Current Biology 23, 462–466, March 18, 2013 ª2013 Elsevier Ltd All rights reserved
http://dx.doi.org/10.1016/j.cub.2013.01.044
Report
Action Video Games Make
Dyslexic Children Read Better
Sandro Franceschini,1,3 Simone Gori,1,2,3 Milena Ruffino,2
Simona Viola,1 Massimo Molteni,2 and Andrea Facoetti1,2,3,*
1Developmental and Cognitive Neuroscience Lab,
Department of General Psychology, University of Padua,
Padua 35131, Italy
2Developmental Neuropsychology Unit, Scientific Institute
E. Medea, Bosisio Parini, Lecco 23842, Italy
Summary
Learning to read is extremely difficult for about 10% of children; they are affected by a neurodevelopmental disorder
called dyslexia [1, 2]. The neurocognitive causes of dyslexia
are still hotly debated [3–12]. Dyslexia remediation is far
from being fully achieved [13], and the current treatments
demand high levels of resources [1]. Here, we demonstrate
that only 12 hr of playing action video games—not involving any direct phonological or orthographic training—
drastically improve the reading abilities of children with
dyslexia. We tested reading, phonological, and attentional
skills in two matched groups of children with dyslexia before
and after they played action or nonaction video games for
nine sessions of 80 min per day. We found that only playing
action video games improved children’s reading speed,
without any cost in accuracy, more so than 1 year of spontaneous reading development and more than or equal to highly
demanding traditional reading treatments. Attentional skills
also improved during action video game training. It has been
demonstrated that action video games efficiently improve
attention abilities [14, 15]; our results showed that this attention improvement can directly translate into better reading
abilities, providing a new, fast, fun remediation of dyslexia
that has theoretical relevance in unveiling the causal role
of attention in reading acquisition.
Results
Dyslexia is a severely invalidating learning disability that
affects literacy acquisition despite normal intelligence and
adequate instruction [1, 2]. Dyslexia is often associated with
undesirable outcomes, such as lower educational attainment
and loss of self-confidence [1, 2], because reading is essential
for all aspects of learning from using older school books to the
latest technology (e.g., ebooks and smart phones).
Although an impaired auditory discrimination of spoken
language (phonological processing) is widely assumed to characterize dyslexic individuals [1, 2, 7, 8], dyslexia remediation is
far from being fully achieved [1]. Improvements in auditoryphonological processing do not automatically increase reading
abilities [13]. Recent evidence suggests that dyslexia could
arise from a basic crossmodal letter-to-speech sound integration deficit [4, 5]. Remediation based on explicit, systematic instruction on letter-to-speech integration (decoding
3These authors contributed equally to this work
*Correspondence: [email protected]
strategies) appears to be the most efficient treatment
[1, 2, 13]. However, all the existing treatments are controversial
and demand high levels of resources. Moreover, the cognitive
processes underlie the improvements in reading ability remain
unclear [1, 4].
Attentional dysfunction is an important core deficit in
dyslexic individuals [6, 9–12, 16–18]. Letters must be precisely
selected from among other cluttering graphemes [19] by rapid
orientation of visual attention [20] before the correct letterto-speech sound integration applies [3–6, 9, 17]. Efficient
attention improves the perception of stimuli [20] and increases
the development of neural connections [21] between letter and
speech sound [4, 5]. An attentional deficit reduces the success
of traditional dyslexia treatments, because learning ability is
hampered by spatial and temporal attention dysfunction.
Thus, treatment of attentional deficits could be crucial in
dyslexia remediation.
Since video game training has been proven to increase
attention abilities [14, 15, 22], we investigated the effects of
video games on children with dyslexia. In contrast to typical
perceptual learning findings in which performance improvement for supra- or subliminal features is strictly stimulus
specific [23, 24], attentional action video game (AVG) training
should produce learning that transfers well beyond the task
domain [22]. It is predicted that AVG training will improve
letter-to-speech sound mapping (phonological decoding)
and, consequently, reading abilities.
To test this hypothesis, we measured the phonological
decoding of pseudowords and word text reading skills in 20
children with dyslexia before (T1) and after (T2) two video
game trainings. Ten dyslexic children were assigned to AVG
and ten to nonaction video game (NAVG) training (see the
Supplemental Experimental Procedures available online).
Chronological age, full intelligence quotient (IQ), reading
severity (measured in speed and errors during reading of
word and pseudoword clinical lists), and phonological skills
were similar in the two groups (see Table S1). The two groups
did not differ at T1 in both reading and attentional measurements (all p values >0.1). Each child was individually treated
by playing a commercial Wii video game (Rayman Raving
Rabbids) for a total of 12 hr. The single minigames were
selected to create the action and nonaction treatments (see
the Supplemental Experimental Procedures and Supplemental
Results). Informed written consent was obtained from the
parents of each child, and the Scientific Institute E. Medea
ethic committee approved the research protocol. The entire
research process was conducted according to the principles
expressed in the Declaration of Helsinki.
Reading Improvements
The reading inefficiency was measured as a ratio between
speed (defined as the time in seconds necessary to read
the specific item, depending of the task) and accuracy
(defined as the ratio between the correct response and the
total number of items). This measure was chosen to control
for the tradeoff between reading speed and accuracy.
Training-related changes in reading inefficiency were analyzed
by a 2 (task: pseudoword decoding and word text reading)
Video Games and Dyslexia
463
A
B
C
D
E
F
Figure 1. Training-Related Changes in Reading Abilities
Pseudoword and word text reading abilities were measured before (T1) and
after (T2) NAVG and AVG treatment in children with dyslexia. The general
reading improvement is the mean between the pseudoword and the word
text reading inefficiency (speed/accuracy) that is reduced by the training.
