Phonological neighborhood density in the picture naming of young children who stutter: Preliminary study

Journal of Fluency Disorders 30 (2005) 125–148

Phonological neighborhood density in the picture naming of young children who stutter: Preliminary study Hayley S. Arnold ∗ , Edward G. Conture, Ralph N. Ohde Department of Hearing and Speech Sciences, Vanderbilt Bill Wilkerson Center for Otolaryngology and Communication Sciences and Disorders, Vanderbilt University Medical Center, 1114 19th Avenue South; Nashville, TN 37212, USA Received 10 March 2004; received in revised form 21 September 2004; accepted 10 January 2005

Abstract The purpose of this study was to assess the effect of phonological neighborhood density on the speech reaction time (SRT) and errors of children who do and do not stutter during a picture-naming task. Participants were nine 3–5-year-old children who stutter (CWS) matched in age and gender to nine children who do not stutter (CWNS). Initial analyses indicated that both CWNS and CWS were significantly faster (i.e., exhibited shorter SRTs) and more accurate on phonologically sparse than phonologically dense words, findings consistent with those found with older children (Newman & German, 2002). Further analyses indicated that talker group differences in receptive language scores weakened these findings. These preliminary findings were taken to suggest that phonological neighborhood density appears to influence the picture-naming speed and accuracy of preschool-aged children. Educational objectives: The reader will learn about and be able to: (1) recognize the relevance of examining phonological variables in relation to childhood stuttering; and (2) describe the method of measuring speech reaction times and errors during a picture-naming task as a means of assessing linguistic skills. © 2005 Elsevier Inc. All rights reserved. Keywords: Stuttering; Preschool children; Phonological neighborhood density; Picture-naming task

∗

Corresponding author. Tel.: +1 615 936 5126; fax: +1 615 936 5013. E-mail address: [email protected] (H.S. Arnold).

0094-730X/$ – see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.jfludis.2005.01.001

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1. Introduction Although many aspects of speech-language planning and production have been discussed with regard to stuttering (see Smith & Kelly, 1997, for multifactorial approaches to stuttering), increased attention has been paid to articulatory, phonetic and phonological contributions to stuttering. The notion that phonological planning and production contribute to stuttering has been theoretically discussed (Postma & Kolk, 1993) and is consistent with the finding that there is a higher incidence of phonological difficulties in children who stutter (CWS) than their normally fluent peers (see Louko, Conture, & Edwards, 1999 for review; cf., Nippold, 2002). For example, children whose stuttering is persistent are more likely to exhibit delayed phonology than children who “spontaneously recover” (Paden, Yairi, & Ambrose, 1999). Likewise, young CWS are more apt to stutter on content words that start with late emerging consonants (Howell, Au-Yeung, & Sackin, 2000). Recently, Melnick, Conture, and Ohde (2003), reported that children who do not stutter (CWNS) with better articulatory mastery also tend to have shorter (i.e., faster) SRTs during picture-naming tasks, but no such relationship was apparent for CWS. Perhaps, therefore, inefficiencies in phonological encoding, a process that matches the syntactic level onto a phonological one, may contribute to childhood stuttering. The present study’s rationale for exploring the connection between childhood stuttering and phonological encoding is based, at least in part, on Postma and Kolk’s (1993) theoretical position paper on the Covert Repair Hypothesis (CRH; also see Kolk & Postma, 1997). The CRH is based on the notion that difficulties with phonological encoding, the ability to retrieve or construct a phonetic plan, may be related to instances of stuttering. Thus, according to this theory, people who stutter are assumed to have a slower-than-normal phonological encoding process, which increases the chances that there will be a mis-selection of intended phonemes, resulting in more potential errors. Subsequently, covert repair of these potential errors, before they become speech-language output, is thought to disrupt overt speechlanguage production, resulting in stuttering. Furthermore, if the speaker speeds up his or her speech-language output, chances of phoneme mis-selection increase even more and, thus, potential errors, increase. In other words, the speech production system is operating beyond the temporal capacity of the phonological encoding system. Rationale for considering the relation between childhood stuttering and phonological encoding is further supported by empirical results from controlled comparisons of CWS and CWNS during tasks believed to measure the speed of phonological encoding. For example, Byrd, Conture, and Ohde (2005) reported that preschool CWS named picture targets faster when the target words were preceded by an end-related phonological prime (e.g., “ed” for “bed”), than when preceded by an onset-related phonological prime (e.g., “b” for “bed”). In contrast, CWNS named picture targets faster when preceded by an onsetrelated prime. These results suggest that the phonological systems of preschool CWS are less well developed and/or efficient than their normally fluent peers. Given the merits of the CRH and its supporting evidence in CWS, the present study seeks to further clarify if and how phonological encoding differs between CWS and CWNS. As mentioned above, one way to experimentally explore the nature of phonological encoding in both CWS and CWNS is to measure their speech reaction times (SRTs) and errors in a picture-naming task (PNT; e.g., Anderson & Conture, 2004; Melnick et al., 2003;

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Pellowski & Conture, in press). For example, a picture-naming task using words that differ by phonological neighborhood density (Luce & Pisoni, 1998) may help identify those variables associated with possible inefficiencies and/or inaccuracies in CWS’s phonological encoding for speech-language planning/production. In brief, words that come from phonologically “dense” neighborhoods have many phonological neighbors and words that come from phonologically “sparse” neighborhoods have few phonological neighbors (see Newman & German, 2002; Vitevitch & Sommers, 2003). Phonological neighbors are typically described (e.g., Luce & Pisoni, 1998) as words that differ by one phoneme substitution, deletion or addition (e.g., the phonological neighbors of “star” include “tar”, “start”, “stir”, and “spar”). Some studies have investigated how phonological neighborhood density influences lexical access during childhood and how it changes with development (Faust, Dimitrovsky, & Davidi, 1997; Jusczyk, Luce, & Charles-Luce, 1994; Newman & German, 2002; Walley & Metsala, 1992). For example, Jusczyk et al. (1994) reported that during early lexical and perceptual development, typical infants and toddlers demonstrate behavioral listening preferences for phonologically dense words (i.e., words with many neighbors). Productively, however, school-aged children name sparse words more accurately than dense words in picture-naming and sentence-completion tasks (Newman & German, 2002), suggesting that many phonological neighbors act as competitors, interfering with selection of the target word. In a similar productive task, however, adults demonstrate more accurate picture naming as well as faster SRTs for phonologically dense targets as opposed to sparse ones, suggesting that simultaneous activation of multiple word forms facilitates speed of speech-language production for adult speakers (Vitevitch, 2002). Differences in phonological neighborhood density effects between children and adults may suggest that the influence of neighborhood factors changes during lexical development (see Newman & German, 2002). Based on the preceding review, one might reasonably suggest that, like typically developing school-aged children, typically developing preschool CWNS may also demonstrate faster SRTs and more accurate picture-naming responses for phonologically sparse rather than dense target pictures. If preschool CWS differ from CWNS in the development of phonological organization, that is, the orderly classification, storage and retrieval of phonological data which allow speech and language to be more quickly planned and produced, then CWS may differ from CWNS in terms of the speed and accuracy of their picturenaming responses in relation to phonological density of targets/pictures to be named. It is presently unknown, however, what facets, if any, of phonological encoding are most problematic for CWS. More specifically, we do not know whether the phonological systems of CWS develop and are organized at the same rate and in the same manner as that of their normally fluent peers during preschool years, the time period when stuttering is more apt to occur (M˚ansson, 2000; Paden et al., 1999; Yaruss, LaSalle, & Conture, 1998). Perhaps, therefore, experimental manipulation of the phonological neighborhood density of pictures to be named may help to circumscribe the number and nature of those aspects of phonological encoding, if any, that preschool CWS find problematic. The current investigators posited two alternative hypotheses regarding the possible influence of phonological density on the SRTs and errors of CWS and CWNS during a picture naming task. The first alternative hypothesis, based on findings for adults, predicted that CWNS would name phonologically dense words faster and more accurately than phonolog-

