Age-of-Acquisition, Word Frequency, and Neighborhood Density Effects on Spoken Word Recognition by Children and Adults

Journal of Memory and Language 45, 468–492 (2001) doi:10.1006/jmla.2000.2784, available online at http://www.academicpress.com on

Age-of-Acquisition, Word Frequency, and Neighborhood Density Effects on Spoken Word Recognition by Children and Adults Victoria M. Garlock Warren Wilson College

Amanda C. Walley University of Alabama at Birmingham

and Jamie L. Metsala University of Maryland, College Park This study assessed how lexical factors associated with vocabulary growth influence spoken word recognition by preschoolers, elementary-school children, and adults. Word frequency effects in gating and word repetition tasks were minimal, whereas age-of-acquisition and neighborhood density effects were found for all listeners. For word repetition, children displayed more of an advantage for the recognition of early-acquired items from sparse vs dense neighborhoods than did adults; adults showed a greater advantage for the recognition of later-acquired items. Regression analyses revealed that the recognition of early-acquired items from sparse neighborhoods contributed to phonological awareness among individual children. In turn, phonological awareness, receptive vocabulary, and verbal short-term memory contributed to word reading. These findings are discussed in terms of recent proposals about the level of processing at which neighborhood density exerts either facilitatory or inhibitory effects. The development of phonological awareness and early reading skill is also discussed. © 2001 Academic Press Key Words: age-of-acquisition; beginning reading; lexical representations; neighborhood density; phonological awareness; spoken word recognition; vocabulary growth; word frequency.

development of phonological awareness and reading ability, less is known about how children represent and process spoken words (see Walley, 1993). In this study, we wanted to learn more about the factors that influence children’s spoken word recognition, and how changes in this ability might contribute to increases in phonological awareness and early reading ability. Over the last decade, there has been a growing consensus that speech representations are not, at the outset, organized around individual speech sounds or phonemic segments, and only gradually, in early through middle childhood, do they become more fully specified and/or undergo segmental restructuring (e.g., Elbro, 1996; Fowler, 1991; Metsala & Walley, 1998; see also Jusczyk, 1993; Nittrouer, 1996). This “emergent” view contradicts the traditional “inaccessibility” one, according to which phonemic segments are present and functional in infancy for

The preschool and early elementary school years span a period of substantial vocabulary growth, a heightened awareness of the phonological structure of spoken words (e.g., in terms of syllables and phonemes), and beginning reading ability. Although much has been learned about the Support for the research reported here was provided by the National Institute for Child Health and Development (HD30398). We thank David Basilico, Norman Bray, Edwin Cook, James Flege, and Michael Sloane for helpful comments regarding this work. In addition, we are grateful to Kristi Carter Guest for her recordings of the stimuli, to Lauren Randazza for assisting with data collection, and to all the families who participated. Three anonymous reviewers provided helpful comments on an earlier version of this paper. Address correspondence and reprint requests to the first or second author: V.M. Garlock, P.O. Box 9000, Campus Box 5035, Warren Wilson College, Asheville, NC 28815 (E-mail: [email protected]); A.C. Walley, Department of Psychology, 415 Campbell Hall, University of Alabama at Birmingham, Birmingham, AL 35294 (E-mail: [email protected]). 0749-596X/01 $35.00 Copyright © 2001 by Academic Press All rights of reproduction in any form reserved.

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basic speech processing, but are not accessible at a conscious level before reading experience with an alphabetic orthography, or metacognitive development more generally (Liberman, Shankweiler, & Liberman, 1989; Morais, Alegria, & Content, 1987; Rozin & Gleitman, 1977; see also Bowey & Francis, 1991; Tunmer & Hoover, 1992). In support, the slopes of 3- to 6year-olds’ identification functions for consonant and vowel continua are shallower than those of older children and adults (Nittrouer & StuddertKennedy, 1987; Walley & Flege, 1999); 5-yearolds classify speech patterns on the basis of overall similarity relations, whereas older listeners use phoneme identity (Treiman & Breaux, 1982; Walley, Smith, & Jusczyk, 1986); and young children are less sensitive to the position in a word of mispronunciations and other disruptions than are older listeners (Cole & Perfetti, 1980; Walley, 1988). While the precise nature of the changes involved is still at issue (see Elbro, 1996), many now agree that developmental advances in basic speech representation and processing provide some foundation for phonological (especially phoneme) awareness and thus reading success (see also Chaney, 1994; Scarborough, 1990; Stanovich, 1988).1 Despite this consensus, the empirical support for such a developmental sequence is meager. Many studies have shown that basic speech processing (e.g., categorical perception) varies as a function of reading ability in both children and adults, and that poor readers are slower and/or 1 The emergent position is closely allied with the “phonological representations” hypothesis of dyslexia (see Goswami, 1999; Snowling & Hulme, 1994), according to which dyslexic children or those at risk for dyslexia have “fuzzy” (i.e., degraded, distorted, or incomplete) speech representations that limit phoneme awareness and ultimately early reading achievement. Those for less frequent, later-acquired word forms may be especially impaired (Gallagher, Frith, & Snowling, 1999; Snowling, Goulandris, Bowlby, & Howell, 1986), suggesting that lexical restructuring in terms of greater segmental specificity and distinctness is delayed, rather than qualitatively different from that in children without reading difficulties (see also Metsala, 1997b). The emergent position is, however, broader inasmuch as it is intended to characterize normal development and individual variation in reading success, as well as the difficulties experienced by children with extreme reading problems.

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less accurate at recognizing spoken words and nonwords than good readers (see Manis et al., 1997). Also, poor readers exhibit deficits in phonological awareness tasks, such as initial phoneme isolation (saying the first sound in “cat”) and initial phoneme deletion (saying “cat” without the first sound), and these problems persist into adulthood (see Brady & Shankweiler, 1991). Yet few studies have attempted to relate all three abilities—i.e., speech processing, phonological awareness, and reading ability. One exception is a study by Swan and Goswami (1997), in which poor readers, reading age (RA) matched, and chronological age (CA) matched children performed similarly well in syllable and onset-rime awareness tasks for words with representations of adequate quality (as indexed by picture naming accuracy). In contrast, poor readers’ phoneme awareness was impaired relative to that of CA controls, who performed more poorly than older, RA controls. Another exception is a study by McBride-Chang, Wagner, and Chang (1997), who found that speech perception (identification of stimuli from a voice-onset-time continuum as either “bath” or “path”), along with verbal short-term memory (STM) and IQ, predicted 26% of the gains made in phoneme awareness by kindergarteners and 42% of their phoneme awareness in grade 1. Further, kindergarteners with high and low speech perception scores later differed in word reading, but not when phoneme awareness was controlled. More recently, Elbro, Borstrøm, and Petersen (1998) found that kindergarteners’ pronunciation accuracy, a measure of the completeness of speech representations, predicted phoneme awareness in grade 2 for both normal children and those at risk for reading problems. These studies suggest that developmental changes in the nature of basic speech representations play a crucial role in the emergence of phoneme awareness and early reading ability, but evidence about the basis for these changes is still lacking. Several researchers have proposed that spoken word representations become more completely specified and/or segmentally structured with vocabulary growth—i.e., as a result

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of pressures to distinguish between an increasing number of items in the mental lexicon (e.g., Charles-Luce & Luce, 1995; Fowler, 1991; Jusczyk, 1993; Metsala, 1997a; Nittrouer, 1996; Walley, 1993). Indeed, differences in vocabulary knowledge have been observed for children varying in phoneme awareness and/or reading proficiency (Chaney, 1994; Gallagher et al., 1999; McBride-Chang et al., 1997; Metsala, 1999; Scarborough, 1990). However, such differences have not been related to variations in speech processing itself, and there is little information about what other factors promote advances in speech perception and spoken word recognition ability. Two factors that have received extensive attention in the adult literature are word familiarity and phonological similarity, and both have figured prominently in models of visual and spoken word recognition (e.g., Luce & Pisoni, 1998; Marslen-Wilson, 1989; McClelland & Elman, 1986; Morton, 1979). Therefore, we might consider what this research has revealed. What makes one word more familiar than another, and thus more readily recognized? Word familiarity encompasses at least two constructs, experienced frequency and age-of-acquisition (AOA). Although correlated, these dimensions of word familiarity are conceptually distinct (Carroll & White, 1973). For example, “cartoon” is acquired early by most children, but it may not be encountered all that often by either children or adults. In contrast, “cartilage” is usually acquired much later, but it is encountered frequently by some (e.g., osteologists). Whereas experienced frequency is typically operationalized using objective word counts, AOA has most often been operationalized using subjective estimates from adults. Several points warrant emphasis here. First, past research has confirmed the validity of adults’ subjective AOA estimates. For example, when adults estimate a particular word to have been acquired at age 5, then most children of this age respond correctly to the word in tasks such as picture naming and mispronunciation detection (Gilhooly & Watson, 1981; Walley & Metsala, 1992). In contrast, it is merely a widespread assumption that words occurring more

often in objective frequency counts (e.g., counts of materials written for children or adults) have actually been experienced more frequently than those occurring less often (but see Gardner, Rothkopf, Lapan, & Lafferty, 1987). Second, high-frequency words are more likely than lowfrequency words to overlap on a segmental basis with many other words, and these “neighbors” also tend to be of high frequency (Landauer & Streeter, 1973). Therefore, it is possible that neighborhood density and frequency contribute substantially to word frequency effects (see below). Third, objective counts are subject to sampling biases, including the underrepresentation of low-frequency words and thus restricted range effects (Carroll, 1971; Lachman, Shaffer, & Hennrikus, 1974), so that subjective estimates may be better predictors of psychological familiarity (Gernsbacher, 1984; Gordon, 1985). A number of studies have attempted to assess the relative influence of word frequency and AOA on adult recognition using multiple regression. In some studies, only AOA predicted picture naming latencies (Carroll & White, 1973; Gilhooly & Gilhooly, 1979; Morrison, Ellis, & Quinlan, 1992) and word naming speed (Brown & Watson, 1987; Gilhooly & Logie, 1981b; Morrison & Ellis, 1995). In other studies, only word frequency predicted visual recognition thresholds (Gilhooly & Logie, 1981a) and lexical decision speed (Gilhooly & Logie, 1982). In still other studies, AOA and word frequency contributed independently to picture naming, word naming, and lexical decision times (Cirrin, 1983, 1984; Lachman et al., 1974; Morrison & Ellis, 1995). Gerhand and Barry (1998) suggest that these inconsistencies are due largely to the limitations of a regression-based approach, including the possibility of predictor variables being suppressed when they are correlated with other variables. In their study, AOA and word frequency exerted independent effects on immediate visual word naming—a result attributed primarily to the use of a fully factorial stimulus design, in which AOA and word frequency varied orthogonally (cf. Morrison & Ellis, 1995; Turner, Valentine, & Ellis, 1998). Based on these and other results, Gerhand and Barry argue that word frequency affects the early stages of visual word recognition,