Only AVG players showed significant general reading improvements (A).
The general reading inefficiency is showed before (T1) and after (T2) training
in NAVG and AVG group (B). Pseudoword (C) and word text (D) reading
improvements were significant only in AVG group. Pseudoword (E) and
word text reading inefficiency (F) is showed before (T1) and after (T2)
training in NAVG and AVG group. Pseudoword and word text reading
inefficiency were both significantly reduced only in AVG players. The
reading improvements—induced by the AVG training—involve both
phonological decoding and lexical reading. The two groups did not differ
at T1 in all the reading measurements. *, significant difference. Error bars
represent the SE. See also Tables S1–S3, S5, and S6.
*2 (time: T1 and T2) *2 (group: AVG and NAVG) mixed ANOVA.
The mean between the three pseudoword reading inefficiencies
and the word text reading inefficiency (see Table S2) was
labeled general reading abilities. The time main effect was
significant [F(1,18) = 5.50, p = 0.03, h2p = 0.23], showing an
improvement in general reading abilities across the two
groups. Crucially, the time*group interaction was also significant [F(1,18) = 6.40, p = 0.02, h2p = 0.26]; general reading
abilities improved in the AVG (mean = 39.33) but not the
NAVG (mean = 21.5; see Figures 1A and 1B) players. Pseudoword phonological decoding and word text reading were both
significantly improved in the AVG compared to the NAVG
players [see Figure 1C, t(18) = 3.30, p < 0.01 and Figure 1D,
t(18) = 1.97, p = 0.03, respectively; see also Figures 1E and 1F
and Tables S2 and S3 for details]. The reading improvements
after the AVG training were characterized by the increased
reading speed without a cost in accuracy. This result is in agreement with the improved speed of processing already found
associated with AVG [25].
To establish the reliability of these findings, we computed
the analysis in syllables per seconds, which is an important
clinical reading index used in both consistent and inconsistent
orthographies [11]. In the reading speed of pseudoworddecoding tasks, the AVG group (mean 0.18 syllable [syll]/s)
showed a bigger improvement [t(18) = 2.79, p = 0.01] than the
NAVG group (mean 0.05 syll/s). The relevance of this result
can be fully appreciated by noting that the pseudoworddecoding improvements obtained after 12 hr of AVG training
(mean 0.18 syll/s) were higher than the mean improvements
expected in a dyslexic child (0.15 syll/s) after 1 year of spontaneous reading development. Similarly, the AVG group (mean
0.39 syll/s) posted a larger improvement [t(18) = 2.52, p =
0.02] in word text reading skills than the NAVG group (mean
0.08 syll/s). Consistently, the improvement in word text
reading speed obtained after 12 hr of AVG training (mean
0.39 syll/s) was higher than the improvement expected (0.3
syll/s) in a dyslexic child without treatment for one year. Moreover, the AVG speed reading improvements were bigger than
those obtained by the highly demanding traditional phonological and orthographic treatments and equal to the letterto-speech integration training (see the Supplemental Results).
Thus, AVG training improves not only the basic letter-tospeech sound integration—indexed by increased pseudoword
reading efficiency—but also lexical recognition, measured by
the word text reading as recently suggested by Vidyasagar
and Pammer [6]. Finally, to quantify the reliability at individual
level of this group improvement, we analyzed the improvement
in the general reading abilities (see Figure 1A). Eight out of ten
(80%) AVG players statistically differed from the NAVG group’s
mean improvements. In addition, seven out of ten (70%) AVG
players were at least 1 SD above the mean of the NAVG in
the general reading improvements.
Considering that children with dyslexia could present
reading comprehension problems as consequence of the
core reading decoding deficit, further studies could directly
investigate the possible effect of AVG on this higher level
reading parameter.
We also measured changes in phonological skills after treatment, using a phoneme-blending task (see Table S1 and the
Supplemental Experimental Procedures). A 2 (time: T1 and
T2) *2 (group: AVG and NAVG) mixed ANOVA revealed no
significant main effects or interaction, suggesting that reading
enhancement driven by AVG training is unrelated to phonological short-term memory improvements.
Two months after the end of the treatment (T3), we followed
up on reading improvements induced by AVG training by
retesting the phonological decoding skill in six out of ten
dyslexic children that did not perform any treatment or training
between T2 and T3. A dependent sample t test comparison
revealed a nonsignificant difference in pseudoword-decoding
skill between T2 and T3 performance, indicating a longlasting reading improvement from AVG training (see Tables
S2 and S3).
Controlling for the speed/accuracy tradeoff, the reading
improvements demonstrate that AVG training did not simply
result in a trigger-happy behavior. The explanation of these
Current Biology Vol 23 No 6
464
A
B
C
Figure 2. Focused and Distributed Spatial Attention Tasks and Results
D
0.25
0.25
0.20
0.20
0.15
0.15
0.10
0.10
0.5
0.5
0.0
0.0
E
F
0.55
0.55
0.5
0.5
0.45
0.45
0.4
0.4
0.35
0.35
0.3
0.3
0.25
0.25
0.2
0.2
results includes the possibility that video games with specific
characteristics increase abilities to allocate attentional
resources in space and time [14, 15, 22, 25, 26].