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ically sparse words (Vivevitch, 2002), but based on the CRH, no such differences would be found for CWS. Such findings might be taken to support the possibility that the phonological systems of CWS may not be as well developed and/or “organized” in terms of sparse/dense neighborhoods as the more “adult-like” systems exhibited by their normally fluent peers. Conversely, the second alternative hypothesis, also based on the CRH as well as findings for children, predicted that CWNS would name phonologically dense targets slower and less accurately than phonologically sparse targets (Newman & German, 2002), but no such differences would be found for CWS. Such findings might be taken to support the notion that increased phonological density actually creates competition in the relatively “organized”, normally developing phonological systems of CWNS, but not in the hypothetically less well organized phonological systems of CWS. Therefore, it was the specific purpose of this study to empirically test the influence of phonological density on the SRTs and accuracy of CWS and CWNS during a picture naming task. If the SRTs and/or accuracy of CWS during the picture naming task differ from those of CWNS as a result of phonological density, then one might reasonably suggest that these aspects of phonological encoding contribute to the difficulties CWS experience establishing reasonably fluent speech-language production.

2. Method 2.1. Participants Participants were nine (4 females, 5 males), monolingual, Standard American English speaking 3–5-year-old children who exhibit stuttering, matched in age (±4 months) and gender to nine (4 females, 5 males) children who do not stutter. Neither the CWNS nor CWS who participated in this study had received any prescribed speech-language treatment prior to their participation. Participants were paid volunteers na¨ıve to the purposes and methods of the study and referred to the investigators by their parents; other speech-language pathologists; or daycare, preschool or school personnel. Participants had no known or reported hearing, neurological, developmental, academic, intellectual or emotional problems. Besides stuttering exhibited by the CWS, no participant, either CWS or CWNS, exhibited any other known or reported speech and/or language problems, with criteria for each behavior described below. All participants were part of an ongoing series of studies concerning the relation between stuttering and language/phonology (e.g., Anderson & Conture, 2000; Anderson & Conture, 2004; Melnick et al., 2003; Pellowski & Conture, 2002; Pellowski & Conture, in press; Zackheim & Conture, 2003). Children who stutter were identified for participation in these studies by their parents who were informed about it through (a) an advertisement in a free, widely-read, monthly parent-oriented magazine circulated throughout Middle Tennessee (i.e., the “Nashville Parent”; estimated monthly readership of 230,000); or (b) Middle Tennessee area speech-language pathologists, health care providers, daycare centers, and so forth; or (c) self or professional referral to the Vanderbilt Bill Wilkerson Hearing and Speech Center for initial assessment of childhood stuttering.

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2.2. Classification and inclusion criteria 2.2.1. Children who stutter Participants were assigned to the CWS group if they (a) exhibited three or more stutterings (part-word repetitions, single-syllable word repetitions, sound prolongations, blocks, and tense pauses) per 100 words of conversational speech (Yairi & Ambrose, 1992) and (b) received a total overall score of 11 or above (a severity equivalent of at least “mild”) on the Stuttering Severity Instrument – 3 (SSI-3; Riley, 1994; CWS had a mean SSI-3 score of 17.44, standard deviation [S.D.] = 6.69). Similar indices of stuttering have been reported elsewhere (e.g., Yairi & Ambrose, 1992; Yairi & Lewis, 1984), with the specific definition of the “constituent members” or different disfluency types representative of stuttering also described elsewhere (Williams, Silverman, & Kools, 1968). 2.2.2. Children who do not stutter To be classified as CWNS, participants (a) exhibited two or fewer stutterings per 100 words of conversational speech (Yairi & Ambrose, 1992) and (b) received a total overall score of 10 or below (a severity equivalent of less than “mild” for preschool children) on the SSI-3 (CWNS had a mean SSI-3 score of 4.78, S.D. = 3.38). 2.2.3. Speech, language and hearing abilities Prior to experimental testing, all participants were administered, during a visit to each child’s home, the Goldman–Fristoe Test of Articulation (GFTA; Goldman & Fristoe, 1986), The Peabody Picture Vocabulary Test –3 (PPVT; Dunn & Dunn, 1997), the Expressive Vocabulary Test (EVT; Williams, 1997) and the Test of Early Language Development – 3 (TELD-3; Hresko, Reid, & Hamill, 1991) to assess articulation abilities, receptive vocabulary, expressive vocabulary, and receptive and expressive language skills, respectively. Requirements for inclusion in the present study were that children scored at or above the 20th percentile rank (approximately 1 S.D. below the mean) for their age group. In addition, participants were required to pass a hearing screening (bilateral pure tone testing at 20 dB HL for .5, 1, 2, and 4 kHz) and exhibit normal tympanograms.

3. Description of independent measures 3.1. Instrumentation Approximately 1–2 weeks after testing in the child’s home, the child participated in experimental testing. Situated in a quiet room, the child was instructed to sit in front of a standard (Pentium 200 MHz) computer with a 20-inch Sony Trinitron monitor and told to name the pictures displayed on the screen (“as soon as you see it”). In total, there were two lists of 10 pictures each (20 pictures total) that were presented to the child in one “sitting,” with a brief break (1–2 min) between conditions to permit the preparation of the next condition. The two word lists, which consisted of the same words in different order, were counterbalanced for each participant according to talker group in attempts to minimize word list order effects on SRT and errors.