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whereas AOA affects the later retrieval or production of a word’s stored phonological representation. In another recent study, Turner et al. (1998) found that word frequency affected visual, but not auditory lexical decision times when AOA was controlled. In contrast, AOA affected both visual and auditory lexical decision times when word frequency was controlled. Thus, modality differences, as well as different task requirements and design/analysis techniques, may influence the extent to which word frequency and AOA effects are found. Surprisingly, questions about the impact of word familiarity have often been pursued without any consideration of the developmental status of the perceiver. In the few existing developmental studies of spoken word recognition, word familiarity has seldom been manipulated, and variations in performance as a function of different levels of word familiarity at a given age have not been assessed (see Walley, 1993).2 However, Cirrin (1983, 1984) observed that a juvenile word frequency measure, not AOA, contributed to kindergarteners’, first-graders’, and third-graders’ picture naming latencies. In contrast, both variables influenced auditory lexical decision speed (except for third-graders). Walley and Metsala (1990, 1992) found systematic improvements with age in the recognition of words from three AOA levels, with frequency, according to both juvenile and adult counts, controlled. For example, 5- and 8-year-olds were as sensitive as adults to errors in early-acquired words; 8-year-olds were as sensitive as adults for words of intermediate AOA status, whereas 5-year-olds were less able to discriminate mispronounced and intact versions; each of the age comparisons was significant for later-acquired words. (Dyslexic children also have particular difficulty 2

This is surprising given the essentially developmental nature of both the AOA and experienced frequency constructs; i.e., the familiarity of a word does not reside in the stimulus itself but must be some function of both the perceiver and the status of that item in the speech to which he/she is exposed. Of course, this is why juvenile and adult word counts exist, but there has been little cross-referencing of such counts and the perceptual/cognitive consequences of the differences have not been studied in any systematic, comprehensive manner.

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recognizing and repeating low-frequency words and nonwords; Gallagher et al., 1999; Snowling et al., 1986). Metsala (1997a) studied the recognition of high- and low-frequency words by 7-, 9-, and 11-year-olds and adults in the gating task, where increasing amounts of acoustic-phonetic input are presented from word-onset over a series of trials. (Word frequency was defined by an adult count, but all the words had AOA ratings suggesting they were acquired by age 7.) In general, older listeners needed less input than younger ones to identify the targets, especially those of high frequency. However, low-frequency items tended to have later AOA ratings than high-frequency items, a confounding that applies to other developmental studies (e.g., Brady, Shankweiler, & Mann, 1983) and many studies in the adult perceptual/cognitive and memory literature (see Turner et al., 1998). Thus, while there is evidence that AOA and word frequency affect children’s recognition, we know little about their relative importance or how they might interact. In studies of adult spoken word recognition, considerable attention has been paid to the effects of phonological similarity—specifically, neighborhood density. Neighbors are words that differ from one another by a single phoneme addition, deletion, or substitution in any position (see Luce, 1986). Thus, “mash” has many neighbors (e.g., “smash”, “ash”, “cash”, “mush”, “mat”), so it resides in a dense neighborhood, while “fudge” has only a few neighbors (e.g., “judge”, “fun”) and resides in a sparse neighborhood. A number of experiments by Luce and colleagues (see Luce & Pisoni, 1998) have shown that words from sparse neighborhoods enjoy an advantage in recognition. For example, words from sparse neighborhoods that are presented in noise are recognized more accurately than words from dense neighborhoods. Also, naming and lexical decisions are faster for words from sparse rather than dense neighborhoods. (Similarly, words from low-frequency neighborhoods are generally recognized more accurately and quickly than those from high-frequency neighborhoods.) These results support the assumption embodied in most current adult models that lexical candidates compete with one another during recognition (e.g.,

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Luce & Pisoni, 1998; Marslen-Wilson, 1989; McClelland & Elman, 1986; Norris, 1994). The notion of competition among an increasing number of words in the child’s lexicon that overlap on an acoustic-phonetic basis is also central to recent proposals regarding developmental advances in spoken word recognition (e.g., Fowler, 1991; Metsala & Walley, 1998; see also Lindblom, 1992). Yet to our knowledge, only two studies have examined the impact of neighborhood density on recognition from a developmental perspective. Metsala (1997a) found that 7- to 11year-olds and adults performed better in the gating task for high-frequency words from sparse, as opposed to dense, neighborhoods (less input was needed for recognition). In contrast, they did better for low-frequency words from dense neighborhoods. This interaction between word frequency and neighborhood density (see also Andrews, 1997; Luce & Pisoni, 1998) was explained in terms of two functionally distinct and opposing influences—namely, online effects that enhance the processing of frequently heard words with few competitors, and structural effects that reflect pressures in acquisition for the segmental restructuring of words resembling many others. However, Pitrat, Logan, Cockell, and Gutteridge (1995) observed a very different pattern of results for much younger children in a picture-pointing task. Specifically, 2-year-olds identified high-frequency words with several neighbors better than those with few neighbors (with word frequency defined according to child production norms). This effect was smaller for 3-year-olds, and 4year-olds did not display it at all. Thus, neighbors appear to impede the recognition of words that are well-known by older children and adults, but they enhance the performance of very young children. More data for the same tasks from children of an age spanning that in these two studies might help to resolve this discrepancy. Overall then, we still know very little about spoken word recognition by children, especially those below first-grade age. In particular, the effects of word familiarity and phonological similarity remain unclear. This lack of clarity is due, in part, to the use of designs that have employed only a small number of stimuli, assessed recognition in only one task, or involved a confound-

ing of AOA and word frequency—problems that characterize virtually all existing developmental studies. Our study sought to address these limitations by assessing how AOA, word frequency, and neighborhood density influence preschoolers’, elementary-school children’s and adults’ recognition of the same words in two tasks, gating and word repetition. For these tasks, we employed a factorial stimulus design in which AOA and word frequency (and neighborhood density) varied orthogonally, even though it has been suggested “there are simply not enough early-acquired, low-frequency words or (especially) later-acquired, high-frequency words to permit the creation of word sets in which the two variables are fully crossed while being matched on other relevant factors such as word length” (Morrison & Ellis, 1995, p. 119). Like Gerhand and Barry (1998), however, we contend that we have a word sample that is sufficient for such a design and the detection of interactions between the lexical factors of interest. While their study was the first to assess the role of AOA and word frequency in visual word naming using a fully factorial design, ours is the first to do so (and include neighborhood density) in assessing spoken word recognition from a developmental perspective. We used adults’ subjective AOA ratings and objective word frequency as measures of word familiarity to allow comparison of our results with existing findings. It was assumed that words with early ratings would be well known by both children and adults, but that there would be agerelated differences, as well as greater individual variation among young children in the extent to which words with later ratings are known (see Walley & Metsala, 1992). Word frequency was initially defined by an adult count and then cross-referenced with a child count—i.e., we attempted to select words that are similar in their relative frequency of occurrence (high vs low) for adults and children. Both of our spoken word recognition tasks have been used extensively with adults (see Grosjean & Frauenfelder, 1996), but less so with children. In the gating task, participants heard increasing amounts of speech from word onset and attempted to say the target word. In the

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word repetition task, they attempted to repeat words presented in the clear or in white noise (and thus with various segments sometimes masked). It was assumed that both tasks tap some sensitivity to sublexical information and the ability to use this in recognizing spoken words. Since the same words were employed across tasks, our results should provide converging evidence about the development of word familiarity and neighborhood density effects. What expectations about the effects of AOA, word frequency, and neighborhood density on spoken word recognition follow from the emergent position? By this view, lexical representations become more completely specified and/or more segmental over the course of childhood. Some researchers have explicitly maintained that changes in lexical representation unfold primarily as a result of vocabulary growth. According to Nittrouer, Studdert-Kennedy, and McGowan (1989, p. 131), “[as] the number and diversity of the words in a child’s lexicon increase, words with similar acoustic and articulatory patterns begin to cluster . . . ultimately [precipitating] the coherent units of sound and gesture that we know as phonetic segments.” Fowler (1991) suggests that for young children, as well as potentially poor readers, highly familiar lexical items gradually become more fully specified in phonemic or gestural terms than less familiar items. In our Lexical Restructuring Model (Metsala & Walley, 1998), vocabulary growth refers to increases in the overall size of the lexicon and variations in rate of expansion, as well as to changes in the familiarity status and phonological similarity relations of individual lexical items. A first expectation, according to these versions of the emergent position, was that developmental change in spoken word recognition should be greatest for items that are least robust and stable in terms of their familiarity and neighborhood density status. Thus, age differences in performance should be most marked for later-acquired and/or low-frequency words from dense neighborhoods, the recognition of which presumably requires more fine-grained representations. Performance should be most similar across age for early-acquired and/or

