Fine-Grained Auditory Discrimination and Performance on Tests of Receptive Vocabulary and Receptive Language Lois L. Elliott Michael A. Hammer Margo E. Scholl Northwestern University Evanston, Illinois
This research investigated the relation between children's performance on two measures of receptive language and children's auditory discrimination of consonant-vowel sounds having frequency and temporal acoustic differences. The measures of fine-grained auditory discrimination produced significant multiple regression coefficients against both receptive vocabulary (Peabody Picture Vocabulary Test-Revised) and receptive language (Token Test for Children) scores. Validation analyses conducted by predicting receptive vocabulary and language scores for a new sample of children and relating them to the actual scores led to significant outcomes. It was concluded that fine-grained auditory discrimination is particularly important in the relatively early stages of language learning.
Introduction It has often b e e n r e p o r t e d that i n d M d u a l s with language-learning problems have auditory processing difficulties (e.g., Eisenson 1968;
The cooperation of the participating children, teachers, principals, and school district administrators is gratefully acknowledged. This research was supported, in part, by a grant from NINCDS (NIH). Annals of Dyslexia, Vol. 40, 1990. Copyright ©1990by The Orton Dyslexia Society ISSN 0736-9387
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Wepman 1960). For example, they have been found to have poorer auditory discrimination than normal listeners of the same age (e.g., Aten and Davis 1968; Elliott and Busse 1987; Tallal and Piercy 1974; Tallal et al. 1981). Investigations of auditory processing in these listeners have employed stimuli such as pure tones (Lowe and Campbell 1965), naturally-produced speech sounds (McReynolds 1966) and computerproduced, synthesized syllables. These latter stimuli have sometimes represented "good" tokens of natural speech (e.g., Tallal and Piercy 1974) while, at other times, the synthesized syllables have represented intermediate steps along a continuum between two end-point syllables (Brandt and Rosen 1980; Godfrey et al. 1981). During the past nine years our research has used a task that measures the smallest differences that can be discriminated along continua of synthesized consonant-vowel (CV) syllables that represent stop consonants differing in place of articulation (POA; e.g., [ba-da-ga]) or voice onset time (VOT; e.g., [ba-pa]). Because the differences to which most listeners respond are smaller than can be produced by the vocal mechanism, the task has been referred to as "fine-grained auditory discrimination." Elliott, Longinotti, Meyer, Raz, and Zucker (1981), Elliott (1986), and, Elliott, Busse, Partridge, Rupert, and DeGraaff (1986) have demonstrated that young children require larger acoustic differences to discriminate these CVs than do young adults. Elliott and Busse (1987), in testing a group of normally-hearing young adults with learning disabilities, found that 90 percent of group members exhibited finegrained auditory discrimination that was as poor (or poorer) as that of normally-achieving six-year-olds. Subsequently, a field project was begun to study fine-grained auditory discrimination in a population of young, school-aged children. All data have been collected within a medium-sized Midwestern school district that is generally representative of the U.S. population. One objective of this project has been to determine how prevalent fine-grained auditory discrimination problems are in a population of young children who progress normally in school and in a population of children who exhibit language-learning problems. This report will describe the test procedures and some of the results during the initial years of the project.
Methods
Subjects were selected using these criteria: 1. chronological age of 6 to 11 years at time of testing 2. English as the first language
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3. normal hearing for pure tones 4. normal middle ear impedance 5. school placement in either a regular classroom or in a class for children of normal intelligence who exhibit language-learning problems 6. absence of other problems such as behavioral disorders, and 7. written parental permission for participation. Children with language-learning problems were included in order to have a wide range of performances on the language measures that were administered. Although children with language-learning problems were not specifically selected as being dyslexic, as a group, they had a high incidence of reading problems. Letters requesting parental permission for participation were sent to the homes of all children in classes for those with language-learning problems who appeared to meet selection criteria and to parents of approximately equal numbers of normally-progressing children. All children whose parents returned permission forms and who met the other criteria were tested. In total, 175 children achieving regular school progress and 189 children exhibiting language-learning problems were tested. This included 145 six- and seven-year-olds anti 219 eight- to eleven-year-olds. Some of these children have been tested additional times in the longitudinal component of the project; those results (Elliott and Hammer 1988) are not reported here. Stimuli
Two continua of consonant-vowel (CV) stimuli were computerproduced using Klatt's synthesizer program (Klatt 1980 Indiana University version). A five-formant1, eight-item continuum that varied in voice onset time (VOT) from 0 to 35 msec in 5-msec steps ([ba-pa]) was created. The presence of a silent period--that is, voice onset time-between release of the initial part of the consonant sound and beginning of voicing distinguishes a voiceless consonant, such as the "p" in [pa], from a voiced consonant, such as "b." Thus, items of the first continuum varied mainly in a temporal dimension. The second continuum represented the place-of-articulation feature of speech production ([ba-da-ga]) and consisted of 13 items, each having five formants. The major acoustic differences along this continuum were the onset frequencies of the second and third formants. All stimuli were 300 msec in duration. Additional information about the stimuli is reported in EUiott, Longinotti, Meyer, Raz, and Zucker (1981), Elliott, Busse, Partridge, Rupert, and DeGraaff (1986), and Elliott (1986). lIn general terms, a formant is a band of acoustic energy of voiced speech sounds.
