Judgment of Disfluency in People who Stutter and People who do not Stutter: Results from Magnitude Estimation

Lickley, Robin J.; Hartsuiker, Robert J.; Corley, Martin; Russell, Melanie; Nelson, Ruth

doi:10.1177/00238309050480030301

Cited by 22 publications

(18 citation statements)

References 30 publications

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“…Further, Postma and Kolk [43] found AWS were equally accurate when identifying their speech errors under auditory masking as AWNS, suggesting that any self-monitoring differences in AWS are not perception-based, but instead associated with a phonological processing deficit (see also [44]). In this same study, however, and in contrast to Lickley et al [41], AWS detected significantly fewer errors in third-party speech than AWNS, suggesting that AWS may have simply generated fewer phonological errors than AWNS. Thus, available data indicate that error monitoring in AWS may be atypical compared to AWNS (cf.…”

Section: Speech Monitoring In Awscontrasting

confidence: 86%

“…Nevertheless, all 3 theories cite evidence for overly critical evaluation of internal and external speech production by AWS. For example, Lickley et al [41] found AWS are more likely to judge external speech as less fluent regardless of whether the speaker is an AWS or a typically fluent adult. Although these participants provided subjective ratings of “fluency,” the authors interpreted AWS’s propensity to judge third-party speech that was ostensibly fluent more harshly than AWNS as evidence for the VCH [32].…”

Section: Speech Monitoring In Awsmentioning

confidence: 99%

“…Although these participants provided subjective ratings of “fluency,” the authors interpreted AWS’s propensity to judge third-party speech that was ostensibly fluent more harshly than AWNS as evidence for the VCH [32]. Lickley et al [41] attributed this pattern in AWS to (a) small vocal deviations that may be present in the fluent speech of AWS compared to the fluent speech of AWNS, and (b) a monitoring system in AWS that was more sensitive to these subtle differences. Differences in perceptual evaluation of phonological code in AWS are not restricted to third-party spoken word production.…”

Section: Speech Monitoring In Awsmentioning

confidence: 99%

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Uniqueness Point Effects during Speech Planning in Adults Who Do and Do Not Stutter

Coalson

Byrd

Kuylen

2017

Folia Phoniatr Logop

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Background/Aims: Previous studies employing a variety of tasks have demonstrated that adults who stutter (AWS) present with phonological encoding differences compared to adults who do not stutter (AWNS). The present study examined whether atypical preverbal monitoring also influenced AWS performance during one such paradigm – the silent phoneme monitoring task. Specifically, we investigated whether monitoring latencies for AWS were accelerated after the word’s uniqueness point – the phoneme that isolates the word from all lexical competitors – as observed for AWNS when monitoring internal and external speech. Methods: Twenty adults (10 AWS, 10 AWNS) completed a silent phoneme monitoring task using stimuli which contained either (a) early uniqueness points (EUP), (b) late uniqueness points, or (c) no uniqueness point (NUP). Response latency when identifying word-final phonemes was measured. Results: AWNS exhibited the expected uniqueness point effect when monitoring internal speech; word-final phonemes were accessed more rapidly for words with EUP than NUP. In contrast, AWS did not differ in the phoneme monitoring speed. That is, AWS did not exhibit the expected uniqueness point effects. Conclusion: Findings suggest that inefficient or atypical preverbal monitoring may be present in AWS and support theories that implicate the internal speech monitor as an area of deficit.

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Section: Speech Monitoring In Awscontrasting

confidence: 86%

Section: Speech Monitoring In Awsmentioning

confidence: 99%

Section: Speech Monitoring In Awsmentioning

confidence: 99%

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Uniqueness Point Effects during Speech Planning in Adults Who Do and Do Not Stutter

Coalson

Byrd

Kuylen

2017

Folia Phoniatr Logop

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“…Verbal self-monitoring is a crucial part of language production, especially when one considers that producing speech errors hampers the fluency of speech and can sometimes lead to embarrassment, for instance, when taboo words are uttered unintentionally (Motley, Camden, & Baars, 1982). Furthermore, malfunction of verbal monitoring is often implicated in disorders such as aphasia (for an overview, see Oomen, Postma, & Kolk, 2001), stuttering (Lickley, Hartsuiker, Corley, Russell, & Nelson, 2005), and schizophrenia (for an overview, see Seal, Aleman, & McGuire, 2004).…”

