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Superior Discrimination of Speech
Pitch and Its Relationship
to Verbal Ability in Autism Spectrum Disorders
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https://www.ncbi.nlm.nih.gov/pubmed/18720290
Cogn Neuropsychol. 2008
Sep;25(6):771-82. doi: 10.1080/02643290802336277. Epub 2008 Aug 22.
https://www.ncbi.nlm.nih.gov/pubmed/18720290
Abstract
Whilst hypersensitivity to pitch information
appears to be characteristic of many individuals with autism spectrum disorders
little is known about the implications of such a tendency for language
acquisition and development. Discrimination of systematically varied pitch
differences between pairs of words, nonwords, and nonspeech pitch contour
analogues was assessed in children with autism spectrum disorders (ASD) and
matched controls. The findings revealed superior performance in ASD, although,
like controls, discrimination of pitch in speech stimuli was poorer in this
group than for nonspeech stimuli. Whilst it was hypothesized that enhanced
processing of speech pitch would correlate negatively with receptive language
skills in ASD, the findings did not fully support this, and enhanced
discrimination skills were observed in individuals without significant language
impairment. The implications of these findings for understanding heterogeneity
of language ability in ASD are discussed.
Full Article
Autism is diagnosed on the basis of abnormalities in social
interaction, communication, and cognitive flexibility that are in evidence
before age three (American Psychiatric Association, 1994). Language onset is
significantly delayed in autism, andresearch into language skills shows
considerable variability in the extent to which these subsequently develop.
Thus, whilst between 25% and 50% of diagnosed individuals never acquire
functional language (Gillberg & Coleman, 2000; Klinger, Dawson, & Renner, 2002),
relatively well-developed language skills are also observed in some individuals
(Boucher, 2003; Kjelgaard & Tager-Flusberg, 2001; Lord & Paul, 1997).
Recently, researchers have turned their attention to identifying
factors associated with this wide variability in language onset and development
in very young children with autism. For example, Luyster, Kadlec, Carter, and
Tager-Flusberg (in press) studied a group of 164 toddlers, aged
18�33 months, with autism and found that responsiveness to joint attention cues
and nonverbal cognitive ability were important in predicting language
development.
Although not currently included as Diagnostic and Statistical Manual of Mental
Disorders� Fourth Edition (DSM-IV) diagnostic criteria for autism (American
Psychiatric Association, 1994), processing abnormalities across sensory domains
are frequently noted (Leekam, Nieto, Libby, Wing, & Gould, 2006) and are
included in the Autism Diagnostic Interview (ADI; Lord, Rutter, & LeCouteur,
1994). Recent work into early occurring sensory abnormalities in autism
spectrum disorders (ASD) has identified a pattern of sensory abnormalities
characterized by underresponsiveness and avoiding behaviours and a low frequency
of seeking behaviours in toddlers with ASD (Ben-Sasson et al., 2007). It is
interesting, given these sensory difficulties, that superior perceptual
discrimination is also characteristic of many individuals with autism (e.g., see
Mottron, Dawson, Soulieres, Hubert, & Burack, 2006).
For example, enhanced discrimination and memory for complex and
simple tones has been shown in a number of experiments (Applebaum, Egel, Koegel,
& Imhoff, 1979; Bonnel et al., 2003; Heaton, 2003, 2005; Heaton, Hermelin, &
Pring, 1998; Heaton, Pring, & Hermelin, 1999; Mottron, Peretz, & M�nard, 2000).
Theories of enhanced perceptual functioning (EPF; Mottron & Burack, 2001;
Mottron et al., 2006) and weak central coherence (WCC; Frith, 1989; Happ�, 1999;
Happ� & Frith, 2006) provide a current theoretical context in which findings of
enhanced perceptual discrimination can be interpreted. Both accounts propose
that cognition in autism is perceptually and locally biased, although the EPF
model does not invoke weak top-down or central processes, but instead
outlines an atypical relationship between intact high-level processes and
overdeveloped low-level or perceptual processes. WCC also differs from EPF in
providing a more constrained account that does not seek to explain communicative
and social abnormalities. These are typically interpreted within the context of
a �mindblindness� account of autism (Baron-Cohen, 1995).
However, findings from a number of studies suggest that pervasive
abnormalities in brain development and connectivity are characteristic in autism
(Belmonte et al., 2004a; Courchesne & Pierce, 2005a, 2005b; Just, Cherkassy,
Keller, & Minshew, 2004), and Herbert (2005) suggests that behavioral features
may be consequences of widespread abnormalities that preferentially target
mental functions requiring highly co-ordinated activity. According to Herbert�s
account, diagnostic features of autism may also be adaptive or compensatory
secondary effects of primary sensory and processing abnormalities.
