Basic Auditory Processing Deficits in Dyslexia: Systematic Review of the Behavioral and Event-Related Potential/Field Evidence

Abstract

A review of research that uses behavioral, electroencephalographic, and/or magnetoencephalographic methods to investigate auditory processing deficits in individuals with dyslexia is presented. Findings show that measures of frequency, rise time, and duration discrimination as well as amplitude modulation and frequency modulation detection were most often impaired in individuals with dyslexia. Less consistent findings were found for intensity and gap perception. Additional factors that mediate auditory processing deficits in individuals with dyslexia and their implications are discussed.

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Journal of Learning Disabilities
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DOI: 10.1177/0022219411436213
2013 46: 413 originally published online 8 February 2012J Learn Disabil
Jarmo A. Hämäläinen, Hanne K. Salminen and Paavo H. T. Leppänen
Potential/ Field Evidence
Basic Auditory Processing Deficits in Dyslexia: Systematic Review of the Behavioral and Event-Related
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Regular Article
Developmental dyslexia is a specific learning disability
manifested by difficulties in learning to read and write
despite having adequate cognitive ability, motivation, access
to instruction, and intact peripheral sensory mechanisms
(Lyon, Shaywitz, & Shaywitz, 2003). It is widely accepted
that deficits in phonological processing underlie the poor
reading performance of the majority of individuals with
dyslexia (Bradley & Bryant, 1983; Stanovich, 1998; Wagner
& Torgesen, 1987). Two broad lines of research emphasize
different cognitive-level manifestations and/or causes, either
bottom-up or top-down, for the phonological processing def-
icit. Bottom-up explanations suggests basic auditory pro-
cessing problems are the underlying basis of the phonological
deficit (Farmer & Klein, 1995; Tallal & Gaab, 2006). In this
account, poor auditory and speech processing leads to fuzzy
or inexact speech sound representations, which in turn con-
strain phonological processing (Pasquini, Corriveau, &
Goswami, 2007; Talcott & Witton, 2002). On the other
hand, top-down explanations suggest that phonological
problems are a consequence of deficits in higher level lin-
guistic processes at lexical and sublexical levels (Ramus
et al., 2003; White et al., 2006). In this view, lower level
auditory processing difficulties may co-occur with a phono-
logical deficit, but they do not contribute to phonological pro-
cessing difficulties and thus play no causal role in the
expression of dyslexia. The current review focuses on the
empirical behavioral and neural-level evidence of auditory
processing deficits in individuals with dyslexia.
Different sound features have been investigated in dys-
lexia depending on the theoretical paradigm. One theory
that has been studied extensively is the rapid auditory pro-
cessing deficit hypothesis, which posits that individuals
with dyslexia have problems processing either brief audi-
tory cues or auditory information presented rapidly, such
as in stop consonants where rapid changes in formants (fre-
quency bands) are important for phoneme identification
(Farmer & Klein, 1995; Tallal & Gaab, 2006). In this view,
slow processing of rapid auditory information could lead
to inaccurate perception of certain phonemic contrasts and
thus to the development of less precise phonological repre-
sentations in individuals with dyslexia.
Another hypothesis with a slightly different emphasis
states that processing of dynamic features of auditory stim-
uli, such as amplitude and frequency modulations (AM,
FM) in a speech signal, is impaired in individuals with dys-
lexia (Talcott & Witton, 2002; Witton, Stein, Stoodley,
Rosner, & Talcott, 2002). AM refers to the fluctuations of
sound intensity in time, and FM refers to similar fluctua-
tions of sound frequency in time.
436213LDXXXX10.1177/0022219411436213Hä
mäläinen et al.Journal of Learning Disabilities
1University of Jyväskylä, Jyväskylä, Finland
Corresponding Author:
Jarmo A. Hämäläinen, Department of Psychology, P.O. Box 35, FI-40014
University of Jyväskylä, Jyväskylä, Finland.
Email: jarmo.a.hamalainen@jyu.fi
Basic Auditory Processing Deficits in
Dyslexia: Systematic Review of the
Behavioral and Event-Related Potential/
Field Evidence
Jarmo A. Hämäläinen, PhD1, Hanne K. Salminen, MA1,
and Paavo H. T. Leppänen, PhD1
Abstract
A review of research that uses behavioral, electroencephalographic, and/or magnetoencephalographic methods to investigate
auditory processing deficits in individuals with dyslexia is presented. Findings show that measures of frequency, rise time,
and duration discrimination as well as amplitude modulation and frequency modulation detection were most often impaired
in individuals with dyslexia. Less consistent findings were found for intensity and gap perception. Additional factors that
mediate auditory processing deficits in individuals with dyslexia and their implications are discussed.
Keywords
auditory processing, dyslexia, neuropsychology
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414 Journal of Learning Disabilities 46(5)
An alternative hypothesis suggests that detecting the lon-
ger time-scale patterns of intonation, rhythm, and stress in
speech prosody is particularly problematic for children and
adults with dyslexia (Goswami et al., 2002; Pasquini et al.,
2007). The prosody-related sound features would include
slowly varying rise times (the time from sound beginning to
its maximum amplitude), AM, FM, and changes in syllable
and phoneme duration. The deficit in processing these sound
features is thought to constrain the segmentation of the
speech stream into smaller elements. Basic auditory pro-
cessing in terms of other features of the acoustic signals, that
is, frequency (how high a tone is), duration (how long a tone
is), and intensity (how loud a tone is) of tones (e.g.,
Baldeweg, Richardson, Watkins, Foale, & Gruzelier, 1999;
Richardson, Thomson, Scott, & Goswami, 2004), could also
be atypical in individuals with dyslexia.
A growing body of evidence has associated deficits in
auditory processing with impaired reading for some but not
all individuals with dyslexia. This finding has led to the
conclusion that auditory problems may mediate but are not
necessary to cause reading problems. For example, auditory
processing deficits may be associated with language learn-
ing impairments (LLI) that constrain reading development
(Bishop, Carlyon, Deeks, & Bishop, 1999). In this view,
auditory processing problems may exist as a deficit that
constrains development of phonological and literacy skills
beyond that which is expected from the language impair-
ment alone. Alternatively, young children with LLI and
reading impairments may have a maturational lag in the
development of the central nervous system, which would
also be reflected in the functioning of the auditory pathway
(McArthur & Bishop, 2004; B. A. Wright & Zecker, 2004).
In this case, auditory deficits that are present early in chil-
dren’s development could affect the formation of speech
sound representations. However, the magnitude of this
effect is expected to diminish as children grow older.
The present review focuses on findings from studies that
use nonlinguistic auditory stimulation to investigate audi-
tory perception of individuals with dyslexia. This approach
is narrow in scope and contrasts with earlier reviews of
research on auditory processing among diverse clinical pop-
ulations that include children and adults with dyslexia (e.g.,
Bishop, 2007; Tallal & Benasich, 2002) or on the neural
basis of dyslexia, which includes studies in genetics and
neurobiology (e.g., Démonet, Taylor, & Chaix, 2004;
Galaburda, Loturco, Ramus, Fitch, & Rosen, 2006). The
current review both complements and extends previous
summaries of research that have focused on narrow topics
related to auditory processing, such as studies on rapid
auditory processing, for example the capacity of those with
dyslexia to make temporal order judgments or the effects of
decreasing interstimulus intervals on the perception of audi-
tory stimuli (e.g., Farmer & Klein, 1995; McArthur &
Bishop, 2001). One aim of this review is to establish the
prevalence rate of auditory deficits in dyslexia. Second, the
review attempts to identify whether some auditory features
are more difficult than other features to process for indi-
viduals with dyslexia. Third, the association between audi-
tory processing abilities and reading and spelling skills is
reviewed.
Method
Definition of Dyslexia
Studies selected for the current review met the following
selection criteria: participants in each study had either a
diagnosis of dyslexia or performance at or below the 16th
percentile or below a reading age of 1.5 years on a stan-
dardized measure of reading and/or spelling. In addition,
the participants had Performance IQs on the Wechsler
Intelligence Scales in the average or above range (i.e., IQ >
80). Participants were from several different language
backgrounds: Chinese, Dutch, English, Finnish, French,
German, Hebrew, Norwegian, and Spanish. One study
investigating children at risk for dyslexia was also included.
Studies conducted up to and including January 2010 were
located through searches of the Google Scholar, Medline,
and PsycINFO databases and reviews of reference lists of
topic-related articles. Keywords used were (dyslexia or read-
ing disability) and auditory processing and (frequency or
frequency modulation or intensity or amplitude modulation
or rise time or duration or gap detection). Out of the 74 stud-
ies found, 14 study samples were rejected because partici-
pants did not meet the above criteria for reading problems,
leaving 61 studies to be analyzed. Out of the 61 studies, 17
used brain research methods.
Assessment of Auditory Processing
Measures used to probe auditory processing include (a)
behavioral nonadaptive and (b) behavioral-adaptive dis-
crimination or detection tasks and (c) brain event-related
potential (ERP) or event-related field (ERF; recorded with
magnetoencephalography (MEG)) measures.
In all of the studies, the sound stimuli were presented
through headphones, except for one ERP study, in which the
stimuli were presented through loudspeakers. A total of 10
studies used monaural stimulation (right ear, best ear, or
both ears separately); the rest of the studies used binaural
stimulation. In behavioral studies, motor or verbal response
was required, whereas the ERP studies were passive in the
sense that they did not require any response from the par-
ticipants. Participants were engaged in a cover task such as
watching videos during sound presentation. ERPs thus
allow the measurement of sensory processing without
effects of task demands, such as active attention, motiva-
tion, or understanding of instructions.
