Edited by: Antoni Rodriguez-Fornells, University of Barcelona, Spain
Reviewed by: Nina Kraus, Northwestern University, USA; Cyril R. Pernet, University of Edinburgh, UK
*Correspondence: Daniele Schön, Faculté de Médecine la Timone, UMR 1106 - Institut de Neurosciences des Systèmes, Aix-Marseille Université, Aile rouge - 5éme étage, 27 bd Jean Moulin 13005, Marseille, France e-mail:
This article was submitted to the journal Frontiers in Human Neuroscience.
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Rhythm organizes events in time and plays a major role in music, but also in the phonology and prosody of a language. Interestingly, children with developmental dyslexia—a learning disability that affects reading acquisition despite normal intelligence and adequate education—have a poor rhythmic perception. It has been suggested that an accurate perception of rhythmical/metrical structure, that requires accurate perception of rise time, may be critical for phonological development and subsequent literacy. This hypothesis is mostly based on results showing a high degree of correlation between phonological awareness and metrical skills, using a very specific metrical task. We present new findings from the analysis of a sample of 48 children with a diagnosis of dyslexia, without comorbidities. These children were assessed with neuropsychological tests, as well as specifically-devised psychoacoustic and musical tasks mostly testing temporal abilities. Associations were tested by multivariate analyses including data mining strategies, correlations and most importantly logistic regressions to understand to what extent the different auditory and musical skills can be a robust predictor of reading and phonological skills. Results show a strong link between several temporal skills and phonological and reading abilities. These findings are discussed in the framework of the neuroscience literature comparing music and language processing, with a particular interest in the links between rhythm processing in music and language.
Music is a complex activity that taps onto several sensory-motor, cognitive and emotional mechanisms. Over the last two decades many studies have tested the hypothesis that music training (implying formal training and/or regular practice) can impact non-musical abilities. Most of these studies have addressed this issue by comparing a population of musicians, either professional or amateur, and a population of non-musicians, namely participants with little or no music training. Overall, these studies have shown a clear effect of music-dependent brain plasticity affecting brain activity both at the functional and structural level in adults (Herholz and Zatorre,
Music shares many basic processes with other human activities, and this is particularly evident when comparing music and speech (Besson and Schön,
While a common belief is that music is mostly challenging with respect to pitch, music making puts a high challenge on all these sound features, and most importantly on complex spectral features, because sound quality (and not just being in tune) is what a musician has to work on from the very start. This may explain why music training enhances processing of sound features that play a major role in speech processing as well (Kraus and Chandrasekaran,
One of the most important properties of music being its structuring sounds in time and in a tonal space, it is not surprising that music-dependent brain plasticity goes well beyond subcortical and primary auditory and sensorimotor cortex, thus affecting more integrated functions. For instance, there is evidence that music training facilitates language learning. Children taking music classes are better at segmenting a new artificial language on the sole basis of its statistical properties (François et al.,
A number of studies have also reported an association between music and reading skills. For example, pitch perception was positively correlated with phonemic awareness and reading abilities in children (Anvari et al.,
The fact of showing, on one side that music and language share several sensory and cognitive processes, and on the other side that music training enhances several language abilities, has brought several researchers to hypothesize that music training may be effective in rehabilitation of several motor and cognitive disorders in different clinical populations (Tallal and Gaab,
Our study focuses on the relation between musical abilities and reading skills in children with developmental dyslexia. Developmental dyslexia is a disorder characterized by a specific and long lasting difficulty in reading acquisition, limited to written text decoding with no sensory or neurological deficits (Snowling and Hulme,
Reading results are slow and inaccurate, despite adequate intelligence, socio-cultural background and instruction. Difficulties arise typically from a phonological core deficit with an indirect impact on reading comprehension, requiring lexical, morpho-syntactic, memory and prediction abilities that are not directly affected by this disorder (Lyon et al.,
In Italy, prevalence of developmental dyslexia ranges from 1.5 to 5% (Cornoldi and Tressoldi,
While the neurobiological and genetic basis of developmental dyslexia is now widely accepted in the scientific community, it is not clear whether there is a specific neuropsychological function that, once impaired, determines such heterogeneous landscape of difficulties in reading acquisition. Indeed, if the reading disorder is best described in terms of phonological deficits and to a certain extent visual deficits, there are other deficits of working memory, sequencing, mental calculation, motor coordination or music processing that are often associated with the main reading disorder (Ramus,
These observations have brought to the emergence of multiple hypotheses relative to the functional deficit of developmental dyslexia that may be accounted for by a multifocal brain abnormality approach (Pernet et al.,
What still remains to be understood is the precise temporal scale(s) that may be impaired, thus causing a phonological deficit. For instance, Tallal (
An alternative hypothesis seems to rely on a longer time scale, that of amplitude envelope, and more precisely that of “rise time” which in the case of speech can be very important to distinguish different voice onset times (VOT) allowing to categorize /ch/ of chip vs. /sh/ of ship or /b/ of bull vs. /p/ of pool (Rosen,
Impaired auditory perception of slow (<10 Hz) temporal modulations in speech is thus likely to cause poor perception of speech rhythm and syllable stress (Goswami,
Following the idea of a neural oscillatory phase-locking to speech modulation patterns (e.g., Ghitza,
In this work we present data collected on an Italian highly selected sample of children with developmental dyslexia. In the light of what has been documented in the literature, we investigate the relation between musical temporal, phonological, and decoding (reading) skills. The starting point is the hypothesis of a temporal sampling deficit as possible cause of the poor phonological representation and reading ability. We present a multivariate approach first describing correlations between reading and temporal processing outcomes. Then, we analyse, within the limits of a cross sectional approach, the (predictive) links between several “temporal processing” measures and reading abilities. Finally, we interpret our findings within the theoretical framework described above and give our contribution to the development of a targeted and rehabilitative hypothesis of developmental dyslexia via music training.