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high-frequency words from sparse neighborhoods, which could be recognized either by holistic or more segmental processes. In contrast, by the accessibility view, speech representations undergo little structural change beyond infancy. Recognition should therefore improve with age and greater word familiarity, but neighborhood density would not be expected to impact performance as a function of age or to interact with word familiarity. A second expectation was that performance should be better for familiar words from sparse, as opposed to dense, neighborhoods to the extent that words compete with one another during recognition. Of particular interest is whether young children show this sort of competition effect, and how it might vary with development, since the existing evidence is unclear (cf. Metsala, 1997a; Pitrat et al., 1995). On the one hand, competition effects might be smaller for young children, who do not know as many words as adults. If representations are not as differentiated and/or segmental, the recognition of dense words might not be as impeded relative to sparse words. On the other hand, competition effects might be larger for young children. If their representations are undergoing substantial structural change, the recognition of words from dense and therefore unstable neighborhoods might be especially hampered. Either pattern of results might not be as marked for less familiar words that do not have very robust representations. Finally, we assessed the relation between children’s performance in the gating and word repetition tasks and their receptive vocabulary knowledge, phonological awareness, verbal STM, and reading ability. A third expectation, according to the emergent position, was that variations in spoken word recognition would contribute to phonological awareness, which would in turn contribute to word reading. As noted earlier, there is little research linking all three abilities for the same children. Metsala (1997b) found that the recognition of words from sparse neighborhoods in the gating task accounted for unique variance in word and pseudoword reading by 6- to 8-year-olds, apart from receptive vocabulary and phoneme awareness, suggesting there are direct links between spoken

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word recognition and early reading (cf. McBride-Chang et al., 1997). Such words have likely undergone little refinement, but children who were very poor at recognizing these words from partial input also displayed poor reading ability, because, it was suggested, their more holistic representations limit the use of sound–letter correspondences. Yet Metsala did not examine what factors contributed to phoneme awareness, and she studied children with and without reading problems. For the normally developing children in our study, we were interested in how different markers of vocabulary growth might influence phonological awareness and word reading. We also considered the role of verbal STM, which has been implicated in early literacy, independent of phonological awareness (see Gathercole & Baddeley, 1993). METHOD Participants The participants were 64 preschoolers and kindergarteners (young children), 64 first- and second-graders (older children), and 64 adults (mean age ⫾SE ⫽ 5.6 ⫾ 0.1, 7.6 ⫾ 0.1, and 25.6 ⫾ 0.6 years, respectively), who were recruited via advertisements. All were monolingual English speakers with no known history of speech, hearing, or reading disorder. Over 85% were native Alabamians and all but 3 were from the Southeast. African-Americans made up 23%, 28%, and 38% of the three groups; the rest were Caucasian. There were equal numbers of males and females in each group. Twenty-five other individuals were excluded: 7 young children, 4 older children, and 3 adults failed a puretone hearing screening; 8 young children, 2 older children, and 1 adult chose not to complete the study. Tasks Each participant completed three standardized and five experimental tasks. The standardized tasks were the Peabody Picture Vocabulary Test-Revised (PPVT-R; Dunn & Dunn, 1981), a measure of receptive vocabulary; the reading subscale of the Wide Range Achievement Test-3 (WRAT-3; Wilkinson, 1993), a measure of word

reading; and the backward digit span subscale of the Wechsler Intelligence Scale for Children (WISC-R; Wechsler, 1974), a measure of verbal STM. The experimental tasks were: gating and word repetition, measures of spoken word recognition; initial phoneme isolation and initial phoneme deletion, measures of phonological awareness3; and nonword repetition (see Gathercole, Willis, Emslie, & Baddeley, 1991), another measure of verbal STM. Gating. A series of trials with increasing amounts of acoustic-phonetic input from word onset were presented. The initial trial consisted of the first 100 ms of the target; subsequent trials increased in 50-ms steps until the entire word was presented. Listeners were told that they would hear “a tiny part of a word, then more and more of it” and were asked to guess the word aloud. On each trial, they also made a confidence rating by pointing to a scale containing the numbers 1–7, with a sad face above the 1, a straightmouthed face above the 4, and a happy face above the 7. A total of 32 words was presented across two sessions; thus, listeners heard 166–186 gates per session, depending on the particular sublist of words heard (see Design). Word repetition. Thirty-two words were presented both in the clear and embedded in white noise (64 stimuli), along with 16 nonwords (32 stimuli). The latter were included to make the total number of trials (96) more similar to the number and duration of trials per session in the gating task and to make the tasks more similar experientially, inasmuch as early gating trials are usually heard as nonwords. However, we have called this task “word repetition” because most of the stimuli were words and our focus was on the ability to recognize these items. Nonwords were created by replacing the initial phoneme in a word with a similar segment (e.g., “fudge” became “sudge”). Eight words from each sublist (see Design) were selected for change and these 32 words were then divided into two lists of 16 (4 from each sublist), so that 3

We refer to these tasks as measures of phonological awareness, since, as one reviewer pointed out, they do not necessarily assess phoneme, as opposed to onset-rime, awareness.

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nonwords were not derived solely from the words heard by particular listeners. The 96 stimuli were presented in one of two quasi-random orders. For order 1, half the intact words and nonwords occurred before their noise counterpart, half occurred after; for order 2, the first list was reversed and different nonwords presented. Listeners were told they would hear real and pretend words, some of which would sound “noisy”, and they were asked to repeat what they heard. Initial phoneme isolation. Each listener heard 32 words. After each word, he/she was told to say only the beginning sound (e.g., to say “f”, given “foot”). Initial phoneme deletion. Each listener heard 32 words. After each word, he/she was told to repeat it without the beginning sound (e.g., to say “oot”, given “foot”). Nonword repetition. Participants repeated 1-, 2-, 3-, and 4-syllable nonwords immediately after their presentation. Ten items of each length were presented in random order. Stimuli The 128 test words for the spoken word recognition and phonological awareness tasks were chosen from a database of about 900 monosyllabic CVC words (from Luce, 1986), all of which had been rated as highly familiar by adults. The test words, which are shown in the Appendix, were selected to vary on three major dimensions, AOA (early vs late), word frequency (high vs low), and neighborhood density (sparse vs dense). AOA ratings by adults were available from previous research (Metsala, 1997a). Half the words had AOA ratings less than 4.5 (1.5–4.4; M ⫽ 3.3), the other half had ratings greater than 4.5 (4.8–7.4; M ⫽ 5.9) on a 9-point scale (Carroll & White, 1973), where 1 means acquired at 0–2 years, 5 means acquired at 6 years, and 9 means acquired at 13 years or older. Thus, 4.5 marks the half-way point on this scale; it was also the median AOA rating for the 900-word database. The ratings of 16 adults from Alabama were similar to the original ones in absolute terms for both early words (M ⫽ 3.8) and late words (M ⫽ 6.1), and the Pearson’s r correlation

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between the two sets of ratings was .91 (df ⫽ 126, p ⬍ .0001). Word frequency was initially based on Kucˇera and Francis (1967). High-frequency words occurred 40 times or more per million (40–240; M ⫽ 90); low-frequency words occured less than 10 times per million (1–7; M ⫽ 3). This measure was cross-validated with Kolson’s (1961) count of words spoken by kindergarteners. Sixty-nine of our words do not appear in this count, but the values for the remaining 40 high-frequency words (M ⫽ 95) are higher than those for the remaining 19 low-frequency words (M ⫽ 15). The correlation between the Standard Frequency Index (SFI) values (a log-linear transform; Carroll, 1970) for these two frequency measures was .45 (df ⫽ 57, p ⬍ .001). We also obtained a more recent and comprehensive frequency count for printed materials (Zeno, Ivens, Millard, & Duvvuri, 1995). Only 32 of our test words do not appear in the corpus for kindergarteners and first-graders. The Zeno et al. values (per million words) for the remaining 55 high-frequency words (M ⫽ 87) are higher than those for the remaining 41 low-frequency words (M ⫽ 18). The correlation between these grade-level (SFI) values and those in Kucˇera and Francis was .39 (df ⫽ 94, p ⬍ .001); the correlation between the SFI values for the entire corpus and those in Kuc˘era and Francis was .89 (df ⫽ 126, p ⬍ .0001). Neighborhood density measures were also available from previous research (Metsala, 1997a). Half the words were from sparse neighborhoods (1–9 neighbors; M ⫽ 5.8); the other half were from dense neighborhoods (11–25 neighbors; M ⫽ 14.6). Our 2(AOA) ⫻ 2(Word Frequency) ⫻ 2(Neighborhood Density) stimulus matrix is shown in Table 1. A series of ANOVAs with each lexical factor as the dependent variable confirmed that relevant cells varied only in the target characteristic. Across cells, the number of words beginning with a stop, fricative/affricate, nasal/liquid, or semivowel was similar. Also, the cells did not differ in terms of other potentially important characteristics such as the isolation point (all words diverged from others in the source lexicon at about the third phoneme),