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Hämäläinen et al. 415
Behavioral Measures
In nonadaptive tasks, a preselected set of stimuli are used to
test each participant’s auditory perception. Usually in these
studies two sounds are presented and the participant decides
whether the sounds were the same or different. This type of
task is called a two-interval, two-alternative, forced-choice
task (two stimuli are presented and two response options are
given). Adaptive behavioral tasks utilize an algorithm that
adapts to the participants’ performance, trying to find the
discrimination threshold where the difference between
stimuli is perceived usually with 75% accuracy. This thresh-
old is called just noticeable difference.
ERP and ERF Measures
ERPs and ERFs are measures of electromagnetic activity
driven by changes in cognitive processing that are usually
time locked to stimuli. To obtain a clearly visible signal,
stimuli of interest are typically presented in 100 or more
trials and brain responses are averaged across individual
stimuli. The most common measures of ERP activity are
peak amplitudes and latencies or mean amplitude over a
time window. Peak amplitude refers to the strength of acti-
vation or voltage of the electrical signal at its highest
period, whereas peak latency refers to the time between the
onset of the stimulus and when the amplitude reached its
peak value.
In the majority of the reviewed ERP studies (15 of 17 stud-
ies; 88%), preattentive auditory discrimination responses,
mismatch negativity (MMN; n = 15; Näätänen, 1992), and
late discriminative negativity (LDN; n = 5; Cheour, Korpilahti,
Martynova, & Lang, 2001) were examined.
MMN is thought to reflect the detection of change in a
sound stream at the level of the sensory memory (Näätänen,
1992; Näätänen & Alho, 1997). MMN is typically investi-
gated using an oddball paradigm, where one standard sound
occurs regularly and several deviant sounds differing in
some feature or features occur rarely. The neural trace of the
deviant sounds does not match the trace generated by the
repeated sound. This mismatch elicits a negative response
at the fronto-central scalp locations at about 150–250 ms
from the deviancy onset. In the same paradigm, LDN with a
frontal distribution starting at about 400 ms can be observed.
The function of LDN is not clear, but it may reflect ongoing
processing of the deviant-standard difference (Cheour et al.,
2001). In the present review, we use the terms MMN and
LDN for the change detection responses occurring around
150–250 ms and after 400 ms, respectively.
In some of the reviewed ERP studies, amplitude modula-
tion following response (AMFR) was examined. The wave-
form structure of AMFR follows the amplitude changes of
the modulated sound, showing the phase locking of brain
activity to the rate of the AM in a sound (McAnally & Stein,
1997). In addition, some of the studies examined the N1
response that has been proposed to reflect detection of tran-
sient changes, for example sound onsets, in the auditory
environment. The N1 response peaks about 100 ms from
stimulus onset and has the largest amplitude at the fronto-
central scalp locations (Näätänen & Picton, 1987).
Analyses
Effect sizes for each study were calculated using Cohen’s d
(mean (dyslexic group) – mean (control group)/square root
of (SD (dyslexic group)2 + SD (control group)2/2)). Also,
95% confidence intervals were calculated. Average effect
sizes were calculated for each auditory feature using sample-
size-weighted effect sizes (Cohen’s d × (sample size of
study/total sample size) summed over all studies).
The variability of the performance in auditory processing
tasks was examined by comparing the standard deviations of
performance in the auditory tasks between participants with
dyslexia and those with typical reading skills. The standard
deviation of dyslexic readers was divided by that of the typi-
cal readers for each of the auditory tasks in each of the stud-
ies. This figure was then averaged over the studies. Thus, a
value of 1 indicates similar variability between groups, and a
value of 2 indicates 2 times greater variability in those with
dyslexia than controls.
Auditory Processing and Dyslexia
Processing of Sound Frequencies
Altogether 30 studies compared discrimination of sounds at
different frequencies between groups of dyslexic and non-
dyslexic readers (see Table 1). Of the 14 (71%) studies that
used adaptive tasks to measure frequency discrimination, 10
showed group differences that were statistically detectible
across conditions. A minority of studies (4 out of a total 14
studies; 29%) report significant group differences on adap-
tive frequency measures on some, but not all, experimental
conditions (Ahissar, Lubin, Putter-Katz, & Banai, 2006;
Amitay, Ahissar, & Nelken, 2002; Banai & Ahissar, 2006;
Walker, Shinn, Cranford, Givens, & Holbert, 2002). For
instance, Ahissar et al. (2006) found that differences in group
performance on the frequency discrimination task reached
statistical significance when the reference sound remained
constant. However, when the reference sound changed in
frequency from trial to trial, group performance differences
were no longer statistically detectible. Poorer average perfor-
mance of children with dyslexia relative to nondyslexics on
this type of an auditory discrimination task may reflect the
inability of those with dyslexia to use the repeated reference
sound as an anchor for comparing sounds.
Nonadaptive behavioral studies have not found group
differences as often as the adaptive studies (see Table 1). In
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416 Journal of Learning Disabilities 46(5)
Table 1. Effect Sizes and 95% Confidence Intervals (CIs) for the Differences Between Individuals With Typical Reading Skills (C;
controls) and With Reading Problems (RD) for Frequency Perception.
Study Age
N (C/
RD) Effect Size 95% CI Method and Significance Level
Studies with small frequency changes
Goswami et al., 2010 (English) 10.5 y 27/44 2.2 1.70–2.67 Frequency discrimination threshold (***)
Renvall & Hari, 2003 30 y 11/8 2.0 1.02–2.96 MMF amplitude, left (*), right (ns) hemisphere
Ahissar et al., 2006 13.1 y 12/16 1.6a0.80–2.30 Frequency discrimination threshold, constant
reference tone (**)
Walker et al., 2002 20.6 y 9/9 1.6 0.59–2.55 Frequency discrimination threshold (ns)
Baldeweg et al., 1999 33.4 y 10/10 1.3 0.36–2.20 Frequency detection, MMN amplitude,
latency (**)
McAnally & Stein, 1996 28.0 y 26/23 1.0 0.43–1.58 Frequency discrimination threshold (***)
McArthur et al., 2008 10.3 y 37/68 1.0a0.63–1.44 Frequency discrimination threshold (**)
Gibson et al., 2006 9.8 y 44/44 0.8 0.35–1.19 Frequency discrimination threshold (**)
Halliday & Bishop, 2006a 10.7 y 28/28 0.8 0.30–1.37 Frequency discrimination threshold (**)
Banai & Ahissar, 2004 17–30 y 59/48 0.7a0.32–1.09 Frequency discrimination threshold (**)
Banai & Ahissar, 2006 13.1 y 12/22 0.7a−0.02–1.44 Frequency discrimination threshold, 1 (ns) &
2 (**) reference sounds
Heath et al., 2006 36.3 y 41/49 0.7 0.26–1.10 Frequency discrimination threshold (*)
Thomson & Goswami, 2008 10.8 y 23/25 0.7 0.07–1.22 Frequency discrimination threshold (*)
Amitay, Ben-Yehudah, et al., 2002 21.5 y 30/30 0.6 0.11–1.15 Frequency discrimination threshold, 50 &
250 ms tones (*)
Lachmann et al., 2005 9.8 y 12/16 0.6 −0.23–1.33 MMN amplitude (** in one subgroup)
Amitay, Ahissar, et al., 2002 22 y 27/23 0.5 0.01–1.04 Frequency discrimination threshold (ns)
Watson & Miller, 1993 24 y 54/24 0.5 0.05–1.02 Frequency discrimination (ns)
Adlard & Hazan, 1998 10.8 y 12/13 0.3 −0.51–1.12 Formant & F0 frequency discrimination (ns)
Maurer et al., 2003 6.6 y 29/31 0.2a−0.32–0.71 Frequency detection (ns), eMMR (ns), MMR &
LDN (**) amplitudes
Schulte-Körne et al., 1998a 12.5 y 15/19 –0.1 −0.77–0.63 MMN & LDN amplitude (ns)
Kujala et al., 2006 33 y 11/9 –0.3 −1.26–0.60 MMN amplitude, optimal (*), oddball (ns)
Watson, 1992 Adults 25/20 –0.6b −1.17–0.04 Frequency discrimination (ns)
Total, frequency — 554/582 0.7 — —
Studies with large frequency changes
Schulte-Körne et al., 2001 30.5 y 13/12 0.5 −0.36–1.28 MMN & LDN amplitude (ns)
Hämäläinen et al., 2008 9.3 y 25/21 0.2 −0.44–0.74 MMN, LDN amplitude in pair with long
interval (ns)
Sharma et al., 2006 10.3 y 19/15 0.2a−0.58–0.87 Frequency detection, MMN amplitude (***
only for 1.1 kHz missing harmonic task)
No effect size calculated because of small sample size or lack of data
Corbera et al., 2006 11.6 y 11/13 NA NA MMN amplitude (ns), latency (***)
France et al., 2002 Adults 20/16 NA NA Frequency discrimination threshold, 1 ref
tone (*), 6 ref tones, 10 & 200 ms ISI (ns),
400 & 1000 ms (***)
Hugdahl et al., 1998 11.8 y 25/25 NA NA MMN amplitude (*), latency (*)
Kujala et al., 2003 28 y 8/8 NA NA MMN amplitude, group × electrode
interaction (*)
Meng et al., 2005 11.1 y 7/11 NA NA MMN amplitude (ns)
Note: The total effect size is weighted by sample size. Studies have been arranged according to effect size (in bold). Testing method (for event-related
potential studies: MMF = mismatch field; MMN = mismatch negativity; MMR = mismatch response; LDN = late discriminative negativity) and significance
level of the group difference are also shown. ns = not significant; ISI = inter-stimulus interval.
aMean and SD provided by the authors of the study.
bEffect size estimated from a figure.