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Fine-Grained Auditory Discrimination Task The purpose of this measure of auditory discrimination was to determine the smallest acoustic differences among the CV syllables (i.e., just noticeable differences UNDs]) that could be discriminated. Stimuli were presented at 80 dB SPL 2. On every trial, two syllables were presented sequentially with a 500 msec intersyllabic interval. The listener's task was to judge them as "same" or "different" by pushing one of two response buttons. Trial-by-trial feedback was given in the form of a smiling face that appeared for 650 msec immediately after every correct response. JNDs were measured with regard to the [ba] and [pal ends of the VOT continuum. For the place-of-articulation continuum, JNDs were measured relative to [da], separately, in the directions of [ba] and [gal. Thus, four different types of JND measures were obtained. A computer-controlled, up-down adaptive procedure (Levitt 1971) was used to track the 50 percent correct response level. For example, the VOT continuum contained stimuli numbered I through 8. To obtain the JND relative to [pal (stimulus #8 of this example), test trials employing stimuli #3 and #8, #8 and #4, and #8 and #5 might be presented and the subject might respond "different" to each pair (correct responses). If stimuli #6 and #8 were next presented, the listener might respond "same." Because this would be an incorrect response, on the next test trial the acoustic difference between the two members of a pair of stimuli would be increased by presenting #5 and #8. This alternation between easy test trials on which the listener responded correctly and difficult test trials on which the listener made errors defined the adaptive procedure. The adaptive procedure was used in order to collect maximum information in the smallest number of test trials and, thereby, to keep test sessions brief for the young listeners. Although the anchor stimulus (i.e., the one for which the JND was being measured) was presented on every test trial, its position as first or second of the pair was randomly varied. Each series of test trials began with a pair of stimuli that, for most children, was very easily discriminated. In addition to the test trials, there were catch trials in which two identical stimuli were randomly selected from among the anchor position and stimuli within two positions of the anchor. The computer inserted catch trials pseudorandomly so as to have approximately equal numbers of test and catch trials. The correct answer to catch trials was "same," and the smiling-face reinforcement was used. Catch trials helped prevent the listener from developing a bias toward 2In general terms, Sound Pressure Level (SPL) is a system of describing sound intensity that employs an absolute, physical reference. In contrast, Hearing Level (HL) is a system of describing sound intensity that uses as a reference the sensitivity of young adults who have no history of ear disease or noise exposure. Clinical audiometric information is typically reported in dB HL.
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responding "different." Performance on catch trials was scored separately from performance on test trials. The catch trial procedure also helped the experimenter monitor subject's understanding and ability to perform the task. Numerous errors on catch trials at the beginning of a test session were assumed to be evidence of possible lack of understanding of the same/different concept; therefore, backup procedures (see below) were used. When the child had previously demonstrated understanding of the same/different concept, poor catch trial performance was interpreted as indicating poor fine-grained auditory discrimination. Pairs of stimuli were presented until the subject's response to test trials changed from "different" to "same," or the reverse, 10 times. The stimulus numbers of the comparison stimuli for the last 8 reversals were averaged and the absolute difference of this number from the value of the anchor stimulus was taken as the measure of the JND. Catch trial performance was scored separately by the computer program. The procedure was self-paced, and listeners had unlimited time in which to respond. They were, however, encouraged to make their decision quickly. In summary, in some ways, the fine-grained auditory discrimination task resembled Tallal and Piercy's (1974) sequencing task in that two stimuli were presented on every trial; those stimuli could be identical or could be different. However, several characteristics of the finegrained auditory discrimination task rendered it unique. The smallest acoustic differences that could be discriminated were measured; each listener received test trials that bracketed, in difficulty, that listener's 50 percent correct level of responding; performance on catch trials provided a measure of attention to the task; and, the number of trials presented was determined by each listener's responses. Specially developed portable computer hardware and software were used to administer the auditory discrimination task in quiet school settings. In order to provide additional attenuation of background sounds, children wore Audiocups over TDH 39 headphones with MX/41 cushions. The right ear was tested unless the child was lefthanded and preferred to use the telephone with the left ear. Two measures of each of the four JNDs were obtained and averaged. Practice sessions were also given to introduce the procedure and as a reminder, when testing carried over to a second or third session. Whenever a child experienced difficulty understanding the task, a series of backup procedures was employed in which naturally-produced words and syllables, or even colored yarns, were discriminated to confirm that the child understood the same/different concept. If a child mastered these procedures, but could not discriminate the endpoints of the synthesized CV continuum, testing for that JND measure was halted, and
