Listener expectations and the perceptual accommodation of talker variability: A pre-registered replication

Published: 04 May 2021Researchers have hypothesized that in order to accommodate variability in how talkers produce their speech sounds, listeners must perform a process of talker normalization. Consistent with this proposal, several studies have shown that spoken word recognition is slowed when speech is produced by multiple talkers compared with when all speech is produced by one talker (a multitalker processing cost). Nusbaum and colleagues have argued that talker normalization is modulated by attention (e.g., Nusbaum & Morin, 1992, Speech Perception, Production and Linguistic Structure, pp. 113–134). Some of the strongest evidence for this claim is from a speeded monitoring study where a group of participants who expected to hear two talkers showed a multitalker processing cost, but a separate group who expected one talker did not (Magnuson & Nusbaum, 2007, Journal of Experimental Psychology, 33[2], 391–409). In that study, however, the sample size was small and the crucial interaction was not significant. In this registered report, we present the results of a well-powered attempt to replicate those findings. In contrast to the previous study, we did not observe multitalker processing costs in either of our groups. To rule out the possibility that the null result was due to task constraints, we conducted a second experiment using a speeded classification task. As in Experiment 1, we found no influence of expectations on talker normalization, with no multitalker processing cost observed in either group. Our data suggest that the previous findings of Magnuson and Nusbaum (2007) be regarded with skepticism and that talker normalization may not be permeable to high-level expectations.This research was supported by NSF 1754284, NSF IGERT 1144399 & NSF NRT 1747486 (PI: JSM) and NSF BCS 1554810 & NIH R01 DC013064 (PI: EBM). This research was also supported in part by the Basque Government through the BERC 2018- 2021 program and by the Agencia Estatal de Investigación through BCBL Severo Ochoa excellence accreditation SEV-2015-0490. SL was supported by an NSF Graduate Research Fellowshi

Neural substrates of subphonemic variation and lexical competition in spoken word recognition

In spoken word recognition, subphonemic variation influences lexical activation, with sounds near a category boundary increasing phonetic competition as well as lexical competition. The current study investigated the interplay of these factors using a visual world task in which participants were instructed to look at a picture of an auditory target (e.g. peacock). Eyetracking data indicated that participants were slowed when a voiced onset competitor (e.g. beaker) was also displayed, and this effect was amplified when acoustic-phonetic competition was increased. Simultaneously-collected fMRI data showed that several brain regions were sensitive to the presence of the onset competitor, including the supramarginal, middle temporal, and inferior frontal gyri, and functional connectivity analyses revealed that the coordinated activity of left frontal regions depends on both acoustic-phonetic and lexical factors. Taken together, results suggest a role for frontal brain structures in resolving lexical competition, particularly as atypical acoustic-phonetic information maps on to the lexicon.Research was supported by National Institutes of Health (NIH) [grant number: R01 DC013064] to EBM and NIH NIDCD [grant number R01 DC006220] to SEB. SG was supported by the Spanish Ministry of Economy and Competitiveness through the Severo Ochoa Programme for Centres/Units of Excellence in R&D [SEV‐2015‐490]. The contents of this paper reflect the views of the authors and not those of the funding agencies

Does signal reduction imply predictive coding in models of spoken word recognition?

Published online: 14 April 2021Pervasive behavioral and neural evidence for predictive processing has led to claims that language processing depends upon predictive coding. Formally, predictive coding is a computational mechanism where only deviations from top-down expectations are passed between levels of representation. In many cognitive neuroscience studies, a reduction of signal for expected inputs is taken as being diagnostic of predictive coding. In the present work, we show that despite not explicitly implementing prediction, the TRACE model of speech perception exhibits this putative hallmark of predictive coding, with reductions in total lexical activation, total lexical feedback, and total phoneme activation when the input conforms to expectations. These findings may indicate that interactive activation is functionally equivalent or approximant to predictive coding or that caution is warranted in interpreting neural signal reduction as diagnostic of predictive coding.This researchwas supported by NSF 1754284, NSF IGERT 1144399, and NSF NRT 1747486 (PI: J.S.M.). This research was also supported in part by the Basque Government through the BERC 2018- 2021program, and by the Agencia Estatal de Investigación through BCBL Severo Ochoa excellenceaccreditation SEV-2015-0490. S.L. was supported by an NSF Graduate Research Fellowship

Lexical Information Guides Retuning of Neural Patterns in Perceptual Learning for Speech