*p < .05. **p < .01. ***p < .001.
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Hämäläinen et al. 417
two out of six studies, performance of the participants with
dyslexia was poorer compared to that of typical readers
(Baldeweg et al., 1999; Sharma et al., 2006). This difference
in results found using adaptive versus nonadaptive tasks
could be the result of the lesser sensitivity of the latter tasks.
As shown in Table 1, there is considerable variation in
findings from ERP and ERF studies. However, in studies
where the difference in sound frequency between standard
and deviant stimuli is small (< 10%), the ERPs or ERFs of
those with dyslexia have smaller amplitudes and/or later
MMN latencies than typical readers (Baldeweg et al., 1999;
Corbera, Escera, & Artigas, 2006; Hugdahl et al., 1998; Kujala,
Lovio, Lepistö, Laasonen, & Näätänen, 2006; Lachmann,
Berti, Kujala, & Schröger, 2005; Renvall & Hari, 2003).
Maurer, Bucher, Brem, and Brandeis (2003) is the only
study in which a smaller LDN response was found. In con-
trast, in studies in which the difference in sound frequency
of stimuli is large (> 10%), group differences are not statisti-
cally detectable (Hämäläinen Leppänen, Guttorm, & Lyytinen,
2008; Meng et al., 2005; Schulte-Körne, Deimel, Bartling,
& Remschmidt, 2001; Sharma et al., 2006). Bishop (2007),
in her review of findings of ERP studies of frequency pro-
cessing in individuals with language impairment and dys-
lexia, arrived at a similar conclusion regarding the processing
of small and large differences in sound frequency among
those with dyslexia.
There are two exceptions to this pattern of results. Schulte-
Körne, Deimel, Bartling, and Remschmidt (1998a) did not
find any differences in ERPs to a small 5% frequency change
in children with spelling problems compared to nonimpaired
controls. On the other hand, one study using a large frequency
change (50%) revealed a group interaction with electrode posi-
tion (Kujala, Belitz, Tervaniemi, & Näätänen, 2003).
Factors in addition to differences in frequency process-
ing may contribute to differences in MMN amplitude and
latency. For example, when a more complex stimulus pre-
sentation paradigm was used, a significant group difference
in MMN amplitude was found in contrast to the traditional
oddball experiment (Kujala et al., 2006). In addition, one
study found that only those children with dyslexia who had
problems in the reading of frequent real words showed
diminished MMN, whereas children with dyslexia who had
problems in nonword reading showed MMN comparable to
that of control children (Lachmann et al., 2005).
Five studies report associations between auditory pro-
cessing (MMN latency: Baldeweg et al., 1999; discrimination
thresholds: Goswami et al., 2011; Heath, Bishop, Hogben,
& Roach, 2006; McAnally & Stein, 1996; Thomson &
Goswami, 2008) and different literacy measures; the correla-
tions range from .35 to .71 (p < .05) across dyslexic and typi-
cal readers. Four studies report correlations (r = .38–.80, p <
.05) between auditory discrimination thresholds and different
reading measures (word identification, nonword reading,
word reading) in the control or dyslexia group only (France
et al., 2002; Gibson, Hogben, & Fletcher, 2006; Halliday &
Bishop, 2006a; Walker et al., 2002).
In summary, it appears that discriminating a small differ-
ence (< 10%) in frequency is problematic for both adults and
children with dyslexia. The few studies reporting correla-
tions between the varied frequency discrimination measures
and different reading measures show somewhat contradic-
tory findings with the correlation showing up within either
the control or dyslexia group or across combined groups.
Processing of FM
Of the reviewed studies, 14 investigated FM detection in
individuals with dyslexia. As shown in Table 2, group per-
formance differs at slow FM rates (2–40 Hz) in 10 out of
the 11 studies. With fast modulation rates (≥ 60 Hz), group
differences are not statistically detectable (Adlard & Hazan,
1998; Ramus et al., 2003; Witton et al., 1998; Witton et al.,
2002). However, there are also studies deviating from this
pattern of findings. One study with school-aged children
reported statistically significant group differences at only a
40 Hz FM rate but not at 2 or 240 Hz rates (Dawes et al.,
2009). Another study testing FM detection at 2 and 240 Hz
rates found children with dyslexia to perform more poorly
at both rates compared to controls (C. M. Wright & Conlon,
2009). The latter finding of group difference at the 240 Hz
rate could be the result of the increased statistical power
given a very large sample size (N = 122).
Only one study examined FM processing with both
behavioral and ERP measures (Stoodley, Hill, Stein, &
Bishop, 2006). Group differences between adults with and
without dyslexia were found only in MMN amplitude. The
lack of any group difference at slow FM rates when behav-
ioral measures were used differs from the majority of the
other studies, but it should be noted that the participants
were university students reading at a normal level but below
that expected based on their other cognitive skills.
Eight studies report correlations between FM detection
thresholds and reading and/or spelling skills. Three studies
found that associations with either reading or spelling skills
were statistically not significant (Dawes et al., 2009; Heath
et al., 2006; Van Ingelghem et al., 2005). One study found a
significant, moderate correlation between MMN amplitude
to a deviant sound using 20 Hz FM rate and word identifica-
tion but not between behavioral measures of FM detection
and word identification (Stoodley et al., 2006). Associations
between FM detection thresholds and reading skills (word
and nonword reading; r = .21–.73, p < .05) were reported in
four studies (Gibson et al., 2006; Witton et al., 1998; Witton
et al., 2002; C. M. Wright & Conlon, 2009). It seems that
even though group differences in auditory processing
emerge systematically for slow FM rate thresholds, the evi-
dence on correlations with word and nonword reading is
conflicting.
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418 Journal of Learning Disabilities 46(5)
Processing of Sound Intensity
As shown in Table 3, only 2 of the 16 samples of studies
that investigated intensity processing in dyslexia reported a
significant group difference (Goswami et al., 2011;
Thomson, Fryer, Maltby, & Goswami, 2006). The sole ERP
study that examined MMN for a change in sound amplitude
(Kujala et al., 2006) did not find any statistically detectable
differences, which suggests that individuals with dyslexia
process sound intensity in the same way as their non-
dyslexic peers.
Three of the four studies that calculated correlations
between intensity discrimination and literacy skills found no
statistically significant associations with word reading or
spelling (Pasquini et al., 2007; Richardson et al., 2004;
Thomson et al., 2006). One study found statistically signifi-
cant associations between intensity discrimination threshold
and reading skills in English-speaking (r = .28) and Spanish-
speaking (r = .45) schoolchildren (Goswami et al., 2011).
Processing of AM
As reported in Table 4, 6 out of 8 studies that investigated
AM detection show that individuals with dyslexia have
higher discrimination thresholds, indicating poorer perfor-
mance, at least at some AM rates (typically at 10–160 Hz).
In addition, in ERP studies participants with dyslexia have
smaller AMFR, showing corroborating evidence (McAnally
& Stein, 1997; Menell, McAnally, & Stein, 1999). However,
even though group differences were found on measures of
AM perception, when examining the confidence intervals
in Table 4, it can be seen that they encompass zero, which
suggests that there is variation across the different condi-
tions used in the individual studies.
There are also some contradictory findings. For exam-
ple, Witton et al. (2002) found detection of AM at the 2 Hz
rate to be intact but impaired at the 20 Hz modulation rate in
adults with dyslexia. This is in line with results of most other
studies. In contrast, Stuart, McAnally, McKay, Johnston, and
Table 2. Effect Sizes and 95% Confidence Intervals (CIs) for the Differences Between Individuals With Typical Reading Skills (C;
controls) and With Reading Problems (RD) for Frequency Modulation Perception.
Study Age N (C/RD) Effect Size 95% CI Method and Significance Level
Studies with slow modulation rates (< 60 Hz)
Witton et al., 2002 25.4 y 21/17 1.2 0.55–1.87 FM detection threshold, 2 Hz (**)
Boets et al., 2007 7.3 y 28/9 0.9 0.07–1.65 FM detection threshold, 2 Hz (*)
Van Ingelghem et al., 2005 11.3 y 10/10 0.9 0.00–1.85 FM detection threshold, 2 Hz (*)
Gibson et al., 2006 9.8 y 44/44 0.8a0.39–1.23 FM detection threshold, 2 Hz (**)
Ramus et al., 2003 21.1 y 17/17 0.7 0.04–1.42 FM detection threshold, 2 Hz (*)
Witton et al., 1998 30.4 y 23/21 0.7b0.07–1.28 FM detection threshold, 2 Hz (***) & 40 Hz (*)
Dawes et al., 2009 9.8 y 20/19 0.6a−0.04–1.25 FM detection threshold, 2 Hz (ns) & 40 Hz (***)
Heath et al., 2006 36.3 y 41/49 0.6 0.14–0.98 FM detection threshold, 2 Hz (*)
Halliday & Bishop, 2006b 11.8 y 16/16 0.5a−0.24–1.19 FM detection threshold, 2 Hz & 20 Hz (ns)
C. M. Wright & Conlon, 2009 8.6 y 52/70 0.5 0.12–0.85 FM detection threshold, 2 Hz (**)
Stoodley et al., 2006 25.6 y 9/10 0.4 −0.55–1.36 FM detection threshold, 2 Hz & 20 Hz (ns),
MMN/LDN amplitude, 5Hz (ns), 20 Hz (*)
White et al., 2006 10.5 y 22/23 0.3 −0.34–0.86 FM detection threshold, 2 Hz (ns)
Total, FM —303/305 0.6 — —
Studies with fast modulation rates (≥ 60 Hz)
Ramus et al., 2003 21.1 y 17/17 0.6 −0.11–1.28 FM detection threshold, 240 Hz (ns)
C. M. Wright & Conlon, 2009 8.6 y 52/70 0.6 0.27–0.99 FM detection threshold, 240 Hz (**)
Dawes et al., 2009 9.8 y 20/19 0.5 −0.12–1.25 FM detection threshold, 240 Hz (*)
Witton et al., 2002 25.4 y 21/17 0.5 −0.16–1.16 FM detection threshold, 240 Hz (ns)
Adlard & Hazan, 1998 10.3 y 12/13 0.2 −0.67–0.96 Formant FM detection, 60–300 Hz (ns)
Stoodley et al., 2006 25.6 y 9/10 −0.1 −1.33–0.57 FM detection threshold, MMN, LDN, 240 Hz (ns)
Witton et al., 1998 30. 4 y 23/21 −0.3 −0.86–0.35 FM detection threshold, 240 Hz (ns)
No effect size calculated because of lack of data
Talcott et al., 2003 11.4 y 22/17 NA NA FM detection threshold, 2 Hz (*)
Note: The total effect size is weighted by sample size. Studies have been arranged according to effect size (in bold). Testing method (for event-related
potential studies: MMN = mismatch negativity; LDN = late discriminative negativity) and significance level of the group difference are also shown.