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175
the child's JND was scored as one continuum step larger than the maximum difference between continuum endpoints. For example, because the VOT continuum consisted of 8 CVs, the maximum possible true VOT JND would be 7 (8 minus 1). Inability to discriminate the continuum endpoints was scored as 8 - - a n approach that assumed the listener would be able to discriminate a difference if one more item were added to the continuum. (Typical children in this age range can easily discriminate a difference of half this magnitude, or smaller.) Some of the children who received a score of 8 would probably have required considerably larger acoustic differences--that is, equivalent to a score of 10 or 11--to achieve discrimination. Therefore, this was a conservative approach that "biased" outcomes in the direction of not finding differences between children who could and who could not discriminate. Other Tests Administered
Pure tone auditory sensitivity was tested at 500, 1,000, 2,000, and 4,000 Hz. Children had hearing no poorer than 20 dB HL 2 (ANSI 1970) in the test ear; furthermore, nearly all of the children had auditory sensitivity that was close to 0 dB HL. Children were required to pass an impedance test (test of middle ear function) each time they received an auditory test procedure. All children were given the Peabody Picture Vocabulary Test-Revised, (PPVT-R, FORM L, Dunn and Dunn 1981) and the Token Test for Children (TTC; DiSimoni 1978) as a measure of their receptive language. Children were not removed from their classrooms for more than 45 to 60 minutes per session. Multiple sessions were usually required to complete testing.
Results
In all of the analyses reported here, children were divided into groups based on age; six- and seven-year-old children were separated from eight- to eleven-year-old children. Also, within each of these age groups, children whose identification numbers ended in an odd digit were placed in a group referred to as the original sample and those whose identification numbers ended in an even digit were placed in a group referred to as the validation sample. These were the subgroups used in the regression analyses (see below). Mean performance on the PPVT-R and TTC is shown in Table I. Because subjects were selected specifically to insure a range of language skills, it is not surprising that the standard deviations were
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RESEARCHINSIGHTS
Table I Performance on the Peabody Picture Vocabulary Test--Revised (PPVT-R) and the Token Test for Children (TFC): Mean Standard Scores and Standard Deviations Original Samples Validation Samples N Younger Children PPVT-R TTC Older Children PPVT-R TTC
M
SD
N
M
SD
72 72
90.9 493.8
19.3 8.6
72 72
89.5 495.0
16.3 8.4
110 108
89.9 495.5
17.4 7.0
109 108
95.8 498.0
17.2 6.4
larger than those expected for a normal population (i.e., PPVT-R, SD = 10; TTC, SD = 5). Subject selection procedures also led to group means that were numerically lower than those expected for a normal population (i.e., PPVT-R, M = 100; TTC, M = 500) of the type on which these tests were standardized. In each case, the PPVT-R and TTC distributions in this study were slightly skewed toward lower scores. T-tests revealed no differences between Original Sample and Validation Sample means for PPVT-R or TTC among younger children [PPVT-R: t(1,72) = 0.48, p > .05; TTC: t(1,72) = 0.88, p > .05]. Because the groups of older children were so large, differences between means of the two samples achieved statistical significance [PPVT-R: t(1,217) = 2.53, p < .05; TTC: t(1,214) = 2.56, p < .05]. However, the differences between means for the older children had no practical significance in this context.
RegressionAnalyses Regression analyses were used for children of the "original" samples (i.e., those with odd identification numbers in the younger and older groups) to predict performance on the PPVT-R and TTC (Table II). The coefficients derived from the regression analyses were then applied to children in the validation sample (i.e., those with even identification numbers) to establish predicted PPVT-R and TTC scores. These predicted scores were then correlated with the children's actual PPVT-R and TTC scores to provide validation information. Outcomes of the multiple regression procedures and the validation analyses are displayed in Table II. With the JNDs and catch trials as predictors, the multiple regression values were higher for younger children (PPVT-R, R = .57; TTC, R ---- .64) than for older children (PPVT-R, R = .39; TTC, R = .37). The regression coefficients were not
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177
Table II Multiple Regression (R) of the Measures of Fine-Grained Auditory Discrimination Against Receptive Vocabulary and Receptive Language Scores and Validation Correlation Coefficients (r) of Predicted Scores and Actual Scores Validation Samples Original Samples R N p r N p Younger Children PPVT-R .57 72 .001 .43 72 .001 TTC .64 72 .001 .67 72 .001 Older Children PPVT-R .39 110 .001 .25 109 .01 TTC .37 108 .001 .36 108 .001 so high that they accounted for all the variance associated with PPVT-R and TTC scores. However, all multiple R's were statistically significant at the .001 level. When predicted PPVT-R and TTC scores were related to obtained scores in the validation analyses, three resulting Pearson correlation coefficients were significant at the .001 level. The remaining correlation coefficient (PPVT-R, older children) attained significance at the .01 level. Validation correlation coefficients were numerically of the same magnitude as the original multiple correlation coefficients for the TTC. However, validation coefficients for the PPVT-R were (numerically) slightly smaller than the regression coefficients, as often happens.
