Use of a phoneme monitoring task to examine lexical access in adults who do and do not stutter

Journal of Fluency Disorders 57 (2018) 65–73

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Use of a phoneme monitoring task to examine lexical access in adults who do and do not stutter Timothy A. Howell, Nan Bernstein Ratner

T

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Department of Hearing and Speech Sciences, University of Maryland, 0100 Samuel J. LeFrak Hall, 7251 Preinkert Dr., College Park, MD 20742, United States

AR TI CLE I NF O

AB S T R A CT

Keywords: Stuttering Language encoding Phoneme monitoring Grammatical class

Previous work has postulated that a deficit in lexicalization may be an underlying cause of a stuttering disorder (Prins, Main, & Wampler, 1997; Wingate, 1988). This study investigates the time course of lexicalization of nouns and verbs in adults who stutter. A generalized phoneme monitoring (PM) paradigm was used. Adults who stutter (AWS) and typically-fluent peers both showed an expected effect of word class (verbs yielded slower and less accurate monitoring than nouns), as well as phoneme position (word medial/final phonemes yielded slower and less accurate monitoring than word initial phonemes). However, AWS had considerably more difficulty when targets to be monitored were embedded in the medial position. A negative correlation between speed and accuracy was found in typically fluent adults, but not in AWS. AWS also scored nonsignificantly more poorly on an experimental language task. Because of the additional difficulty noted in AWS with word-medial targets, our results provide evidence of phonological encoding differences between the two groups. Expanded use of the PM paradigm is recommended for the exploration of additional aspects of language processing in people who stutter.

1. Introduction 1.1. Lexical access in speech production An ever-growing literature suggests that people who stutter (PWS) show atypical language processing profiles and abilities as compared to people who do not stutter (PWNS). Lexicalization (the act of retrieving and encoding word targets) is one area of the language planning/production process which has been cited as a potential cause of a moment of speaker disfluency in both typical and stuttering speakers (e.g., Prins, Main, & Wampler, 1997; Segalowitz & Lane, 2004; Wingate, 1988). To ground the current study, we review the main aspects of lexicalization. In current psycholinguistic models, such as Weaver ++ (Roelofs, 1997; Levelt, Roelofs, & Meyer, 1999), lexicalization can be divided into two main stages. In the first stage (L1), a semantic/conceptual target (the lemma) is selected and grammatically encoded based on its meaning and syntactic properties. In the second stage (L2), phonological encoding occurs to generate the lexeme (Levelt et al., 1991). There is, at this point, a large body of literature suggesting potential problems at the second, phonological encoding, stage in both adults and children who stutter (AWS/CWS) see review by Sasisekaran (2014). However, our current interest is at the earlier stage, when speakers access the potential word candidates that subsequently undergo phonological realization and production. Lexicalization is a complex construct. Studies in aphasia have provided ample evidence that the mental lexicon distinguishes

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Corresponding author. E-mail address: [email protected] (N. Bernstein Ratner).

https://doi.org/10.1016/j.jfludis.2018.01.001 Received 7 June 2017; Received in revised form 22 January 2018; Accepted 26 January 2018

Available online 06 February 2018 0094-730X/ © 2018 Elsevier Inc. All rights reserved.