ns = not significant.
aMean and SD provided by the authors of the study.
bEffect size estimated from a figure.
*p < .05. **p < .01. ***p < .001.
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Hämäläinen et al. 419
Table 3. Effect Sizes and 95% Confidence Intervals (CIs) for the Differences Between Individuals With Typical Reading Skills (C;
controls) and With Reading Problems (RD) for Intensity Perception.
Study Age N (C/RD) Effect Size 95% CI Method and Significance Level
Goswami et al., 2011 (English) 10.5 y 27/44 0.9 0.43–1.41 Intensity discrimination threshold (*)
Goswami et al., 2011 (Spanish) 11.4 y 18/21 0.9 0.20–1.49 Intensity discrimination threshold (ns)
Nicolson et al., 1995 14.0 y 10/10 0.9a−0.03–1.82 Intensity discrimination threshold (ns)
Thomson et al., 2006 22.3 y 20/19 0.7 0.02–1.31 Intensity discrimination threshold (*)
Fraser et al., 2010 10.2 y 11/11 0.6 −0.28–1.47 Intensity discrimination threshold (ns)
Watson & Miller, 1993 24 y 54/24 0.5 −0.01–0.97 Intensity discrimination threshold (ns)
Pasquini et al., 2007 21.8 y 18/18 0.4 −0.25–1.10 Intensity discrimination threshold (ns)
Amitay, Ben-Yehudah, et al., 2002 21.5 y 30/30 0.3 −0.23–0.80 Intensity discrimination threshold (ns)
Banai & Ahissar, 2004 17–30 y 59/48 0.3a−0.08–0.68 Intensity discrimination threshold (ns)
Nicolson et al., 1995 18.2 y 12/12 0.3a−0.68–1.28 Intensity discrimination threshold (ns)
Richardson et al., 2004 8.8 y 24/24 0.3 −0.26–0.90 Intensity discrimination threshold (ns)
Kujala et al., 2006 33 y 11/9 0.2 −0.76–1.10 MMN amplitude, latency (ns)
Nicolson et al., 1995 9.3 y 9/9 0.2a−0.60–1.07 Intensity discrimination threshold (ns)
Thomson & Goswami, 2008 10.7 y 23/25 –0.1 −0.72–0.44 Intensity discrimination threshold (ns)
Watson, 1992 Adults 25/20 –0.2b−0.84–0.36 Intensity discrimination threshold (ns)
Rocheron et al., 2002 12.7 y 5/10 –0.5a−1.65–0.71 Intensity discrimination threshold (ns)
Total, intensity —356/334 0.5 — —
Note: The total effect size is weighted by sample size. Studies have been arranged according to effect size (in bold). Testing method (for event-related
potential studies: MMN = mismatch negativity) and significance level of the group difference are also shown. ns = not significant.
aMean and SD provided by the authors of the study.
bEffect size estimated from a figure.
*p < .05. **p < .01. ***p < .001.
Table 4. Effect Sizes and 95% Confidence Intervals (CIs) for the Differences Between Individuals With Typical Reading Skills (C;
controls) and With Reading Problems (RD) for Amplitude Modulation Perception.
Study Age N (C/RD) Effect Size 95% CI Method and Significance Level
Rocheron et al., 2002 12.7 y 5/10 1.1a−0.09–2.26 AM detection threshold, 4 Hz & 128 Hz (*)
McAnally & Stein, 1997 27.7 y 15/15 0.6a−0.15–1.33 AMFR amplitude, 20–80 Hz (*)
Menell et al., 1999 27.6 y 20/20 0.6a−0.04–1.23 AM detection threshold, 10–160 Hz (**), AMFR amplitude,
10–160 Hz (*)
Witton et al., 1998 25.4 y 21/17 0.5 −0.14–1.17 AM detection threshold, 2 Hz (ns), 20 Hz (*)
Hämäläinen et al., 2009 9.0 y 30/30 0.4 −0.15–0.87 AM detection threshold, 20 Hz (ns)
Total, AM —91/92 0.5 — —
No effect size calculated because of small sample size or lack of data
Amitay, Ahissar, et al.,
2002
22 y 27/23 NA NA AM detection threshold, 4–500 Hz (ns)
Lorenzi et al., 2000 10.5 y 6/6 NA NA AM detection threshold, 4 Hz (***), 1024 Hz (*), 16–256 Hz (ns)
Stuart et al., 2006 35.5 y 18/13 NA NA AM detection threshold, 1 Hz (**) & 100 Hz (ns)
Note: The total effect size is weighted by sample size. Studies have been arranged according to effect size (in bold). Testing method (for event-related
potential studies: AMFR = amplitude modulation following response) and significance level of the group difference are also shown. ns = not significant.
aMean and SD provided by the authors of the study.
*p < .05. **p < .01. ***p < .001.
Castles (2006) found that detection at the 1 Hz AM rate was
impaired and at the 100 Hz AM rate intact in adults with
dyslexia. The latter finding appears contradictory but may
be the result of different methods used for measuring AM
perception. In the Witton et al. study, the participants indi-
cated which of the two separate sounds was amplitude mod-
ulated. In the Stuart et al. study, the participants listened to
a single 4 s tone and had to decide when they heard AM in
the tone, during either the 2nd or 3rd second of the tone.
Also, Hämäläinen et al. (2009) found no group differences
in schoolchildren with and without dyslexia for 20 Hz AM
detection scores.
Only four of the studies examined associations between
AM detection and different reading measures (including
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420 Journal of Learning Disabilities 46(5)
word and nonword reading and a composite of different
reading measures). In three studies, a moderate correlation
(r = .39–.48, p < .05) was reported across the groups (Menell
et al., 1999; Stuart et al., 2006; Witton et al., 2002). In one
study no statistically significant associations were found
(Hämäläinen et al., 2009). Overall, it appears that group dif-
ferences are rather consistently found with AM detection
tasks, particularly with moderate or fast modulation rates
(at or above 4 Hz). Variation in AM processing also seems
to be associated with differences in a diverse set of reading
skills.
Processing of Sound Rise Time
As reported in Table 5, group differences were found in
sound rise time processing in the 11 samples studied. ERP
studies also show group differences in N1/MMN and LDN
responses. N1 has been found to be less sensitive to differ-
ent rise times in children with dyslexia compared to con-
trols (Hämäläinen, Leppänen, Guttorm, & Lyytinen, 2007).
In one study, MMN was larger in children with dyslexia to
rise time change compared to control children (Hämäläinen
et al., 2008). In contrast, LDN was smaller in response to a
change in sound rise time in the same study in children with
dyslexia. It is interesting that in one study, despite the group
difference found in ERPs, the same children did not show
behavioral group differences in rise time discrimination
(Hämäläinen et al., 2009).
In the 10 samples studied, significant correlations between
rise time perception and word and nonword reading, spelling,
and masked word recognition and nonword or word choice
performance across the reading groups were found (r = .28–
.60; Fraser, Goswami, & Conti-Ramsden, 2010; Goswami
et al., 2002; Goswami et al., 2011; Hämäläinen, Leppänen,
Torppa, Muller, & Lyytinen, 2005; Muneaux, Ziegler, Truc,
Thomson, & Goswami, 2004; Pasquini et al., 2007;
Richardson et al., 2004; Thomson et al., 2006; Thomson &
Goswami, 2008). One study that did not find group differ-
ences in rise time perception reported a statistically signifi-
cant correlation between rise time discrimination and spelling
(r = .39), but only in the children with reading problems
(Hämäläinen et al., 2009).
Processing of Sound Duration
As shown in Table 6, statistically detectable differences in
the performance of individuals with and without dyslexia
were found in 8 of 12 study samples. Two MMN studies
found significant differences (Corbera et al., 2006; Huttunen,
Halonen, Kaartinen, & Lyytinen, 2007), whereas two MMN
studies found no statistically detectable differences between
those with dyslexia and controls (Baldeweg et al., 1999;
Kujala et al., 2006). The only stimulation parameter explain-
ing the difference between study findings could be faster
stimulation rate (onset-to-onset interval of 100–300 ms vs.
500–700 ms) in the studies that found group differences.