Discussion
These relations between fine-grained auditory discrimination and measures of receptive vocabulary and receptive language contribute new evidence to the role of auditory discrimination in language learning (see Introduction). Fine-grained auditory discrimination of both frequency and temporal acoustic differences appears to be particularly important in the earlier years of vocabulary and language-learning. (The youngest children tested in this work were six-year-olds. It seems likely that fine-grained auditory discrimination is even more important for language learning by preschoolers.) This is true even for children who demonstrate normal sensitivity for pure tones and normal middle ear function, according to conventional audiologic clinical standards. Although finegrained auditory discrimination significantly predicts vocabulary and language performance in eight- to eleven-year-old children, the magnitude of this prediction is smaller than in younger children. This may have occurred because the major avenue for vocabulary and language
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RESEARCH INSIGHT'3
cabulary and language learning in young children is the auditory sense, whereas many older children may enhance their vocabulary and language skills through other avenues such as reading and word games. It is not clear why the correlations for younger and older children between predicted and actual scores (i.e., r's) more closely matched the multiple regression values (i.e., R's) for the TTC than for the PPVT-R. Until such time as a definitive explanation for this difference is identified, it is probably best to attribute it to chance variation.
Practical Implications One might ask what educational or training approaches are feasible to improve the fine-grained auditory discrimination of frequency and temporal differences by youngsters who have discrimination difficulties. We have begun work on this topic but have not reached conclusions. In the interim, however, there are a number of approaches that may be employed in classroom and home environments. Young children require more acoustic information than adults to perceive and identify familiar speech stimuli (Elliott 1983; Elliott and Katz 1980). When children are placed in a noisy environment they will have trouble obtaining the academic information they need. Therefore, the strategy currently recommended is to provide for all young children, and particularly for those with language-learning difficulties, the best-possible listening environment. What constitutes a good listening environment? It is one that is free from extraneous noise such as that caused by outdoor traffic, loud talking in hallways and corridors, music, foot shuffling, and conversational sounds generated by others. It is also an environment that has minimum reverberation or "echoing" from walls, blackboards, large glass areas, and hard-surfaced floors. It is an environment where the speaker (teacher or parent) is positioned close to the child so the distance the sound must travel is minimized (and, hence, speech retains maximum possible loudness). It is also an environment in which light falls directly on the face of the talker who is turned to the listener. Think about the modifications that enhance the listening of individuals who have mild-to-moderate hearing loss and follow those guidelines. The same procedures are likely to enhance the speech understanding skills of young children. And, one may hypothesize that with enhanced speech understanding, young children will improve in language learning.
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References American National Standards Specifications for Audiometers (ANSI $3.6-1969, R-1970). New York: ANSI. Aten, J. and Davis, J. 1968. Disturbances in the perception of auditory sequence in children with minimal cerebral dysfunction. Journal of Speech and Hearing Research 11:236-245. Brandt, J. and Rosen, J.J. 1980. Auditory phonemic perception in dyslexia: Categorical identification and discrimination of stop consonants. Brain and Language 9:324337. DiSimoni, F. 1978. The Token Testfor Children. Hingham, MA: Teaching Resources. Dunn, L.M. and Dunn, L.M. 1981. Peabody Picture Vocabulary Test-Revised. Circle Pines, MN: American Guidance Service. Eisenson, J. 1968. Developmental Aphasia: A speculative view with therapeutic implications. Journal of Speech & Hearing Disorders 33:3-13. Elliott, L.L. 1983. Noise-control measures needed in schools. Educational Technology 23:36-37. EUiott, L.L. 1986. Discrimination and response bias for CV syllables differing in voice onset time among children and adults. Journal of the Acoustical Society of America 80:1250-1255. Elliott, L.L. and Busse, L.A. 1987. Auditory processing by learning disabled young adults. In D. Johnson and J. Blalock (eds.). Adults with Learning Disabilities:Clinical Studies. Orlando: Grune and Stratton. Elliott, L.L., Busse, L.A., Partridge, R., Rupert, J. and DeGraaff, R. 1986. Adult and child discrimination of CV syllables differing in voicing onset time. Child Development 57:628-635. Elliott, L.L. and Hammer, M.A. 1988. Longitudinal changes in auditory discrimination in normal children and children with language-learning problems. Journal of