Journal of Fluency Disorders 57 (2018) 65–73

T.A. Howell, N. Bernstein Ratner

grammatical classes (see review in Benetello et al., 2016); different cortical networks may support storage and access of major classes such as nouns and verbs (e.g., Berlingeri et al., 2008; Damasio & Tranel, 1993; Perani et al., 1999). Distinctions between access for open- and closed-class words, as well as nouns and verbs, are readily detected using many behavioral and imaging methodologies (Vigliocco et al., 2011; see 2013 review by Crepaldi et al. 2013). A classic study by Szekely et al. (2005) found that response time profiles for action naming were significantly slower than those for object naming, even after numerous variables (e.g., picture and word properties, name agreement, and complexity) were controlled. Effects were also found within the classes of nouns and verbs: higher-frequency objects elicited faster naming latencies, but higher-frequency verbs elicited slower ones. The authors concluded that mapping between pictures and their names differs for actions (verbs) and objects (nouns). One reason could be that verbs require various numbers of arguments (a mandatory subject, obligatory and optional object[s]), to be ordered and inflected appropriately. Thus, verb lexicalization is thought to be a more complicated process, beginning before utterance onset and potentially causing a slight delay in speech onset (Lindsley, 1976). 1.2. Lexical access in people who stutter Both children and adults who stutter show subtle disadvantage in tasks designed to examine lexical retrieval and confrontation naming (Chen, Zhang, & Xiao, 2011; Newman & Bernstein Ratner, 2007; Pellowski, 2011; Bernstein Ratner, Newman & Strekas, 2009; Watson et al., 1991), as do children who stutter on standardized vocabulary measures (Ntourou, Conture, & Lipsey, 2011). Typical priming effects that speed lexical retrieval in fluent speakers may be absent or reversed (Melnick, Conture, & Ohde, 2003; Maxfield, Pizon-Moore, Frisch, & Constantine, 2012; Pellowski & Conture, 2005). Deficits may appear even in lexical decision tasks (is the stimulus a real word?), where a spoken response is not required (e.g., Álvarez et al., 2014; Hand & Haynes, 1983). This profile is not universal; some studies have failed to show deficits (e.g., Hennessey, Nang, & Beilby, 2008). Because they do not require overt speech or any motor response, event-related brain potentials (ERPs) in both children and adults who stutter have been the subject of a significant amount of research in stuttering, most of which have found differences between PWS and typically-fluent peers. These include studies by Morgan, Cranford, and Burk (1997) for the P300 (stimulus evaluation) response, and numerous studies of the N400 (semantic coherence) response primarily by two laboratories: Weber (-Fox) and colleagues at Purdue University, and Maxfield and colleagues at University of South Florida. Weber-Fox (2001) found reduced amplitude of the N400 in AWS, as well as response to both function (grammatical) words (the N280), as well as content words (lexical entries; the N350). Weber-Fox concluded that neural functions underlying language processing in PWS “perhaps involve shared, underlying processes for lexical access.” Additional anomalous ERP profiles have been reported in further reports (e.g., Weber-Fox, Spencer, Spruill, & Smith, 2004; Weber-Fox & Hampton, 2008; Weber-Fox, Wray, & Arnold, 2013). Using different tasks, Maxfield, Huffman, Frisch, and Hinckley (2010), Maxfield et al. (2012)) and Maxfield, Morris, Frisch, Morphew, and Constantine (2015) also detected atypical N400 responses in AWS participants. Maxfield and colleagues suggested that lemma/word form connections may be attenuated in AWS, a conclusion also reached by Murase, Kawashima, Satake, and Era (2016). Cuadrado & Weber-Fox (2003) additionally found responses to subject-verb agreement violations (marked by the P600 ERP) to be attenuated in AWS, similar to findings reported by Maxfield et al. (2010). This body of research suggests potential differences in lexical representations of people who stutter and fluent peers. 1.3. Verb processing in people who stutter Both AWS and CWS have shown atypical profiles in verb processing. In a study of picture naming in AWS (Prins et al., 1997), the difference in latencies between AWS and AWNS was six times greater during verb naming than during noun naming. Similarly, Ning, Lu, Ma, Peng, and Ding (2007) found production differences between noun phrase and verb phrase production in AWS not attributable to target length. Relative overuse of limited verb forms in spontaneous speech, as well as atypical past tense verb inflection have been reported for CWS (Bauman, Hall, Wagovich, Weber-Fox, & Bernstein Ratner, 2012; Wagovich & Bernstein Ratner, 2007). Stuttering may gravitate to verb-initial constituents in CWS (Bernstein, 1981). Bilingual Spanish-English AWS appear to stutter more on verbs in Spanish (where they are typically utterance-initial) than in English, where they appear later in the utterance (Ardila, Ramos, & Barrocas, 2011; Bernstein Ratner & Benitez, 1985). 1.4. Stuttering research and the phoneme monitoring (PM) paradigm One of the oldest psycholinguistic techniques for investigating lexical access is the phoneme monitoring (PM) paradigm (Gleason & Bernstein Ratner, 1998). In PM, participants are asked to monitor for a specific, predetermined phoneme and press a button when it appears (Connine & Titone, 1996). The stimulus can be auditory or it can be visual (a picture that the participant silently names). In PM tasks, the assumption is that, as the experimental tasks become progressively more complex, increasingly more resources are used for the higher level computations, leading to longer reaction times (RTs) for PM. Thus, phoneme monitoring latencies were originally proposed as a “measuring stick”, of the complexity of other experimental tasks (Frauenfelder & Segui, 1989). In the stuttering literature, PM tasks have been used primarily to investigate phonological encoding proficiency of AWS; most studies have found subtle impairment in monitoring performance in AWS, suggesting likely deficits during the lexicalization phase of speech encoding (Sasisekaran & De Nil, 2006; Sasisekaran, De Nil, Smyth, & Johnson, 2006; Sasisekaran, Brady, & Stein, 2013). However, in the general psycholinguistic literature, the PM paradigm has been used to examine other measures of language-encoding complexity, such as word frequency, morphological complexity, lexical or syntactic ambiguity, or embedded clausal structure (e.g., 66