Attentional Improvements
Focused and distributed spatial attention were also measured
(accuracy) in T1 and T2 with a single-report task. A red dot appeared before (cue condition) or after (probe condition) a multielement display [27] composed of nonverbal stimuli (see
Figures 2A and 2B, respectively). Two separate 2 (time: T1
and T2) *2 (group: AVG and NAVG) mixed ANOVAs were conducted for the cue and probe conditions. In the cue condition,
the time main effect [F(1,18) = 25.56, p < 0.001, h2p = 0.59] and
the time*group interaction [F(1,18) = 6.32, p = 0.02, h2p = 0.26]
were significant, demonstrating that only dyslexic children
treated with AVGs improved their focused attention (see
Figures 2C and 2E, Table S4, and the Supplemental Experimental Procedures). Similarly, in the probe condition,
both the time main effect [F(1,18) = 8.12, p = 0.01, h2p = 0.31]
and the time*group interaction [F(1,18) = 5.12, p = 0.03,
In a single-report task, participants were instructed to keep their eyes on the fixation point
and identify the target symbol that appeared
above the red dot as accurately as possible and
without time limit. The red dot was displayed
before (focused spatial attention, A) or after
(distributed spatial attention, B) the string of
symbols. Dyslexic children treated with AVGs
showed significantly greater improvement (accuracy difference between T2 and T1) in both
focused (C) and distributed (D) spatial attention
tasks. Accuracy in focused (E) and distributed
(F) spatial attention tasks are showed before
(T1) and after (T2) training, in the NAVG and AVG
groups. Accuracy in both focused and distributed
spatial attention was significantly increased only
in AVG players. The two groups did not differ at
T1 in focused and distributed spatial attention.
*, significant difference. Error bars represent
the SE. See also Tables S4–S6.
h2p = 0.22] were significant, showing
that only dyslexic children treated with
AVGs improved their distributed attention (see Figures 2D and 2F, Table S4,
and the Supplemental Experimental
Procedures).
Crossmodal attention was also measured, with an uninformative, peripheral
auditory cue [26] that preceded (by 50
or 100 ms) the display of a visual target
at a correct (valid condition) or incorrect
(invalid condition) spatial location. Bilateral auditory cues (neutral condition)
were also used to distribute attention
(see Figure 3A and the Supplemental
Experimental Procedures). Temporal
attention indexed by reductions in the
reaction time needed to localize the left
or right visual target at longer (100 ms)
versus shorter (50 ms) cue-target
interval was analyzed by a 3 (cue type:
valid, neutral, and invalid) *2 (time: T1
and T2) *2 (group: AVG and NAVG) mixed ANOVA. Importantly,
the Time*Group interaction was significant [F(1,18) = 4.32,
p = 0.05, h2p = 0.19], showing a larger crossmodal alerting
improvement in AVG than in NAVG players (see Figures 3B
and 3C, Table S4, and the Supplemental Results).
These findings are in agreement with several reports documenting the beneficial effects of playing video games for attention [22]. In particular, previous studies have demonstrated
that AVG-controlled training was causally linked to enhancements in spatial (e.g., peripheral target recognition) and
temporal (e.g., attentional blink and backward masking reduction) attention [15, 22]. AVGs are distinguished by NAVGs by
such characteristics as game speed, a high sensory-motor
load, and presentation of multiple, peripheral stimuli [22].
AVG players constantly receive both external and internal
feedback on their performance, producing learning [21]. AVG
players perform better at tasks requiring both distributed
and focused visual spatial attention [14]. They also react
more quickly to stimulus targets preceded by spatiotemporal
cues [28], suggesting a more efficient alerting system.
Video Games and Dyslexia
465
A
improvements (for details, see Table S5). The attentional
enhancements accounted for about 50% of the unique variance of reading improvement (r2 change = 0.48, p = 0.01; see
Table S6), demonstrating a clear, causal link between attentional functioning and reading remediation. AVG training could
directly reduce reading disorders in children with dyslexia,
increasing the efficiency of their attentional orienting [14, 15,
22, 25] and alerting systems [28].
Discussion
B
C
Figure 3. Crossmodal Temporal Attention Task and Results
An auditory spatial-temporal cue was presented in the left, right, or both
loudspeakers placed on either side of the screen. A dog appearing in one
of the two circles was the target stimulus. Children had to press one of
two buttons on the keyboard to indicate whether the target appeared in
the left or the right circle (A). Crossmodal temporal attention improvements
refer to the improvements between T1 and T2 in the reaction time needed to
correctly localize the target at second cue-target interval (100 ms) in
comparison to the first cue-target interval (50 ms). AVG players showed
a significant improvement in temporal attention compared to NAVG players
(B). Temporal attention (reaction time difference between second and first
cue-target interval) is showed before (T1) and after (T2) training in the
NAVG and AVG groups (C). Temporal attention was significantly increased
only in AVG players. The two groups did not differ at T1. *, significant difference. Error bars represent the SE. See also Tables S4–S6.
Previous studies have suggested that visual attention could be
crucial for learning letter identities and their relative positions
(orthographic processing) independently of language knowledge [6, 10, 12, 29]. In agreement with our causal results,
studies have shown that visual attention is impaired not only
in dyslexic children [16, 17] but also in prereaders at familial
risk for dyslexia [18], indicating that attentional disorders are
present before reading acquisition. In addition, a recent longitudinal study demonstrated that prereading visual attention
predicts future reading acquisition skills in second grade,
controlling not only for age, IQ, and phonological processing,
but also for nonalphabetic, visual-to-phonological mapping
[10]. About 60% of future poor readers displayed visual attention deficits as prereaders [10]. The importance of the visual
attention and the phonological factors could vary across
languages based on their orthographic transparency degree.