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Pictures were shown one at a time, with each picture preceded by a 42 ms, 1000 Hz “preparatory beep” 300 ms prior to each onset of picture presentation. This duration of 300 ms between the preparatory beep and the onset of the target or picture ensured no temporal overlap of the auditory preparatory or ready signal and the visual onset of the picture, but was close enough in time to the onset of the picture presentation to provide the child with a signal to attend to the pictures displayed on the computer screen. The onset of subsequent pictures was determined by the child’s voice-activated microphone attached to a co-processor, the New Experimental Stimulus Unit (NESU) developed by the Max Plank Institute, University of Nijmegen, Nijmegen, The Netherlands. The NESU was interfaced, synchronized, and run simultaneously with the Pentium computer. For each participant, prior to the experiment, the experimenter ensured that the voice-key trigger indicator of the NESU display window was consistently showing simultaneously with the onset of the child’s vocal response and, if needed, the voice-key level on the NESU was adjusted so that each child’s response was triggered at its onset. Subsequently, during the experiment, any response onset that did not occur at the same time as the voice-key trigger indicator was shown on the NESU display window was excluded from SRT analyses. Each participant was presented with five phonologically dense (i.e., high neighborhood density) and five phonologically sparse (i.e., low neighborhood density) picture words to name per each of the two word lists. These picture stimuli were randomized into two different lists of 10 words each, with the random orders differing between the two lists. Interspersing dense and sparse words within the two lists was done to reduce any systematic facilitative or inhibitory effects that each word may have had on another. 3.2. Initial selection of picture–word stimuli The stimulus picture words were selected from a corpus of 28 words used by Pellowski and Conture (in press). Pellowski and Conture selected this corpus from a standardized set of 260 pictures developed by Snodgrass and Vanderwart (1980), which were subsequently normed for name agreement, familiarity, and complexity for 5–7-year-old children by Cycowicz, Friedman, Snodgrass, and Rothstein (1997). Accuracy data for pictures reported by Pellowski and Conture indicated that the 10 monosyllabic words chosen for this study (to be described below) were named accurately, not counting misarticulations, on average by 3–5-year-old children (N = 10 in each age group) at least 94% of the time. 3.3. Determination of phonological density, word familiarity, frequency, age of acquisition, and bigram frequency In the present study, a large, on-line lexicon (Sommers, 2002), based on Webster’s Pocket Dictionary (Webster’s Seventh New Collegiate Dictionary, 1967), which contains approximately 20,000 entries, was used to computationally determine each of the 10 word’s familiarity, frequency and phonological neighborhood density. Although this on-line database uses data from adults, child and adult lexical databases have been reported to be positively correlated (Walley & Metsala, 1992; Jusczyk et al., 1994). Furthermore, age of acquisition data were obtained from Snodgrass and Yuditsky (1996). Finally, a bigram analysis, described in further detail below, was completed using the calculations of Solso, Barbuto,

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Table 1 Phonological Neighborhood Density, Frequency of Occurrence, Familiarity Ratings, Age of Acquisition, and Bigram Frequencies of Ten Picture-Naming Targets Picture naming target

Neighborhood density

Frequency of occurrence

Familiarity

AoA

Bigram frequencies

10 13 20 8 12 12.6

118 88 56 8 4 54.8

7 7 7 7 7 7

4.05 3.50 2.90 3.15 2.44 3.21

8,973 8,080 44,382 7,312 8,474 15,444

8 7 8 7 5 7.0

186 59 25 14 6 58.0

7 7 7 7 7 7

3.32 2.58 2.90 3.03 2.45 2.86

49,853 31,621 32,461 22,336 21,956 31,645

Dense words Gun Key Hat Piga Sock Mean Sparse words Heart Tree Star Forka Spoon Mean

AoA, age of acquisition (Snodgrass & Yuditsky, 1996). a Speech reaction times for these words were excluded from analysis due to initially incorrect neighborhood density values (see Pre-analysis data preparation for details).

and Juel (1979). See Table 1 for phonological neighborhood density, frequency, familiarity rating, age of acquisition and bigram frequency values for each of the 10 target words. Phonological neighborhood density was determined based on the number of words which differed from the target picture word by a single phoneme substitution, deletion or addition (e.g., “bat”, “at”, and “scat” would be phonological neighbors of “cat”). The five dense picture-naming stimuli had significantly higher (t[8] = 2.65, p < .05) mean neighborhood density (i.e., 10 and above) than the neighborhood density of the five sparse (i.e., 8 and below) picture-naming stimuli (see values in Table 1). To further check on the appropriateness of the phonological neighborhood density values with regard to a child lexicon, density values were calculated when neighbors, which appeared to be acquired beyond the age of 5, were culled from each target word’s neighborhood (e.g., “sob” and “sake” for “sock”). This culling process was completed by a certified speech-language pathologist (i.e., the first author) with 2 years of experience testing preschool-aged children clinically as well as experimentally, who determined which neighbors to cull based on her subjective judgments of their age-appropriateness. These neighborhood density values, when words acquired after age 5 were culled from the neighborhoods, still significantly differentiated the dense and sparse target words (t[8] = 4.58, p < .01, assuming equal variances), indicating that the neighborhood values from Sommers (2002) adult database were appropriate for the present study with children. With regard to word frequency, all of the 10 picture stimuli (i.e., 5 dense + 5 sparse) had equivalent mean frequencies of occurrence in the English language. All the frequency counts used in the aforementioned online database were based on Kucera and Francis (1967). In order to determine frequency of word usage, Kucera and Francis selected 500 samples of