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Cairns & Kamerman, 1975; Foss, 1969; Foss & Jenkins, 1973; Hakes & Foss, 1970; Hakes, 1971; Swinney, 1979; Swinney & Hakes, 1976). A classic example was Foss (1969), who found that listeners told to monitor for [b] took longer when hearing the phrase “itinerant bassoon player” than when hearing “traveling bassoon player”, despite knowing the meanings of both modifiers. Results suggested that less frequently heard and used words of the language take longer for listeners to access and process, and therefore delay PM responses. Only later was phonological structure itself investigated using the PM paradigm (e.g., Stemberger, Elman, & Haden, 1985; Wheeldon & Levelt, 1995; Wheeldon & Morgan, 2002). In reviewing the literature, we were unable to locate studies outside the stuttering literature that have used PM to assess differences across subject populations, nor did we locate studies using PM to assess potential differences in language processing in AWS other than in phonological encoding. Given past research showing potential disproportionate weakness in verb processing by PWS, we hypothesized that AWS would show correspondingly less accurate, slower PM skills for phonemes in verbs than to those in nouns. 2. Method 2.1. Participants Fifteen adults, who, at the time of the study, self-reported themselves as adults who stutter (AWS) were recruited from a variety of sources, including the University of Maryland, College Park clinic, local area/local therapy groups, and the National Stuttering Association (NSA) yearly convention. After recruiting AWS, AWNS were recruited for a control group. AWNS were recruited to match for age (within five years), gender, handedness, and education (within two years), so that the two groups matched as closely as possible, with the exception of a negative history of fluency or language disorder by the fluent adults. All participants were monolingual, native speakers of English with no other significant educational, psychological or linguistic disorders/conditions, as reported by a self-history questionnaire. Participants were compensated for their time with a $10 gift card, or volunteered as part of an undergraduate course requirement. The final sample of stuttering participants included eight females and seven males (ages 22–58 years; M = 40.53). Three of the AWS were left-handed. Education levels ranged from high-school to graduate school (range 12 to 24 years, M = 17 years). Selfreported age of onset of stuttering ranged from three to nine years (M = 5.25). Ten of the AWS reported a positive family history of stuttering. To gauge level of symptom severity in our particular sample, all participants completed the Stuttering Severity Instrument, 4th edition (SSI-4; Riley & Bakker, 2009), and demonstrated some unambiguous stuttering during the intake process. Scores for AWS on the SSI-4 ranged from 2 to 25 (“below very mild” − “moderate”; M = 13.7 “very mild”). Typically fluent participants (AWNS) also included eight females and seven males (ages 20 to 63 years; M = 41.46). Three of the AWNS were also left-handed. Education levels ranged from 12 to 22 years (M = 17). Two of the AWNS reported a positive family history of stuttering. No AWNS demonstrated any stuttering-like disfluencies on the SSI-4. See Appendix A (in Supplementary material) for participant characteristics. 2.2. Background testing All participants also completed an experimental language task (example questions in Appendix B in Supplementary material), adapted from practice reviews for the Graduate Record Examination and the SAT (found on majortests.com), as a concurrent measure of participants’ language ability. The task had 30 multiple choice questions of three different types: a) spot-the-ungrammaticality (12 questions in 6 min.), b) vocabulary fill-in-the-blank (8 questions in 8 min.), and c) analogies (10 questions in 3 min.). 2.3. Stimuli Line drawings of noun and verb stimuli sets (15 each) (Appendix C in Supplementary material) came from the International Picture Naming Project (IPNP) (Szekely et al., 2004; Szekely et al., 2005). Noun and verb stimuli were matched for number of phonemes, naming agreement, order of acquisition and picture complexity (all from Szekely et al. norms). Word frequency was matched using data from Balota et al. (2007). Number of syllables was matched across groups, as was presence of consonant clusters. Final stimuli included some homophonous nouns and verbs, although only verbs used as nouns less than 25% of the time, and nouns used as verbs less than 25% of the time were included (Kim & Thompson, 2000). 2.4. Procedure Prior to beginning the task, participants were trained on the names expected for each picture. To assure active listening and processing, participants were instructed that the phoneme for which they were monitoring could appear anywhere within the word to ensure attention (Frauenfelder & Segui, 1989), and position of target was varied, simply to prevent use of a positional bias in responding. Each word appeared as an experimental word (in which the target phoneme actually appeared) and as a filler word (in which the target phoneme did not appear) so that participants could not presume that all words contained a target (similar to Sasisekaran et al., 2006). Therefore, each word appeared four times: with target phoneme in the initial position, target phoneme in non-initial position, and twice as a filler. For non-initial position, target phoneme was the second consonant sound to appear in the word. The experiment was presented in eight blocks of 15 stimulus words, either all nouns or all verbs. Word order was pseudo67