However, visual attention deficit in dyslexia was found in
both consistent and inconsistent orthographies [30–36].
Accordingly, extra-large spacing between letters improves
reading efficiency in dyslexic children with consistent and
inconsistent orthographies [11], helping to focus attention
[37] on each successive letter within a written word [3].
Since all AVGs share an extraordinary speed in terms of
transient events and moving objects, a high degree of perceptual and motor load, and an emphasis on peripheral processing, AVG training might mainly improve the efficiency of
the magnocellular-dorsal pathway or ‘‘action’’ stream [6, 9,
12, 17]. Although further studies are necessary to investigate
the specific role of the ‘‘action’’ stream in reading acquisition,
our results demonstrate the causal role of visual spatial and
crossmodal temporal attention in dyslexia.
Our findings—supported by results showing that attention
can be studied [38] and efficiently trained [39] during
infancy—pave the way for low-resource-demanding early prevention programs that could drastically reduce the incidence
of reading disorders.
Supplemental Information
AVG players have faster response times without a loss of
accuracy [25].
Relationship between Attentional and Reading
Improvements
The improvements in spatial and temporal attention correlated
with those in general reading abilities (r = 0.52, p = 0.02 and
r = 0.49, p = 0.03, respectively). To determine the predictive
relationships between attentional and reading improvements,
we performed a three-step, fixed-entry, multiple regression
analysis on the entire sample of children with dyslexia
(n = 20). The dependent variable was the general reading ability
improvements, and the predictors were (1) age and full IQ, (2)
phonological changes, and (3) spatial and temporal attention
Supplemental Information includes seven tables and Supplemental
Experimental Procedures and can be found with this article online at
http://dx.doi.org/10.1016/j.cub.2013.01.044.
Acknowledgments
We thank Elena Olgiati for help in collecting data. This work was funded with
grants from the CARIPARO Foundation (Borse di Dottorato CARIPARO 2009
to S.F.; Progetti di Eccellenza CARIPARO, 2011–2012 to A.F.) and the
University of Padua (Assegni di Ricerca 2009 and 2011 and Senior
Researcher to S.G.; Progetto di Ateneo 2009 and 2011 to A.F.).
Received: November 7, 2012
Revised: January 4, 2013
Accepted: January 15, 2013
Published: February 28, 2013
Current Biology Vol 23 No 6
466
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DOI 10.1007/s00221-005-2387-6
R ES E AR C H A RT I C L E
W. David Hairston Æ Jonathan H. Burdette
D. Lynn Flowers Æ Frank B. Wood Æ Mark T. Wallace
Altered temporal profile of visual–auditory multisensory interactions
in dyslexia
Received: 5 August 2004 / Accepted: 13 October 2004 / Published online: 19 July 2005
! Springer-Verlag 2005
Abstract Recent studies have demonstrated that dyslexia is associated with deficits in the temporal encoding
of sensory information. While most previous studies
have focused on information processing within a single
sensory modality, it is clear that the deficits seen in
dyslexia span multiple sensory systems. Surprisingly,
although the development of linguistic proficiency involves the rapid and accurate integration of auditory
and visual cues, the capacity of dyslexic individuals to
integrate information between the different senses has
not been systematically examined. To test this, we
studied the effects of task-irrelevant auditory information on the performance of a visual temporal-orderjudgment (TOJ) task. Dyslexic subjects’ performance
differed significantly from that of control subjects, specifically in that they integrated the auditory and visual
information over longer temporal intervals. Such a result suggests an extended temporal ‘‘window’’ for
binding visual and auditory cues in dyslexic individuals.
The potential deleterious effects of this finding for rapid
multisensory processes such as reading are discussed.
Keywords Multisensory Æ Cross-modal Æ Dyslexia Æ
Temporal Æ Integration
W. D. Hairston (&) Æ M. T. Wallace
Department of Neurobiology and Anatomy,
Wake Forest University Health Sciences,
Winston-Salem, NC 27157, USA
E-mail: [email protected]
Tel.: +1-336-7164481
Fax: +1-336-7164534
J. H. Burdette
Department of Radiology, Wake Forest University
Health Sciences, Winston-Salem, NC 27157, USA
D. L. Flowers Æ F. B. Wood
Department of Neurology, Section of Neuropsychology,
Wake Forest University Health Sciences,
Winston-Salem, NC, USA
Introduction
Developmental dyslexia is a common neurobehavioral
disorder, with reported prevalence rates as high as 10%
or more (Tallal et al. 1993; Shaywitz et al. 1998; Habib
2000; Ramus 2001). Although a number of conceptual
frameworks have been put forward to explain the spectrum of neurological deficits seen in dyslexia, two have
emerged as the preeminent models (Ramus 2001, 2003).
The first of these, the phonological processing impairment hypothesis, suggests that the core deficit in dyslexia
is an inability to properly decode words into their
component speech sounds (Pennington et al. 1991;
Shaywitz 1998). As a result, individuals have trouble
associating printed representations with the appropriate
speech elements (Wagner 1986). The second framework
focuses on a disruption in sensory-motor processes, and
posits that the core deficit in dyslexia lies in the processing of sensory stimuli, specifically when they are
rapidly occurring. Evidence for this latter hypothesis has
come from studies that have shown temporally based
processing abnormalities for non-speech visual or auditory stimuli (Tallal et al. 1993; Fischer et al. 2000; Laasonen et al. 2001; Stein 2001; Fischer and Hartnegg
2004) as well as in the performance of optokinetic tasks
(Biscaldi et al. 2000). Nonetheless, substantial debate
still exists as to whether such temporal abnormalities can
fully explain the constellation of observed deficits (Ramus 2003). Although much of this work has focused on
the processing of stimuli within a specific sensory
modality, there is growing evidence that temporal processing deficits can be seen across different sensory
modalities as well (Laasonen et al. 2001).
