model providing laryngeal motor control for speech acquisition and production

“…A single study using DIVA was able to replicate this relationship for simulated participant responses, with vowel formant target sizes modeled using Gaussian distributions with varying distribution variances (i.e., vowel formant acuity was inversely related to distribution variance; [ 104 ]). However, in terms of laryngeal motor control, there have been conflicting prior study observations about the relationship between auditory feedback perturbation magnitudes and corresponding vocal f o acuity, suggesting that there could be a complex relationship (or lack thereof) between generating corrective feedback for auditory feedback errors and perceiving those feedback errors [ 68 , 98 , 105 , 106 ]. Including vocal f o acuity as a variable parameter will allow for modeling intra-speaker f o acuity variability in LaDIVA, helping to interpret these recent findings.…”