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randomized in each block, stimuli appeared only once per block, and block order was randomized for each participant. The experiment was designed using the program DMDX (Forster & Forster, 1999). Participants were seated comfortably at a laptop computer. The target phoneme for which the participant monitored was presented for 1000 milliseconds, followed by a picture for 3000 ms, as shown below:

*

/X/

[picture]

Orientating Symbol: 1000 milliseconds

Target phoneme: 1000 milliseconds

Object/Action Picture: 3000 milliseconds

The participant responded by pressing keys on the keyboard corresponding to ‘yes’ or ‘no’ as quickly as possible. Right-handed participants responded using two right-positioned adjacent keys and left handed participants responded using two left-positioned adjacent keys. The next stimulus appeared after a response, or after the 3000 ms. presentation window concluded, if no response was given in that time. Six practice items (three nouns and three verbs) were presented, using words which did not appear during the experiment. Participants were instructed to monitor for a sound, rather than making judgments based on letters in the word’s written form (which was not displayed). 2.5. Statistical Analysis Statistical analysis was conducted using Number Cruncher Statistical System (NCSS-8; ncss.com). Data screening confirmed homogeneity of variance, so parametric tests were used in all cases. Language test scores were compared between groups using t-tests. Group, phoneme position, and part-of-speech profiles were compared using a three-way ANOVA. 3. Results We first conducted data screening. Scores of left-handed participants and scores of participants with a positive familial history of stuttering were examined to see if they were outliers from the rest of the group. For both subgroups (family history and lefthandedness), scores were well within the range of the rest of the group. 3.1. Language test scores Total mean language test score for the AWS group was 17.60 (range = 5–28, SD = 6.08). Mean language test score for the AWNS group was somewhat higher at 20.73 (range = 12–28, SD = 4.23). A two-tailed, two sample t-test showed no significant difference between the two groups (t(28) = 1.64, p = .1126). No significant differences were detected for the different types of questions. See Fig. 1. 3.2. Phoneme monitoring (PM) profiles Two dependent variables were considered: the accuracy of PM responses and the response latency/reaction time (RT). Three-way ANOVAs were used to analyze the data. Accuracy and RT were examined in separate analyses. For accuracy, all responses were included. When examining RT and word position, only experimental trials were used. Responses more than two standard deviations above or below the mean were classified as outliers and excluded from RT analysis.