Despite the fact that such work establishes the
‘‘multisensory’’ nature of dyslexia (in that it impacts
multiple senses in the same individuals), little evidence
has been gathered to directly address the capacity of
dyslexics to integrate information between the different
senses. One possibility is that dyslexic individuals are
preferentially impacted in tasks in which information
475
from different sensory modalities must be synthesized.
This synthesis, collectively known as multisensory integration, has been shown to play important roles in human behavior and perception (Bertelson and Radeau
1981; Stein and Meredith 1993; Massaro et al. 1996;
Slutsky and Recanzone 2001; Hairston et al. 2003;
Morein-Zamir et al. 2003). Most germane in the current
context is that multisensory integration is undoubtedly a
critical process in the development of linguistic skills
(notably reading), given that such skills are vitally
dependent on the rapid and accurate association between the appropriate auditory and visual language
elements (Tallal et al. 1993; Massaro et al. 1996)
Consequently, in the current study, we set out to test
whether the temporal integration of multisensory stimuli
is preferentially altered in dyslexic individuals. To do
this, dyslexic and typical-reading control subjects performed a modified visual temporal-order-judgment
(TOJ) task similar to that described recently (MoreinZamir et al. 2003) and in which subjects showed an
improved performance when a spatially non-informative, task-irrelevant auditory cue was added. Critically,
this effect was seen only as long as the auditory cue was
presented within close temporal proximity to the visual
target. We adopted this task because it shows a multisensory-mediated effect on the perceived timing of
events, and also serves to define a temporal window
within which cross-modal (i.e. auditory influence on vision) interactions occur, thus providing an opportunity
to examine differences in this window between typicalreading and dyslexic individuals.
Materials and methods
Subjects
Subjects were 36 dyslexic (23 males) and 29 typical-reading control (16 males) subjects. The two groups were agematched adults, ranging from 21 to 57 years old, all with
confirmed IQ above 70, and were acquired from a collection of cohorts originally recruited for a series of longitudinal studies (Flowers et al. 1991; Flowers 1993, 1995;
Wood et al. 1996, 2001; Wood and Flowers 1999). All
were paid for their participation, and provided informed
consent prior to participation. Procedures were approved
by the Wake Forest University Internal Review Board,
and conformed to the 1964 Declaration of Helsinki.
Evaluation of dyslexia
Subjects were defined as ‘‘dyslexic’’ on the basis of
standardized tests of decoding, where at least two scores
fell below the 25th percentile. Non-dyslexic subjects
scored above the 25th percentile on all reading skills
tests. The control group consists of samples from the
original normal-reading control group from each
cohort. Participants in each of these cohorts have
undergone extensive testing over the course of several
years as a consequence of participation in a number of
studies (Flowers et al. 1991; Flowers 1993, 1995; Wood
et al. 1996, 2001; Wood and Flowers 1999); members of
each of the cohorts were serially tested in the 3rd, 8th,
and 12th grades. Additionally, all subjects had recently
been tested in conjunction with another concurrent
study. The comprehensive test battery included the
Wechsler Abbreviated Scale of Intelligence, Woodcock
Johnson 3rd edition (word identification, word attack,
reading, math fluency, math calculations and math
applications), Decoding Skills Test of real and non-word
reading, Lindamood Auditory Conceptualization and
Rosner tests of phonemic awareness, Denckla’s Rapid
Automatized Naming, Wechsler Digit Span, Gray Oral
Reading Test, Barkley’s adult ADHD questionnaire,
and Wake Forest Rapid Word Reading Test (Denckla
and Rudel 1976; Lindamood and Lindamood 1979;
Rosner 1979; Richardson and DiBenedetto 1985; Wiederholt and Bryant 1992; Wilkinson 1993; Wechsler
1999; Woodcock et al. 2001).
Stimuli
Visual stimuli consisted of two white circles (covering
approximately 3! diameter visual angle each) against a
black background presented on a PC monitor (LG
915FT+, 200 Hz vertical scan), 10! above and below a
gray fixation cross. The fixation cross remained visible
through all trials and during inter-trial intervals. Auditory stimuli (broadband noise, 20 Hz–20 kHz, 65 dB
SPL, 10 ms duration) were presented via headphones to
both ears, and were simultaneous and of equal amplitude so as to not provide any spatial information.
Stimuli were presented using E-Prime presentation
software (Psychology Software Tools Inc., Pittsburgh,
Pa., USA). The temporal consistency of all stimuli was
externally verified prior to data acquisition.
Task overview
Subjects sat in a sound-attenuated room wearing headphones, and pressed a button in order to respond to
stimuli appearing on the monitor. Each participant
completed all tasks within a single session, which consisted of three different blocks. The first block was a
repeated staircase procedure to obtain threshold for visual TOJ performance, the second was an evaluation of
the threshold, and the third consisted of test trials with
task-irrelevant auditory signals added to the visual TOJ
task (see below for details).
Staircase procedure: visual TOJ
Following instructions, a fixation cross appeared on the
screen, and subjects were instructed to keep their eyes
476
focused on the cross. For each trial, after a delay of
1500 ms, the first of two circles appeared on the screen.