Speech and Hearing Disorders 53:467-474. Elliott, L.L. and Katz, D.R. 1980. Development of a New Children's Speech Discrimination Test. St. Louis: Auditec of St. Louis. Elliott, L.L., Longinotti, C., Meyer, D., Raz, I., and Zucker, K. 1981. Developmental differences in identifying and discriminating CV syllables. Journal of the Acoustical Society of America 70:669-677. Godfrey, J.J., Syrdal-Laskey, A.K., Millay, K.K., and Knox, C.M. 1981. Performance of dyslexic children on speech perception tests. Journal of Experimental Child Psychology 32:401-424. Klatt, D.H. 1980. Software for a cascade/parallel formant synthesizer. Journal of the Acoustical Society of America 67:971-995. Levitt, H. 1971. Transformed up-down methods in psychoacoustics. Journal of the Acoustical Society of America 40:467-477. Lowe, A.D. and Campbell, R.A. 1965. Temporal discrimination in aphasoid and normal children. Journal of Speech and Hearing Research 8:313-314. McReynolds, L.V. 1966. Operant conditioning for investigating speech sound discrimination in aphasic children. Journal of Speech and Hearing Research 9:519-528. Tallal, P. and Piercy, M. 1974. Developmental aphasia: Rate of auditory processing and selective impairment of consonant perception. Neuropsychologia 12:83-93. Tallal, P, Stark, R., Kallman, C., and Mellits, D. 1981. A reexamination of some nonverbal perceptual abilities of language-impaired and normal children as a function of age and sensory modality. Journal of Speech and Hearing Research 24:352-357. Wepman, J. 1960. Auditory discrimination, speech, and reading. Elementary School Journal 9:325-333.
This research investigated the relation between children's performance on two measures of receptive language and children's auditory discrimination of consonant-vowel sounds having frequency and temporal acoustic differences. The measures of fine-grained auditory discrimination produced significant multiple regression coefficients against both receptive vocabulary (Peabody Picture Vocabulary Test-Revised) and receptive language (Token Test for Children) scores. Validation analyses conducted by predicting receptive vocabulary and language scores for a new sample of children and relating them to the actual scores led to significant outcomes. It was concluded that fine-grained auditory discrimination is particularly important in the relatively early stages of language learning.
Introduction It has often b e e n r e p o r t e d that i n d M d u a l s with language-learning problems have auditory processing difficulties (e.g., Eisenson 1968;
The cooperation of the participating children, teachers, principals, and school district administrators is gratefully acknowledged. This research was supported, in part, by a grant from NINCDS (NIH). Annals of Dyslexia, Vol. 40, 1990. Copyright ©1990by The Orton Dyslexia Society ISSN 0736-9387
170
FINE-GRAiNED AUDITORY DISCRIMINATION
171
Wepman 1960). For example, they have been found to have poorer auditory discrimination than normal listeners of the same age (e.g., Aten and Davis 1968; Elliott and Busse 1987; Tallal and Piercy 1974; Tallal et al. 1981). Investigations of auditory processing in these listeners have employed stimuli such as pure tones (Lowe and Campbell 1965), naturally-produced speech sounds (McReynolds 1966) and computerproduced, synthesized syllables. These latter stimuli have sometimes represented "good" tokens of natural speech (e.g., Tallal and Piercy 1974) while, at other times, the synthesized syllables have represented intermediate steps along a continuum between two end-point syllables (Brandt and Rosen 1980; Godfrey et al. 1981). During the past nine years our research has used a task that measures the smallest differences that can be discriminated along continua of synthesized consonant-vowel (CV) syllables that represent stop consonants differing in place of articulation (POA; e.g., [ba-da-ga]) or voice onset time (VOT; e.g., [ba-pa]). Because the differences to which most listeners respond are smaller than can be produced by the vocal mechanism, the task has been referred to as "fine-grained auditory discrimination." Elliott, Longinotti, Meyer, Raz, and Zucker (1981), Elliott (1986), and, Elliott, Busse, Partridge, Rupert, and DeGraaff (1986) have demonstrated that young children require larger acoustic differences to discriminate these CVs than do young adults. Elliott and Busse (1987), in testing a group of normally-hearing young adults with learning disabilities, found that 90 percent of group members exhibited finegrained auditory discrimination that was as poor (or poorer) as that of normally-achieving six-year-olds. Subsequently, a field project was begun to study fine-grained auditory discrimination in a population of young, school-aged children. All data have been collected within a medium-sized Midwestern school district that is generally representative of the U.S. population. One objective of this project has been to determine how prevalent fine-grained auditory discrimination problems are in a population of young children who progress normally in school and in a population of children who exhibit language-learning problems. This report will describe the test procedures and some of the results during the initial years of the project.