Fig. 1. Language test results by group and sub-topic. Differences in vocabulary completion approached, but did not meet, significance.

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Fig. 2. Accuracy of phoneme monitoring by Part of Speech and Within-Word Position (null targets represent cases where the phoneme to be monitored was not actually in the stimulus to be judged).

3.2.1. Accuracy for phoneme monitoring Accuracy was computed as the number of correct trials, out of 120 (including null trials). For AWS, mean score was 105.33 or 87.1% (range = 83–115, SD = 9.64). For AWNS, mean score was 108.20 or 89.2% (range = 88–119, SD = 7.84). A repeated measures ANOVA was run to examine profiles of PM accuracy for nouns and verbs. PM accuracy in nouns was somewhat higher than for verbs across both groups, but this difference did not reach significance (F [1,28] = 3.17, p = .0857). Recall that we had to vary position of the target phoneme to prevent premature response strategy by participants. Analysis of accuracy for PM across target position (initial/medial) showed an expected strong effect of target position (F [2,28] = 36.34, p < .0000), and an unexpected significant interaction by group (F [2,28] = 4.75, p < .012). Post-hoc Tukey-Kramer testing revealed that accuracy of AWS for medial targets was significantly lower than all other conditions (at 66%, compared to accuracy above 90% by both groups for targets in other positions). See Fig. 1. In sum, PM accuracy appeared to be mildly and non-significantly influenced in some expected ways (verbs more difficult than nouns, and embedded targets more difficult than word-initial targets). However, AWS had considerably more difficulty when PM targets were embedded in medial position within words (Fig. 2). 3.2.2. Reaction Time (Latency) Average RTs were computed using a trimmed data set that included only correct responses to a present target, and removed trials in which RT exceeded two standard deviations from the mean group RT. Across both groups, verb RTs were slower than noun RTs: (F [1,28] = 52.9, p < .0000, with no interaction by group (F [1,28] = .29, p. = .59), showing no particular verb processing disadvantage for the AWS. RT latency mirrored results for accuracy when analyzed for target position. There was a significant effect of position (F [2,28] = 65.37, p < .0000), but no group interaction (F [2,28] = 2.31, p = .109). AWS were actually non-significantly quicker than AWNS to respond on filler trials (with a mean RT of 1153.8 msec. compared to the AWNS mean of 1170 msec.), and showed no particular motor handicap in speed of response to stimuli. RT latencies are shown in Fig. 3. PM accuracy and language test scores were significantly correlated (r(28) = 0.46, p < 0.01). However, RT and language test scores were not significantly correlated (r(28) = 0.25, p = .2531). Thus, PM accuracy may have reflected better language skills in general, but RT differences did not (Table 1). 3.2.3. Speed/Accuracy Tradeoff and Inverse Efficiency Scores While AWNS showed an expected and highly significant negative correlation between RT and accuracy (r (14) = −0.88, p < .001) for all responses, AWS showed no significant correlation between RT and accuracy (r(14) = 0.17, p = .55). By part of speech (noun/verb), AWS again showed no significant correlation between RT and accuracy for nouns (r(14) = 0.34, p = .21) or verbs (r(14) = −0.09, p = .74). In contrast, for AWNS, noun responses showed a highly significant speed/accuracy tradeoff (r(14) = −0.85, p < .001), as did those for verbs (r(14) = −0.82, p < .001). Moreover, AWS showed no significant correlation between speed and accuracy for initial position targets (r(14) = 0.19, p = .49), nor for medial targets (r(14) = −0.03, p = .92), nor for null trials (r(14) = 0.10, p = .71). In contrast, AWNS showed a highly significant speed/accuracy trade-off for initial position targets (r(14) = −0.82, p < .001), as well as medial position targets (r (14) = −0.83, p < .001), and on null trials (r(14) = −0.77, p < .001). The Inverse Efficiency Score (IES) (Bruyer & Brysbaert, 2011; Townsend & Ashby, 1978) is a statistical measure which combines speed and error. It is calculated by dividing reaction time by the proportion of correct responses; smaller numbers indicate greater efficiency (Bruyer & Brysbaert, 2011). AWS showed an IES of 1197.08 ms for nouns and 1360.43 ms for verbs. AWNS showed lower IES of 1097.91 ms for nouns and 69