Within the auditory domain, abnormalities in neural processing in
autism have been noted (Boddaert et al., 2003; C � eponiene� et al., 2003;
Gervais et al., 2004; Mu� ller et al., 1999). Behavioural studies have
highlighted auditory filtering difficulties (Rogers, Hepburn, & Wehner, 2003)
and difficulties in spatially focusing auditory attention (Teder-Sa�leja�rvi,
Pierce, Courchesne, & Hillyard, 2005) in autism. Underreactivity to speech is
frequently observed in young children with this diagnosis (e.g., Klin, 1991),
and auditory processing abnormalities identified by Rogers et al. (2003) and
Teder-Salejarvi et al. (2005) may be implicated in this. Research from sources
as divergent as tone-language speakers, musically enriched infants, and
non-human primates provides evidence for learning-induced plasticity in the
auditory system (Kraus & Banai, 2007). Considered within the context of these
data, atypical auditory development in children with poor orientation to
language is unsurprising. Indeed Kuhl, Coffey-Corina, Padden, and Dawson (2005)
showed that a subgroup of childrenwith autism who did not preferentially attend
to child-directed speech failed to show
the neural changes in response to changes in vowel pitch observed in typically
developing controls.
An early, but highly significant, body of research, predating the
EPF and WCC accounts, tested the hypothesis that, when presented with multiple
simultaneous cues, children with autism will focus on a limited number of those
available (Koegel & Rincover, 1976; Lovaas & Schreibman, 1971; Reynolds, Newsom,
& Lovaas, 1974; Rincover & Koegel, 1975; Schreibman, 1975; Schreibman & Lovaas,
1973).
More recently, Samson, Mottron, Jemel, Belin, and Ciocca (2006)
have hypothesized that auditory processing in autism is influenced by
differences in levels of spectro-temporal complexity in stimuli. In a detailed
analysis of the neuropsychological and behavioural literature, they contrasted
findings showing an association between superior task performance and typical
brain activation with findings showing an association between impoverished task
performance and atypical brain activation and concluded that enhanced and
impoverished processing across auditory domains reflects levels of stimulus
complexity and complexity in patterns of neural processes involved in the
processing task.
Speech is a particularly complex auditory stimulus, and
Schreibman, Kohlenberg, and Britten (1986) investigated differential
responsiveness to different components of the speech signal in autistic
participants. Their findings showed that whilst echolalic autistic children
selectively responded to intonation in speech, nonverbal autistic children
responded to sentence content alone. In contrast, typical controls responded to
both intonation and content or to content alone.
The finding that a subgroup of children with autism selectively
respond to intonation in speech has recently been investigated in samples of
verbal children and adolescents with autism. For example, in one study (Jarvinen-Pasley,
Wallace, Ramus, Happ�, & Heaton, 2008) where participants were required to match
sentence pitch contours to their visual analogues (rising, falling, and U or
inverted U shapes), participants with autism performed at significantly higher
levels than did controls. However, when, in a later study, the same participants
were asked to categorize a series of short sentences as either questions or
statements, they were highly insensitive to the semantic function of the
sentence pitch cues. Instead, they relied on a strategy whereby questions
without a �W� word (i.e., what, which, where, who, etc.) were categorized as
statements (Jarvinen-Pasley, Peppe�, King-Smith, & Heaton, 2008). As none of the
sentences presented did include a �W� word, these verbally able individuals
were almost entirely unable to make the question/ statement distinction.
Taken together, these two sets of findings suggest an association
between semantic and pragmatic deficits and a bias towards fine-grained
processing of the perceptual components of speech-signals.
The current experiment extends previous research into the
perception of speech-pitch in autism by systematically varying task demands at
both semantic and perceptual levels. The task is a paired-stimulus tone
discrimination test, comprising pairs of real words, nonsense words, or pitch
analogue tone stimuli in which one of the pair is systematically varied in pitch
height. Two experimental hypotheses are proposed. The first
draws on the EPF theory, predicting that pitch discrimination ability is
enhanced in ASD compared to controls. The rationale for the second draws on WCC
theory, which states that participants with ASD exhibit weak verbal/semantic
coherence. Contrary to controls, who are predicted to show poorer discrimination
performance for stimuli with than for those without semantic content (i.e.,
real-word pairs vs. nonsense word and pitch analogue pairs), children with ASD
are predicted to show no such semantic effect.
Finally, in order to explore the relationship between enhanced
pitch perception and language, discrimination performance is considered with
respect to participant performance on standardized tests of receptive language.
Method
Participants:
Recruitment and screening
A total of 20 children with diagnoses along the autistic spectrum (ASD) and 29
children with moderate learning difficulties or typical development (controls)
were recruited and completed the experiment. However, of these, 6 children
with ASD and 15 control children were unable consistently to discriminate
stimuli even in those experimental conditions where differences between
comparison pairs were most marked (see section �Fidelity check�), and they were
therefore excluded from the principal analysis.
As an aim of the study was to determine whether hypersensitivity
to pitch frequency cues in autism might be negatively associated with atypical
language, all children completed receptive vocabulary (British Picture
Vocabulary Scales; BPVS; Dunn, Whetton, & Pintilie, 1997) and grammar (Test of
Reception of Grammar; TROG; Bishop, 1983) tests. For both tests, children are
shown groups of four pictures and are asked to indicate those associated with
single words (BPVS) or sentences (TROG) read out by the experimenter.