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GARLOCK, WALLEY, AND METSALA TABLE 1 2(AOA) ⫻ 2(Word Frequency) ⫻ 2(Neighborhood Density) Stimulus Matrix AOA Early-acquired

FREQ: ND: Sparse

Dense

Later-acquired

High frequency

Low frequency

High frequency

Low frequency

“foot”

“nail”

“fear”

“badge”

ND ⫽ 5.6(0.4) AOA ⫽ 3.1(0.2) FREQ ⫽ 79.8(5.3)

ND ⫽ 5.9(0.6) AOA ⫽ 3.4(0.1) FREQ ⫽ 2.7(0.4)

ND ⫽ 5.6(0.5) AOA ⫽ 5.9(0.2) FREQ ⫽ 91.6(11.9)

ND ⫽ 5.9(0.4) AOA ⫽ 5.9(0.2) FREQ ⫽ 2.8(0.4)

“game” ND ⫽ 14.5(0.6) AOA ⫽ 3.2(0.2) FREQ ⫽ 89.9(5.8)

“bun” ND ⫽ 14.9(0.7) AOA ⫽ 3.3(0.2) FREQ ⫽ 2.8(0.5)

“fought” ND ⫽ 14.6(0.9) AOA ⫽ 5.8(0.2) FREQ ⫽ 96.9(13.6)

“lame” ND ⫽ 14.6(0.7) AOA ⫽ 5.9(0.2) FREQ ⫽ 2.9(0.3)

Note. AOA, mean age-of-acquisition rating; early ⫽ 3.3 (0.1); late ⫽ 5.9 (0.1). FREQ, mean objective frequency; high ⫽ 89.6 (4.8); low ⫽ 2.8 (0.2). ND, mean neighborhood density; sparse ⫽ 5.7 (0.2); dense ⫽ 14.6 (0.4). There were 16 words in each of the eight cells of the matrix (one example is given for each cell). Standard errors are in parentheses.

neighborhood frequency, phonotactic probability,4 the number of words beginning with a digraph (which might lead to poor phonological awareness in children who can spell; total ⫽ 16/128), or the number of items for which the correct response in the phoneme deletion task formed a word (total ⫽ 50/128). A female speaker from Alabama read each word aloud in a neutral sentence. Sentences were recorded on a Marantz cassette machine in a sound-attenuated booth. The words were excised using COOL Edit (1992), normalized for peak amplitude and stored on disk. For the gating task, stimulus preparation involved storing the first 100 ms of the waveform for a given word, followed by the first 150 ms, the first 200 ms, and so on. If the difference between the last gate and the entire waveform was less than 25 ms (1/2 a gate), this gate was not used. For example, the gated sequence for “bib”, which was 508 ms in length, consisted of 8 gates (100 ms, 150 ms, 200 ms. . .450 ms) plus the entire waveform. All gated stimuli were ramped off over the final 10 ms to prevent clicks. The mean duration of the 128 words was 602 ms; the mean number of gates in a sequence was 11. A 4

In computing phonotactic probability, we used biphone frequency (segment-to-segment co-occurrence probability) for both adult and first-graders’ speech, according to Carterette and Hubbard (1974).

2(AOA) ⫻ 2(Word Frequency) ⫻ 2(Neighborhood Density) ANOVA on word duration revealed no significant differences. For the word repetition task, CSRE (1994) was used to create a white-noise-added version of each word and nonword (0 dB signal-to-noise ratio; see Ryalls & Pisoni, 1997). Stimuli were then recorded on cassette tapes. Those for the STM tasks were recorded directly onto tape. Design Four words from each of the eight cells in the stimulus matrix were used to form sublists (A, B, C, D) of 32 words, so that AOA, word frequency, and neighborhood density means were similar to both overall means and other sublist means. Sublists were counterbalanced across listeners in each age group and the spoken word recognition and phonological awareness tasks, according to a simple Latin square design. Each listener thus heard a given word (with its various gates or its intact and noise versions) only once. For each subgroup of 16 listeners at each age level, half received form L of the PPVT-R and the tan form of the WRAT-3, and half received form M of the PPVT-R and the blue form of the WRAT-3. Procedure Data were collected over the second half of the school year in two sessions lasting 45–60

477

SPOKEN WORD RECOGNITION

min (maximum time between sessions ⫽ 35 days; M ⫽ 13 days for children, 7 days for adults). All participants passed a pure-tone hearing screening (500–2,000 Hz at 20 dB) at the start of each session. The PPVT-R, WRAT-3, first half of the gating task, initial phoneme isolation, and nonword repetition were administered in session 1; the second half of the gating task, word repetition, initial phoneme deletion, and backward digit span were administered in session 2. Practice items and feedback were given at the start of each task. No specific feedback, only general encouragement, was given during testing. All stimuli (except those for the PPVT-R and WRAT-3) were presented via taperecordings over matched and calibrated Sennheiser headphones in a quiet setting. Task order was counterbalanced within each session. Participants were paid after each session. Children were also given stickers after each task and toy prizes after each session. All responses were transcribed by one of two phonetically trained experimenters. The same experimenter tested the same participant in both sessions. Both experimenters were present during eight first sessions and eight second sessions for each age group. Interexperimenter agreement was 94% or more when scoring older children’s and adults’ responses in the gating task, and 88% or more for young children’s responses (see below). Agreement was 95% or more for the other experimental tasks. Data Scoring For the gating task, we calculated isolation points (IPs) and total acceptance points (TAPs). The IP is the stimulus duration at which a word was first correctly identified, regardless of confidence rating; the TAP is the duration at which the word was identified with high confidence (6 or 7), with no subsequent change in identification or confidence rating. When the target was not identified, even after all the word was presented, its total duration plus 50 ms (one gate) was used for the IP. When listeners did not maintain a correct identification or reach/maintain high confidence, the target’s total duration plus 50 ms was used for the TAP. The IPs and TAPs were converted to percentages of a tar-

get’s total duration to adjust for differences across words. Percent correct scores were used for the word repetition and phonological awareness tasks. RESULTS Mean standard PPVT-R scores (⫾SE) for young children, older children, and adults were 103.2 (⫾1.9), 108.2 (⫾1.7), and 106.7 (⫾2.1), respectively. Thus, each group was close to the norm in receptive vocabulary. Mean age-equivalent scores were 5.9 (⫾0.2), 8.7 (⫾0.2), and 28.5 (⫾0.9). Spoken Word Recognition Performance Data from the gating and word repetition tasks were analyzed in separate 3(Group) ⫻ 2(AOA) ⫻ 2(Word Frequency) ⫻ 2(Neighborhood Density) mixed-design ANOVAs. Unless otherwise noted, only those effects that were significant in both subject and item analyses (F1 and F2) at or beyond ␣ ⫽ 0.05 are reported. These were followed up with Holm’s sequential Bonferroni procedure (Seaman, Levin, & Serlin, 1991). Gating—isolation points (IPs). Analysis of IP data revealed a main effect of group (F1(2,189) ⫽ 65.39, F2(2,48) ⫽ 92.38) and of AOA (F1(1,189) ⫽ 439.64, F2(1,24) ⫽ 34.67), and a Group ⫻ AOA interaction (F1(2,189) ⫽ 3.25, F2(2,48) ⫽ 2.82, p ⫽ .07) (Fig. 1, left panel). Young children needed more input to recognize both early and late words than did older children, who needed more than adults: early M ⫽ 67.4, 59.9, and 55.5 (SE ⫽ 0.8, 0.8, 0.6), respectively; late M ⫽ 78.5, 74.8, and 68.4 (SE ⫽ 0.6, 0.7, 0.6). All groups needed less input to recognize early words as opposed to late ones, but this advantage was larger for older children than for young children and adults. A Group ⫻ Neighborhood Density interaction (F1(2,189) ⫽ 5.27, F2(2,48) ⫽ 3.49) was also found (Fig. 1, right panel). Young children needed more input than older children, who needed more than adults: M ⫽ 72.5, 69.0, and 62.9 (SE ⫽ 0.8, 1.0, 0.8), respectively, for words from dense neighborhoods; M ⫽ 73.4, 65.7, and 60.9 (SE ⫽ 0.8, 0.8, 0.7) for words from sparse neighborhoods. Both older children and adults needed less input to isolate words from sparse

478

GARLOCK, WALLEY, AND METSALA

FIG. 1. Mean isolation points (IPs) in the gating task as a function of age-of-acquisition (AOA), word frequency (FREQ), and neighborhood density (ND) for young children (Y), older children (O), and adults (A). IPs are expressed as percentages of total word duration. Lower scores reflect better performance.