Out of 225 children aged 8–11 years with a diagnosis of developmental dyslexia, referred to the health units and rehabilitation centers (IRCCS Burlo Garofolo and ASS1 local health units in Trieste and Villaggio Eugenio Litta in Grottaferrata, Rome), we included 48 children based on the following criteria.
Italian native language; reading performance (accuracy and/or speed) failed on at least two of three school grade standardized Italian tests, as stated in the Original National Guidelines (PARCC DSA,
Comorbidity with Attentional Deficit Disorders with Hyperactivity (ADHD), Specific Language Impairment (SLI), Oppositional Defiant Disorder (ODD), severe emotional-relational impairments, previous formal musical or painting education for more than one year, on-going treatment.
The assessment was carried out by neuropsychologists and neurologists. Children participated only upon formal signed informed consent from their parents.
After the enrolment, the 48 children underwent the following neuropsychological assessment, which includes standardized test and phonological and musical tasks (22 children in Trieste and 26 in Grottaferrata), with mean age of 9 years and 8 months. Two children did not complete the testing.
Parents completed a detailed anamnestic questionnaire providing information about their child's health, relevant family history, and socioeconomic background.
General cognitive abilities and working memory were assessed using the Wechsler Intelligence Scale for Children III (Orsini and Picone,
Auditory Attention was measured using a subtest from the BIA Battery (Marzocchi,
Phonological awareness was assessed using the pseudowords repetition test from the Promea Battery (Vicari,
The ability to read a text aloud was measured using an Italian standardized test for reading abilities (
The ability to read single words and pseudowords aloud was measured on a standardized list of 102 Italian words and 48 Italian pseudowords (
The phonemic blending test included 38 words (nouns) of increasing difficulty, selected from VARLESS Italian data base (Burani et al.,
The phonemic segmentation task also included 38 words, with the same selection criteria described above for the phonemic blending task. Children had to segment every word into its basic sounds (e.g., hear [frog] and produce [f]-[r]-[o]-[g]). Every child performance was recorded with Audacity 1.3 (beta). Dependent variables: number of correct items and time to perform the test.
In this experiment children listened to a sequence of three identical pure tones (800 ms each) with headphones. The onset of one of the tones was varied adaptively (longer ramping) to find the subject's threshold using a Maximum Likelihood Procedure (MLP, Grassi and Soranzo,
In this experiment children listened to a sequence of five identical complex tones (100ms each) with headphones and had to report whether or not a cartoon rabbit was able to perform regular jumps. The gap between tones 3–4 and 4–5 was varied adaptively to find the subject's threshold using a Maximum Likelihood Procedure (MLP, Grassi and Soranzo,
Children had to tap along a 90 pulse/minute metronome for 40 s. Each sound lasted 50 ms, was built using a sinusoidal sound (
Children had to listen and reproduce 10 different rhythms (3–8 tones each; durations spanned from triplets of eight notes to half notes). Each sound of the sequence lasted 65 ms and was built using a MIDI woodblock sound. The sequences were taken and adapted from Fries and Swihart study (
Every item performance was scored by two independent judges from 1 to 9 depending on its similarity to the template stimulus (9 = identical). The final mark for each child was the average of the twenty scores (inter judge correlation was 0.89).
The musical meter task tested and published by Huss et al. (
Each sequence comprised a simple rhythm (2–5 notes) repeated 3 times, to keep short-term memory demands low. Trial length was approximately equated across variations in the number of notes by varying the length of individual notes. Ten trials (5 same, 5 different) were in 4/4 time and 8 trials (4 same, 4 different) were in 3/4 time, with accent conveyed by increasing the intensity of the relevant note in the sequence by 5 dB.
Statistical analysis was performed with SPSS 13.0 and Intercooled Stata 9.0.
Spearman correlation analysis (based on ranks) was performed to test the strength of a relationship between variables. The 95% confidence interval for Rho was calculated with Fisher method.
The interdependence among the measured variables, namely the joint measured variations in response to possible latent (unobserved) variables, was calculated by using a factor analysis with Varimax rotation (maximizing the variances of the squared correlations between variables and factors).