As the task procedures are similar and relatively simple they are
particularly suitable for sampling vocabulary and syntax together in children
with cognitive impairment. Raw scores for both tests can be converted to
age-equivalent scores (verbal-mental age) using the norms provided in the
manual. As already noted, a large proportion of children tested (48% of controls
and 30% of children with ASD) failed to complete the experimental task
satisfactorily.
Psychometric data for subgroups of children who did and did not
understand the task are shown, together, in Table 1. Comparison of data for
groups of participants (ASD and controls) retained in the analysis showed no
significant difference between chronological age (CA) scores, t(27) � 2 0.306,
ns, BPVS Verbal Mental Age-Equivalence (VMA)
scores, t(27) � 0.87, ns, or TROG VMA scores, t(27) � 0.08, ns. CA and VMA
scores for control participants excluded from the analysis were lower than
scores for those control participants who were retained, CA, t(27) � 2 2.09, p ,
.05; BPVS, t(27) � 2 2.12, p , .05; TROG, t(27) � 2 2.07, p , .05, and the same
pattern was observed for included and excluded participants with ASD, CA, t(18)
� 2 2.37, p , .05; BPVS, t(18) � 2 2.12, p , .05; TROG, t(18) � 2 2.07, p , .05.
Comparison of data for excluded children with and without
ASD showed no significant difference for CA, t(19) � 2 1.04, ns, or TROG VMA
scores, t(19) � 2 1.63, ns. However, the BPVS VMA scores were significantly
lower for excluded ASD
children than for excluded controls, t(19) � 2 4.95, p , .001. Excluded
participants were between 2 and 3 years younger than those who were retained in
the analysis, suggesting that the current paradigm is unsuitable for testing
children younger than 8 years.
The children with ASD were all male and were recruited through
two special educational facilities in the south-eastern UK. In order to verify
diagnosis, examination of the children�s school records was carried out,
confirming that all individuals had been diagnosed with ASD by a paediatrician
independent of the study, on the basis of ICD- 10 (International Classification
of Diseases�10th Revision; World Health Organization, 1992) criteria.
Specifically, 11 children were reported to have autistic disorder, 1 child was
diagnosed with Asperger syndrome, and the reports of the remaining 2 children
indicated unspecified ASD. In 2 children, comorbid presentation of attention
deficit/hyperactivity features was noted, with Tourette syndrome also indicated
for one child.
As the children with ASD were heterogeneous in terms of level of
functioning, and given that the
Table 1. HERE
groups were intended to be matched for mean CA and VMA, the
control group (14 boys and 1 girl) included children with both typical
development (TD, n � 2) and moderate learning difficulties (MLD, n � 13). The
latter group were recruited from the same specialist educational facilities as
were the children with ASD, while the former were recruited from a mainstream
primary school. Children�s school records were again checked to ensure that no
child with ASD was included in the control group.
Experimental stimuli
Speech stimuli were generated to cover a range of 10 vowel sounds
commonly spoken in British English, with 5 monosyllabic real words (e.g., meat,
seat, heat, feet, sweet) and 5 monosyllabic nonsense words (e.g., deat, veat,
yeat, geat, leat) generated for each vowel sound. Multiple versions
of these word and nonsense word stimuli were recorded by an adult female, and
the stimulus set was selected. For each selected word and nonsense word, four
pairs were created using PRAAT software (Boersma, 2001;
https://www.praat.org/).
For the first set of pairs, the original recorded stimulus was
repeated. For the second, the overall pitch contour of each original stimulus
was shifted a pitch distance equivalent to 1 semitone away from the original,
and then paired with the original. For the third and fourth sets, pitch contours
were shifted away from the originals by distances equivalent to 3 and 6
semitones, respectively, and again paired with the original recorded stimuli.
The pitch contours of the recorded words and nonwords were again traced, and, on
the basis of these, nonvocal pitch analogue pairs were created, with the second
of the pair again being the same as the original or differing by 2, 3, or 6
semitones.
From the available bank of selected recorded words, a random
sample of 40 was selected for the block of trials comprising the real-word
pairs. Of these, 10 were �same� stimulus pairs, with the remaining 30
�different� stimuli pairs including 10 with 2-semitone differences, 10 with
3-semitone differences, and 10 with 6- semitone differences. Blocks of 40
nonsense words and 40 tone contour pairs were created and organized in the same
way.
Design and procedure
The design was mixed factorial, with group as the between-subject
factor (ASD and control) and stimulus type (real words, nonsense words, pitch
contours) and interval type (no, small, medium, and large differences) as the
within-group factors. As pilot testing had shown that participants became
confused when the stimulus types were presented randomly together (e.g., with
words, nonwords, and pitch contours all combined within bocks), presentation of
the 120 stimuli was blocked by stimulus type. The order of presentation of the
three blocks was then counterbalanced across the participants of each group in a
Latin square design, while within each block, the ordering of the stimulus pairs
was held constant.