neighborhoods as opposed to dense ones, but neighborhood density did not influence young children’s responses. There was no main effect of word frequency and this factor did not interact with any other in both subject and item analyses. The middle panel of Fig. 1 therefore simply shows the main effect of group. We also considered children’s performance as a function of word frequency for early-acquired items only. All 32 high-frequency items and 25 low-frequency items appear in Zeno et al.’s (1995) child count, where their frequency (M ⫽ 127 and 24) is similar to, but slightly higher than that in Kucˇera and Francis (1967) (M ⫽ 85 and 3; for SFI values, r ⫽ .65). The missing words were “bib”, “loaf”, “burp”, “chirp”, “bead”, “moth”, and “shove”. Analyses again revealed a Group ⫻ Neighborhood Density interaction, but no effect of word frequency. Gating—total acceptance points (TAPs). Analysis of TAP data revealed results very similar to those for IPs. In particular, a Group ⫻ AOA interaction was found (F1(2,189) ⫽ 4.48, F2(2,48) ⫽ 6.52), as was a Group ⫻ Neighborhood Density interaction (F1(2,189) ⫽ 7.60, F2(2,48) ⫽ 8.63). All three groups needed less input to recognize early rather than late words with high con-

fidence: early M ⫽ 83.4, 78.5, and 80.5 (SE ⫽ 0.9, 1.0, 0.6), for young children, older children, and adults, respectively; late M ⫽ 88.7, 87.5, and 85.8 (SE ⫽ 0.7, 0.7, 0.5), and the advantage for early words over late ones was greatest for older children. There were no differences between young children’s, older children’s, and adults’ TAPs for words from dense neighborhoods: M ⫽ 86.0, 85.3, and 85.0 (SE ⫽ 0.8, 0.9, 0.6), respectively. However, young children’s TAPs for words from sparse neighborhoods were longer than those for older children and adults, whose responses did not differ: M ⫽ 86.1, 80.8, and 81.4 (SE ⫽ 0.8, 0.9, 0.6). Older children and adults needed less input for confident recognition of words from sparse as opposed to dense neighborhoods, but neighborhood density did not influence young children’s responses. A Group ⫻ Word Frequency interaction (F1(2,189) ⫽ 4.30, F2(2,48) ⫽ 3.34) was also found. Young children and adults needed less input for confident recognition of high-frequency as opposed to low-frequency words, whereas this advantage did not obtain for older children: high M ⫽ 85.2, 83.0, and 81.6 (SE ⫽ 0.8, 0.8, 0.6) for young children, older children, and adults, respectively; low M ⫽ 87.0, 83.1, and 84.7 (SE ⫽ 0.8, 1.0, 0.6).

SPOKEN WORD RECOGNITION

Although young children’s IPs and TAPs were generally longer than older listeners’, they did not need to hear all of the targets in order to identify them (for late words, young children’s TAP M ⫽ 88.7). While this may be some reflection of our scoring procedure, in which listeners who failed to ever identify a word were given credit for doing so (albeit with a 50-ms penalty), the absolute number of such cases was low (young children’s M for late words ⫽ 2.4). Thus, it would seem that young children have at least partially fixed representations for most of our test items. Word repetition. The results reported here focus on average percent correct scores for words presented in the clear and in noise. (Separate ANOVAs showed a similar pattern.) Analysis of these scores revealed main effects of group (F1(2,189) ⫽ 60.66, F2(2,48) ⫽ 81.80), AOA (F1(1,189) ⫽ 92.15, F2(1,24) ⫽ 6.14), and neighborhood density (F1(1,189) ⫽ 73.42, F2(1,24) ⫽ 4.40), as well as Group ⫻ Neighborhood Density (F1(2,189) ⫽ 5.24, F2(2,48) ⫽ 3.26) and Group ⫻ AOA ⫻ Neighborhood Density interactions (F1(2,189) ⫽ 4.72, F2(2,48) ⫽ 3.63). For early words (Fig. 2, left panel), all listeners identified more items from sparse as opposed to dense neighborhoods: for young children, M ⫽ 69.5 and 62.4 (SE ⫽ 1.3 and 1.1), for older chil-

479

dren, M ⫽ 75.9 and 64.9 (SE ⫽ 1.2 and 1.1), for adults, M ⫽ 78.7 and 73.9 (SE ⫽ 1.2 and 1.1). However, young children did not perform as well as older children for words from sparse neighborhoods, whereas older children performed as well as adults. The two groups of children performed similarly for words from dense neighborhoods and more poorly than adults. For late words (Fig. 2, right panel), young children’s performance for items from sparse and dense neighborhoods did not differ (M ⫽ 56.6 and 58.1; SE ⫽ 1.4 and 1.3). In contrast, older children identified more items from sparse than from dense neighborhoods (M ⫽ 66.6 and 61.9; SE ⫽ 1.4 and 1.1), as did adults (M ⫽ 75.1 and 68.8; SE ⫽ 1.1 and 1.1). Young children performed more poorly for words from sparse neighborhoods than did older children, who performed more poorly than adults. The two groups of children performed similarly for words from dense neighborhoods and more poorly than adults. For the early vs late conditions, each of the unconfounded comparisons was significant, except that older children performed similarly for early and late words from dense neighborhoods, and adults performed similarly for early and late words from sparse neighborhoods. Finally, a 3(Group) ⫻ 2(Lexical Status: late words vs nonwords) ANOVA revealed a two-

FIG. 2. Mean percent correct scores in the word repetition task as a function of age-of-acquisition (AOA) and neighborhood density (ND) for young children (Y), older children (O), and adults (A).

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GARLOCK, WALLEY, AND METSALA

way interaction (F1(2,189) ⫽ 23.51; F2(2,60) ⫽ 17.65). Adults identified more late words than did older children, who identified more late words than did young children (M ⫽ 72.0, 64.3, 57.4; SE ⫽ 0.9, 1.0, 1.2, respectively). Performance was more similar across age for nonwords (M ⫽ 54.7, 52.9, 51.1; SE ⫽ 0.8, 0.9, 1.1). The word–nonword comparison was significant for all groups, a result which again suggests that young children have at least partially fixed representations for the late words. Phonological Awareness Performance For initial phoneme isolation, a 3(Group) ⫻ 2(AOA) ⫻ 2(Word Frequency) ⫻ 2(Neighborhood Density) ANOVA of percent correct scores revealed only a main effect of group (F1(2, 189) ⫽ 35.79, F2(2,48) ⫽ 672.19). Adults and older children both performed better than young children (M ⫽ 86.6, 86.5, 52.3, respectively; SE ⫽ 1.0, 0.9, 1.9). No other effects were significant in both subject and item analyses. For initial phoneme deletion, adults’ performance (M ⫽ 98.4; SE ⫽ 0.3) was at ceiling. Older children also performed very well and much better than young children (M ⫽ 93.7 and 27.0; SE ⫽ 0.7, 1.8). A 2(AOA) ⫻ 2(Word Frequency) ⫻ 2(Neighborhood Density) ANOVA of older children’s responses revealed an AOA ⫻ Word Frequency interaction (F1(1, 63) ⫽ 8.69, F2(1,24) ⫽ 6.22). Performance was poorer for late-acquired, high-frequency words (M ⫽ 90.6, SE ⫽ 1.7) than for either late-acquired, low-frequency words (M ⫽ 95.3, SE ⫽ 1.4) or early-acquired, high-frequency words (M ⫽ 94.9, SE ⫽ 1.4; for early-acquired, lowfrequency words, M ⫽ 93.8, SE ⫽ 1.4). Perhaps this result indicates that representations for words that occur often but have just recently been acquired are especially unstable and difficult for children to manipulate. Analysis of young children’s scores revealed no effects of word familiarity or neighborhood density. Although these tasks were selected to avoid floor and ceiling effects, 17 young children scored 0% on phoneme isolation (7 older children and 10 adults scored 100%); 29 older children and 41 adults scored 100% on phoneme deletion (36 young children scored 0%). We

therefore considered the consequence of averaging scores across the two tasks (see also below). For this composite measure, 13 young children scored 0% and only 5 older children and 9 adults scored 100%. Analysis of these scores further revealed an effect of AOA (F1(1,189) ⫽ 7.23, F2(1,24) ⫽ 3.88, p ⫽ .06), such that all listeners’ performed slightly better for early words (M ⫽ 74.8; SE ⫽ 1.2) than for late words (M ⫽ 73.4; SE ⫽ 1.2). To this point, we have seen that both children’s and adults’ performance in the spoken word recognition tasks varied with AOA and/or neighborhood density, but that frequency effects were minimal. The impact of these lexical factors on phonological awareness was much less apparent, likely because of floor and ceiling effects. Therefore, we now focus on individual children’s phonological awareness as it relates to their performance in our other tasks. Individual Children’s Performance across Tasks We next employed hierarchical regression analyses in order to examine what factors contribute to phonological awareness and early word reading. Table 2 provides descriptive statistics for our major measures. Inspection of all children’s WRAT-3 reading scores revealed that 7 correctly read only 1 word and 29 could not read at all. The data of these 36 (young) children were excluded. The gender and ethnic makeup of the 92 remaining children was similar to our larger sample (47% male, 53% female; 27% African-American, 73% Caucasian). Raw scores are shown for the WRAT-3 (number of words read), PPVT-R, nonword repetition, and digit span. The four spoken word recognition (SWR) measures are averages of standardized scores for the gating and word repetition tasks (see below). Percent correct scores are shown for initial phoneme isolation and initial phoneme deletion, as well as for a composite phonological awareness (PA) measure, which is the average of scores for the two tasks. (For this sample, the mean standard PPVT-R score was 107.9, SE ⫽ 1.4, range ⫽ 63–135; the mean age-equivalent score was 8.0, SE ⫽ 0.2, range ⫽ 4.6–13.4; the mean standard WRAT-3 score was 112.2, SE ⫽ 1.5, range ⫽ 78–155).