Methods
Subjects were selected using these criteria: 1. chronological age of 6 to 11 years at time of testing 2. English as the first language
172
RESEARCH InsIc~rrs
3. normal hearing for pure tones 4. normal middle ear impedance 5. school placement in either a regular classroom or in a class for children of normal intelligence who exhibit language-learning problems 6. absence of other problems such as behavioral disorders, and 7. written parental permission for participation. Children with language-learning problems were included in order to have a wide range of performances on the language measures that were administered. Although children with language-learning problems were not specifically selected as being dyslexic, as a group, they had a high incidence of reading problems. Letters requesting parental permission for participation were sent to the homes of all children in classes for those with language-learning problems who appeared to meet selection criteria and to parents of approximately equal numbers of normally-progressing children. All children whose parents returned permission forms and who met the other criteria were tested. In total, 175 children achieving regular school progress and 189 children exhibiting language-learning problems were tested. This included 145 six- and seven-year-olds anti 219 eight- to eleven-year-olds. Some of these children have been tested additional times in the longitudinal component of the project; those results (Elliott and Hammer 1988) are not reported here. Stimuli
Two continua of consonant-vowel (CV) stimuli were computerproduced using Klatt's synthesizer program (Klatt 1980 Indiana University version). A five-formant1, eight-item continuum that varied in voice onset time (VOT) from 0 to 35 msec in 5-msec steps ([ba-pa]) was created. The presence of a silent period--that is, voice onset time-between release of the initial part of the consonant sound and beginning of voicing distinguishes a voiceless consonant, such as the "p" in [pa], from a voiced consonant, such as "b." Thus, items of the first continuum varied mainly in a temporal dimension. The second continuum represented the place-of-articulation feature of speech production ([ba-da-ga]) and consisted of 13 items, each having five formants. The major acoustic differences along this continuum were the onset frequencies of the second and third formants. All stimuli were 300 msec in duration. Additional information about the stimuli is reported in EUiott, Longinotti, Meyer, Raz, and Zucker (1981), Elliott, Busse, Partridge, Rupert, and DeGraaff (1986), and Elliott (1986). lIn general terms, a formant is a band of acoustic energy of voiced speech sounds.
FINE-GRAINEDAUDITORYDISCRIMINATION
173
Fine-Grained Auditory Discrimination Task The purpose of this measure of auditory discrimination was to determine the smallest acoustic differences among the CV syllables (i.e., just noticeable differences UNDs]) that could be discriminated. Stimuli were presented at 80 dB SPL 2. On every trial, two syllables were presented sequentially with a 500 msec intersyllabic interval. The listener's task was to judge them as "same" or "different" by pushing one of two response buttons. Trial-by-trial feedback was given in the form of a smiling face that appeared for 650 msec immediately after every correct response. JNDs were measured with regard to the [ba] and [pal ends of the VOT continuum. For the place-of-articulation continuum, JNDs were measured relative to [da], separately, in the directions of [ba] and [gal. Thus, four different types of JND measures were obtained. A computer-controlled, up-down adaptive procedure (Levitt 1971) was used to track the 50 percent correct response level. For example, the VOT continuum contained stimuli numbered I through 8. To obtain the JND relative to [pal (stimulus #8 of this example), test trials employing stimuli #3 and #8, #8 and #4, and #8 and #5 might be presented and the subject might respond "different" to each pair (correct responses). If stimuli #6 and #8 were next presented, the listener might respond "same." Because this would be an incorrect response, on the next test trial the acoustic difference between the two members of a pair of stimuli would be increased by presenting #5 and #8. This alternation between easy test trials on which the listener responded correctly and difficult test trials on which the listener made errors defined the adaptive procedure. The adaptive procedure was used in order to collect maximum information in the smallest number of test trials and, thereby, to keep test sessions brief for the young listeners. Although the anchor stimulus (i.e., the one for which the JND was being measured) was presented on every test trial, its position as first or second of the pair was randomly varied. Each series of test trials began with a pair of stimuli that, for most children, was very easily discriminated. In addition to the test trials, there were catch trials in which two identical stimuli were randomly selected from among the anchor position and stimuli within two positions of the anchor. The computer inserted catch trials pseudorandomly so as to have approximately equal numbers of test and catch trials. The correct answer to catch trials was "same," and the smiling-face reinforcement was used. Catch trials helped prevent the listener from developing a bias toward 2In general terms, Sound Pressure Level (SPL) is a system of describing sound intensity that employs an absolute, physical reference. In contrast, Hearing Level (HL) is a system of describing sound intensity that uses as a reference the sensitivity of young adults who have no history of ear disease or noise exposure. Clinical audiometric information is typically reported in dB HL.