The blocks of trials were presented to the children as separate
activities, with up to 10 practice trials (with feedback) preceding each. The
practice trials were structured in the same way as the main blocks of trials,
utilizing stimuli that had been recorded and generated in the same way as the
test stimuli, but had not been included in the final stimulus set. Practice and
test trials were presented on the computer. An instruction, recorded from the
same female voice as the test stimuli, was presented prior to each practice
pair� �Listen carefully. Are these two the same?�.
Following each practice trial response, an automatic feedback
recording was presented��Yes, you got it right�, or �No you got it wrong this
time. Try again�. The experimental blocks also began with the computerized vocal
instruction presented once at the start of the block. However, no feedback was
provided.
Children were tested individually at the schools. No specific
order of testing was maintained, with the verbal measures (BPVS and TROG)
sometimes presented prior to or following the computerized task, in order to
facilitate maintained concentration on all tasks. For some children, the tasks
were completed over two sessions on separate days. During the computer task
presentation, the experimenter sat with the child, offering encouragement for
effort (i.e., not contingent upon their correct responses). Practice trialswere
curtailed for children who appeared to grasp the task requirements quickly, and
in order to avoid fatigue and biased responding, those children who continued to
respond with error on the practice trials nonetheless proceeded on to the
experimental trials, with fidelity checking later used to screen out any child
who had clearly failed to understand the task. Children recorded �same� or �not
same� responses by pressing one of two buttons (marked �yes� and �no�) on a
purpose-built response box. This was connected directly to the computer that
recorded the responses.
Results
The response data for each child on the computerized pitch
discrimination task were imported into SPSS for fidelity checking and analysis.
Fidelity check
Signal detection analysis (SDA; Green & Swets, 1966/1974) was
initially used to confirm the examiner�s sense that the data for some children
should be discarded due to failure to grasp the task requirements. Children were
considered to have understood the task if they responded accurately within the
two easiest conditions of the study (i.e., showing ability to distinguish
between those pitch analogue pairs in which the stimuli differed
greatly�equivalent to 6 semitones� and those pitch analogue pairs in which the
stimuli did not differ at all��same� pairs).
For each child, the SDA parameter d-prime (d0) was therefore
computed on the response data pertaining to these �same� and �large difference�
pitch analogue pairs. The parameter d0 is a measure of the perceived difference
between the conditions being compared and is distributed around 0, with a large
d0 parameter indicating that the child understood the task requirements (i.e.,
was able to correctly respond in such a way that indicated awareness that the
�same� pairs were the same, and the �very different� pairs were not the same).
By contrast, a small d0 parameter would indicate that a child was
responding as if perceiving no difference between these �same� and �very
different� pairs (evidencing lack of understanding of task requirements in this
simplest experimental condition).
Such d0 parameters were calculated for all children assessed and
were found to vary between 3.29 (consistently responding correctly) and �2.61
(consistently responding incorrectly). Any child with a d0 parameter below 0.8
was considered to be a poor discriminator at even this most basic level of the
task and was hence considered not to have understood the task requirements. The
data for 21 of the children tested (N � 4 ASD and 17 control) were therefore
omitted from the analysis, resulting in the final sample of 29 children
described above and presented in Table 1.
Main analysis
Means, standard deviations, and ranges for correct scores across
experimental conditions for the two groups are shown in Table 2. It was
predicted that enhanced pitch discrimination would be observed in the children
with ASD in comparison with controls and that the presence of semantic meaning
within the stimuli would hamper pitch discrimination for controls, but not for
children with ASD.
The data were analysed using a mixed factorial analysis of
variance (ANOVA) with the betweensubjects factor of group (2 levels; ASD and
control) and within-subjects factors of stimulus type (3 levels; real words,
nonsense words, and
pitch analogues) and interval (4 levels; same, and small, medium, and large
differences). The dependent variable was the number of correct responses across
the 10 trials at each level of stimulus type by interval, and given that a
priori hypotheses had been specified, these were tested directly with no
adjustment made to the significance level for multiple testing.
The analysis revealed a significant main effect of group, F(1,
26) � 11.38, p , .01) with children with ASD making more correct pitch
discriminations than controls (M � 9.51, SD � 2.81 for ASD group, and M � 6.69,
SD � 2.32 for control group). There was a highly significant main effect of
stimulus type, F(2) � 8.47, p � .001, with no significant stimulus type by group
interaction, F(2) � 0.32. This is shown in Figure 1.
Across both groups, correct discrimination scores for the real
and nonsense words did not differ, t(28) � 0.57, ns, M � 0.65, SD � 0.23, and
were lower than pitch analogue scores, t(28) � 2 4.02, p , .001; M � 0.74, SD �
0.20.
Thus, participants from both groups showed poorer discrimination
performance when the stimuli included linguistic content. The main effect of
interval size was significant, F(3) � 32.71, p , .001, with correct
discrimination scores improving with increases in interval sizes (all
comparisons p , .01). There was a significant interval size by group
interaction, F(1.77, 47.74) � 6.25, p , .01, with participants with ASD making
more correct decisions than controls on all �different� pair conditions, t(27) �
33, p � .033; ASD, M � 0.76, SD � 0.21; control M � 0.48, SD � 0.24, but not on
�same� pair conditions, t(27) � 0.64, ns; ASD, M � 0.88, SD � 0.13; control M �
0.84, SD � 0.17. It is unlikely that a participant, even with low discrimination
ability, would falsely identify �same� pairs as �different�. Therefore the
�same� condition is likely to elicit fewer errors, and the interaction, shown in
Figure 2, may be an artefact of a ceiling effect on this condition.