Only four studies examined correlations between liter-
acy skills and duration processing. Two behavioral studies
found a significant association between duration discrimi-
nation threshold and word reading (r = .36–.42, p < .05;
Thomson et al., 2006; Thomson & Goswami, 2008). In
addition, one study found no statistically significant asso-
ciation between word reading and duration discrimination
thresholds in English speakers but did find an association in
Spanish speakers (Goswami et al., 2011). In contrast, the
association between MMN amplitude or latency and word
and nonword reading skills was not statistically detectable
(Baldeweg et al., 1999).
Processing of Gap Duration
As shown in Table 7, 9 articles with 10 different samples
investigated processing of gap detection among individuals
with dyslexia. Of these study samples, 7 showed no group
differences between those with dyslexia and controls.
However, gap detection thresholds may vary as children grow
older and mature. In a cross-sectional study using small
sample sizes, Hautus, Setchell, Waldie, and Kirk (2003)
found gap detection thresholds to be elevated in 6- and
8-year-olds but not in 10-, 12-, or 25-year-olds with dyslexia.
Two studies found group differences also in 10- and 11-year-
olds (Sharma et al., 2006; Van Ingelghem et al., 2001). In
contrast, findings from a longitudinal study conducted by
Boets, Wouters, van Wieringen, and Ghesquiere (2007)
showed no group differences in gap detection at 5 years in
children who were classified as either poor or typical readers
based on a composite of their word, pseudoword, and non-
word reading and word spelling skills at 7 years. In addition,
two other studies with 10- to 12-year-old children found no
statistically detectable group differences (Adlard & Hazan,
1998; Schulte-Körne, Deimel, Bartling, & Remschmidt,
1998b); similarly, three studies with adults found no statisti-
cally detectable group differences in gap detection perfor-
mance (King, Lombardino, Crandell, & Leonard, 2003;
McAnally & Stein, 1996; Schulte-Körne et al., 1998b). In
the only ERP study, group differences were found to be not
statistically detectable for MMN to a gap deviancy in adults
with and without dyslexia (Kujala et al., 2006).
Of the four studies that examined correlation between
gap detection thresholds and literacy measures, two studies
did not find any associations with word reading or spelling
(King et al., 2003; Schulte-Körne et al., 1998b). Two stud-
ies that also showed group differences showed a significant
correlation between gap detection and word and pseudo-
word reading skills (words: r = .60, nonwords: r = .57, p <
.05; Van Ingelghem et al., 2001) and gap detection and
nonword reading skills (r = .38, p < .05; Sharma et al.,
2006). Overall, the majority of the studies did not find gap
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Hämäläinen et al. 421
Table 5. Effect Sizes and 95% Confidence Intervals (CIs) for the Differences Between Individuals With Typical Reading Skills (C;
controls) and With Reading Problems (RD) for Rise Time Perception.
Study Age N (C/RD) Effect Size 95% CI Method and Significance Level
Goswami et al., 2002 9.0 y 25/24 1.4 0.85–1.99 5-ramp rise time discrimination threshold (***)
Hämäläinen et al., 2005 37 y 13/19 1.2 0.48–1.94 Rise time detection in paired tones (**)
Fraser et al., 2010 10.4 y 11/11 1.1 0.23–1.98 2-ramp (**) and 1-ramp (ns) rise time
discrimination threshold
Muneaux et al., 2004 11.4 y 20/18 1.1 0.45–1.76 5-ramp rise time discrimination threshold (***)
Thomson et al., 2006 22.3 y 20/19 1.0 0.36–1.65 2-ramp (**) and 1-ramp (*) rise time
discrimination threshold
Goswami et al., 2011 (English) 10.5 y 27/44 0.8 0.35–1.33 2-ramp (**) and 1-ramp (***) rise time
discrimination threshold
Hämäläinen et al., 2008 9.3 y 25/21 0.7 0.14–1.33 LDN to rise time change in paired tones (*)
Richardson et al., 2004 8.8 y 24/24 0.7 0.16–1.31 2-ramp (**) & 1-ramp (*) rise time discrimination
threshold
Thomson & Goswami, 2008 10.8 y 23/25 0.7 0.15–1.31 Multiple-ramp (*) and 1-ramp (**) rise time
discrimination threshold
Goswami et al., 2011 (Spanish) 11.4 y 18/21 0.5 −0.15–1.14 2-ramp (ns) and 1-ramp (*) rise time
discrimination threshold
Pasquini et al., 2007 21.8 y 18/18 0.5 −0.15–1.19 5-ramp (*), 2-ramp (ns) and 1-ramp (ns) rise time
discrimination threshold
Hämäläinen et al., 2009 9.0 y 21/26 0.2 −0.18–0.58 2-ramp and 1-ramp rise time discrimination
threshold (ns); same sample of children as in
Hämäläinen et al., 2008
Total, rise time —234/244 0.8 — —
Note: The total effect size is weighted by sample size. Studies have been arranged according to effect size (in bold). Testing method (for event-related
potential studies: LDN = late discriminative negativity) and significance level of the group difference are also shown. ns = not significant.
*p < .05. **p < .01. ***p < .001.
Table 6. Effect Sizes and 95% Confidence Intervals (CIs) for the Differences Between Individuals With Typical Reading Skills
(C; controls) and With Reading Problems (RD) for Duration Perception.
Study Age N (C/RD) Effect Size 95% CI Method and Significance Level
Thomson & Goswami, 2008 10.8 y 23/25 1.4 0.81–1.97 Duration discrimination threshold (*)
Goswami et al., 2011 (Spanish) 11.4 y 18/21 1.1 0.46–1.75 Duration discrimination threshold (**)
Thomson et al., 2006 22.4 y 20/19 1.0 0.39–1.68 Duration discrimination threshold (**)
Banai & Ahissar, 2004 17–30 y 59/48 0.9a0.48–1.25 Duration discrimination threshold for 100 ms (***)
and 1,000 ms (***) sounds
Banai & Ahissar, 2006 13.1 y 12/22 0.9a0.13–1.59 Duration discrimination threshold for 100 ms (*)
and 400 ms (**) sounds
Watson & Miller, 1993 24 y 54/24 0.8 0.27–1.25 Duration discrimination (ns at α = .004)
Goswami et al., 2011 (English) 10.5 y 27/44 0.6 0.15–1.12 Duration discrimination threshold (*)
Kujala et al., 2006 33 y 11/9 0.5 −0.43–1.43 MMN amplitude, latency (ns)
Baldeweg et al., 1999 33.4 y 10/10 0.3 −0.59–1.26 MMN amplitude, latency and behavioral accuracy (ns)
Total, duration —234/222 0.9 — —
No effect size calculated because of small sample size or lack of data
Corbera et al., 2006 11.6 y 11/13 NA NA MMN amplitude (*), latency (***)
Huttunen et al., 2007 11.8 y 21/21 NA NA MMN amplitude, hemisphere × group interaction (*)
Watson, 1992 Adults 25/20 3.5bNA Duration discrimination (*), excluded as an outlier
value
Note: The total effect size is weighted by sample size. Studies have been arranged according to effect size (in bold). Testing method (for event-related
potential studies: MMN = mismatch negativity) and significance level of the group difference are also shown. ns = not significant.
aMean and SD provided by the authors of the study.
bEffect size estimated from a figure.
*p < .05. ** p < .01. *** p < .001.
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422 Journal of Learning Disabilities 46(5)
detection differences that were statistically significant
between dyslexic and typical readers, and the reported cor-
relations were found only in studies also showing group
differences.
Effect Size Summary and Variability Across
Different Auditory Features
A funnel plot was drawn using the effect sizes from all
studies. Funnel plots are used to examine the possibility of
publication bias: It is possible that only those studies using
small sample sizes but demonstrating large effects are pub-
lished. However, as can be seen from Figure 1, the plot
showed a triangle-like distribution of the effect sizes; that
is, the smaller studies had both smaller and larger effect
sizes compared to those of the studies with larger sample
sizes. This indicates that there is no evidence for publica-
tion bias in the reviewed studies.
To answer the question of the prevalence of auditory
deficits in dyslexia, an average of the sample-size-weighted
effect sizes was calculated across the different sound fea-
tures and study samples. This average effect size indicates
that approximately 38% to 43% of the distributions for con-
trol and dyslexia groups do not overlap (i.e., effect size of
0.65 on average, calculated across individual studies). This
is close to a previous estimation, according to which 39% of
individuals with dyslexia show auditory processing deficits
(Ramus, 2003). However, for some sound features the mean
sample-size-weighted effect sizes are larger. Table 8 shows
that for duration perception, the effect size is 0.9 (52% non-
overlap), for rise time perception 0.8 (47% nonoverlap), for
frequency perception 0.7 (43% nonoverlap), for FM
perception 0.6 (38% nonoverlap), and for intensity, AM,
and gap perception 0.5 (33% nonoverlap).
It is often the case that the task performance in the dys-
lexia samples has more variation than that of typical read-
ers, indicating possible existence of subgroups or other
confounding factors such as attention problems. As Table 8
Table 7. Effect Sizes and 95% Confidence Intervals (CIs) for the Differences Between Individuals With Typical Reading Skills (C;
controls) and With Reading Problems (RD) for Gap Perception.
