Production Variability and Categorical Perception of Vowels Are Strongly Linked

Chao, Sara Ching; Ochoa, D.O. Calderas; Daliri, Ayoub

doi:10.3389/fnhum.2019.00096

Cited by 17 publications

(14 citation statements)

References 52 publications

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“…Thus the magnitude of adaptation (learning from errors) and error sensitivity are larger when errors are task relevant. Based on the results of this study and previous studies (Chao et al 2019;Guenther 2016;Niziolek and Guenther 2013;Perkell 2012;Villacorta et al 2007), we speculate that the CNS uses a combination of strategies to determine relevance of errors and to assign weights to perceived auditory errors. First, the CNS may use the distance between the received auditory feedback and predicted auditory consequences of its speech motor outputs (auditory prediction error).…”

Section: Discussionsupporting

confidence: 62%

“…These two vowels were selected because the vowel // was perturbed toward the vowel /ae/ in the adaptation task, and therefore, it was important to examine participants' perception of the auditory perturbations. We used a procedure similar to the one used in our previous study (Chao et al 2019). Based on the extracted median tokens, a participant-specific vowel continuum (-ae) was generated in 10 successive equal increments (in Hz) such that the first token coincided with the participant's median // and the last token coincided with the participant's median /ae/ (Fig.…”

Section: B E Dmentioning

confidence: 99%

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Successful auditory motor adaptation requires task-relevant auditory errors

Daliri

Dittman

2019

Journal of Neurophysiology

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When we produce speech movements, we also predict the auditory consequences of the movements. We use discrepancies between our predictions and incoming auditory information to modify our future movements (adapt). Although auditory errors are crucial for speech motor learning, not all perceived auditory errors are consequences of our own actions. Therefore, the brain needs to evaluate the relevance of perceived auditory errors. In this study, we examined error assessment processes involved in auditory motor adaptation by systematically manipulating the correspondence between speech motor outputs and their auditory consequences during speaking. Participants ( n = 30) produced speech while they received perturbed auditory feedback (e.g., produced “head” but heard a word that sounded like “had”). In one condition, auditory errors were related to participants’ productions (task-relevant errors). In another condition, auditory errors were defined by the experimenter and had no correspondence with participants’ speech output (task-irrelevant errors). We found that the extent of adaptation and error sensitivity (derived from a state-space model) were greater in the condition with task-relevant auditory errors compared with those in the condition with task-irrelevant auditory errors. Additionally, participants with smaller perceptual targets (derived from a categorical perception task) adapted more to auditory perturbations, and participants with larger perceptual targets adapted less. Similarly, participants with smaller perceptual targets were more sensitive to errors in the condition with task-relevant auditory errors. Together, our results highlight the intricate mechanisms, involving both perception and production systems, that the brain uses to optimally integrate auditory errors for successful speech motor learning. NEW & NOTEWORTHY Feedback monitoring is essential for accurate speech production. By providing empirical results and a computational framework, we show that 1) the brain evaluates relevance of auditory errors and responds more to relevant errors, and 2) smaller perceptual targets are associated with more sensitivity to errors and more auditory motor adaptation.

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Section: Discussionsupporting

confidence: 62%

Section: B E Dmentioning

confidence: 99%

Successful auditory motor adaptation requires task-relevant auditory errors

Daliri

Dittman

2019

Journal of Neurophysiology

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“…However, this potentially introduces a mismatch between the perceptual measure and the production variability measure, since participants are likely to make different perceptual judgments when hearing their own recorded speech versus model speakers' speech. This point is bolstered by the findings reported in Chao et al (2019), where there was a strong correlation between participants' categorical perception boundary and a productionbased categorical boundary for the same /ε/ -/ae/ contrast. In that study, participants' own speech was used to create the synthesized vowel continuum for the identification task, specifically using participants' median F1 and F2 values for each vowel as the two endpoints.…”

Section: Relationship Between Perception Ability Production Contrast Distinctness and Production Variabilitymentioning

confidence: 55%

“…However, averaging over repeated trials omits information about trial-to-trial variability, which may be of relevance to both perception and production. In fact, Chao et al (2019) found that the location of participants' perceptual boundary between /ε/-/ae/ in American English, derived using an identification task, was correlated with the location of the boundary between these categories in production space, derived based on the distribution of tokens across repeated productions. That is, the categorical boundary was further away from the more variable vowel in the contrast.…”

Section: Links Between Speech Perception and Production Variabilitymentioning

confidence: 99%

“…Specifically, while we proposed to improve on previous perceptual measures by using a measure of categorical labeling consistency, we did not use participants' own productions to create the synthesized vowel contrast continuum used in the identification task. Given that Chao et al (2019) reported a strong relationship between the perception-based categorical boundary and the production-based boundary when selfproduced speech was used to create the perceptual stimuli, it is of importance to investigate whether associations between categorical labeling consistency, vowel contrast distance, and the two measures of production variability might be observed if labeling consistency is measured using individual-specific acoustic continua. Furthermore, in the specific context of the centering ratio measure, it seems likely that a task measuring individuals' ability to detect within-category differences may be more relevant for examining the relationship between perception and production variability.…”

Section: Relationship Between Perception Ability Production Contrast Distinctness and Production Variabilitymentioning

confidence: 99%

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Examining the Relationship Between Speech Perception, Production Distinctness, and Production Variability

Cheng

Niziolek

Buchwald

et al. 2021

Front. Hum. Neurosci.

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Several studies have demonstrated that individuals’ ability to perceive a speech sound contrast is related to the production of that contrast in their native language. The theoretical account for this relationship is that speech perception and production have a shared multimodal representation in relevant sensory spaces (e.g., auditory and somatosensory domains). This gives rise to a prediction that individuals with more narrowly defined targets will produce greater separation between contrasting sounds, as well as lower variability in the production of each sound. However, empirical studies that tested this hypothesis, particularly with regard to variability, have reported mixed outcomes. The current study investigates the relationship between perceptual ability and production ability, focusing on the auditory domain. We examined whether individuals’ categorical labeling consistency for the American English /ε/–/æ/ contrast, measured using a perceptual identification task, is related to distance between the centroids of vowel categories in acoustic space (i.e., vowel contrast distance) and to two measures of production variability: the overall distribution of repeated tokens for the vowels (i.e., area of the ellipse) and the proportional within-trial decrease in variability as defined as the magnitude of self-correction to the initial acoustic variation of each token (i.e., centering ratio). No significant associations were found between categorical labeling consistency and vowel contrast distance, between categorical labeling consistency and area of the ellipse, or between categorical labeling consistency and centering ratio. These null results suggest that the perception-production relation may not be as robust as suggested by a widely adopted theoretical framing in terms of the size of auditory target regions. However, the present results may also be attributable to choices in implementation (e.g., the use of model talkers instead of continua derived from the participants’ own productions) that should be subject to further investigation.

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