These results clearly show that sensitivity to pitch cues in
speech is heightened in ASD, although semantic content was found to hamper
Table 2. HERE
Figure 1. HERE
this ability in a similar way to that seen for controls. In order
to explore the relationship between pitch discrimination and language levels, CA
and VMA measures were correlated with scores for separate conditions and total
test scores (summed across all three conditions).
Discrimination scores for each of the stimulus types (real words,
nonwords, and pitch analogues) were found to be highly related, with
coefficients varying between .67 and .87 for ASD and between .71 and .86 for
controls (all p , .01),
and this justified the summing of conditions.
For the ASD group BPVS VMA scores correlated with total
discrimination scores (r � .61, p , .05) and with non-word scores (r � .66, p ,
.01). The BPVS VMA pitch analogue and real-word correlations approached
significance (pitch analogue, r � .5, p � .06; real words, r � .48, p , .07).
Neither CA nor TROG VMA scores correlated with any of the discrimination scores
for the ASD group. For controls, TROG VMA scores correlated with scores on
nonwords (r � .57, p , .05), and the TROG VMA correlations approached
significance for real words (r � .48, p � .07) and total discrimination scores
(r � .52, p � .052). Neither CA nor BPVS VMA scores correlated with any of the
discrimination scores for controls. This differential pattern of correlations
across groups is somewhat difficult to interpret. For controls, the correlation
between BPVS and TROG VMA scores was significant (r � .74, p , .01) although VMA
scores derived from the TROG were significantly lower than those derived from
the BPVS (p , .05). The correlations showing that control Children with higher
TROG VMA scores also achieved higher discrimination scores may therefore reflect
the role of intelligence in task performance. Whilst there was a similar
discrepancy between VMA scores derived from the TROG and BPVS (p , .05) for the
children with ASD, the correlation between the two measures appeared to be
weaker than that for controls (r � .43, ns) suggesting greater heterogeneity in
the relationship between receptive vocabulary and syntax in this group. Whilst
no clear conclusions can be drawn from the differential pattern of correlations
observed for the ASD group, it is plausible to suggest that pitch discrimination
is under the influence of different mechanisms in this group.
Discussion
The current results showed that children with ASD were
exceptionally sensitive to changes in pitch contours across different types of
auditory stimuli. The group mean discrimination scores were uniformly higher for
participants with ASD than for controls, and whilst only 3 of the 15 controls
achieved discrimination scores above 62%, only 2 participants from the ASD group
scored below 64%. The findings therefore support the EPF theory and replicate
previous studies showing enhanced discrimination of musical (Applebaum et al.,
1979; Bonnel et al., 2003; Heaton, 2003, 2005; Heaton et al., 1998, 1999;
Mottron et al., 2000) and linguistic (Heaton, Davies, & Happ�, 2008; Jarvinen
Pasley & Heaton, 2007; Jarvinen Pasley et al., 2008) pitch.
The second aim of the study was to test the effect of semantic
content on information processing in autism. WCC theory (Happ�, 1999) proposes
that coherence at verbal/semantic levels is weak, and experiments supporting
this hypothesis have typically observed a reduced tendency to process language
for meaning in autism. For example, several studies have shown that participants
with autism are less likely to capitalise on available semantic information when
required to disambiguate homographs (Frith & Snowling, 1983; Happ�, 1997;
Jolliffe & Baron-Cohen,1999; L�pez & Leekam, 2003).
However, these studies have also shown that when participants are
specifically instructed to �read for meaning�, increased attention to semantic
content is observed (Happ�, 1994; Jolliffe & Baron-Cohen, 1999; Snowling &
Frith, 1986).
In the current study understanding of the test words was not
directly tested but it was assumed that controls (but not participants with
ASD), would be captured by meaning and perform at lower levels on the word than
on the non-word and pitch contour conditions. The findings did not show a
difference between real words and nonsense words for either group, and it
appeared that speech, rather than speech content, was the important factor in
predicting poorer pitch discrimination. Whilst differences in psychoacoustic
complexity between speech and nonspeech stimuli are likely to have influenced
performance, highly significant correlations between speech and nonspeech
discrimination scores were observed for both groups of participants, and this
suggested that similar cognitive processes were recruited for the different
conditions.
Although it was predicted, based on WCC theory, that
discrimination performance would be less disrupted by semantic content for
participants with ASD, this was not the case, and group differences on the task
resulted from difference in perceptual discrimination thresholds only.