Posted Online August 31, 2020A listener's interpretation of a given speech sound can vary probabilistically from moment to moment. Previous experience (i.e., the contexts in which one has encountered an ambiguous sound) can further influence the interpretation of speech, a phenomenon known as perceptual learning for speech. This study used multivoxel pattern analysis to query how neural patterns reflect perceptual learning, leveraging archival fMRI data from a lexically guided perceptual learning study conducted by Myers and Mesite [Myers, E. B., & Mesite, L. M. Neural systems underlying perceptual adjustment to non-standard speech tokens. Journal of Memory and Language, 76, 80-93, 2014]. In that study, participants first heard ambiguous /s/-/∫/ blends in either /s/-biased lexical contexts (epi_ode) or /∫/-biased contexts (refre_ing); subsequently, they performed a phonetic categorization task on tokens from an /asi/-/a∫i/ continuum. In the current work, a classifier was trained to distinguish between phonetic categorization trials in which participants heard unambiguous productions of /s/ and those in which they heard unambiguous productions of /∫/. The classifier was able to generalize this training to ambiguous tokens from the middle of the continuum on the basis of individual participants' trial-by-trial perception. We take these findings as evidence that perceptual learning for speech involves neural recalibration, such that the pattern of activation approximates the perceived category. Exploratory analyses showed that left parietal regions (supramarginal and angular gyri) and right temporal regions (superior, middle, and transverse temporal gyri) were most informative for categorization. Overall, our results inform an understanding of how moment-to-moment variability in speech perception is encoded in the brain.This work was supported by NSF IGERT DGE-1144399, NIH R03 DC009395 (PI: Myers), NIH R01 DC013064 (PI: Myers), and an NSF Graduate Research Fellowship to S. L. The authors report no conflict of interes

Using TMS to evaluate a causal role for right posterior temporal cortex in talker-specific phonetic processing

Available online 21 April 2023Theories suggest that speech perception is informed by listeners’ beliefs of what phonetic variation is typical of a talker. A previous fMRI study found right middle temporal gyrus (RMTG) sensitivity to whether a phonetic variant was typical of a talker, consistent with literature suggesting that the right hemisphere may play a key role in conditioning phonetic identity on talker information. The current work used transcranial magnetic stimulation (TMS) to test whether the RMTG plays a causal role in processing talker-specific phonetic variation. Listeners were exposed to talkers who differed in how they produced voiceless stop consonants while TMS was applied to RMTG, left MTG, or scalp vertex. Listeners subsequently showed near-ceiling performance in indicating which of two variants was typical of a trained talker, regardless of previous stimulation site. Thus, even though the RMTG is recruited for talker-specific phonetic processing, modulation of its function may have only modest consequences.This research was supported by NSF 1554810 (PI: EBM), NIH NIDCD 2R01 DC013064 (PI: EBM) and NSF NRT 1747486 (PI: JSM). This research was supported in part by the Basque Government through the BERC 2022–2025 program, by the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation CEX2020-001010- S and award PID2020-119131 GB-I000

Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again

First published: 20 April 2021A long-standing question in cognitive science is how high-level knowledge is integrated with sensory input. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech sound, but do such effects reflect direct top-down influences on perception or merely postperceptual biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation for coarticulation (LCfC). Previous LCfC studies have shown that a lexically restored context phoneme (e.g., /s/ in Christma#) can alter the perceived place of articulation of a subsequent target phoneme (e.g., the initial phoneme of a stimulus from a tapes-capes continuum), consistent with the influence of an unambiguous context phoneme in the same position. Because this phoneme-to-phoneme compensation for coarticulation is considered sublexical, scientists agree that evidence for LCfC would constitute strong support for top–down interaction. However, results from previous LCfC studies have been inconsistent, and positive effects have often been small. Here, we conducted extensive piloting of stimuli prior to testing for LCfC. Specifically, we ensured that context items elicited robust phoneme restoration (e.g., that the final phoneme of Christma# was reliably identified as /s/) and that unambiguous context-final segments (e.g., a clear /s/ at the end of Christmas) drove reliable compensation for coarticulation for a subsequent target phoneme.We observed robust LCfC in a well-powered, preregistered experiment with these pretested items (N = 40) as well as in a direct replication study (N = 40). These results provide strong evidence in favor of computational models of spoken word recognition that include top–down feedback

Brain-behavior relationships in incidental learning of non-native phonetic categories

Available online 12 September 2019.Research has implicated the left inferior frontal gyrus (LIFG) in mapping acoustic-phonetic input to sound category representations, both in native speech perception and non-native phonetic category learning. At issue is whether this sensitivity reflects access to phonetic category information per se or to explicit category labels, the latter often being required by experimental procedures. The current study employed an incidental learning paradigm designed to increase sensitivity to a difficult non-native phonetic contrast without inducing explicit awareness of the categorical nature of the stimuli. Functional MRI scans revealed frontal sensitivity to phonetic category structure both before and after learning. Additionally, individuals who succeeded most on the learning task showed the largest increases in frontal recruitment after learning. Overall, results suggest that processing novel phonetic category information entails a reliance on frontal brain regions, even in the absence of explicit category labels.This research was supported by NIH grant R01 DC013064 to EBM and NIH NIDCD Grant R01 DC006220 to SEB. The authors thank F. Sayako Earle for assistance with stimulus development; members of the Language and Brain lab for help with data collection and their feedback throughout the project; Elisa Medeiros for assistance with collection of fMRI data; Paul Taylor for assistance with neuroimaging analyses; and attendees of the 2016 Meeting of the Psychonomic Society and the 2017 Meeting of the Society for Neurobiology of Language for helpful feedback on this project. We also extend thanks to two anonymous reviewers for helpful feedback on a previous version of this manuscript