174
RESEARCH INSIGHTS
responding "different." Performance on catch trials was scored separately from performance on test trials. The catch trial procedure also helped the experimenter monitor subject's understanding and ability to perform the task. Numerous errors on catch trials at the beginning of a test session were assumed to be evidence of possible lack of understanding of the same/different concept; therefore, backup procedures (see below) were used. When the child had previously demonstrated understanding of the same/different concept, poor catch trial performance was interpreted as indicating poor fine-grained auditory discrimination. Pairs of stimuli were presented until the subject's response to test trials changed from "different" to "same," or the reverse, 10 times. The stimulus numbers of the comparison stimuli for the last 8 reversals were averaged and the absolute difference of this number from the value of the anchor stimulus was taken as the measure of the JND. Catch trial performance was scored separately by the computer program. The procedure was self-paced, and listeners had unlimited time in which to respond. They were, however, encouraged to make their decision quickly. In summary, in some ways, the fine-grained auditory discrimination task resembled Tallal and Piercy's (1974) sequencing task in that two stimuli were presented on every trial; those stimuli could be identical or could be different. However, several characteristics of the finegrained auditory discrimination task rendered it unique. The smallest acoustic differences that could be discriminated were measured; each listener received test trials that bracketed, in difficulty, that listener's 50 percent correct level of responding; performance on catch trials provided a measure of attention to the task; and, the number of trials presented was determined by each listener's responses. Specially developed portable computer hardware and software were used to administer the auditory discrimination task in quiet school settings. In order to provide additional attenuation of background sounds, children wore Audiocups over TDH 39 headphones with MX/41 cushions. The right ear was tested unless the child was lefthanded and preferred to use the telephone with the left ear. Two measures of each of the four JNDs were obtained and averaged. Practice sessions were also given to introduce the procedure and as a reminder, when testing carried over to a second or third session. Whenever a child experienced difficulty understanding the task, a series of backup procedures was employed in which naturally-produced words and syllables, or even colored yarns, were discriminated to confirm that the child understood the same/different concept. If a child mastered these procedures, but could not discriminate the endpoints of the synthesized CV continuum, testing for that JND measure was halted, and
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the child's JND was scored as one continuum step larger than the maximum difference between continuum endpoints. For example, because the VOT continuum consisted of 8 CVs, the maximum possible true VOT JND would be 7 (8 minus 1). Inability to discriminate the continuum endpoints was scored as 8 - - a n approach that assumed the listener would be able to discriminate a difference if one more item were added to the continuum. (Typical children in this age range can easily discriminate a difference of half this magnitude, or smaller.) Some of the children who received a score of 8 would probably have required considerably larger acoustic differences--that is, equivalent to a score of 10 or 11--to achieve discrimination. Therefore, this was a conservative approach that "biased" outcomes in the direction of not finding differences between children who could and who could not discriminate. Other Tests Administered
Pure tone auditory sensitivity was tested at 500, 1,000, 2,000, and 4,000 Hz. Children had hearing no poorer than 20 dB HL 2 (ANSI 1970) in the test ear; furthermore, nearly all of the children had auditory sensitivity that was close to 0 dB HL. Children were required to pass an impedance test (test of middle ear function) each time they received an auditory test procedure. All children were given the Peabody Picture Vocabulary Test-Revised, (PPVT-R, FORM L, Dunn and Dunn 1981) and the Token Test for Children (TTC; DiSimoni 1978) as a measure of their receptive language. Children were not removed from their classrooms for more than 45 to 60 minutes per session. Multiple sessions were usually required to complete testing.
Results
In all of the analyses reported here, children were divided into groups based on age; six- and seven-year-old children were separated from eight- to eleven-year-old children. Also, within each of these age groups, children whose identification numbers ended in an odd digit were placed in a group referred to as the original sample and those whose identification numbers ended in an even digit were placed in a group referred to as the validation sample. These were the subgroups used in the regression analyses (see below). Mean performance on the PPVT-R and TTC is shown in Table I. Because subjects were selected specifically to insure a range of language skills, it is not surprising that the standard deviations were
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Table I Performance on the Peabody Picture Vocabulary Test--Revised (PPVT-R) and the Token Test for Children (TFC): Mean Standard Scores and Standard Deviations Original Samples Validation Samples N Younger Children PPVT-R TTC Older Children PPVT-R TTC
M
SD
N
M
SD
72 72
90.9 493.8
19.3 8.6
72 72
89.5 495.0
16.3 8.4
110 108
89.9 495.5
17.4 7.0
109 108
95.8 498.0
17.2 6.4
larger than those expected for a normal population (i.e., PPVT-R, SD = 10; TTC, SD = 5). Subject selection procedures also led to group means that were numerically lower than those expected for a normal population (i.e., PPVT-R, M = 100; TTC, M = 500) of the type on which these tests were standardized. In each case, the PPVT-R and TTC distributions in this study were slightly skewed toward lower scores. T-tests revealed no differences between Original Sample and Validation Sample means for PPVT-R or TTC among younger children [PPVT-R: t(1,72) = 0.48, p > .05; TTC: t(1,72) = 0.88, p > .05]. Because the groups of older children were so large, differences between means of the two samples achieved statistical significance [PPVT-R: t(1,217) = 2.53, p < .05; TTC: t(1,214) = 2.56, p < .05]. However, the differences between means for the older children had no practical significance in this context.