The final aim of the study addressed developmental issues by
attempting to determine whether increased sensitivity to non-semantic
information in speech stimuli might contribute to the undercutting of language
development in ASD. In the study by Schreibman et al. (1986), echolalic autistic
children selectively attended to intonation, and it is indeed difficult to
understand how such a tendency would not result in constrained language
development. However, the findings from the current study failed to reveal a
simple negative relationship between attention to pitch information and language
skills in ASD.
A total of 4 individuals with ASD obtained discrimination scores
that were higher than 90% on the most difficult discrimination condition (i.e.,
comparing real words with only very small interval differences), and for 2 of
these receptive language scores were very low. However, language scores for the
other 2 high-scoring individuals were within the normal range, and correlations
carried out on the discrimination and receptive vocabulary scores (VMA) for the
whole group were positive and significant. This suggests that whilst enhanced
perception of pitch information in speech may contribute to the undercutting of
language for some individuals, language impairment is not a necessary outcome of
this tendency. Previously cited research into early precursors of language in
autism (Luyster et al., in press) have highlighted the importance of joint
attention and nonverbal intelligence, and these factors may serve to limit any
negative effects resulting from sensory abnormalities. However, it may also be
the case
that sensory difficulties are less marked in children with relatively unimpaired
joint attention and non-verbal intelligence.
The nature of the relationship between enhanced perceptual
discrimination shown on experimental tasks and the sensory abnormalities noted
in clinical settings in ASD is little understood.
Sensory abnormalities are not confined to ASD, although low
perceptual discrimination thresholds do not appear to be characteristic in other
neurodevelopmental disorders associated with sensory abnormalities. In the study
by
Ben-Sasson et al. (2007), toddlers with ASD showed increased sensory
abnormalities compared to chronological- or mental-age-matched controls, with
avoiding behaviours and low awareness of sensations being most marked. However,
as the authors noted, these items on the test scales involved social components,
and this may have biased and increased underresponsiveness scores. The results
from the diagnostic tests revealed high rates of unusual sensory interests in
these toddlers, and the authors concluded that sensory seeking in ASD may not
differ from sensory seeking in typical development in terms of frequency but in
quality. An important question is whether differences in perceptual
discrimination thresholds, as noted in the current study, may be downstream
effects of these early and highly atypical sensory interests and preoccupations.
Current influential accounts of autism, for example the WCC theory and theory of
mind deficit hypothesis, explain social and nonsocial abnormalities within
different theoretical frameworks.
However, interrelationships between assets in non-social and
deficits in social domains are currently not well understood. If enhanced
perception results from a disruption in the early occurring bias to process
social stimuli, a more holistic approach to studying development in autism would
then be justified.
Summary
The present findings confirm that discrimination of pitch information across
speech and non-speech auditory domains is enhanced in ASD. Task difficulty
resulted in the exclusion of ASD and control participants with low CA and VMA
scores. As outstanding questions about the genesis of enhanced perceptual
discrimination and the implications of sensory abnormalities for language
development may be better addressed in studies of younger children, a future
goal will be to develop a paradigm suitable for testing pitch discrimination in
younger and less able children with autism. The inclusion of such a task in a
battery of language, intelligence, and sensory processing tests may enable
researchers to identify interrelationships between sensory abnormalities, low
perceptual thresholds, and language skills.
REFERENCES
American Psychiatric Association. (1994). Diagnostic and
statistical manual of mental disorders (4th ed.). Washington, DC: Author.
Applebaum, E., Egel, A. L., Koegel, R. L., & Imhoff, B. (1979). Measuring
musical abilities of autistic children. Journal of Autism and Developmental
Disorders, 9, 279�285.
Baron-Cohen, S. (1995). Mindblindness: An essay on autism and theory of mind.
Boston: MIT Press/ Bedford Books.
Belmonte, M. K., Allen, G., Beckel-Mitchener, A., Boulanger, L. M., Carper, R.
A., & Webb, S. J. (2004a). Autism and abnormal development of brain
connectivity. Journal of Neuroscience, 24, 9228�9231.
Ben-Sasson, A., Cermak, S. A., Orsmond, G. I., Tager- Flusberg, H., Carter, A.
S., Kadlec, M. B., et al. (2007). Extreme sensory modulation behaviours in
toddlers with autism spectrum disorders. American Journal of Occupational
Therapy, 61, 584�592.
Bishop, D. V. M. (1983). T.R.O.G. Test for Reception of
Grammar. Manchester, UK: Chapel Press.
Boddaert, N., Belin, P., Chabane, N., Poline, J.-B., Barth�le�my, C.,
Mouren-Simeoni, M.-C., et al. (2003). Perception of complex sounds: Abnormal
mpattern of cortical activation in autism. American Journal of Psychiatry, 160,
2057�2060.
Boersma, P. (2001). PRAAT, a system for doing phonetics
by computer. Glot International, 5, 341�345.
Bonnel, A., Mottron, L., Peretz, I., Trudel, M., Gallun, E., & Bonnel, A. M.
(2003). Enhanced pitch sensitivity in individuals with autism: A signal
detection analysis. Journal of Cognitive
Neuroscience, 15, 226�235.
Boucher, J. (2003). Language development in autism.