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Fig. 3. Latency of phoneme monitoring judgments across Part of Speech and Within-Word position of target to be monitored.

Table 1 Descriptive Statistics. Group

Noun/Verb

Initial/Medial/Null

Mean ACC

Mean Latency

Median Latency

SD of Latency

SE of Latency

Min Latency

Max Latency

AWNS AWNS AWNS AWNS AWNS AWNS AWS AWS AWS AWS AWS AWS

Noun Noun Noun Verb Verb Verb Noun Noun Noun Verb Verb Verb

Initial Medial Null Initial Medial Null Initial Medial Null Initial Medial Null

0.95 0.81 0.94 0.95 0.79 0.91 0.94 0.70 0.95 0.93 0.64 0.94

878.46 1009.43 1031.94 932.12 1128.90 1143.60 932.91 1107.66 1032.64 967.55 1241.18 1159.59

850.58 1022.53 992.88 899.94 1080.79 1118.41 898.24 1161.35 1010.91 946.68 1231.69 1182.89

150.45 137.04 142.00 172.26 184.86 161.01 160.23 242.34 202.27 158.40 210.99 179.21

38.85 35.38 36.66 44.48 47.73 41.57 41.37 62.57 52.23 40.90 54.48 46.27

693.74 837.19 848.97 702.03 834.03 840.89 658.27 505.92 623.02 747.69 940.69 841.11

1229.07 1271.36 1299.20 1360.14 1530.14 1487.55 1231.99 1450.15 1498.13 1314.91 1777.16 1530.38

1250.27 ms for verbs. These differences were not significant (nouns: (t(28) = −1.15, p = .19;, verbs: (t(28) = −0.89, p = .38). By phoneme position, AWNS mean IES was 970.28 ms (initial position), while AWS mean IES was 1015.25 ms. This difference was not significant (t(28) = −0.59, p = .56). For medial position, AWNS mean IES was 1486.81 ms.; AWS mean IES was 2645.16 ms., nonsignificant (t(28) = −1.47, p = .15). For null trials, AWNS mean IES was 1181.70 ms, AWS mean IES was non-significantly lower (more efficient) at 1155.02; this difference was not significant (t(28) = 0.35, p = .73). To summarize, AWNS showed a non-significantly higher level of efficiency for PM across word classes and stimulus position. However, AWS showed a non-significantly higher level of efficiency for null trials. 4. Discussion We used a phoneme monitoring (PM) paradigm to examine possible differences in lexicalization between AWS and typicallyfluent peers. AWS were non-significantly slower and less accurate than fluent adults when monitoring for phonemes in verbs rather than nouns; differences between groups were non-significant when comparing how quickly or accurately nouns and verbs were accessed for their phonemic properties. In order to conduct a PM task properly, however, it is necessary to vary where inside the stimulus word the target phoneme is embedded. Otherwise, participants do not process past word onset. It was this aspect of the design that yielded the most evident differences between groups. AWS were more prone to error when monitoring for targets embedded in medial positions within words. Thus, while designed to examine whether AWS may access verbs less well than AWNS, our results actually provided evidence of potential phonological encoding differences between the two groups of speakers, consistent with a number of other prior studies, most notably those conducted by Sasisekaran et al. (2006). To our knowledge, this is the first study in which a phoneme monitoring paradigm has been used with AWS as originally designed by Hakes and Foss (1970). Sasisekaran et al. (2006) used PM latencies as direct indices of phonological encoding skill. In contrast, in the original PM task, the monitoring latencies are not direct measurements of anything, but rather a “yardstick”, by which one can measure the relative difficulty of other concomitant language processing tasks (Frauenfelder & Segui, 1989). Thus, it is not surprising that, across both groups, PM in verbs was significantly slower than PM in noun targets. This is consistent with previous work (such as Szekely et al., 2005). This effect cannot be attributed to name agreement, number of phonemes, syllable structure, age of acquisition, 70