The circle could be either 5 cm above (i.e. 10!) or below
the fixation cross. Following a variable stimulus onset
asynchrony (SOA), a second circle appeared at the
location opposite the illuminated first circle. The subject’s task was to respond, using a button press, as to
which of the circles appeared first. Following the response, both circles disappeared, and a new trial began.
Subjects ran approximately 15 practice trials before
starting the series.
Because of the common comorbidity of attention
deficits in dyslexia, an adaptive staircase procedure was
used to quickly determine the SOA (i.e. the time between
the presentation of the two visual stimuli) necessary to
perform the visual TOJ task at threshold. Two staircase
procedures ran independently, one starting at an SOA of
10 ms (ascending series) and the other starting at an
SOA of 80 ms (descending series). For both, the initial
step size was 20 ms and decreased to 10 ms after three
response reversals. The SOA increased one step after
each incorrect response, and decreased one step only
after two consecutive correct responses. Each staircase
terminated after ten reversals in response accuracy, and
an average was calculated from the last five reversal
values. The entire procedure was repeated three times,
and an average was then determined for the six staircases, rounded up to the nearest minimum value compatible with the vertical scan rate of the monitor. This
process converged on perceptual thresholds with average
performance rates of 70–75% accuracy. Such accuracy
rates represent the average percentage of trials in which
subjects correctly judged which of the two visual stimuli
appeared first.
Fig. 1 Schematic of the trial sequence. Two visual stimuli (V1 and
V2 top and middle lines) appeared on a monitor with a stimulus
onset asynchrony—subjects reported which appeared first. During
initial testing, the visual SOA varied according to a staircase
procedure, and the visual cues were presented alone. During the
experimental phase, this value was fixed at threshold, and
additional auditory stimuli (A, bottom line) were presented with a
variable delay (0–350 ms) between the second visual and auditory
stimuli
of the second light. A randomly interleaved no-sound
condition provided baseline performance. Each auditory
SOA condition, plus the no-sound condition, was presented 32 times, with the presentations split between two
5 min blocks. Subjects were instructed to perform the
same task as they had previously (i.e. report which of the
two lights came on first); however, they were told that
they would also hear sounds. Since the sounds were
presented binaurally through headphones with no interaural timing or level differences, they did not provide
any spatial, task-relevant information. However, although not relevant for task performance, it must be
noted that the auditory cues did provide temporal
information that could be used in the paradigm.
Staircase verification: visual TOJ
In order to verify that the SOA derived from the staircase procedure was near threshold, subjects performed a
second series, with SOA values set relative to their previously determined thresholds. Three SOAs were used
relative to this value: 0 ms (i.e. threshold), 10 ms above,
and 10 ms below. Each of these three SOAs was repeated 20 times in a random order. This sequence was
repeated with new values if performance was not near
threshold.
Experimental procedure: visual TOJ with auditory cues
Figure 1 shows the trial sequence. On each trial, the
SOA between the two visual stimuli was fixed according
to the threshold value derived in the staircase procedure
described above. Additionally, two identical sounds
were presented on most trials through headphones.
While the first sound was always synchronous with the
first visual onset, the onset of the second sound was
delayed by 0–350 ms (50 ms steps) relative to the onset
Trimming of below-threshold scores
Somewhat unexpectedly, the average accuracy scores for
the visual-only baseline trials were noticeably lower than
those determined immediately following the threshold
procedure. That is, inclusion of the visual-only trials
with threshold SOAs among a block of visual–auditory
trials led to a significant decrease in accuracy compared
to performance on a block of similar visual-only trials
for the entire subject sample [t(64)=4.32, P<0.001].
This decrease was robust, with an average decline in
accuracy of 7%, and occurred in the majority (75.4%) of
the subjects. However, the average amount of decrease
was not statistically distinguishable between the two
experimental groups [t(63)=0.046, P>0.05]. For final
analyses, subjects in which accuracy scores were below
65% for visual-only (baseline) trials were excluded. It is
unclear as to whether these subjects were responding to a
visual SOA that was indeed near their perceptual
threshold or whether the task was simply too difficult
and they were guessing. A total of 11 normal-reading
477
and 15 dyslexic subjects were excluded based on this
criterion. For the final evaluation of performance
enhancement, these baseline values were subtracted from
scores on cross-modal trials to examine the amount of
gain conferred by having the additional auditory temporal cue.
Results
Performance on the visual TOJ task
In agreement with previous studies examining the temporal aspects of visual processing in dyslexia (Tallal
et al. 1993; Laasonen et al. 2001; Ramus 2001), performance on the visual TOJ task differed between dyslexic
and typical-reading control subjects. Dyslexic subjects
required over 33% more time to perform at the same
level of accuracy on the task (Fig. 2). Thus, whereas
dyslexic subjects required an average of 63.3±3.0
(SEM) ms between visual onsets to perform at threshold,
typical-reading control subjects required only
47.4±2.3 ms to perform at the same level, a highly
significant difference [t(63)=3.97, P<0.001]. Even
though the sample of dyslexic subjects contained more
males, the effect did not depend on gender, as males and
females performed identically when reading ability was
not considered [t(63)=0.24, P>0.05]. Interestingly,
there was a strong correlation between age and visual
threshold (r=0.372, P<0.01), with older subjects requiring longer SOAs. This was not a factor for the
current study, however, as there were no differences in
age between the two groups [t(63)=0.88, P>0.05], and
the difference between groups remained significant even
after accounting for the effect of age as a covariate with
Fig. 2 Differences in visual TOJ threshold between control and
dyslexic subjects. The average threshold stimulus onset asynchrony
(SOA) for the visual-only TOJ task was significantly longer for the
dyslexic group (dark bar) when compared with typical-reading
controls (light bar). To perform at an equivalent perceptual
threshold (74.6±1.1% accuracy), dyslexic subjects required over
33% more time. Inset shows the visual TOJ paradigm; the subject’s
task is to report which visual stimulus appears first. Error bars
represent group SEM. *P<0.05
linear regression [t(65)=3.87, P<0.001]. Reading ability
was consistently found to be correlated with performance on this visual temporal discrimination task; ad
hoc logistic regression showed that visual threshold
scores were a significant, reliable predictor of which
group a subject belonged to [R2=0.29, P<0.01], with a
74% overall prediction accuracy. Along with confirming
the presence of visual temporal processing differences in
dyslexics, the staircasing procedure allowed us to choose
SOAs for each individual at which task performance was
equivalent. With such values in hand, we could then
assess the temporal nature of the changes in performance due to the addition of task-irrelevant auditory
information.