International Journal of Pediatric Otorhinolaryngology, 67(Suppl. 1), S159�S163.
Ceponiene, R., Lepisto�, T., Shestakova, A., Vanhala, R., Alku, P., Naatanen,
R., & Yaguchi, K. (2003). Speech�sound selective auditory impairment in children
with autism: They can perceive but not
attend. Proceedings of the National Academy of Sciences, 100, 5567�5572.
Courchesne, E., & Pierce, K. (2005a). Brain overgrowth in autism during a
critical time in development: Implications for frontal pyramidal neuron and
interneuron development and connectivity. International Journal of Developmental
Neuroscience, 23, 153�170.
Courchesne, E., & Pierce, K. (2005b). Why the frontal cortex in autism might be
talking only to itself: Local over-connectivity but long-distance disconnection.
Current Opinion in Neurobiology, 15, 225�230.
Dunn, L. M., Whetton, C., & Pintilie, D. (1997). British Picture Vocabulary
Scale. Windsor, UK: NFER-Nelson.
Frith, U. (1989). Autism: Explaining the enigma. Oxford,
UK: Blackwell.
Frith, U., & Snowling, M. (1983). Reading for meaning and reading for sound in
autistic and dyslexic children. Journal of Developmental Psychology, 1, 329�342.
Gervais, H., Belin, P., Boddaert, N., Leboyer, M., Coez, A., Sfaello, I., et al.
(2004). Abnormal cortical voice processing in autism. Nature Neuroscience, 7,
801�802.
Gillberg, C., & Coleman, M. (2000). The biology of the autistic syndromes (3rd
ed.). London: Mac Keith Press.
Green, D., & Swets, J. (1974). Signal detection theory and psychophysics.
Huntington, NY: Krieger. (Reprinted from original work published 1966, New York:
Wiley)
Happ�, F. (1994). An advanced test of theory of mind: Understanding of story
characters� thoughts and feelings in able autistic, mentally handicapped, and
normal children and adults. Journal of Autism and Developmental Disorders, 24,
129�154.
Happ�, F. (1997). Central coherence and theory of mind in autism: Reading
homographs in context. British Journal of Developmental Psychology, 15, 1�12.
Happ�, F. (1999). Autism: Cognitive deficit or cognitive style? Trends in
Cognitive Sciences, 3, 216�222.
Happ�, F., & Frith, U. (2006). The weak coherence account: Detail-focused
cognitive style in autism spectrum disorders. Journal of Autism and
Developmental Disorders, 1, 1�21.
Heaton, P. (2003). Pitch memory, labelling and disembedding
in autism. Journal of Child Psychology and Psychiatry, 44, 1�9.
Heaton, P. (2005). Interval and contour processing in autism. Journal of Autism
and Developmental Disorders, 8, 1�7.
Heaton, P., Davis, R. E., & Happ�, F. (2008). Research note: Exceptional
absolute pitch perception for spoken words in an able adult with autism.
Neuropsychologia, 46, 2095�2098.
Heaton, P., Hermelin, B., & Pring, L. (1998). Autism and pitch processing: A
precursor for savant musical ability? Music Perception, 15, 291�305.
Heaton, P., Pring, L., & Hermelin, B. (1999). A pseudo-savant: A case of
exceptional musical splinter skills. Neurocase, 5, 503�509.
Herbert, M. R. (2005). Autism: A brain disorder, or a disorder that affects the
brain? Clinical Neuropsychiatry, 2, 354�379.
Jarvinen-Pasley, A. M., & Heaton, P. (2007). Evidence for reduced specialisation
in auditory processing in autism. Developmental Science, 10, 786�793.
Jarvinen-Pasley, A. M., Peppe�, S., King-Smith, G., & Heaton, P. (2008). The
relationship between form and function level receptive prosodic abilities in
autism. Journal of Autism and Developmental Disorders, 38, 1328�1340.
Jarvinen-Pasley, A. M., Wallace, G. L., Ramus, F., Happ�, F., & Heaton, P.
(2008). Enhanced perceptual processing of speech in autism. Developmental
Science, 11, 109�121.
Jolliffe, T., & Baron-Cohen, S. (1999). A test of central coherence theory:
Linguistic processing in highfunctioning adults with autism or Asperger
syndrome: Is local coherence impaired? Cognition, 71, 149�185.
Just, M. A., Cherkassy, V. L., Keller, T. A., & Minshew, N. J. (2004). Cortical
activation and synchronization during sentence comprehension in high-functioning
autism: Evidence of underconnectivity. Brain, 127, 1811�1821.
Kjelgaard, M., & Tager-Flusberg, H. (2001). An investigation of language
impairment in autism: Implications for genetic subgroups. Language and Cognitive
Processes, 16, 287�308.
Klin, A. (1991). Young autistic children�s listening preferences
in regard to speech: A possible characterisation of the symptom of social
withdraw. Journal of Autism and Developmental Disorders, 21, 29�42.
Klinger, L., Dawson, G., & Renner, P. (2002). Autistic disorder. In E. Mash & R.