RegressionAnalyses Regression analyses were used for children of the "original" samples (i.e., those with odd identification numbers in the younger and older groups) to predict performance on the PPVT-R and TTC (Table II). The coefficients derived from the regression analyses were then applied to children in the validation sample (i.e., those with even identification numbers) to establish predicted PPVT-R and TTC scores. These predicted scores were then correlated with the children's actual PPVT-R and TTC scores to provide validation information. Outcomes of the multiple regression procedures and the validation analyses are displayed in Table II. With the JNDs and catch trials as predictors, the multiple regression values were higher for younger children (PPVT-R, R = .57; TTC, R ---- .64) than for older children (PPVT-R, R = .39; TTC, R = .37). The regression coefficients were not
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Table II Multiple Regression (R) of the Measures of Fine-Grained Auditory Discrimination Against Receptive Vocabulary and Receptive Language Scores and Validation Correlation Coefficients (r) of Predicted Scores and Actual Scores Validation Samples Original Samples R N p r N p Younger Children PPVT-R .57 72 .001 .43 72 .001 TTC .64 72 .001 .67 72 .001 Older Children PPVT-R .39 110 .001 .25 109 .01 TTC .37 108 .001 .36 108 .001 so high that they accounted for all the variance associated with PPVT-R and TTC scores. However, all multiple R's were statistically significant at the .001 level. When predicted PPVT-R and TTC scores were related to obtained scores in the validation analyses, three resulting Pearson correlation coefficients were significant at the .001 level. The remaining correlation coefficient (PPVT-R, older children) attained significance at the .01 level. Validation correlation coefficients were numerically of the same magnitude as the original multiple correlation coefficients for the TTC. However, validation coefficients for the PPVT-R were (numerically) slightly smaller than the regression coefficients, as often happens.
Discussion
These relations between fine-grained auditory discrimination and measures of receptive vocabulary and receptive language contribute new evidence to the role of auditory discrimination in language learning (see Introduction). Fine-grained auditory discrimination of both frequency and temporal acoustic differences appears to be particularly important in the earlier years of vocabulary and language-learning. (The youngest children tested in this work were six-year-olds. It seems likely that fine-grained auditory discrimination is even more important for language learning by preschoolers.) This is true even for children who demonstrate normal sensitivity for pure tones and normal middle ear function, according to conventional audiologic clinical standards. Although finegrained auditory discrimination significantly predicts vocabulary and language performance in eight- to eleven-year-old children, the magnitude of this prediction is smaller than in younger children. This may have occurred because the major avenue for vocabulary and language
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cabulary and language learning in young children is the auditory sense, whereas many older children may enhance their vocabulary and language skills through other avenues such as reading and word games. It is not clear why the correlations for younger and older children between predicted and actual scores (i.e., r's) more closely matched the multiple regression values (i.e., R's) for the TTC than for the PPVT-R. Until such time as a definitive explanation for this difference is identified, it is probably best to attribute it to chance variation.
Practical Implications One might ask what educational or training approaches are feasible to improve the fine-grained auditory discrimination of frequency and temporal differences by youngsters who have discrimination difficulties. We have begun work on this topic but have not reached conclusions. In the interim, however, there are a number of approaches that may be employed in classroom and home environments. Young children require more acoustic information than adults to perceive and identify familiar speech stimuli (Elliott 1983; Elliott and Katz 1980). When children are placed in a noisy environment they will have trouble obtaining the academic information they need. Therefore, the strategy currently recommended is to provide for all young children, and particularly for those with language-learning difficulties, the best-possible listening environment. What constitutes a good listening environment? It is one that is free from extraneous noise such as that caused by outdoor traffic, loud talking in hallways and corridors, music, foot shuffling, and conversational sounds generated by others. It is also an environment that has minimum reverberation or "echoing" from walls, blackboards, large glass areas, and hard-surfaced floors. It is an environment where the speaker (teacher or parent) is positioned close to the child so the distance the sound must travel is minimized (and, hence, speech retains maximum possible loudness). It is also an environment in which light falls directly on the face of the talker who is turned to the listener. Think about the modifications that enhance the listening of individuals who have mild-to-moderate hearing loss and follow those guidelines. The same procedures are likely to enhance the speech understanding skills of young children. And, one may hypothesize that with enhanced speech understanding, young children will improve in language learning.
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