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written English, containing approximately 2000 words each, from several different genres of writing, including journalism, fiction, humor, hobbies, religion, and scientific writing, among others. The entire corpus consists of 1,014,232 words. Furthermore, all 10 picture stimuli had a similar word familiarity rating of 7 based on a rating scale ranging from (1) “don’t know the word” to (7) to “know the word and know its meaning”. The subjective familiarity ratings were based on the Nusbaum, Pisoni, and Davis (1984) study of adults, which was, of course, an approximation to word familiarity for preschool children in the absence of norms for these young, preschool children. Additionally, dense and sparse picture stimuli did not differ with respect to Age of Acquisition (AoA) ratings based on Snodgrass and Yuditsky (1996). Ratings were obtained for all 250 of the Snodgrass and Vanderwart (1980) words by asking adults to approximate the age at which they first learned each word and its meaning in either spoken or written form. Snodgrass and Yuditsky (1996) used the same rating scale and instructions that Carroll and White (1973) used to obtain AoA ratings. According to an independent samples t-test, the five dense and five sparse stimuli did not differ according to age of acquisition, t(8) = 1.12, p = .295. Because the dense and sparse stimuli were not as comparable in phonetic structure (i.e., having the same number of words containing consonant clusters) as would have been ideal, a metric was sought that might relate to the frequency that two phonemes within a word occur in the English language. Therefore, a bigram frequency analysis was completed (Westbury & Buchanan, 2002). Bigram frequency is the frequency of two letters occurring in sequence within a word. The non-word-length-matched, non-position-controlled bigram frequencies were obtained from Solso et al. (1979), who calculated bigram frequencies using the Kucera and Francis (1967) norms. A mean bigram frequency was calculated for each stimulus by taking an average of all the bigram frequencies within the word. The overall mean bigram frequencies did not significantly differ between the dense and sparse words. It is not clear how closely bigram frequency, which relates to a word’s orthographic characteristics, would impact the affects of phonological neighborhood density, which relates to the word’s phonological characteristics, on picture-naming in young, pre-literate children. 4. Definition/description of dependent measures 4.1. Dependent variables for practice effects For the purposes of this study, two dependent measures (i.e., errors and SRTs) were used to assess differences between children who do and do not stutter in terms of naming pictures differing by phonological neighborhood density. Criteria for tabulating or measuring each of these variables are described immediately below. 4.2. Pre-analysis data preparation Subsequent reliability checks of each target word’s phonological neighbors revealed an error with regard to classification of “pig,” a word initially classified as dense. The online database (Sommers, 2002) used for querying phonological neighbors apparently generated “neighbors,” which differed by a single phoneme as well as by tense/lax vowel classification

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(e.g., [pik] and [pit ] for the target [pIg]). Because these words were not classified as neighbors according to the definition stipulated by Luce and Pisoni (1998), SRTs for the word “pig” and its sparse cognate according to frequency, “fork”, were discarded from statistical analysis of the final data corpus. Thus, final assessment of SRT during picturenaming in association with dense and sparse words was based on four dense and four sparse words. These remaining dense and sparse words still significantly differed according to phonological neighborhood density, but continued to be comparable in frequency, familiarity rating, age of acquisition and bigram frequency values. 4.3. Errors During the picture-naming task, error data were collected for each trial response by a speech-language pathologist and later verified by the first author watching the audio–video recordings of the experiment. The number of errors was summed for each participant according to experimental condition. A set of exclusionary criteria detailed below were used prior to error analysis. 4.3.1. CWNS From an initial group of 19 CWNS, five participants (26%), consisting of four 3-year-olds and one 4-year-old, were excluded from the study because one or more of their standardized test scores fell below the 20th percentile criterion. The remaining 14 CWNS participants (i.e., three 3-year-olds, five 4-year-olds and six 5-year-olds) provided picture-naming responses for 224 trials (8 trials per condition × 2 density conditions × 14 participants). Of these 224 responses, the 8 (3.6%) trial responses containing speech disfluencies, either normal or stuttering, were discarded, even if these responses also contained errors (e.g., “uh, spoon” for the target picture “fork”). Of the remaining 216 responses, the 5 (2.3%) trial responses containing phonological/articulatory errors were discarded, even if these responses also contained other errors. The remaining 211 fluent, correctly articulated picture-naming responses were classified as follows: (1) 205 (97%) accurate responses, (2) 3 (1.4%) mislabeled responses (e.g., ‘box’ for ‘hat’), (3) 3 (1.4%) trials with no overt verbal response to the stimuli (i.e., no response, NR), and (4) no off-task behaviors (e.g., talking about something else during the task). Next, because any participant’s data, containing greater than 40% (>3 out of 8) misarticulations or disfluencies, were excluded from analysis; the complete set of responses for one 5-year-old CWNS was excluded. Finally, only 9 (69%) of these 13 remaining CWNS qualified as age and gender matches to the usable CWS (the latter to be discussed directly below). These nine CWNS were matched to the age and gender of the nine CWS. In one case, there were two possible CWNS to match to one available CWS; in this situation, the CWNS to match was randomly selected via coin flip. Thus, for the purpose of error analysis, the final corpus of data for the nine CWNS participants consisted of 137 (67 sparse and 70 dense) fluent, accurate picture-naming responses without articulation/phonological errors. 4.3.2. CWS From an initial group of 23 CWS, eight participants (35%), consisting of two 3-year-olds, five 4-year-olds, and one 5-year-old, were excluded from the study because one or more

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of their standardized test scores fell below the 20th percentile criterion. The remaining 15 CWS participants (i.e., seven 3-year-olds, six 4-year-olds and two 5-year-olds) provided picture-naming responses for 240 trials (8 trials per condition × 2 density conditions × 15 participants). Of these 240 responses, the 11 (4.6%) trial responses containing speech disfluencies, either normal or stuttering, were discarded, even if these responses also contained errors (e.g., “uh, spoon” for the target picture “fork”). Of the remaining 229 responses, the 8 (3.5%) phonological/articulatory errors were discarded, even if these responses also contained other errors. The remaining 221 fluent, correctly articulated picture-naming responses were classified as follows: (1) accurate responses, (2) 11 (5.0%) mislabeled responses (e.g., ‘box’ for ‘hat’), (3) 14 (6.3%) non-responses to the stimuli (i.e., NRs), and (4) 1 (.5%) off-task behavior (e.g., talking about something else during the task). As stated above, any participant’s data, containing greater than 40% (>3 or more out of 8) misarticulations and/or disfluencies, were excluded from analysis. Because none of the 15 CWS demonstrated greater than 40% misarticulations and/or speech disfluencies, none of these 15 CWS were excluded at this stage. Finally, only 9 (60%) of these 15 remaining CWS qualified as age and gender matches to the usable CWNS. Thus, for the purpose of error analysis, the final corpus of data for the nine CWS participants consisted of 140 (70 sparse and 70 dense) fluent picture-naming responses without articulation/phonological errors. 4.4. Speech reaction time During the picture-naming task for both word lists, the computer controlled the presentation of targets and recorded the naming latencies (SRT, in ms) measured from target onset to the triggering of the voice key by the participant’s spoken response. The SRT data were collected and analyzed using the aforementioned NESU hardware and software, a collection/analysis system for chronometric data (i.e., reaction time, RT). Mean SRT was calculated for each of the participant’s responses for dense and sparse words for both the first and second trial blocks of picture-naming targets. For this SRT analysis, only fluent, accurate trial responses, for which the voice-key trigger indicator of the NESU was shown simultaneously with the onset of the child’s vocal response, were analyzed (Melnick et al., 2003; Pellowski & Conture, in press). Of the 139-response corpus from the CWNS previously analyzed for errors, the following exclusions were made for the purpose of SRT analysis. 4.4.1. CWNS Of the 139 fluent, correctly articulated responses, 3 (2.2%) response trials were excluded from SRT analysis because the voice key was triggered prematurely (e.g., child makes noise with kicking feet) or late (e.g., initial portion of child’s naming response not loud enough to trigger voice key). Of the remaining 136 responses, 5 (3.7%) were excluded because they had an SRT that was ±2 S.D. from the mean for the talker group to which the participant is assigned (see Ratcliff, 1993 for further discussion of motivated procedures for dealing with reaction time outliers). Of the remaining 131 responses, the 6 (4.6%) that were designated as mislabels, non-responses, or off-task behaviors were discarded from SRT analysis. Finally, if greater than 40% (>3 or more out of 8) of a participant’s trial responses were excluded for SRT analysis, the entire set of responses for that participant was excluded from SRT