481

SPOKEN WORD RECOGNITION TABLE 2 Descriptive Statistics for Children’s Performance (n ⫽ 92) in the Experimental and Standardized Tasks

Age (years) WRAT-3 (no. words read) PPVT-R SWR-ES SWR-ED SWR-LS SWR-LD NW Rep Digit span IPI IPD PA

M

SE

Range

Skewness

Kurtosis

7.1 12.1 88.9 0.0 0.0 0.0 0.0 31.6 3.5 77.3 73.4 75.4

0.1 0.7 1.7 0.1 0.1 0.1 0.1 0.4 0.9 2.9 4.1 3.1

4.4–8.6 2–33 52–118 ⫺2.4–1.7 ⫺2.2–1.7 ⫺2.4–2.0 ⫺2.1–1.6 19–38 1–5 0–100 0–100 0–100

⫺0.487 0.299 ⫺0.409 ⫺0.500 ⫺0.017 ⫺0.447 ⫺0.186 ⫺0.725 ⫺0.279 ⫺1.946 ⫺1.232 ⫺1.478

⫺0.361 ⫺0.219 ⫺0.456 0.380 0.322 1.090 0.091 0.721 0.551** 2.785** ⫺0.338** 0.948**

Note. SWR, spoken word recognition (a composite gating and word repetition score; see text); ES, early sparse; ED, early dense; LS, late sparse; LD, late dense; NW Rep, nonword repetition; IPI, initial phoneme isolation; IPD, initial phoneme deletion; PA, phonological awareness (a composite score; see text). **p < .01: distribution is nonnormal.

In examining the role of spoken word recognition in phonological awareness and word reading, we constructed four composite measures of spoken word recognition (SWR). Specifically, scores for early/sparse (ES), early/dense (ED), late/sparse (LS), and late/dense words (LD) in the gating and word repetition tasks were standardized across the 92 children in this sample and then averaged. (Gating IP scores were inverted before being combined with those from word repetition.) Only the influence of AOA and neighborhood density was considered, since word frequency effects were minimal in the ANOVAs. We used this composite measure because we had no a priori expectations about differences between the gating and word repetition tasks in predicting phonological awareness or word reading. This also served to reduce the number of potential predictor variables in the regression analyses and to increase the reliability of our measure(s) of spoken word recognition. Kologorov–Smirnov tests with a Lilliefors correction using a conservative but conventional ␣-level of 0.01 (Tabachnick & Fidell, 1989, p.73) revealed that the distributions for age, the WRAT-3, PPVT-R, nonword repetition, and four spoken word recognition measures were not statistically different from normal, whereas those for digit span, initial phoneme isolation, initial

phoneme deletion, and the composite phonological awareness measure were (Table 2). Predicting phonological awareness. Since the distributions for initial phoneme isolation and deletion were not normal, a composite phonological awareness measure (PA) was employed as our criterion variable to address this problem, as well as to simplify analysis and presentation. Although PA scores were also not normally distributed, the results of analyses using an inverse log transformation (Tabachnick & Fidell, 1992, p. 82), which did serve to normalize the distribution, were very similar to those for untransformed scores. (A similar pattern was found in separate analyses of initial phoneme isolation and deletion scores.) Therefore, we report results for untransformed PA scores. Only four children had scores of 0%; five had scores of 100%. These children were retained, because they were considered to be true outliers and we wanted to examine predictors of phonological awareness and word reading for the same children. Table 3 shows Pearson’s r correlations for our various measures. With the exception of nonword repetition, all were significantly correlated with PA. In a first set of regressions, we examined the relative contributions of spoken word recognition (SWR-ES, SWR-ED, SWR-LS, and SWR-LD) in predicting PA. Age was always en-

482

GARLOCK, WALLEY, AND METSALA TABLE 3 Correlations between Relevant Variables for Children’s Performance (n ⫽ 92)

1. Age (years) 2. WRAT-3 (no. words read) 3. PPVT-R 4. SWR-ES 5. SWR-ED 6. SWR-LS 7. SWR-LD 8. NW Rep 9. Digit span 10. IPI 11. IPD 12. PA

1

2

3

4

5

6

7

8

9

10

11

12

* .72 .66 .53 .24 .36 .24 .20 .34 .40 .66 .63

* .65 .33 .10 .34 .22 .41 .40 .28 .59 .52

* .35 .03 .35 .20 .27 .24 .19 .48 .41

* .46 .44 .24 .01 .27 .54 .53 .61

* .38 .13 ⫺.10 .19 .34 .30 .36

* .32 .15 .02 .32 .42 .43

* .09 .12 .18 .22 .23

* .07 .09 .12 .12

* .25 .27 .29

* .56 .83

* .92

*

Note. SWR, spoken word recognition (a composite gating and word repetition score; see text); ES, early sparse; ED, early dense; LS, late sparse; LD, late dense; NW Rep, nonword repetition; IPI, initial phoneme isolation; IPD, initial phoneme deletion; PA, phonological awareness (a composite score; see text). For correlations of a magnitude ⱖ.21, p ⬍ .05; for those ⱖ.27, p ⬍ .01.

tered on Step 1, followed by the spoken word recognition measures in one of four permutations, such that across regressions, each measure was entered immediately after age, as well as last in the equation. Age accounted for almost 40% of the variance associated with PA. Each spoken word recognition measure, except SWRLD, accounted for additional variance when entered on Step 2. However, only SWR-ES accounted for additional variance when entered after the other SWR measures. Thus, among our spoken word recognition measures, SWR-ES was the most powerful predictor of PA, accounting for about 5 to 10% of unique variance. In a second analysis, we examined the contribution of SWR-ES in predicting PA, after the variance due to our other major measures was taken into account. As shown in Table 4 (top panel), age was a significant predictor of PA. Variance associated with the WRAT-3, PPVT-R, digit span, and nonword repetition was removed in Steps 2–5, even though none of these measures accounted for additional variance in PA after that was accounted for by age. Step 6 shows that SWR-ES accounted for unique variance in PA, after variance due to age and our other measures was removed. Similar results were found for SWR-ED and SWR-LS, but not SWR-LD.

Predicting word reading. In a third analysis, we examined the contributions of our various measures in predicting number of words read on the WRAT-3. Initial phoneme deletion was used as a predictor because it was more highly correlated with word reading than either initial phoneme isolation or our composite phonological awareness (PA) measure (Table 3). Further, in the preceding analysis, PA and word reading were not associated at a significant level. As shown in Table 4 (bottom panel), after variance due to age was taken into account, the PPVT-R, nonword repetition, digit span, and initial phoneme deletion each accounted for unique variance in word reading. In contrast, SWR-ES did not predict additional, unique variance. Nor did SWR-ED, SWR-LS, or SWR-LD in subsidiary analyses. DISCUSSION This study is the first to assess the effects of AOA, word frequency, and neighborhood density on spoken word recognition from a developmental perspective using a factorial design. In the gating task, young children, older children, and adults all required less input to first isolate and recognize with high confidence early words as opposed to late ones, when frequency was balanced. Similarly, word repetition was gener-

483

SPOKEN WORD RECOGNITION TABLE 4 Results of Hierarchical Regression Analyses Step variable

R

R2 change

F to enter

Final b

Final F

0.392 0.012 0.002 0.004 0.002 0.110

57.97*** 1.74 0.23 0.55 0.29 19.47***

0.290 0.234 ⫺0.068 0.013 ⫺0.021 0.399

69.45*** 2.07 0.27 0.66 0.35 19.47***

94.93*** 11.43** 12.99*** 6.43* 4.88* 2.49

0.375 0.232 0.239 0.173 0.220 ⫺0.121

135.60*** 14.64*** 14.66*** 6.83* 4.96* 2.49

Regressions predicting phonological awareness (PA) 1. Age (years) 2. WRAT-3 (no. words read) 3. PPVT-R 4. Digit span 5. NW Rep 6. SWR-ES

0.623 0.635 0.636 0.639 0.640 0.722

Regressions predicting word reading (no. words read on the WRAT-3) 1. Age (years) 2. PPVT-R 3. NW Rep 4. Digit span 5. IPD 6. SWR-ES

0.717 0.754 0.790 0.806 0.818 0.824

0.513 0.055 0.056 0.023 0.019 0.009

Note. SWR, spoken word recognition (a composite gating and word repetition score; see text); ES, early sparse. *p ⬍ .05. **p ⬍ .01. ***p ⬍ .001.

ally better for early words. Previous studies have shown that early acquisition confers a performance advantage on children (e.g., Cirrin, 1984; Walley & Metsala, 1992) and adults (e.g., Carroll & White, 1973; Gilhooly & Gilhooly, 1979). Our results extend this finding to gating and word repetition, and they are consistent with the proposal that representations for earlyacquired words are more robustly specified than those for later-acquired items, so they are better recognized from partial or degraded input (e.g., Brown & Watson, 1987; Fowler, 1991; Metsala & Walley, 1998). Word frequency effects were less apparent, despite our having tested a large number of listeners at three age levels. In fact, research with adults indicates that the influence of word frequency in immediate visual and auditory word naming may be due to an AOA confound or to other dimensions of word familiarity (e.g., Connine, Mullenix, Shernoff, & Yelen, 1990; Morrison & Ellis, 1995). However, Morrison and Ellis observed independent effects of AOA and word frequency on adults’ lexical decision latencies and therefore suggested that frequency effects are minimal for tasks that tap the early stages of recognition, and are greater

for those that are more influenced by postperceptual decision factors (see also Connine et al., 1990; Luce & Pisoni, 1998). Our results are consistent with this view inasmuch as word frequency did not affect initial target isolation but did have some influence on total acceptance points in the gating task. More recently, Gerhand and Barry (1998) showed that AOA and word frequency exert independent effects on immediate visual word naming—a result attributed largely to the use of a fully factorial stimulus design in which these lexical factors varied orthogonally, as opposed to the semifactorial design of Morrison and Ellis (1995) and the regression-based approach of many others. It was therefore argued that word frequency affects the early stages of visual word recognition, whereas AOA affects the later retrieval or production of a word’s phonological representation. We also employed a fully factorial design but did not find much evidence of a word frequency effect on auditory word recognition (see also Connine et al., 1990; Turner et al., 1998). While this effect is not typically found for adults’ response times in delayed naming tasks (which both our recognition tasks