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picture complexity, or word frequency, as none of these factors varied significantly between noun and verb stimuli. Our finding is consistent with the account that verbs are simply more semantically complex than nouns. Our AWS participants did not evidence any specific or disproportionate difficulty with PM in verbs, as opposed to nouns, when targets were word-initial. However, our finding that word-medial features of target verb lexemes were apparently less accessible (significantly lower accuracy when targets were word-medial) could have an impact on the ability of a speaker to maintain fluency in conversational speech. Slower lexicalization or phonological encoding of elements following word onsets could then trigger stalls (blocks), repetitions of segments or other stuttering-like disfluencies during speech, particularly when the large literature suggesting limited capacity for concurrent task completion in AWS is taken into account (e.g., Bosshardt, 2002; Smits-Bandstra & De Nil, 2009). Certainly, PM is a dual-task paradigm, in which monitoring for the target phoneme is thought to be directly influenced by the concurrent difficulty of processing other attributes of the task stimuli. In that sense, our findings are thus also consistent with dualtask impacts on phonological encoding seen in AWS by researchers such as Jones, Fox, and Jacewicz (2012) and Tsai & Bernstein Ratner (2016). Across groups, accuracy and language test scores were significantly correlated (r(28) = 0.46, p < .01). However, reaction time and language test scores were not significantly correlated (r(28) = 0.25, p = .2531). This indicates that accuracy may be linked to having better language skills in general, regardless of participant group, but reaction time differences cannot be attributed to having better language abilities overall. Newman & Bernstein Ratner (2007) similarly found that AWS were as fast but less accurate at word retrieval tasks than AWNS. In some of our conditions, AWS were actually faster to respond than AWNS, making motor slowing or inefficiency a less reasonable account than differences in phonological processing. 4.1. Phoneme Position Recall that phoneme position and PM performance was not a research question, but one cannot conduct a well-designed PM task without varying targets across positions within words to avoid incomplete stimulus processing. As expected, both groups showed significantly slower monitoring latencies for embedded phonemes in medial position than those in initial position, but AWS showed disproportionately lower accuracy when monitoring word-internal phonemes. The particular difficulty we noted in AWS in working with word-internal phonological elements is consistent with previous research (notably, Sasisekaran et al., 2006), and more recent findings using a similar task by Coalson & Byrd (2015). Burger & Wijnen (1999) also found results consistent with this profile when they noted that phonological priming in AWS required both word-initial consonants plus a following vowel to obtain benefit. WeberFox, Spruill, Spencer, & Smith, (2008) found ERP differences signaling relative disadvantage in phonological encoding when working with children who stutter, particularly those whose stuttering became persistent. Subsequent work in a different lab (e.g., Maxfield et al., 2012) has shown some evidence of atypical neurological indices of phonological processing in AWS. However, others have found little evidence of slowing or impairment in this domain in either adults or children who stutter (a full listing is beyond the scope of our current report, but see, for example, work by Hennessey et al., 2008; Vincent, Grela, & Gilbert, 2012). 4.2. Limitations and future directions Having shown with this study that PM monitoring tasks can be used with AWS to investigate language processing at levels other than phonological encoding, a worthwhile future research direction may be to develop a more challenging version of our tasks. In some sense, what was found here mirrors work done with CWS, which tends to find consistent levels of non-significantly lower performance between groups within individual studies; significant differences are more likely to emerge when studies are combined to obtain greater power (as in Ntourou et al., 2011). We saw subtle differences in the speed and accuracy of language tasks completed by our AWS. The challenge may be to find tasks that impose greater difficulty on participants, as well as to identify additional aspects of language processing that may inform our understanding of stuttering. Another potential study limitation is that, while the PM task does not require a spoken response, it does require a button press. Although we did not detect any significantly different latencies by group suggestive of a motor disadvantage for our AWS, future studies may wish to conduct a baseline response to a non-linguistic task, such as button-press to a tone, to ensure no differences in motor coordination across groups that might impact response times. Most of our stimuli were single-syllable words, with a relatively early age of acquisition. It is possible that these words did not provide a high enough level of difficulty to result in enough stress on our participants’ language processing systems to detect significant differences in performance. Another way to make the task more difficult would be to speed stimulus presentation and automatic time-out, although this may result in an unacceptably high number of unanalyzable responses. Using more complicated, multi-syllabic words could also make the task more difficult and potentially induce larger group differences, although more complex words may also be more difficult to match across all relevant features known to influence lexical access. Critically, the PM task has been used in typical speakers to analyze an array of language skills not yet appraised to date in stuttering adults or children. For example, the PM paradigm may be able to inform whether there are differences between people who stutter in processing of lexical, phrasal or syntactic ambiguity, memory for clausal elements, or processing of phrases that violate linguistic expectations. In this sense, our work demonstrates the feasibility of using a well-explored paradigm to make additional aspects of language processing accessible to study in PWS. It is also suitable for implementation across other language communities. Another limitation of this study was that the sample of stuttering participants was skewed towards the milder end of the stuttering spectrum, and included more women than men. Replicating the study with a sample that includes more severe participants, and includes participants in a more typical gender ratio for PWS could strengthen the findings of this study. 71