Study Age N (C/RD) Effect Size 95% CI Method and Significance Level
Van Ingelghem et al., 2001 11.3 y 10/10 1.5 0.53–2.38 Gap detection threshold (**)
Sharma et al., 2006 10.3 y 19/15 0.9a0.17–1.57 Threshold when two sound heard as one (**)
Adlard & Hazan, 1998 10.3 y 12/13 0.6 −0.18–1.45 Gap detection (ns)
Boets et al., 2007 7.3 y 28/9 0.6 −0.22–1.36 Gap detection threshold (ns)
King et al., 2003 24.4 y 14/11 0.6 −0.23–1.42 Gap detection threshold (ns)
McAnally & Stein, 1997 28.0 y 26/23 0.4a−0.15–0.89 Gap detection threshold (ns)
Schulte-Körne et al., 1998b 12.5 y 14/15 0.4 −0.41–1.10 Gap detection threshold (ns)
Kujala et al., 2006 33 y 11/9 0.2 −0.77–1.09 MMN amplitude, latency (ns)
Schulte-Körne et al., 1998b 27.5 y 22/9 0.2 −0.61–1.02 Gap detection threshold (ns)
Total, gap —156/114 0.6 — —
No effect size calculated because of small sample size
Hautus et al., 2003 6.1–25.4 y 6–11/4–6 NA NA Gap detection threshold for 6–8 y (*), 10–25 y (ns)
Note: The total effect size is weighted by sample size. Studies have been arranged according to effect size (in bold). Testing method (for event-related
potential studies: MMN = mismatch negativity) and significance level of the group difference are also shown. ns = not significant.
aMean and SD provided by the authors of the study.
*p < .05. ** p < .01. *** p < .001.
Figure 1. Funnel plot showing the effect size and sample size of
all reviewed studies.
Filled circles are studies examining frequency perception, open
circles frequency modulation, filled diamonds intensity, open
diamonds amplitude modulation, filled rectangles rise time, open
triangles duration, and open squares gap detection.
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Hämäläinen et al. 423
shows, the variability in performance of participants with
dyslexia is 1.4 to 2.5 times greater compared to that of
controls (variability in the dyslexic groups divided by that
of controls). It seems that the same auditory features show-
ing the largest effect sizes (frequency, FM, duration) also
show the largest variability between groups (1.8, 2.1, 1.8,
respectively). An exception to this seems to be rise time
perception, where the variability in individuals with dys-
lexia compared to controls was only 1.4 times greater. The
larger variability in general could indicate that a subpopula-
tion of individuals with dyslexia have problems in process-
ing AM and FM as well as duration and frequency. However,
perception of gaps and intensity show less variability between
groups (1.5 and 1.4, respectively). Although the performance
of individuals with dyslexia does demonstrate more variabil-
ity in these tasks as well, the variability is greater in those
tasks showing group differences. This suggests that the
increased variability is the result of dyslexia subgroups
instead of, for example, general attention problems that
would affect all tasks equally.
Discussion
The present review examined the nature of auditory process-
ing deficits in children and adults with dyslexia. Overall,
statistically detectable differences in auditory processing
between groups of dyslexic and typical readers were
reported on measures of duration, rise time, and slow FM
rates. On average, children and adults with dyslexia also
appear to have more difficulty processing small differences
in sound frequency and the AM perception. In most of the
reviewed studies, perceiving a gap between sounds as well
as the intensity of sounds was found to be typical in indi-
viduals with dyslexia.
For FM and frequency perception, a pattern seems to
emerge: Group differences are found at slower FM rates
(less than 60 Hz) and for smaller frequency differences
(10% or smaller). This is in line with the observation made
by Bishop (2007) that in MMN studies group differences
between language-learning-impaired individuals and con-
trols are found only when small frequency differences need
to be detected. For AM perception, group differences are
observed, interestingly, at high modulation rates (10–320
Hz; except for one study), whereas at slow modulation rates
(1–4 Hz) the results are less consistent.
One explanation for the different findings at fast and
slow AM and FM rates could be related to the perception of
frequencies and intensity in general. It has been suggested
that FM perception at slow modulation rates relies on fre-
quency perception (Moore, 2003). This would explain why
reader group differences are seen at the slow FM rates. On
the other hand, in the present review perception of intensity
was found to be intact in individuals with dyslexia in most
studies, and thus AM detection at slow rates, possibly
related to the perception of intensity (Moore, 2003), does
not show consistent group differences.
Of the reviewed studies, 17 used ERP or ERF measures.
Results obtained with ERP or ERF measures were mainly in
line with those obtained from behavioral studies. However,
with ERPs it is possible to examine the time course of audi-
tory processing and to try to find which auditory processing
stages are different in individuals with dyslexia. Response
to oscillations of AM stimuli (AMFR) showed diminished
amplitudes in participants with dyslexia relative to controls
(McAnally & Stein, 1997; Menell et al., 1999), which sug-
gests that basic auditory processing problems can exist at
the thalamic or early cortical level. Participants with dys-
lexia also have atypical processing of changes in sound
streams, indicating poorer discrimination at preattentive
levels in ERP or ERF studies. This is manifested in differ-
ences in MMN amplitude and latency (e.g., Baldeweg et al.,
1999; Corbera et al., 2006; Hugdahl et al., 1998; Kujala
et al., 2003; Kujala et al., 2006; Lachmann et al., 2005). In
addition to the atypical change detection response, LDN,
Table 8. Numbers of Study Samples in Which Auditory Processing in Individuals With Dyslexia Was Investigated and the Number (and
percentage) of Study Samples That Showed Group Differences Between Individuals With Dyslexia and Typical Reading Skills.
FrequencyaFMbIntensity AM Rise Time Duration Gap
Number of studies 25 13 16 8 11 12 10
Group difference found (n/%) 19/76% 12/92% 2/13% 6/75% 11/100% 9/75% 3/30%
Weighted mean effect size 0.7 (43%) 0.6 (38%) 0.5 (33%) 0.5 (33%) 0.8 (47%) 0.9 (52%)c0.5 (33%)
SD of RD/SD of C 1.8 2.1 1.5 2.5 1.4 1.8 1.4
Note: Weighted (by sample size) mean effect size (Cohen’s d) (in parentheses is the percentage of nonoverlap between the two groups’ distributions)
and the standard deviation of dyslexia samples divided by that of control samples. C = controls; RD = participants with reading disability.
aFive studies using large frequency differences between standard and deviant sound excluded (Hämäläinen et al., 2008; Kujala et al., 2003; Meng et al.,
2005; Schulte-Körne et al., 2001; Sharma et al., 2006); see Table 1.
bStudies and conditions using FM rates faster than or equal to 60 Hz excluded; see Table 2.
cOutlying value (3.5) of Watson (1992) removed.
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424 Journal of Learning Disabilities 46(5)
reflecting further processing of stimulus difference, has also
been reported to be smaller in individuals at risk for and
with dyslexia (Hämäläinen et al., 2008; Maurer et al., 2003).
However, there are inconsistencies in findings on group
differences also when brain responses are examined. The
seemingly contradictory findings could be related to several
confounding factors such as variation in the severity of the
reading problems and heterogeneity of the neural origins of
dyslexia. These include the question of whether a specific
subpopulation of individuals with dyslexia is more likely to
have auditory processing deficits, as hinted by the increased
variability in auditory task performance.
Most of the reviewed studies were carried out with adults
or school-aged children. In these samples, auditory deficits
did not seem to diminish as a function of age (see Tables
1–7) as predicted by the hypothesis of a maturational lag in
children with LLI (McArthur & Bishop, 2004; B. A. Wright
& Zecker, 2004). However, the development of auditory
skills in younger children at risk for dyslexia is mainly
uncharted territory. It has been suggested that auditory pro-
cessing deficits present at birth are ameliorated in later
infancy and childhood, possibly obscuring the effects of
these early processing anomalies on the development of
neural networks that lead to reading impairment (Galaburda
et al., 2006). For instance, infants at risk for familial dys-
lexia have been shown to differ in their brain responses to
speech sounds, and variation in these responses is related to
differences in later reading related language skills (Guttorm
et al., 2005; Guttorm, Leppänen, Richardson, & Lyytinen,
2001; Leppänen et al., 2002; Leppänen, Pihko, Eklund, &
Lyytinen, 1999; Pihko et al., 1999). Further longitudinal
studies are needed to investigate the effect of brain develop-
ment on basic auditory and speech processing skills over
extended periods of time.
The studies in this review showed that at least a sub-
group of individuals with dyslexia have auditory processing
problems in dynamic and speech prosody-related sound
features (FM, AM, rise time, duration) as well as in percep-
tion of sound frequency. However, the anomalous process-
ing of these and other sound features by individuals with
dyslexia does not necessarily indicate causal connections,
and the significance of these deficits in the development of
dyslexia remains an open question.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) disclosed receipt of the following financial support
for the research and/or authorship of this article: This study was
supported by grants from the Academy of Finland (127277,
44858, 213486).
References
Adlard, A., & Hazan, V. (1998). Speech perception in children
with specific reading difficulties (dyslexia). Quarterly Journal
of Experimental Psychology A: Human Experimental Psychol-
ogy, 51, 153–177.
Ahissar, M., Lubin, Y., Putter-Katz, H., & Banai, K. (2006). Dyslexia
and the failure to form a perceptual anchor. Nature Neuroscience,
9, 1558–1564.
Amitay, S., Ahissar, M., & Nelken, I. (2002). Auditory processing
deficits in reading disabled adults. Journal of the Association
for Research in Otolaryngology, 3, 302–320.
Amitay, S., Ben-Yehudah, G., Banai, K., & Ahissar, M. (2002).
Disabled readers suffer from visual and auditory impairments
but not from a specific magnocellular deficit. Brain: A Journal
of Neurology, 125, 2272–2284.
Baldeweg, T., Richardson, A., Watkins, S., Foale, C., &
Gruzelier, J. (1999). Impaired auditory frequency discrimina-
tion in dyslexia detected with mismatch evoked potentials.