Section: Introductionmentioning

confidence: 99%

Brain Error–monitoring Activity is Affected by Semantic Relatedness: An Event-related Brain Potentials Study

Ganushchak

Schiller

2008

Journal of Cognitive Neuroscience

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Abstract& Speakers continuously monitor what they say. Sometimes, self-monitoring malfunctions and errors pass undetected and uncorrected. In the field of action monitoring, an event-related brain potential, the error-related negativity (ERN), is associated with error processing. The present study relates the ERN to verbal self-monitoring and investigates how the ERN is affected by auditory distractors during verbal monitoring. We found that the ERN was largest following errors that occurred after semantically related distractors had been presented, as compared to semantically unrelated ones. This result demonstrates that the ERN is sensitive not only to response conflict resulting from the incompatibility of motor responses but also to more abstract lexical retrieval conflict resulting from activation of multiple lexical entries. This, in turn, suggests that the functioning of the verbal self-monitoring system during speaking is comparable to other performance monitoring, such as action monitoring. &

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“…Furthermore, verbal-monitoring is often implicated in disorders such as aphasia (for an overview see , stuttering (Lickley et al, 2005), and schizophrenia (for overview see Seal et al, 2004). …”

Section: Introductionmentioning

confidence: 99%

Effects of time pressure on verbal self-monitoring: An ERP study

Ganushchak

Schiller

2006

Brain Research

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The Error-Related Negativity (ERN) is a component of the event-related brain potential (ERP) that is associated with action monitoring and error detection. The present study addressed the question whether or not an ERN occurs after verbal error detection, e.g., during phoneme monitoring. We obtained an ERN following verbal errors which showed a typical decrease in amplitude under severe time pressure. This result demonstrates that the functioning of the verbal self-monitoring system is comparable to other performance monitoring, such as action monitoring. Furthermore, we found that participants made more errors in phoneme monitoring under time pressure than in a control condition. This may suggest that time pressure decreases the amount of resources available to a capacity-limited self-monitor thereby leading to more errors. IntroductionError monitoring is an important executive function, which helps to adapt, anticipate, learn, correct, and mend the consequences of actions. The neural basis of error monitoring has become a key issue in cognitive neuroscience due to its importance to the aforementioned cognitive skills. A better understanding of its working may offer new insights into the dysfunctions of self-monitoring seen in a range of clinical conditions such as schizophrenia , opiate addicts (Forman et al., 2004), and obsessive-compulsive disorder (Gehring et al., 2000). Progress in identifying the functional characteristics of the error monitoring system has been mainly achieved through the study of an electrophysiological index thought to be associated with error processing, i.e., Error-Related Negativity (ERN; Falkenstein et al., 1991;Gehring et al., 1993). The ERN is a component of the event-related potential (ERP) that has a fronto-central scalp distribution and peaks about 80 ms after an overt incorrect response (Bernstein et al., 1995;Holroyd and Yeung, 2003;Scheffers et al., 1996). The early onset latency of the ERN with respect to the incorrect response is suggestive of an error monitoring system. The generation of the ERN has been localized in the anterior cingulate cortex (ACC; Dehaene et al., 1994;Holroyd and Coles, 2002). Several hypotheses of performance monitoring have been proposed to account for the ERN, for instance, the mismatch hypothesis put forward by Falkenstein et al. (1991), the response conflict hypothesis proposed by Carter et al. (1998), and the reinforcement learning theory by Holroyd and Coles (2002).The mismatch hypothesis considers the ERN as the result of a mismatch between the intended and the actual response execution (Bernstein et al., 1995). This hypothesis assumes a comparison between the internal representation of the intended correct response, arising from ongoing stimulus processing, and the internal representation of the actual

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Judgment of Disfluency in People who Stutter and People who do not Stutter: Results from Magnitude Estimation

Cited by 22 publications

References 30 publications

Uniqueness Point Effects during Speech Planning in Adults Who Do and Do Not Stutter

Uniqueness Point Effects during Speech Planning in Adults Who Do and Do Not Stutter

Brain Error–monitoring Activity is Affected by Semantic Relatedness: An Event-related Brain Potentials Study

Effects of time pressure on verbal self-monitoring: An ERP study

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