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We sought to explore potential differences in lexicalization between people who do and do not stutter, similar to the differences seen in studies such as (Prins et al., 1997). We could not find evidence of significant differences between AWS/AWNS for retrieving nouns and verbs. It was varying the location of targets to monitor that produced our strongest finding: that AWS had considerable difficulty in accurately identifying targets embedded within phonological sequences, rather than word-initial representations. Our findings thus most strongly support work that identifies phonological encoding as a potential weak skill in stuttering speakers, a deficit that logically could hamper ongoing speech fluency in connected speech. However, we additionally demonstrate that use of the PM paradigm may be employed to make comparisons between people who do and do not stutter on numerous aspects of language processing that go beyond phonological encoding. Appendix A. 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Early childhood stuttering and electrophysiological indices of language processing. Journal of Fluency Disorders, 38, 206–221. Wheeldon, L. R., & Levelt, W. M. (1995). Monitoring the time course of phonological encoding. Journal of Memory and Language, 34(3), 311–334. http://dx.doi.org/10. 1006/jmla.1995.1014. Wheeldon, L. R., & Morgan, J. L. (2002). Phoneme monitoring in internal and external speech. Language and Cognitive Processes, 17(5), 503–535. http://dx.doi.org/10. 1080/01690960143000308. Wingate, M. E. (1988). The structure of stuttering: A psycholinguistic analysis. New York: Springer-Verlag. Timothy Howell, MA, is a research associate at an independent healthcare research company. Prior to that work, Mr. Howell was a research assistant at the Center for Advanced Study of Language (CASL) at the University of Maryland. Mr. Howell holds a Master of Arts degree in speech-language pathology from the University of Maryland, College Park, where he worked extensively in the lab of Dr. Nan Bernstein Ratner. Mr. Howell also holds Bachelor of Arts degrees in linguistics and Germanic studies, also from the University of Maryland, College Park. Nan Bernstein Ratner, Ed.D., is Professor, Hearing and Speech Sciences, University of Maryland, College Park. She publishes frequently in the areas of fluency development and disorders, as well as the psychology of language. Professor Bernstein Ratner was named a distinguished researcher of the International Fluency Association, is an honoree of the American Speech-Language and Hearing Association, and is a Fellow of the American Association for the Advancement of Science.

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