Barkley (Eds.), Child psychopathology (2nd ed., pp. 409�454). New York: Guilford
Press.
Koegel, R. G., & Rincover, A. (1976). Some detrimental effects of using extra
stimuli to guide learning in normal and autistic children. Journal of Abnormal
Child Psychology, 4, 1, 59�71.
Kraus, N., & Banai, K. (2007). Auditory-processing malleability: Focus on
language and music. Current Directions in Psychological Science, 16, 105�114.
Kuhl, P. K., Coffey-Corina, S., Padden, D., & Dawson, G. (2005). Links between
social and linguistic processing of speech in preschool children with autism:
Behavioural and electrophysiological measures. Developmental Science, 8, F9�F20.
Leekam, S. R., Nieto, C., Libby, S. J., Wing, L., & Gould, J. (2006). Describing
the sensory abnormalities of children and adults with autism. Journal of Autism
and Developmental Disorders, 37, 894�910.
L�pez, B., & Leekam, S. R. (2003). Do children with autism fail to process
information in context? Journal of Child Psychology and Psychiatry, 44, 285�300.
Lord, C., & Paul, R. (1997). Language and communication in autism. In D. J.
Cohen & F. R. Volkmar (Eds.), Handbook of autism and pervasive developmental
disorders (2nd ed., pp. 195�225). New York: John Wiley & Sons.
Lord, C., Rutter, M., & LeCouteur, A. (1994). Autism Diagnostic
Interview�Revised: A revised version of a diagnostic interview for caregivers of
individuals with possible pervasive developmental disorders. Journal of Autism
and Developmental Disorders, 24, 659�685.
Lovaas, L., & Schreibman, L. (1971). Stimulus overselectivity of autistic
children in a two stimulus situation. Behavioural Research Therapy, 9, 305�310.
Luyster, R. J., Kadlec, M. B., Carter, A., & Tager- Flusberg, H. (in press).
Language assessment and development in toddlers with autism spectrum. Journal of
Autism and Developmental Disorders.
Mottron, L., & Burack, J. (2001). Enhanced perceptual functioning in the
development of persons with autism. In J. A. Burack, T. Charman,
N. Yirmiya, & P. R. Zelazo (Eds.), The development of autism: Perspectives from
theory and research (pp. 131�148). Mahwah, NJ: Lawrence Erlbaum Associates.
Mottron, L., Dawson, M., Soulie`res, I., Hubert, B., & Burack, J. (2006).
Enhanced perceptual functioning in autism: An update, and eight principles of
autistic perception. Journal of Autism and Developmental Disorders, 2, 1�17.
Mottron, L., Peretz, I., & M�nard, E. (2000). Local and global processing of
music in high-functioning persons with autism: Beyond central coherence? Journal
of Child Psychology and Psychiatry, 41,
1057�1065.
Mu ler, R. A., Behen, M. E., Rothermel, R. D., Chugani, D. C., Muzik, O.,
Mangner, T. J. & Chugani, H. T. (1999). Brain mapping of language and auditory
perception in high-functioning autistic adults: A PET study. Journal of Autism
and
Developmental Disorders, 29, 19�31.
Reynolds, C. D., Newsom, C. D., & Lovaas, O. I. (1974). Auditory overselectivity
in autistic children. Journal of Abnormal Child Psychology, 2, 253�263.
Rincover, A., & Koegel, R. L. (1975). Setting generality and stimulus control in
autistic children. Journal of Applied Behaviour Analysis, 8, 235�246.
Rogers, S. J., Hepburn, S., & Wehner, E. (2003). Parent reports of sensory
symptoms in toddlers with autism and those with other developmental disorders.
Journal of Autism and Developmental Disorders, 33, 631�642.
Samson, S.,Mottron, L., Jemel, B., Belin, P.,& Ciocca,V. (2006). Can spectro-temporal
complexity explain the autistic pattern of performance on auditory tasks?
Journal of Autism and Developmental Disorders, 36, 65�76.
Schreibman, L. (1975). Effects of within-stimulus and extra-stimulus prompting
on discrimination learning in autistic children. Journal of Applied Behaviour
Analysis, 8, 91�112.
Schreibman, L., Kohlenberg, B. S., & Britten, K. R. (1986). Differential
responding to content and intonation components of a complex auditory stimulus
by non-verbal and echolalic autistic children. Analysis and Intervention in
Developmental Disabilities, 6, 109�125.
Schreibman, L., & Lovaas, O. I. (1973). Overselective response to social stimuli
by autistic children. Journal of Abnormal Child Psychology, 1, 1973.
Snowling, M., & Frith, U. (1986). Comprehension in �hyperlexic� readers. Journal
of Experimental Child Psychology, 42, 392�415.
Teder-Sa�leja�rvi, W. D., Pierce, K. L., Courchesne, E., & Hillyard, S. A.
(2005). Auditory localisation and attention deficits in autistic adults. Brain
Research Cognitive Brain Research, 23, 221�234.
World Health Organization. (1992). The ICD-10 classification
of mental and behavioural disorders. Geneva, Switzerland: Author.
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