484

GARLOCK, WALLEY, AND METSALA

resemble more closely than immediate naming), it has been found for naming accuracy for words presented with and without noise. For example, Brady et al. (1983; see also Snowling et al., 1986) found that both good and poor readers (aged 8 1/2 years) identified more high-frequency noise-masked words (e.g., “door”, “road”, “ships”) than low frequency items (“frond”, “nymph”, “skiff”). A word-frequency effect was also found for adults by Grosjean (1980) with his introduction of the gating paradigm. Yet in these studies, as in many others, word frequency was likely confounded with AOA. Our results indicate that when this is not the case, AOA may be a more sensitive index of lexical familiarity within and across developmental levels. Despite mounting empirical evidence that AOA influences word recognition, it has received little theoretical attention. This neglect may be due to the difficulty encountered by currently popular connectionist models in accounting for AOA effects. In many such models, learning takes place via back propagation, so that newly learned patterns overwrite existing ones. However, as discussed by Morrison and Ellis (1995; see also Turner et al., 1998), training regimes in which lexical items are introduced gradually (rather than in all-or-none fashion, as is usually the case), or self-organizing systems, in which new information must be accommodated in terms of previously learned information, may better account for AOA effects. Indeed, AOA effects have recently been shown to be a natural property of models that employ back-propagation, when training for early- and later-acquired items is cumulative or interleaved (Ellis & Lambon Ralph, 2000). Our findings underscore the need for further evaluation of theoretical alternatives of this sort. The effect of neighborhood density in our study was more complex. For both gating and word repetition, adults showed a competition effect, such that recognition was better for all items from sparse, as opposed to dense, neighborhoods (see also Luce & Pisoni, 1998). In the gating task, older children showed a competition effect (see Metsala, 1997a) that was similar to that of adults, whereas young children did not

show this effect in terms of either isolation or total acceptance points. In the word repetition task, all groups showed a competition effect, but this was qualified by the Group ⫻ AOA ⫻ Neighborhood Density interaction. For adults, the size of this effect was again similar for early and late words; for older children, it was larger for early words than for late words; for young children, it was apparent only for early words. Thus, by at least age 5 1/2 years, children do display a competition effect, but a restricted one. However, this effect was actually larger for children than for adults for early-acquired words. This was because performance was best and most similar across age for words from sparse neighborhoods, whereas it was poorest for words from dense neighborhoods among children. How do these findings fit with our original expectations? We first considered the implications of the claim that vocabulary growth (including increases in the number of phonologically similar items) contributes to developmental changes in spoken word representation and processing (e.g., Fowler, 1991; Metsala & Walley, 1998; Nittrouer et al., 1989). This claim does not translate simply into the expectation that children’s recognition performance should be best for words from dense neighborhoods (although this pattern might be expected in some tasks; see below). Rather, by this view, age differences should be greatest for words with very dynamic representations, and smallest for words with more robust representations and few competitors. In general, our results confirm these predictions, with those for word repetition fitting the expected pattern especially well; i.e., recognition was best and age differences were smallest for early words from sparse neighborhoods, and it improved the most between childhood and adulthood for other words. A second question was whether young children show a competition effect, and if so, how it compares across age. In the gating task, young children did not display this effect, while older children and adults displayed one of similar magnitude. In the word repetition task, competition effects for familiar, early-acquired words

SPOKEN WORD RECOGNITION

were larger for child than for adult listeners, but this pattern was reversed for less familiar, lateracquired words. With increases in age and word familiarity then, competition effects emerge and become more widespread. These results are consistent with structural analyses showing that word neighborhoods are relatively sparse for young children and that recognition might therefore be accomplished via holistic processes (e.g., Charles-Luce & Luce, 1995; cf. Dollaghan, 1994). However, with vocabulary growth, lexical representations become more fine-grained and/or segmental, and thus the ability to recognize words from partial input improves (e.g., Fowler, 1991; Metsala & Walley, 1998). To sum to this point, spoken word recognition was generally best for early-acquired words and those from sparse neighborhoods. Word repetition was more sensitive to both listener and stimulus characteristics than was gating, presumably because of differences in task structure and demands. In particular, while neighborhood density had no effect on young children’s performance in the gating task, performance was better for early-acquired, sparse vs dense items in the word repetition task. In the latter task, the entire target was presented, with some segments (i.e., fricatives) in variable word positions masked to a greater extent than others by the noise manipulation. In the gating task, an increasing amount of partial but intact speech input was presented from word onset. Thus, our measure of phonological similarity (a single phoneme change in any word position) most closely matches the word repetition task. We did not find very marked or consistent effects of AOA, word frequency, or neighborhood density on phonological awareness. Such effects might have been expected according to the claim that awareness depends on the restructuring of lexical representations (e.g., Fowler, 1991; Metsala & Walley, 1998). We employed initial phoneme isolation and deletion because they have been used in other recent work (e.g., Elbro et al., 1998). In retrospect, however, these tasks may not have been the most suitable ones for our participants, whose performance was often at floor or ceiling. Perhaps stronger effects

485

would be observed for a different and/or larger set of tasks. Indeed, Metsala (1999) found that lexical status, AOA, and neighborhood density influenced young children’s performance in a variety of phonological awareness tasks. In particular, 3- and 4-year-olds’ phoneme blending was better for familiar words from dense, as opposed to sparse, neighborhoods in a picturepointing task. Similarly, Goswami and De Cara (2000) found that 5- and 6-year-olds were better at making rime judgments for words from dense neighborhoods. Thus, an interesting pattern is beginning to emerge in the literature. In three studies now (the present one and Metsala, 1997a, 1997b), children’s recognition of familiar (early-acquired or high-frequency) words has proved better for words residing in sparse vs dense neighborhoods. In two other studies (Goswami & De Cara, 2000; Metsala, 1999), the opposite pattern has been found for phonological awareness. Thus, inhibitory/competition effects of neighborhood density may be primary during spoken word recognition, which involves discriminating among multiple lexical candidates. In contrast, the facilitatory influence of probabilistic phonotactics (the frequencies and sequences of phonemic segments), which is positively correlated with neighborhood density, may be primary for phonological awareness tasks. In these tasks, attention is typically focused not on the word level, but rather on the sublexical structure of a single speech pattern. Recent adult research has demonstrated the dissociable effects of probabilistic phonotactics and neighborhood density. Specifically, Vitevitch and Luce (1998) showed that when a lexical level of processing is induced with the use of word stimuli in a naming task, competitive effects of neighborhood density are found (e.g., response times are slower for words from dense vs sparse neighborhoods). In contrast, when nonword stimuli are used and thus lexical processing is made more difficult, facilitative effects of probabilistic phonotactics are observed (response times are faster for high- vs low-probability phonotactic patterns); this advantage, it was proposed, arises by virtue of higher activation levels that are associated with such patterns

486

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in a connectionist sort of framework. (Recall that Metsala (1997a) offered a similar account of the Word Frequency ⫻ Neighborhood Density interaction that she observed; see also below.) This analysis helps to explain the results of Pitrat et al. (1995), who found that 2-year-olds better identified words with many vs few neighbors in a picture-pointing task. This effect was smaller for 3-year-olds and nonsignificant for 4year-olds. Perhaps for the younger children, the test items had not yet achieved full or robust lexical status, and therefore facilitative effects of phonotactic probability, which varied with neighborhood density, were observed (see also Logan & Ridgeway, 2000; Storkel, 1999). Similarly, Metsala (1997a) suggested that although competition effects may predominate in the processing of frequently heard words, the processing of low-frequency words (which resemble nonwords, in that they may fail to make contact with a single lexical representation) is more influenced by structural (sublexical) factors. The result is that recognition is sometimes better for items from dense vs neighborhoods. In our word repetition task, all listeners identified later-acquired words better than nonword fillers, but only adults and older children showed a competition effect for the words.Yet young children did show a competition effect for early-acquired words. Thus, apparently only when lexical representations have been firmly established do we see this sort of effect. In light of these recent studies, it seems clear that expectations about the precise effects of neighborhood density will depend crucially on the demands of a given task and the level of processing engaged, and that more data from the same children across different tasks is needed. An ancillary goal of our study was to examine the relations among children’s speech processing, phonological awareness, and reading ability. Spoken word recognition, especially that for early-acquired items from sparse neighborhoods, accounted for significant variance in phonological awareness (initial phoneme isolation and deletion), after age, receptive vocabulary, verbal memory skills, and word reading were taken into account. In turn, initial phoneme

deletion, together with age, receptive vocabulary knowledge, and verbal memory, contributed to the variance in word reading ability. Spoken word recognition did not predict word reading. These findings support the claim that developmental advances in speech representation and processing provide some foundation for later phonological awareness, and that variations in the familiarity of individual lexical items and their phonological similarity relations are associated with these advances (e.g., Fowler, 1991; Metsala & Walley, 1998). Specifically, the recognition of early-acquired words that are similar to only a few other items appears to be a particularly sensitive indicator of phonological awareness. Why is this? These words are better recognized than those from dense neighborhoods even by young children, apparently because they are subject to less competition from other items. In the case of young children, these words may not have undergone extensive restructuring. Yet, to the extent this has occurred, recognition of these words predicts phonological awareness. Our results are consistent with those of Metsala (1997b), who found that words from sparse vs dense neighborhoods were better recognized by normally achieving (NA) children, but not by reading-disabled (RD) children (about 9 years of age). Although the two group’s performance did not differ for words from dense neighborhoods, RD children were especially poor at recognizing words from sparse neighborhoods. It was therefore suggested that spoken word recognition in RD children is relatively holistic and is best characterized in terms of developmental delays in the segmental restructuring of lexical representations. Indeed, their performance resembles that of our 5-year-olds, whose gating performance was not affected by neighborhood density, and whose word repetition performance for later-acquired words was also unaffected. Further, for a subset of both NA and RD children (7 years of age), Metsala found that the recognition of words from sparse neighborhoods, together with phoneme awareness, predicted word and pseudoword reading. Thus, we now have two studies implicating the recogni-