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analysis. All the remaining nine CWNS were included in the SRT analysis because they all met the above criteria for number of usable responses. Application of the above exclusionary criteria resulted in 126 picture-naming responses (67 sparse and 59 dense), for the purposes of assessing speech-reaction time, that were fluent, error-free and within a time frame (i.e., typically 300–2000 ms from picture onset) that can be reasonably assumed to span the temporal epoch that preschool children are planning for and then producing picture-naming responses. 4.4.2. CWS Of the 140 fluent, correctly articulated responses, 8 (5.7%) response trials were excluded from SRT analysis because the voice key was triggered prematurely (e.g., child makes noise with kicking feet) or late (e.g., initial portion of child’s naming response not loud enough to trigger voice key). Of the remaining 132 responses, 6 (4.5%) were excluded because they had an SRT that was ±2 S.D. from the mean for the talker group to which the participant is assigned (Ratcliff, 1993). Of the remaining 126 responses, the 5 (4.0%) that were designated as mislabels, non-responses, or off-task behaviors were discarded from SRT analysis. Finally, if greater than 40% (>3 or more out of 8) of a participant’s trial responses were excluded for SRT analysis, the entire set of responses for that participant was excluded from SRT analysis. All the remaining nine CWNS were included in the SRT analysis because they all met the above criteria for number of usable responses. Application of the above exclusionary criteria resulted in 120 picture-naming responses (62 sparse, and 58 dense), for the purposes of assessing speech-reaction time, that were fluent, error-free and within a time frame (i.e., typically 300–2000 ms from picture onset) that can be reasonably assumed to span the temporal epoch that preschool children are planning for and then producing picture-naming responses. 4.5. Intra- and inter-judge measurement reliability 4.5.1. Speech disfluency measures Intra-judge and inter-judge measurement reliability were calculated for judgments of stuttering and other speech disfluencies based on three randomly selected conversational speech samples (representing 33% of the total data corpus). All three speech samples were obtained from the group of CWS. The total data corpus for measurement reliability purposes consisted of 900 total words, with 300 words obtained per participant in each talker group. The first author and a doctoral student, both of whom are certified speech-language pathologists and experienced in the assessment of stuttering, observed video recordings of the conversational speech samples of the three children and re-identified all instances of stuttering and other speech disfluencies within each sample. Utilizing the following measurement reliability index, ([A + B]/[A + B + C + D]) × 100, where A, number of words judged stuttered on both occasions; B, number of words judged not stuttered on both occasions; C, number of words judged stuttered on one occasion but not the other occasion; and D, number of words judged fluent on one occasion but not the other occasion, the measurement reliability percentages included the following: (a) intra-judge, 99% and (b) inter-judge, 97%.

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4.5.2. Response accuracy measures For the measure of response accuracy, another three CWS and three age- and gendermatched CWNS were randomly selected (N = 6). This resulted in approximately 17% of the total data (16 responses × 6 participants = 96 responses) being used for intra-judge and interjudge measurement reliability for response accuracy. Intra-judge reliability was assessed by having the first author judge each response for accuracy on two separate occasions. Interjudge reliability was assessed by having the first author and a trained observer judge each response for response accuracy. As with the speech disfluency measures, intra-judge and inter-judge reliability scores for response accuracy measures were assessed across participants using the following measurement reliability index, ([A + B]/[A + B + C + D]) × 100, where A, number of responses judged to contain errors or speech disfluency on both occasions; B, number of responses judged to be without error or disfluency on both occasions; C, number of responses judged to contain errors or speech disfluency on one occasion but not the other occasion; and D, number of responses judged to be accurate and fluent on one occasion but not the other occasion. The measurement reliability percentages included the following: (a) intra-judge, 96% and (b) inter-judge, 96%. 5. Data analysis 5.1. Between- and within-group comparisons For the current study, there were two dependent variables, SRT and errors, for the picturenaming task. Talker group membership and phonological neighborhood density conditions were the independent variables. Statistical analyses involved mixed-model repeated measures analyses of variance (ANOVA) for each dependent variable (i.e., SRT and errors). Unexpectedly, initial, informal pre-analysis of the data suggested that there may be both SRT and error differences between CWS and CWNS in picture-naming across trial-block number (first trial block = first set of pictures to be named, both sparse and dense versus second trial block = second set of pictures to be named, both sparse and dense). Therefore, to rule out any potential effect trial-block number had on between-groups neighborhood density effects, three-way ANOVAs for each dependent variable (i.e., SRT and errors) were completed. Finally, the influence of any potential group differences in standardized speech and language measures on between-group SRT or error differences was assessed by means of an analysis of covariance (ANCOVA). The statistical program used to complete all these parametric, inferential statistical analyses was SPSS 12.0 for Windows and the level of significance applied for all the statistical tests was .05. 6. Results 6.1. Descriptive information 6.1.1. Stuttering/speech disfluencies Based on the 300-word conversational speech sample, and as would be expected from employing the aforementioned participant selection criteria, CWS (N = 9) exhibited signif-

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Table 2 Mean Standard Scores (Standard Deviation) by Participant Group (CWS, CWNS) for the Standardized SpeechLanguage Tests Speech-language test

CWS standard score (S.D.)