Performance on the multisensory TOJ task
To examine the influence of adding a task-irrelevant
sound to the TOJ task for both groups, we first compared performance on the multisensory trials in which
there was no delay between the visual and auditory
stimuli with performance in the baseline visual-only
trials. Confirming the utility of the staircase procedure in
establishing equivalent baseline performance, accuracy
for visual-only trials was statistically indistinguishable
Fig. 3 The TOJ accuracy for visual-only and simultaneous visual–
auditory trials. For visual-only baseline trials in which threshold
visual SOAs were used from the staircase procedure, performance
for the two groups was equivalent (light bars). Addition of sounds
whose onsets were simultaneous with the lights resulted in a
significant improvement in dyslexic subjects’ performance (dark
bars), while control subjects showed no significant improvement.
Consequently, dyslexic subjects show a significant average gain in
performance relative to controls. Error bars represent the group
SEM. Note that this difference in multisensory performance cannot
be attributed to the different baselines, as all subjects were tested
with SOAs at their own perceptual threshold, and at equal baseline
accuracy rates. Inset shows the multisensory TOJ task, in which the
two auditory stimuli were delivered with their onsets simultaneous
with the two lights. Note also that the sounds are spatially noninformative
478
for the two groups [t(63)=1.53, P>0.05; Fig. 3, light
bars]. When the sounds were presented simultaneous
with the visual stimuli, there was a significant increase in
average accuracy in comparison to visual-only trials for
the pooled sample [t(38)=2.57, P<0.01]. However,
further analysis revealed that this effect was solely the
result of performance for the dyslexic group (Fig. 3,
compare dark bars to light bars). That is, while the small
difference seen with control subjects between visual-only
and multisensory trials with no auditory delay failed to
reach statistical significance [accuracy increase of 1.2%,
t(17)=0.50, P>0.05], dyslexic subjects showed a highly
significant benefit (accuracy increase of 6.5%) when the
sound was added [t(20)=3.43, P<0.005]. Overall, the
total performance gain (increase above baseline) was
significantly larger for dyslexic subjects when compared
to control subjects [t(37)=1.70, P<0.05].
A
B
Effect of auditory delay
Consistent with previous reports (Morein-Zamir et al.
2003), delaying the onset of the second auditory signal
relative to the onset of the second visual signal led to a
considerable increase in performance accuracy; however,
the extent of benefit depended on the auditory delay
[F(7,224)=2.33, P<0.05]. A comparison across these
conditions revealed a striking difference for the two
groups. Figure 4a shows the increase in accuracy for
control subjects, with scores significantly higher than
baseline being denoted by asterisks (*). Note that with a
delay of 100 ms, accuracy is improved by approximately
7%, a significant performance benefit [t(17)2.62,
P<0.05]. The benefit is seen with delays of up to a
250 ms, although its magnitude appears to decline after
150 ms. With delays longer than 250 ms, the multisensory-mediated benefit disappears [t(17)=1.36, P>0.05,
300 ms; t(17)=0.72, P>0.05, 350 ms]. These data are
consistent with previous reports using the same paradigm (Morein-Zamir et al. 2003), and with the concept
of a specific temporal ‘‘window’’ of cross-modal integration (Meredith et al. 1987; Stein and Meredith 1993).
In contrast to their typical-reading counterparts,
dyslexic subjects exhibited a significant benefit from the
additional auditory stimulus across all tested delays
(Fig. 4b). Improvements were seen with delays as small
as 50 ms [t(20)=4.32, P<0.05] and as large as 350 ms
[t(20)=2.49, P<0.05]. Note also that, even at the
100 ms delay, where both groups show the greatest
performance enhancement, the overall gain was much
greater for dyslexic subjects than for control subjects
[t(37)=1.80, P<0.05]. This difference remained statistically significant when accounting for age [t(36)=2.16,
P<0.05].