SPOKEN WORD RECOGNITION

tion of these words in beginning reading and reading-related abilities. Although our data are correlational, they also support the claim that the relation between speech representation/processing and reading ability is mediated by phonological awareness (e.g., Elbro et al., 1998; Fowler, 1991; McBrideChang et al., 1997; Metsala & Walley, 1998). (Of course, reading experience with an alphabetic orthography may later enhance phoneme awareness; see Goswami, 2000). In particular, we found an association between word reading and initial phoneme deletion, which was quite difficult for young children, but not between word reading and our composite measure of phonological awareness, which included scores for the easier initial phoneme isolation task. Our results are compatible with McBride-Chang’s (1996) study of third and fourth-graders in which structural equation modeling showed that model fit was not enhanced when a direct link between speech perception (assessed in several forced-choice phonetic identification tasks) and word reading was included, but was much poorer when the link between speech perception and phoneme awareness was excluded. McBride-Chang et al. (1997) found that speech perception, together with verbal STM and IQ, predicted growth in phoneme awareness from kindergarten through grade 1; speech perception in kindergarten was linked to later word reading, but not when phoneme awareness was controlled. In another longitudinal study, Elbro et al. (1998) found that the distinctness of lexical representations in kindergarten (assessed by pronunciation accuracy) predicted phoneme awareness in grade 2 for normal children and those at risk for dyslexia. In contrast, Metsala (1997b) found that spoken word recognition (in the gating task with stimuli similar to ours) contributed directly to word reading, after accounting for phoneme awareness. However, she did not examine predictors of phoneme awareness, and our results provide a more detailed picture of the relation between these abilities. The convergence between these studies highlights the need for more research delineating the relations among various speech processing tasks. One possibility is that they tap a single

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underlying speech ability; another is that they tap separate abilities that interact during development (e.g., mature speech perception may depend on increases in lexical knowledge; Walley & Flege, 1999). The relations between speech processing and various reading-related, phonological processes also warrant further study. For example, verbal STM has been found to contribute to early reading independently of phonological awareness (cf. Gathercole & Baddeley, 1993; Torgesen & Burgess, 1998). Indeed, in our study, backward digit span and nonword repetition each accounted for unique variance in word reading, but neither was linked to phonological awareness. The opposite pattern was found for spoken word recognition, suggesting that these tasks do tap different abilities. However, we still know little about how spoken word recognition and verbal STM are themselves related. For example, new word learning might be differentially supported by existing lexical representations in long-term memory or by the ability to retain verbal material for short periods of time (see Metsala, 1999). In any event, our results add to a growing body of work indicating that comprehensive models of reading acquisition must incorporate speech processing abilities. Finally, although we found that receptive vocabulary (PPVT-R scores) predicted word reading, it did not predict phonological awareness. The latter result is problematic, given the claim that vocabulary growth drives the restructuring of lexical representations and thus the emergence of phonological/phoneme awareness (e.g., Fowler, 1991; Walley & Metsala, 1998). Several studies have found an association between vocabulary knowledge and phonological awareness, but most have focused on this relation as it pertains to reading development and little attention has been paid to precursors of phonological awareness itself. One exception is McBride-Chang et al.’s (1997) study, in which vocabulary knowledge was found to contribute uniquely to growth in phoneme deletion ability between kindergarten and grade 1. However, Elbro et al. (1998) found that neither receptive nor productive vocabulary contributed uniquely to initial phoneme isolation or deletion ability in

488

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grade 2, after phoneme awareness in preschool was taken into account. Although these two studies used different test–retest designs and analyses, we have no definitive explanation for the discrepancy or our own failure to find a link between receptive vocabulary and phonological awareness. Perhaps a more robust relation exists for awareness of segments in any word position as opposed to those in word-initial position only (Metsala & Taboada, 2000). In the future, it might also be informative to assess whether rate of vocabulary growth is a better predictor of phonological awareness than static estimates of vocabulary size. SUMMARY AND CONCLUSIONS The present study assessed AOA, word frequency, and neighborhood density effects on children’s and adults’ performance in gating and word repetition tasks. Our results suggest that AOA is a more sensitive measure of lexical familiarity for making developmental comparisons of spoken word recognition. Many current models of recognition do not incorporate the effects of AOA, but our results indicate that it merits greater theoretical attention (see also Ellis & Lambon Ralph, 2000). Of most interest, children displayed a greater advantage for the repetition of early-acquired words from sparse

vs dense neighborhoods than older children and adults; for later-acquired words, the opposite pattern obtained. To our knowledge, this study is the first to show such competition effects for young children. However, there is still much to be learned, for example, about how the requirements of different tasks might influence the observation of facilitatory or inhibitory effects of neighborhood density. Our results for individual children’s performance across tasks add to a growing body of work that seeks to identify the precursors of phonological awareness, and they are consistent with the theoretical claim that changes in speech processing associated with vocabulary growth support the development of such awareness and early reading ability (e.g., Fowler, 1991; Metsala & Walley, 1998). However, we still need to know more about how various speech processing abilities are related to one another and to other reading-related, phonological processes. This information will be essential for building comprehensive models of reading acquisition and will undoubtedly facilitate the early identification of children at risk for reading disabilities—an endeavor made more pressing by recent reports that training phonological awareness skills in the elementary school period may be quite difficult (see Torgesen & Burgess, 1998).

APPENDIX Word

Sublist

ND

AOA

FREO

Word

Sublist

ND

AOA

FREO

bib nail fudge loaf foot safe chain choice sock chick mash lace rock heat wide suit badge goof nudge

A A A A A A A A A A A A A A A A A A A

9 5 5 4 7 4 4 3 22 14 16 16 19 16 16 13 8 4 5

2.63 3.25 3.69 3.94 1.81 3.13 3.69 4.44 1.50 2.56 4.06 4.06 2.50 4.13 3.69 4.25 4.81 5.31 6.75

2 6 1 4 70 58 50 113 4 3 1 7 75 97 125 48 5 1 2

jade fear faith doubt theme peck tame wick jot fought wine pace mass goat yawn geese leash teeth knife

A A A A A A A A A A A A A B B B B B B

6 8 6 8 4 12 12 20 16 19 14 13 15 8 5 6 6 5 3

7.25 4.75 5.25 5.75 7.06 4.75 4.75 5.56 7.38 5.31 5.13 6.25 6.50 2.81 3.50 3.94 4.19 1.94 2.75

1 127 111 114 55 5 5 4 1 46 72 43 110 6 2 3 3 103 76

489

SPOKEN WORD RECOGNITION

Word

Sublist

ND

AOA

FREO

Word

Sublist

ND

AOA

FREO

learn chief lick bun chalk cub fell pick wish bit cheat mole weave gem curve worth tour firm hike gag sash lame loss cause pope sought peach burp sour chirp reach watch rule join suck bead poke mitt game shape race shop howl yam thud

B B B B B B B B B B B B B B B B B B B B B B B B B B C C C C C C C C C C C C C C C C C C C

8 5 18 15 16 12 14 18 11 17 7 7 5 6 8 4 2 6 11 13 11 14 14 11 14 25 8 3 1 5 8 7 7 4 14 11 14 16 12 11 15 13 4 5 6

3.31 4.44 2.50 2.63 3.38 4.31 2.13 2.88 2.81 3.19 4.94 5.31 6.00 6.38 4.94 5.38 7.06 5.19 5.38 5.69 6.50 5.94 5.13 6.06 6.50 7.25 3.00 3.06 3.50 4.13 3.25 2.69 3.56 3.75 3.13 3.94 3.75 2.56 2.63 3.69 2.94 3.38 4.75 6.56 5.75

84 119 3 1 3 1 92 55 110 101 3 4 4 4 45 94 43 109 4 4 3 2 86 130 40 55 3 1 3 1 106 81 73 65 5 1 1 1 123 85 103 63 4 1 3

beige serve youth league term numb gut dine reap base laid deal sake kite worm shove moth dirt neck page bar hop mop peep dip ship hill gun pass vine fuzz chess daze south goal vote wage fade kit shin gawk hung type bill rate

C C C C C C C C C C C C C D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D D

4 4 4 7 6 12 14 18 15 13 13 13 17 9 8 5 7 6 7 7 5 13 11 13 17 15 16 12 14 9 5 7 7 3 7 6 7 15 20 13 17 12 11 15 14

6.75 4.81 6.31 6.63 7.06 6.06 5.81 5.88 6.75 5.69 4.94 4.94 6.69 3.38 2.38 3.50 3.78 2.25 2.44 2.56 3.63 2.81 3.44 4.44 4.06 4.13 2.25 3.44 3.75 5.63 5.00 6.25 6.44 5.25 5.13 6.06 7.19 6.31 6.00 4.94 7.19 4.88 5.63 5.00 7.19

1 107 82 69 79 4 1 2 3 91 77 142 41 1 4 2 1 49 81 66 82 2 3 2 6 83 72 118 89 4 3 3 1 240 60 75 56 2 2 3 1 65 200 143 209

Note: ND, Neighborhood Density (based on Luce, 1986); AOA, subjective age-of-acquisition ratings; FREQ, frequency of occurrence (per million words, based on Kucˇera & Francis, 1967).

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