Annals of Neurology, 45, 495–503.
Banai, K., & Ahissar, M. (2004). Poor frequency discrimination
probes dyslexics with particularly impaired working memory.
Audiology & Neurotology, 9, 328–340.
Banai, K., & Ahissar, M. (2006). Auditory processing deficits in dys-
lexia: Task or stimulus related? Cerebral Cortex, 16, 1718–1728.
Bishop, D. V. M. (2007). Using mismatch negativity to study
central auditory processing in developmental language and
literacy impairments: Where are we, and where should we be
going? Psychological Bulletin, 133, 651–672.
Bishop, D. V. M., Carlyon, R. P., Deeks, J. M., & Bishop, S. J.
(1999). Auditory temporal processing impairment: Neither
necessary nor sufficient for causing language impairment in
children. Journal of Speech, Language and Hearing Research,
42, 1295–1310.
Boets, B., Wouters, J., van Wieringen, A., & Ghesquiere, P.
(2007). Auditory processing, speech perception and phonolog-
ical ability in pre-school children at high-risk for dyslexia: A
longitudinal study of the auditory temporal processing theory.
Neuropsychologia, 45, 1608–1620.
Bradley, L., & Bryant, P. (1983). Categorizing sounds and learning
to read: A causal connection. Nature, 301, 419–421.
Cheour, M., Korpilahti, P., Martynova, O., & Lang, A. H. (2001).
Mismatch negativity and late discriminative negativity in
investigating speech perception and learning in children and
infants. Audiology & Neuro-Otology, 6, 2–11.
Corbera, S., Escera, C., & Artigas, J. (2006). Impaired duration
mismatch negativity in developmental dyslexia. Neuroreport,
17, 1051–1055.
Dawes, P., Sirimanna, T., Burton, M., Vanniasegaram, I., Tweedy, F.,
& Bishop, D. V. M. (2009). Temporal auditory and visual
motion processing of children diagnosed with auditory pro-
cessing disorder and dyslexia. Ear & Hearing, 30, 675–686.
Démonet, J. F., Taylor, M. J., & Chaix, Y. (2004). Developmental
dyslexia. Lancet, 363, 1451–1460.
at Jyvaskylan Yliopisto on March 16, 2014ldx.sagepub.comDownloaded from

Hämäläinen et al. 425
Farmer, M. E., & Klein, R. M. (1995). The evidence for a temporal
processing deficit linked to dyslexia: A review. Psychonomic
Bulletin & Review, 2, 460–493.
France, S. J., Rosner, B. S., Hansen, P. C., Calvin, C., Talcott, J. B.,
Richardson, A. J., & Stein, J. F. (2002). Auditory frequency
discrimination in adult developmental dyslexics. Perception &
Psychophysics, 64, 169–179.
Fraser, J., Goswami, U., & Conti-Ramsden, G. (2010). Dyslexia
and specific language impairment: The role of phonology
and auditory processing. Scientific Studies of Reading, 14,
8–29.
Galaburda, A. M., Loturco, J., Ramus, F., Fitch, R. H., & Rosen, G. D.
(2006). From genes to behavior in developmental dyslexia.
Nature Neuroscience, 9, 1213–1217.
Gibson, L. Y., Hogben, J. H., & Fletcher, J. (2006). Visual and
auditory processing and component reading skills in develop-
mental dyslexia. Cognitive Neurospsychology, 23, 621–642.
Goswami, U., Thomson, J., Richardson, U., Stainthorp, R.,
Hughes, D., Rosen, S., & Scott, S. K. (2002). Amplitude
envelope onsets and developmental dyslexia: A new hypoth-
esis. Proceedings of the National Academy of Sciences of the
United States of America, 99, 10911–10916.
Goswami, U., Wang, H.-L. S., Cruz, A., Fosker, T., Mead, N.,
& Huss, M. (2011). Language-universal sensory deficits in
developmental dyslexia: English, Spanish, and Chinese. Jour-
nal of Cognitive Neuroscience, 23, 325–337.
Guttorm, T. K., Leppänen, P. H. T., Poikkeus, A.-M., Eklund, K. M.,
Lyytinen, P., & Lyytinen, H. (2005). Brain event-related poten-
tials (ERPs) measured at birth predict later language develop-
ment in children with and without familial risk for dyslexia.
Cortex, 41, 291–303.
Guttorm, T. K., Leppänen, P. H. T., Richardson, U., & Lyytinen, H.
(2001). Event-related potentials and consonant differentiation
in newborns with familial risk for dyslexia. Journal of Learn-
ing Disabilities, 34, 534–544.
Halliday, L. F., & Bishop, D. V. M. (2006a). Auditory frequency
discrimination in children with dyslexia. Journal of Research
in Reading, 29, 213–228.
Halliday, L. F., & Bishop, D. V. M. (2006b). Is poor frequency
modulation detection linked to literacy problems? A compari-
son of specific reading disability and mild to moderate senso-
rineural hearing loss. Brain and Language, 97, 200–213.
Hämäläinen, J. A., Leppänen, P. H. T., Eklund, K., Thomson, J.,
Richardson, U., Guttorm, T. K., . . . Lyytinen, H. (2009). Com-
mon variance in amplitude envelope perception tasks and their
impact on phoneme duration perception and reading and spell-
ing in Finnish children with reading disabilities. Applied Psy-
cholinguistics, 30, 511–530.
Hämäläinen, J. A., Leppänen, P. H. T., Guttorm, T. K., & Lyytinen, H.
(2007). N1 and P2 components of auditory event-related poten-
tials in children with and without reading disabilities. Clinical
Neurophysiology, 118, 2263–2275.
Hämäläinen, J. A., Leppänen, P. H. T., Guttorm, T. K., & Lyytinen, H.
(2008). Event-related potentials to pitch and rise time change
in children with reading disabilities and typically reading chil-
dren. Clinical Neurophysiology, 119, 100–115.
Hämäläinen, J., Leppänen, P. H. T., Torppa, M., Muller, K., &
Lyytinen, H. (2005). Detection of sound rise time by adults
with dyslexia. Brain and Language, 94, 32–42.
Hautus, M. J., Setchell, G. J., Waldie, K. E., & Kirk, I. J. (2003).
Age-related improvements in auditory temporal resolution in
reading-impaired children. Dyslexia: An International Journal
of Research and Practice, 9, 37–45.
Heath, S. M., Bishop, D. V. M., Hogben, J. H., & Roach, N. W.
(2006). Psychophysical indices of perceptual functioning in
dyslexia: A psychometric analysis. Cognitive Neuropsychol-
ogy, 23, 905–929.
Hugdahl, K., Heiervang, E., Nordby, H., Smievoll, A. I., Steinmetz, H.,
Stevenson, J., & Lund, A. (1998). Central auditory process-
ing, MRI morphometry and brain laterality: Applications to
dyslexia. Scandinavian Audiology, 27, 26–34.
Huttunen, T., Halonen, A., Kaartinen, J., & Lyytinen, H. (2007).
Does mismatch negativity show differences in reading-disabled
children compared to normal children and children with atten-
tion deficit? Developmental Neuropsychology, 31, 453–470.
King, W. M., Lombardino, L. J., Crandell, C. C., & Leonard, C. M.
(2003). Comorbid auditory processing disorder in develop-
mental dyslexia. Ear & Hearing, 24, 448–456.
Kujala, T., Belitz, S., Tervaniemi, M., & Näätänen, R. (2003).
Auditory sensory memory disorder in dyslexic adults as
indexed by the mismatch negativity. European Journal of
Neuroscience, 17, 1323–1327.
Kujala, T., Lovio, R., Lepistö, T., Laasonen, M., & Näätänen, R.
(2006). Evaluation of multi-attribute auditory discrimination
in dyslexia with the mismatch negativity. Clinical Neurophysi-
ology, 117, 885–893.
Lachmann, T., Berti, S., Kujala, T., & Schröger, E. (2005). Diag-
nostic subgroups of developmental dyslexia have different
deficits in neural processing of tones and phonemes. Interna-
tional Journal of Psychophysiology, 56, 105–120.
Leppänen, P. H. T., Pihko, E., Eklund, K. M., & Lyytinen, H.
(1999). Cortical responses of infants with and without a
genetic risk for dyslexia: II. Group effects. Neuroreport, 10,
969–973.
Leppänen, P. H. T., Richardson, U., Pihko, E., Eklund, K. M.,
Guttorm, T. K., & Aro, M. (2002). Brain responses to changes
in speech sound durations differ between infants with and
without familial risk for dyslexia. Developmental Neuropsy-
chology, 22, 407–422.
Lorenzi, C., Dumont, A., & Füllgrabe, C. (2000). Use of tempo-
ral envelope cues by children with developmental dyslexia.
Journal of Speech, Language and Hearing Research, 43,
1367–1379.
Lyon, G. R., Shaywitz, S. E., & Shaywitz, B. A. (2003). A defini-
tion of dyslexia. Annals of Dyslexia, 53, 1–14.
Maurer, U. B., Bucher, K., Brem, S., & Brandeis, D. (2003).
Altered responses to tone and phoneme mismatch in kinder-
gartners at familial dyslexia risk. Neuroreport, 14, 2245–2250.
at Jyvaskylan Yliopisto on March 16, 2014ldx.sagepub.comDownloaded from

426 Journal of Learning Disabilities 46(5)
McAnally, K. I., & Stein, J. F. (1996). Auditory temporal coding in
dyslexia. Proceedings of the Royal Society of London. Series
B: Biological Sciences, 263, 961–965.