PPVT-III EVT TELD-3 Expressive subtest Receptive subtesta GFTA-2

104 (9.8) 111 (8.2) 106 (15.1) 107 (12.9) 104 (14.6) 113 (9.3)

CWNS standard score (S.D.) 113 (15.8) 113 (15.8) 117 (11.6) 108 (11.0) 120 (11.3) 112 (8.7)

Note: CWS, Children who stutter; CWNS, Children who do not stutter; S.D., standard deviation; PPVT-III, Peabody Picture Vocabulary Test-III; EVT, expressive vocabulary test; TELD-3, Test of Early Language Development-3; GFTA-2, Goldman–Fristoe Test of Articulation-2. a CWS were significantly different from CWNS for the TELD-3 receptive subtest, p < .05.

icantly greater mean total (i.e., stuttering plus other disfluencies; M = 29.44, S.D. = 7.06), t(16) = 7.37, p < .0001, as well as more frequent stuttering (M = 20.78, S.D. = 10.13), t(16) = 4.97, p < .01, than CWNS (N = 9; total: M = 7.56, S.D. = 5.43; stuttering: M = 3.11, S.D. = 3.33). 6.1.2. Speech and language abilities Although all participants scored within normal limits (i.e., at or above the 20th percentile) on a variety of standardized speech-language tests (e.g., PPVT-III, EVT, TELD-3, and GFTA-2; see Table 2), a multivariate analysis of variance (MANOVA) revealed significant between-group differences on one of the subscales of one of these four standardized measures of speech-language. Specifically, CWS were found to score significantly lower than CWNS on the Receptive subtest of the TELD-3, t(16) = 2.32, p < .05. The possible influence of this between-group difference on the study’s dependent measures (i.e., SRT and errors) will be discussed below. 6.2. Speech reaction time (ms) 6.2.1. Dense versus sparse phonological neighborhood conditions Speech reaction time data were subjected to a three-way mixed-model analysis of variance (ANOVA) with talker group (CWS or CWNS) as a between-subjects variable and conditions (dense or sparse words) as well as trial-block number (first or second) as withinsubjects variables. Results, illustrated in Fig. 1, indicated a significant main effect for neighborhood density condition—that is, SRT was slower in the dense condition than in the sparse condition for both talker groups, F(1,16) = 4.73, p = .045. There was no two-way interaction effect for talker group by neighborhood density condition (F[1,16] = .05, p = .826) nor was there a three-way interaction effect for talker group by neighborhood density by trialblock number (F[1,16] = .07, p = .795). These findings suggest that during picture naming, preschool children (i.e., both CWS and CWNS) demonstrate a neighborhood density effect that produced a slower SRT in the dense than sparse neighborhood conditions. The mean difference in SRT between dense and sparse conditions for both talker groups was 79 ms (S.D. = 150). This period of time (79 ms) is considered sizable given the extreme ra-

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Fig. 1. Mean speech reaction time (in ms) for naming phonologically dense versus sparse words for children who do (CWS) and do not (CWNS) stutter. Y-axis bars show the standard errors of measurement.

pidity of speech-language planning and production (e.g., Hagoort, Brown, & Groothusen, 1993). Findings also revealed a two-way talker group by trial number interaction effect, which approached significance, F(1,16) = 4.04, p = .06. Though this finding was unexpected and not part of the original purpose of this study, it appeared to suggest that regardless of density condition; the difference between first and second productions of target words differed between CWNS and CWS, that is, CWNS got faster from first to second productions whereas CWS became slower. However, because this finding did not reach significance, no further analyses were deemed appropriate relative to SRT and trial-block number. The overall trends of phonological neighborhood density were relatively consistent for individual CWNS. That is, 78% (N = 7) of the nine CWNS exhibited slower SRTs in the dense condition. The phonological neighborhood density trends were a bit less consistent among CWS. That is, 56% (N = 5) of the nine participants who stutter exhibited slower SRT in the dense condition. To assess whether the between-groups difference in performance on the TELD-3 Receptive subtest influenced participants’ SRT in the dense versus sparse conditions, an ANCOVA was conducted using the TELD-3 Receptive subtest standard score as a covariate. In general, using ANCOVA in this way controls for differences in receptive language skills, as measured by a standardized test, by taking into account the relation between performance on this subtest and participants’ SRT when naming dense versus sparse words. Specifically, to rule out possible confounding effects of receptive language, one covariate was added to the Group × Density × Trial Number ANOVA model. This expanded ANCOVA model was limited by having low power for between-subjects effects and only six subjects per variable (i.e., 18 participants/3 variables). Results suggested that the effect of

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the receptive language covariate was nonsignificant (F[1,15] = .780, p = .39); however, the effect of density that was significant (p = .045) in the smaller model became nonsignificant (p = .29) in the expanded model. These results suggest that using a larger model on our small sample failed to clarify the results and that the phonological neighborhood density effect initially found relative to SRT was not robust enough, in part due to the variability of individual responses, to withstand more complex analysis using a relatively small sample size.

7. Errors 7.1. Dense versus sparse phonological neighborhood conditions Error data were subjected to a three-way mixed-model analysis of variance with talker group (CWS or CWNS) as a between-subjects variable and conditions (dense or sparse words) as well as trial-block number (first or second) as within-subjects variables. Results, illustrated in Fig. 2, indicated a significant main effect for neighborhood density condition—that is, there were more errors in the dense condition than in the sparse condition for both talker groups, F(1,16) = 11.5, p = .004. There was no two-way interaction effect for talker group by neighborhood density condition (F[1,16] = 1.28, p = .275), or for talker group and trial-block number, (F[1,16] = 2.286, p = .150), nor was there a three-way interaction effect for talker group by neighborhood density by trial-block number (F[1,16] = 2.0,

Fig. 2. Group means for summed errors, not including misarticulations, stutterings, or other speech disfluencies for naming phonologically dense versus sparse words for children who do (CWS) and do not (CWNS) stutter. Y-axis bars show the standard errors of measurement.