Discussion
Fig. 4 The TOJ performance is differentially affected in control
and dyslexic subjects by auditory delay. The temporal window for
behavioral enhancement on the multisensory TOJ task is expanded
in dyslexic subjects. In both groups, significant enhancements in
performance above visual baseline (asterisks single-sample t-test at
P<0.05) are seen with various temporal delays of the auditory
stimulus. Across both groups, the extent of gain above baseline
depended on the amount of auditory delay. a For typical-reading
control subjects, significant performance increases are seen when
the onset of the second sound is delayed by between 100 and
250 ms relative to the onset of the second light. b Dyslexic subjects
tended to be more accurate overall, and performed significantly
better across the entire range of tested auditory delays. Error bars
represent the group SEM for each condition. Inset shows the
multisensory TOJ task in which the second sound was delayed by a
variable interval relative to the second light
The results of this study are the first to show a preferential alteration in multisensory processing in a dyslexic
cohort. It’s important to note that even though dyslexic
subjects exhibited a difference in their ability to discriminate which of two visual stimuli appeared first, a
finding in keeping with a number of observations
showing visual temporal processing abnormalities in
dyslexia (Stein and Walsh 1997; Laasonen et al. 2001),
when this difference was corrected by having each individual work at or very near their own perceptual
thresholds, a multisensory performance difference persisted. Most intriguingly, this difference manifested as an
expanded window of time within which visual and
auditory stimuli could interact to alter psychophysical
performance.
In the paradigm used in the current study, we have
taken advantage of previous work that has established
that a temporally offset auditory cue can strongly
influence a subject’s judgment of the perceived onset
time of a pair of visual cues, a phenomenon that has
479
been described as ‘‘temporal ventriloquism.’’ This benefit is conferred not by any spatial cues, since the auditory stimulus is devoid of any useful spatial information,
but rather by the temporal relationship of the auditory
cue to the visual cues.
There is now a substantial body of evidence suggesting prelinguistic deficits in visual, visuomotor and
auditory tasks in dyslexic subjects (Tallal et al. 1993;
Biscaldi et al. 2000; Fischer et al. 2000; Laasonen et al.
2001; Stein 2001; Fischer and Hartnegg 2004). While a
number of studies have shown that these deficits in the
different sensory systems involve alterations in temporal
information processing (Tallal 1980; Brannan and Williams 1988; Reed 1989; Kinsbourne et al. 1991; Laasonen et al. 2000, 2001), our results extend these findings
and provide additional support for a theory of altered
cross-modal processing over time (Laasonen et al. 2000,
2001; Laasonen et al. 2002a, b). Here we add to this
framework by showing that the alterations cannot be
explained simply on the basis of the changes in unisensory temporal processes, and that they extend to include
multisensory temporal processes, specifically the time
over which auditory cues can influence visual discrimination. Although the current study is limited to examining interactions between vision and audition, the
evidence for temporal processing abnormalities in other
sensory systems suggest that similar effects may be seen
between other modalities, such as vision and touch.
Additionally, it should be noted that in the current
study, although it is clear that the differences lie in the
temporal realm, it is not clear which aspects of multisensory temporal processing are preferentially affected.
For example, the observed differences may relate to
difficulties in the processing rate of the stimuli them-
A
B
Fig. 5 Model in which the temporal window of multisensory
integration is expanded in dyslexia. Performance enhancement on
the multisensory TOJ task is likely the result of the integration of
the latter visual and auditory signals, thereby perceptually delaying
the second visual onset. In typical-reading individuals, this crossmodal integration occurs over a range of delays spanning 250 ms or
less (a). Stimuli separated by larger temporal intervals are
processed as separate events (b). In contrast, here we show that
dyslexic individuals exhibit this performance improvement over a
much wider range of temporal delays, suggesting that they integrate
visual and auditory stimuli over abnormally large windows of time
selves, or rather they may be due to differences in the
encoding of the rate of change between stimuli.
Together, these results support a model of altered
multisensory processing in dyslexia in which the temporal window within which sensory stimuli from different modalities can interact is expanded (Fig. 5). Such an
expanded temporal window would be expected to result
in difficulties in processes that are dependent on the
rapid and accurate binding of cues from multiple senses.
One such process is reading, in which the nervous system
must map the appropriate visual (i.e. graphemic) elements of the written word with the corresponding
auditory (i.e. phonemic) representations. Expanding the
window of time within which such mappings can take
place will likely result in inappropriate correspondences,
which will ultimately manifest as errors in phoneme/
grapheme associations, and consequently in declines in
both the speed and accuracy at which printed representations can be decoded.
Although the results of this study and its associated
model are readily reconcilable with both the disrupted
phonological processing model and the disrupted temporal processing model, they provide powerful new
evidence for both the stage at which information processing is impacted in dyslexia and for a multisensory
specificity to the disorder. Thus, it is readily evident
from this study that information processing is affected at
prelinguistic stages in dyslexia, since differences are
readily demonstrable with stimuli devoid of linguistic
content, a finding in keeping with other recent evidence
(Tallal et al. 1993; Fischer et al. 2000; Laasonen et al.
2001; Stein 2001; Fischer and Hartnegg 2004). Furthermore, it is clear that there are alterations in multisensory processing in dyslexia that go beyond those
expected based on the deficits seen in the individual
sensory systems. Such a finding fits nicely with recent
studies linking decreased cross-modal temporal performance in individuals with abnormal phonological processing (Laasonen et al. 2001, 2002b), as well as with the
success of multisensory strategies that are employed to
remediate the reading skills of dyslexic individuals
(Oakland et al. 1998; Hook et al. 2001; Joshi et al. 2002;
Overy 2003). With such knowledge in hand, future
studies directed at elucidating the neurobiological basis
for dyslexia can focus on presumptive multisensory
areas of the cerebral cortex, specifically to areas that are
involved in the early convergence and integration of
multisensory information.
Acknowledgements The authors would like to thank Debbie Hill
for her assistance in arranging subjects, and Dr. Paul Laurienti for
his helpful discussion. Funding for this study was provided in part
by NIH DC 00057, HD21887, and MH63861.
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