McAnally, K. I., & Stein, J. F. (1997). Scalp potentials evoked
by amplitude-modulated tones in dyslexia. Journal of Speech,
Language, and Hearing Research, 40, 939–945.
McArthur, G. M., & Bishop, D. V. (2001). Auditory perceptual
processing in people with reading and oral language impair-
ments: Current issues and recommendations. Dyslexia: The
Journal of the British Dyslexia Association, 7, 150–170.
McArthur, G. M., & Bishop, D. V. M. (2004). Which people with
specific language impairment have auditory processing defi-
cits? Cognitive Neuropsychology, 21, 79–94.
McArthur, G. M., Ellis, D., Atkinson, C. M., & Coltheart, M.
(2008). Auditory processing deficits in children with read-
ing and language impairments: Can they (and should they) be
treated? Cognition, 107, 946–977.
Menell, P., McAnally, K. I., & Stein, J. F. (1999). Psychophysical
sensitivity and physiological response to amplitude modula-
tion in adult dyslexic listeners. Journal of Speech, Language,
and Hearing Research, 42, 797–803.
Meng, X., Sai, X., Wang, C., Wang, J., Sha, S., & Zhou, X. (2005).
Auditory and speech processing and reading development in
Chinese school children: Behavioural and ERP evidence. Dys-
lexia: An International Journal of Research and Practice, 11,
292–310.
Moore, B. J. C. (2003). An introduction to the psychology of hear-
ing. San Diego, CA: Academic Press.
Muneaux, M., Ziegler, J. C., Truc, C., Thomson, J., & Goswami,
U. (2004). Deficits in beat perception and dyslexia: Evidence
from French. Neuroreport, 15, 1255–1259.
Näätänen, R. (1992). Attention and brain function. Hillsdale, NJ:
Lawrence Erlbaum.
Näätänen, R., & Alho, K. (1997). Mismatch negativity—The mea-
sure for central sound representation accuracy. Audiology &
Neuro-Otology, 2, 341–353.
Näätänen, R., & Picton, T. W. (1987). The N1 wave of the human
electric and magnetic response to sound: A review and an
analysis of the component structure. Psychophysiology, 24,
375–425.
Nicolson, R. I., Fawcett, A. J., & Dean, P. (1995). Time estima-
tion deficits in developmental dyslexia: Evidence of cerebel-
lar involvement. Proceedings of the Royal Society of London.
Series B: Biological Sciences, 259, 43–47.
Pasquini, E. S., Corriveau, K. H., & Goswami, U. (2007). Audi-
tory processing of amplitude envelope rise time in adults
diagnosed with developmental dyslexia. Scientific Studies of
Reading, 11, 259–286.
Pihko, E., Leppänen, P. H. T., Eklund, K. M., Cheour, M.,
Guttorm, T. K., & Lyytinen, H. (1999). Cortical responses of
infants with and without a genetic risk for dyslexia: I. Age
effects. Neuroreport, 10, 901–905.
Ramus, F. (2003). Developmental dyslexia: Specific phonological
deficit or general sensorimotor dysfunction? Current Opinion
in Neurobiology, 13, 212–218.
Ramus, F., Rosen, S., Dakin, S. C., Day, B. L., Castellote, J. M.,
White, S., & Frith, U. (2003). Theories of developmental dys-
lexia: Insights from a multiple case study of dyslexic adults.
Brain, 126, 841–865.
Renvall, H., & Hari, R. (2003). Diminished auditory mismatch
fields in dyslexic adults. Annals of Neurology, 53, 551–557.
Richardson, U., Thomson, J. M., Scott, S. K., & Goswami, U.
(2004). Auditory processing skills and phonological represen-
tation in dyslexic children. Dyslexia: An International Journal
of Research and Practice, 10, 215–233.
Rocheron, I., Lorenzi, C., Füllgrabe, C., & Dumont, A. (2002).
Temporal envelope perception in dyslexic children. Neurore-
port, 13, 1683–1687.
Schulte-Körne, G., Deimel, W., Bartling, J., & Remschmidt, H.
(1998a). Auditory processing and dyslexia: Evidence for a
specific speech processing deficit. Neuroreport, 9, 337–340.
Schulte-Körne, G., Deimel, W., Bartling, J., & Remschmidt, H.
(1998b). Role of auditory temporal processing for reading and
spelling disability. Perceptual and Motor Skills, 86, 1043–1047.
Schulte-Körne, G., Deimel, W., Bartling, J., & Remschmidt, H.
(2001). Speech perception deficit in dyslexic adults as mea-
sured by mismatch negativity (MMN). International Journal
of Psychophysiology, 40, 77–87.
Sharma, M., Purdy, S. C., Newall, P., Wheldall, K., Beaman, R.,
& Dillon, H. (2006). Electrophysiological and behavioral evi-
dence of auditory processing deficits in children with reading
disorder. Clinical Neurophysiology, 117, 1130–1144.
Stanovich, K. E. (1998). Refining the phonological core deficit
model. Child and Adolescent Mental Health, 3, 17–21.
Stoodley, C. J., Hill, P. R., Stein, J. F., & Bishop, D. V. M. (2006).
Auditory event-related potentials differ in dyslexics even
when auditory psychophysical performance is normal. Brain
Research, 1121, 190–199.
Stuart, G. W., McAnally, K. I., McKay, A., Johnston, M., &
Castles, A. (2006). A test of the magnocellular deficit theory
of dyslexia in an adult sample. Cognitive Neuropsychology,
23, 1215–1229.
Talcott, J. B., Gram, A., Van Ingelghem, M., Witton, C., Stein, J. F.,
& Toennessen, F. E. (2003). Impaired sensitivity to dynamic
stimuli in poor readers of a regular orthography. Brain and
Language, 87, 259–266.
Talcott, J. B., & Witton, C. (2002). A sensory-linguistic approach
to normal and impaired reading development. In E. Witruk,
A. D. Friederici, & T. Lachmann (Eds.), Basic functions of lan-
guage, reading and reading disability (pp. 213–240). Dordrecht,
Netherlands: Kluwer.
Tallal, P., & Benasich, A. A. (2002). Developmental language
learning impairments. Development & Psychopathology, 14,
559–579.
Tallal, P., & Gaab, N. (2006). Dynamic auditory processing, musi-
cal experience and language development. Trends in Neurosci-
ences, 29, 382–390.
Thomson, J. M., Fryer, B., Maltby, J., & Goswami, U. (2006).
Auditory and motor rhythm awareness in adults with dyslexia.
Journal of Research in Reading, 29, 334–348.
at Jyvaskylan Yliopisto on March 16, 2014ldx.sagepub.comDownloaded from

Hämäläinen et al. 427
Thomson, J. M., & Goswami, U. (2008). Rhythmic processing
in children with developmental dyslexia: Auditory and motor
rhythms link to reading and spelling. Journal of Physiology—
Paris, 102, 120–129.
Van Ingelghem, M., Boets, B., van Wieringen, A., Onghena, P.,
Ghesquière, P., Vandenbussche, E., & Wouters, J. (2005). An
auditory temporal processing deficit in children with dyslexia?
In P. Ghesquière & A. J. J. M. Ruijssenaars (Eds.), Children
with learning disabilities: A challenge to teaching and instruc-
tion (pp. 47–63). Leuven, Belgium: University Press.
Van Ingelghem, M., van Wieringen, A., Wouters, J., Vandenbuss-
che, E., Onghena, P., & Ghesquiére, P. (2001). Psychophysical
evidence for a general temporal processing deficit in children
with dyslexia. Neuroreport, 12, 3603–3607.
Wagner, R. K., & Torgesen, J. K. (1987). The nature of phonologi-
cal processing and its causal role in the acquisition of reading
skills. Psychological Bulletin, 101, 192–212.
Walker, M. M., Shinn, J. B., Cranford, J. L., Givens, G. D., & Holbert, D.
(2002). Auditory temporal processing performance of young
adults with reading disorders. Journal of Speech, Language,
and Hearing Research, 45, 598–605.
Watson, B. U. (1992). Auditory temporal acuity in normally
achieving and learning disabled college students. Journal of
Speech & Hearing Research, 35, 148–156.
Watson, B. U., & Miller, T. K. (1993). Auditory perception, pho-
nological processing, and reading ability/disability. Journal of
Speech & Hearing Research, 36, 850–863.
White, S., Milne, E., Rosen, S., Hansen, P., Swettenham, J., Frith, U.,
& Ramus, F. (2006). The role of sensorimotor impairments in
dyslexia: A multiple case study of dyslexic children. Develop-
mental Science, 9, 237–255.
Witton, C., Stein, J. F., Stoodley, C. J., Rosner, B. S., &
Talcott, J. B. (2002). Separate influences of acoustic AM
and FM sensitivity on the phonological decoding skills of
impaired and normal readers. Journal of Cognitive Neurosci-
ence, 14, 866–874.
Witton, C., Talcott, J. B., Hansen, P. C., Richardson, A. J.,
Griffiths, T. D., Rees, A., . . . Green, G. G. R. (1998). Sensitiv-
ity to dynamic auditory and visual stimuli predicts nonword
reading ability in both dyslexic and normal readers. Current
Biology, 8, 791–797.
Wright, B. A., & Zecker, S. G. (2004). Learning problems, delayed
development, and puberty. Proceedings of the National
Academy of Sciences of the United States of America, 101,
9942–9946.
Wright, C. M., & Conlon, E. G. (2009). Auditory and visual pro-
cessing in children with dyslexia. Developmental Neuropsy-
chology, 34, 330–355.
at Jyvaskylan Yliopisto on March 16, 2014ldx.sagepub.comDownloaded from