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p = .176). These findings suggest that during picture naming, preschool children (i.e., both CWS and CWNS) demonstrate a neighborhood density effect that produced more errors in the dense than sparse neighborhood conditions. The total difference in errors between dense and sparse conditions for both talker groups was 12 errors and the mean difference was .67 (S.D. = .84). These overall trends of phonological neighborhood density were variable for individual CWNS. That is, 44% (N = 4) of the nine CWNS exhibited more errors in the dense condition, 56% (N = 5) exhibited no difference in number of errors across density conditions, and 0% exhibited more errors in the sparse as opposed to the dense condition. The phonological neighborhood density trends for the CWS were identical to those of the CWNS. That is, 44% (N = 4) of the nine participants who stutter exhibited more errors in the dense condition, 56% (N = 5) exhibited no difference in number of errors across density, and 0% exhibited more errors in the sparse as opposed to the dense condition. To assess whether the between-groups difference in performance on the TELD-3 Receptive subtest influenced each participant’s number of errors in the dense versus sparse conditions, an ANCOVA was conducted using the TELD-3 Receptive subtest standard score as a covariate. As with the ANCOVA relative to the SRT analysis, results suggested that the effect of the receptive language covariate was nonsignificant (F[1,15] = 2.063, p = .171); however, the effect of density that was significant (p = .004) in the smaller model became nonsignificant (p = .298) in the expanded model. As with the SRT analyses, the results of the ANCOVA suggest that using a larger model on our small sample failed to clarify the results and that the phonological neighborhood density effect initially found relative to SRT was not robust enough to withstand more complex analysis using a relatively small sample size. Additionally, the fact that only 44% of all participants demonstrated fewer errors when naming the sparse as opposed to dense targets suggests that the individual variability might have also made it difficult for the previously significant findings to withstand more in-depth analysis.

8. Discussion 8.1. Main findings: An overview In the present investigation, a picture naming task using two groups of words, differing in phonological neighborhood density, was used to examine experimentally the time course and accuracy of phonological encoding processes in CWS and CWNS, the findings of which are considered below. The present study resulted in three main findings: First, picture naming for 3–5-year-old children was slower and less accurate for words that are phonologically dense than for sparse words, a pattern that appeared consistent with that found in older children (Newman & German, 2002). Second, there was no apparent difference in SRT or errors between preschool CWNS and CWS relative to phonological neighborhood density. Third, findings indicated that, the mean receptive subtest score of the TELD-3 was higher for the CWNS than CWS. Although further analyses indicated no apparent covariance of these

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receptive language standard scores with SRT or error data relative to phonological neighborhood density, they appeared to account for enough variance in the analyses to render the neighborhood density effects, for both SRT and error data, statistically non-significant for preschool children, regardless of talker group. 8.2. Influence of neighborhood density on SRT and errors for young children First, present findings suggest that both temporal processing and accuracy of naming single words by preschool children were affected by the phonological neighborhood density of the words. This finding is consistent with Newman and German (2002) findings that school-aged children (aged 7–12 years) demonstrate more accurate picture naming and sentence completion when the naming targets are phonologically sparse as opposed to dense. Although current findings as well as those of Newman and German (2002) appear to support Vitevitch’s (2002) general observation that adults’ picture-naming speed was influenced by neighborhood density, Vitevitch (2002) reported that adults named the dense targets faster than the sparse ones whereas the preschool children in the present study named dense targets slower than sparse targets. Dense phonological neighborhoods appear to facilitate the speed of picture naming for adults due to simultaneous activation of multiple word forms. For preschool children, however, dense neighborhoods appear to inhibit the speed and accuracy of picture naming perhaps because multiple word forms create competition for the desired picture naming target. The developmental differences apparent in neighborhood density effects seem to suggest that this aspect of phonological encoding differs between skills acquisition (i.e., in childhood) and skills maintenance (i.e., in adulthood) phases of speech-language production. Perhaps, with development/maturation, a change occurs in how phonological neighborhoods influence speakers’ speech-language planning and production. Because the present study does not experimentally compare children and adults on their performance on the same task, the possibility that picture naming relative to neighborhood density changes with development, remains an open question that must await future, empirical research. 8.3. Talker group differences in picture naming relative to neighborhood density Second, contrary to initial predictions based on the CRH, there did not appear to be a difference between CWS and CWNS in terms of how phonological neighborhood density influenced participants’ SRTs and picture naming accuracy. This result is consistent with the Melnick et al. (2003) findings, which indicated minimal differences between talker groups with regard to SRT in response to related and unrelated phonological primes. However, the present findings were inconsistent with Byrd et al. (2005), who found talker group differences with regard to picture naming when given a phonological offset-related prime (i.e., almost the whole portion of the target word) as opposed to when given a phonological onset-related prime (i.e., only the initial portion of the word). Apparent differences in the phonological encoding abilities of young CWS among these empirical studies may be related to the different methodologies used. In the Byrd et al. (2005) study of CWS’s phonological abilities, there were comparisons between young children’s

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sound/syllable and their whole-word processing. In contrast, the present study investigated how the similarity of phonological constituents between whole words influenced the speed and accuracy of participants’ picture naming. Likewise, Melnick et al. (2003) did not directly compare sound/syllable processing to whole-word processing but assessed the effects of different sound/syllable primes on the speed of participants’ picture naming. Thus, the possibility that CWS and CWNS differ in their phonological abilities is still an open question that seems highly dependent on the nature of the methodology used to study these abilities. 8.4. Receptive language differences between CWS and CWNS Finally, results indicated that CWNS exhibited significantly higher standard scores on the receptive subtest of the TELD-3 than CWS, though all scores of included participants were within or above normal limits. This is consistent with the findings of others (Anderson & Conture, 2000; Murray & Reed, 1977; Ratner & Silverman, 2000; Yairi, Ambrose, Paden, & Throneburg, 1996) that CWS may demonstrate less robust language skills as compared to CWNS. Despite the fact that the neighborhood density effects did not differ according to talker group, the authors attempted to rule out any possible influence talker group differences in receptive language subtest scores had on their errors and SRTs during the picture naming task. As mentioned earlier, the addition of the covariate may have contributed, along with less than robust consistency of individual responses, to the weakening of the effect due to the sheer addition of a variable to the model. Additionally, it seems appropriate to raise the question of how relevant these scores of receptive language are to the children’s performance on an expressive task of picture naming. The test scores seemingly more relevant to the skills required for the present picture-naming task, that is, the articulation/phonological, expressive vocabulary, and expressive language, did not significantly differ between the two talker groups.

9. Caveats and future directions and conclusions 9.1. Child versus adult lexical databases In the present study, an online database of an adult lexicon (Sommers, 2002) was used to determine the phonological neighborhood density and frequency of the pictures to be named. Others have found this database to be highly correlated with child databases (Geirut & Storkel, 2002). Furthermore, the Kucera and Francis (1967) corpus, on which the frequency data is based, contains data on many more words than child databases (cf. Dollaghan, 1994), which generally include data on children of a particular age or grade (German & Newman, 2004), and would be less appropriate given the range of ages sampled in this study (i.e., 36–71 months). Despite these qualifications, the phonological neighborhood density of the target words should ideally be determined by constructing neighborhoods through examination of word acquisition data for preschool children. It seems reasonable to suggest that the lexicon of a preschool child differs appreciably from that of an adult, a fact that may account for the different pattern of naming speed that children and adults have for dense and sparse words.