The Influence of Sentence Context on Phonetic Recalibration

Individual talkers vary considerably in how they produce different speech sounds, and a challenge for the listener is to learn the appropriate mapping between acoustics and phonetic categories for an individual talker. Several studies have shown that listeners are able to leverage various sources of context (e.g., coincident visual information, lexical knowledge) to guide this process, sometimes termed perceptual learning of speech. Here, we examine how sentence-level semantic information – specifically, whether preceding sentence context is predictive of an upcoming word – might modulate the size of learning effects. Across a series of perceptual learning experiments, we manipulate how learning compares between groups who receive neutral or predictive sentence contexts, also varying whether contexts are presented in the auditory or written modality. Though we observed greater learning for subjects who read predictive contexts than for subjects who read neutral contexts, this finding did not replicate in an identical follow-up experiment, suggesting that potential influences of sentence context on phonetic recalibration may be small. These findings are discussed in the context of the broader literature on perceptual learning

TimedUCM: Specifying scenario models over time

This thesis introduces TimedUCM (Timed Use Case Maps), an extension of the current UCM (Use Case Maps) standard, which enables the modeling and analysis of a comprehensive set of changes to a scenario model over time. Scenario models are used to capture functional requirements in terms of causal relationships among scenario steps and combine scenarios into a high-level system view where behavior is superimposed over structural elements. Scenarios are analyzed with a traversal mechanism that checks whether expected pre- and post-conditions of a scenario hold when the model is updated. Generally, these analyses focus on one snapshot in time, i.e., the model is assumed to not change over time. However, desired system qualities, stakeholder objectives, potential solutions, and their relationships as well as other model elements may change as the time progresses, and sometimes this evolution over time is predictable or can be reasonably estimated. It may also be useful to explore several "what if" scenarios in terms of how the system model could evolve. Furthermore, some systems may require the problem to be analyzed over a longer period of time.TimedUCM proposes capturing the scenario model and the changes to the scenario model in a single model to facilitate system evolution. The metamodel for TimedURN (Timed User Requirements Notation) is presented and the usefulness of TimedUCM is illustrated with a hypothetical but realistic example to build an advanced research facility, and the comprehensiveness of the supported types of changes is implemented and assessed in jUCMNav, a requirements engineering tool. Moreover, a detailed case study has been performed to show TimedUCM's functionality and usefulness.Cette thèse présente TimedUCM (Timed Use Case Maps), une extension de la norme actuelle UCM (Use Case Maps), qui permet de modéliser et d'analyser un ensemble complet de modifications d'un modèle de scénario dans le temps. Les modèles de scénarios sont utilisés pour capturer les exigences fonctionnelles en termes de relations causales entre les étapes d'un scénario, et combiner les scénarios dans une vue système de haut niveau où le comportement est superposé aux éléments structurels. Les scénarios sont analysés avec un mécanisme de traversée qui vérifie si les pré- et post-conditions d'un scénario sont maintenues lorsque le modèle est mis à jour. Généralement, ces analyses se concentrent sur un moment particulier dans le temps, c'est-à-dire que le modèle est supposé ne pas changer avec le temps. Cependant, les qualités souhaitées du système, les objectifs des parties prenantes, les solutions potentielles et leurs relations ainsi que d'autres éléments du modèle peuvent changer avec le temps, et parfois cette évolution est prévisible ou peut être raisonnablement estimée. Il peut également être utile d'explorer plusieurs scénarios anticipatifs (what if) en termes d'évolution du modèle du système. De plus, certains systèmes peuvent exiger que le problème soit analysé sur une période plus longue.TimedUCM propose de capturer le modèle de scénario et les modifications apportées au modèle de scénario dans un seul modèle pour faciliter l'évolution du système. Le métamodèle pour TimedURN (Timed User Requirements Notation) est présenté et l'utilité de TimedUCM est illustrée avec un exemple hypothétique mais réaliste de construction d'une installation de recherche avancée, et l'exhaustivité des types de changements pris en charge est implémentée et évaluée dans jUCMNav, un outil d'ingénierie des exigences. De plus, une étude de cas détaillée a été réalisée pour montrer la fonctionnalité et l'utilité de TimedUCM