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9.2. Number of picture-naming targets The present study included relatively few (i.e., 8 words × 2 trial blocks) opportunities for picture naming in each neighborhood density condition. After error, disfluent, and outlier responses were excluded from analysis; the sample of individual SRTs was relatively small, although quite quantitatively and qualitatively comparable between the two talker groups. In a future study of this type, obtaining a larger corpus of picture-naming targets should increase the number of usable responses that can be analyzed, thereby perhaps increasing the generalizability/validity of the results. 9.3. Neighborhood density differences between dense and sparse stimuli Although the dense and sparse stimuli for this study differed according to phonological neighborhood density at a statistically significant level, they do not differ as much as the stimuli used in past studies of phonological neighborhood density (Newman & German, 2002; Vitevitch, 2002). Thus, comparisons between the current study and previous ones are limited given the inherent differences in the nature or extent of the word contrasts being used as well as inherent differences in age of participants among the various studies. Nevertheless, our results show a trend in preschool-aged children relative to phonological neighborhood density that was consistent with the trend shown in school-aged children (Newman & German, 2002). 9.4. Other phonological structure differences of dense versus sparse stimuli The present authors acknowledge that the dense and sparse stimuli were not as comparable in phonological structure (i.e., having the same number of words containing consonant clusters) as would have been ideal. However, it should be noted how extremely difficult it would be to achieve such an ideal and, at the same time, select “picturable” stimuli that met all our other criteria such as being monosyllabic, differing by neighborhood density, and having comparable levels of familiarity and frequency of use. More importantly, however, given the acknowledged preliminary nature of our study, there were more sparse stimuli that contained consonant onset strings as well as beginning with /s/ than there were dense stimuli words. Thus, if phonetic factors such as “late emerging” consonants as well as consonant onset strings resulted in slower responses by preschool children, one would expect them to name the sparse, not the dense, picture stimuli more slowly and less accurately. However, our results indicated just the opposite. 9.5. Voiceless onsets of target stimuli Given the fact that SRT was calculated based on the first sound detected by the voiceactivated microphone, it is important to note that some of both sparse and dense target stimuli begin with voiceless consonants. However, there was only one additional stimulus beginning with a voiceless consonant in the sparse as opposed to the dense corpus, which should have, in theory, rendered sparse SRT slower, but exactly the opposite was found. Whatever the case, it might have been better to have all target words begin with a voiced

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phoneme but it should be remembered that these preschool participants could not read printed words and stimuli had to be picturable. This reality, that is, only being able to use picturable target words that are accurately, fluently and rapidly named, makes it difficult to control for word-initial voiced/voiceless characteristics. 9.6. Range of speech and language abilities of participants Finally, the authors acknowledge that despite the fact that participants were matched according to age and gender, children at the top and bottom ends of the 3- and 5-years age range probably have widely differing speech and language abilities. Although the matching procedure rendered these age-related differences similar for both talker groups, differences in neighborhood size between children of different ages (Charles-Luce & Luce, 1990; Charles-Luce & Luce, 1995) may have influenced the picture-naming performance of this study’s participants. Given the preliminary nature and, thus, small sample size for this study, analyses of differences between age groups were not deemed appropriate. However, future empirical studies of phonological neighborhood density and childhood stuttering may want to stratify participants according to age. For example, 3-year-old versus 4-year-old CWS, to better address these issues.

10. Conclusions The present study found that contrary to adult talkers, preschool children name phonologically sparse targets faster and more accurately than they do dense targets. Furthermore, CWS and CWNS did not differ in terms of speed and accuracy of picture naming relative to changes in phonological neighborhood density. Future study of this topic may want to consider differences in spoken productions of phonologically dense versus sparse neighborhoods during a developmental (i.e., in children) phase when compared to a maintenance (i.e., in adults) phase. Overall, the present preliminary, findings indicate that although phonological neighborhood density appears to influence the speed and accuracy of picture naming, it does not differentially influence the phonological processes of CWS and CWNS, thus perhaps making minimal contributions to the difficulties children who stutter have establishing reasonably fluent speech-language productions.

Acknowledgements This research was supported in part by an NIH grant (DC00523) to Vanderbilt University. The authors would like to thank Drs. Daniel H. Ashmead, J. Kathryn Bock, Herman H.J. Kolk, and Warren Lambert for their thoughtful and insightful reviews of earlier versions of this manuscript and to Dr. Michael S. Vitevitch for familiarizing the authors with phonological neighborhood density resources. The authors would also like to give special thanks to Dr. Courtney Byrd for her help with creating the computerized experiment, Kia N. Hartfield for her help with the inter-judge measurement reliability, and Dr. Corrin G. Graham for her help with the data collection process. The authors are also grateful to

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the parents and children who participated in this study, without which there would be no study. CONTINUING EDUCATION Phonological neighborhood density in the picture naming of young children who shutter: Preliminary study QUESTIONS

1. Research examining phonological skills of children who stutter has indicated that: a. there is a higher incidence of phonological difficulties in children who stutter than their normally fluent peers b. children whose stuttering is persistent are more likely to exhibit delayed phonology than children who “spontaneously recover” c. phonological skills are unrelated to stuttering in preschool children d. a and b e. all of the above 2. A phonological neighbor is a word that: a. is problematic for preschool children who stutter b. Is easy for preschool children who stutter c. is semantically related to the target word d. differs from the target word by one phoneme addition, substitution, or deletion e. differs from the target word in orthographic spelling, but is phonemically identical 3. Words that reside in dense phonological neighborhoods: a. have few phonological neighbors b. have many phonological neighbors c. do not have any phonological neighbors d. have more phonemes than words that reside in sparse neighborhoods e. are acquired at later ages than words that reside in sparse neighborhoods 4. The Covert Repair Hypothesis is based on the notion that: a. speech motor execution may be related to instances of stuttering b. temperamental differences in preschool children may be related to instances of stuttering c. difficulties with phonological encoding may be related to instances of stuttering d. covert dysfunction in a preschool child’s family may be related to instances of stuttering e. parental correction or “repair” of a preschool child’s speech errors may be related to instances of stuttering 5. Research examining phonological neighborhood density in adults has indicated that: a. adults name phonologically dense words more quickly and more accurately than phonologically sparse words b. adults name phonologically sparse words more quickly and more accurately than phonologically dense words

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c. adults name phonologically sparse words less quickly but more accurately than phonologically dense words d. adults name phonologically dense words more quickly and less accurately than phonologically sparse words e. adults have less dense phonological neighborhoods than children

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