Logistic regression analyses were carried out in order to evaluate which measures were associated with the six dependent variables of the reading tests. All associations were adjusted for sex, school level, city of recruitment and IQ were always controlled (see Tables
Figures
Correlations between all the temporal processing tasks and measures of phonology and literacy are provided in Tables
MT text accuracy | 0.006 (−0.288/0.299) | −0.280 (−0.528/0.011) | −0.154 (−0.425/0.143) | 0.301 (0.012/0.544) | −0.135 (−0.409/0.162) | −0.165 (−0.434/0.132) | −0.342 (−0.575/−0.057) | −0.131 (−0.406/0.166) |
MT text speed | −0.346 (−0.578/−0.062) | 0.387 (0.109/0.609) | 0.269 (−0.023/0.519) | −0.274 (−0.523/0.018) | −0.161 (−0.431/0.135) | 0.240 (−0.054/0.495) | 0.065 (−0.229/0.349) | 0.305 (0.0159/0.547) |
Word accuracy | −0.245 (−0.499/0.049) | 0.204 (−0.091/0.467) | 0.278 (−0.013/0.526) | −0.224 (−0.483/0.071) | −0.168 (−0.437/0.128) | |||
Word time | −0.317 (−0.556/−0.029) | 0.239 (−0.055/0.495) | 0.189 (−0.108/0.454) | −0.238 (−0.495/0.056) | −0.025 (−0.313/0.267) | |||
Pseudoword accuracy | 0.191 (−0.108/0.459) | −0.162 (−0.432/0.134) | 0.303 (0.014/0.545) | 0.000 (−0.290/0.291) | −0.285 (−0.531/0.006) | −0.189 (−0.454/0.107) | −0.170 (−0.439/0.126) | |
Pseudoword time | 0.292 (−0.001/0.539) | −0.229 (−0.487/0.065) | −0.284 (−0.530/0.007) | 0.069 (−0.226/0.352) | 0.159 (−0.138/0.429) | −0.123 (−0.399/0.174) | −0.020 (−0.308/0.272) | 0.312 (-0.553/−0.024) |
Pseudoword reproduction, accuracy | 0.131 (−0.165/0.406) | −0.209 (−0.470/0.087) | −0.246 (−0.500/0.048) | ||
Phonemic segmentation accuracy | −0.252 (−0.508/0.045) | 0.340 (0.055/0.574) | 0.200 (−0.095/0.464) | −0.090 (−0.371/0.205) | −0.015 (−0.304/0.277) |
Phonemic blending accuracy | 0.259 (−0.034/0.511) | −0.015 (−0.304/0.277) | −0.101 (−0.380/0.195) |
As observed in Table
Overall, Tables
Table
Tapping | |||||
Rhythm reproduction | |||||
Meter Perception | 0.319 (0.028/0.560) | ||||
MLP Rise time | 0.203 (−0.096/0.468) | −0.186 (−0.455/0.113) | −0.294 (−0.540/−0.000) | ||
MLP Temporal anisochrony | −0.084 (−0.369/0.214) |
The factor analysis included accuracy and speed measures in the tests measuring reading abilities, phonological awareness, temporal processing, auditory attention, and digit span. Preliminary testing showed that our model was satisfactorily adequate. Indeed the Kaiser-Meyer-Olkin (KMO) index measuring the sampling adequacy gave a value of 0.764 (recommended is >0.6). Also the Bartlett's test of sphericity rejecting the null hypothesis of an identity matrix was significant (
MT text reading speed | −0.816 | ||
Word reading accuracy | 0.803 | ||
Word reading time | 0.874 | ||
Pseudoword reading accuracy | 0.813 | ||
Pseudoword reading time | 0.826 | ||
Phonemic segmentation | 0.842 | ||
Phonemic blending | 0.818 | ||
Pseudoword repetition | −0.443 | 0.527 | |
Auditory attention | 0.671 | ||
Digit span | 0.486 | ||
Metrical task | 0.548 | ||
Tapping | −0.586 | ||
Rhythm reproduction | 0.551 | 0.511 | |
Rise time | 0.540 | ||
Temporal anisochrony | −0.802 |
The first factor shows high factor loadings (i.e., correlation coefficients between variables and factors) for speed and accuracy scores in all reading tests and surprisingly in rise time threshold. Thus, this first factor can be interpreted as describing reading abilities.
The second factor shows high factor loadings for the temporal anisochrony threshold and auditory attention test while slightly lower factor loadings for tapping coefficient of variation, accuracy in rhythm reproduction task, musical meter perception task, pseudoword repetition test and the verbal short term memory test of WISC III. It can thus be interpreted as a factor describing broad auditory temporal processing.
The third factor shows high factor loadings for accuracy in the phonemic blending and phonemic segmentation tests and slightly lower loading for the pseudoword repetition and rhythm reproduction tasks. It can thus be interpreted as a factor describing broad phonological processing.
In the logistic regression analyses (Tables
City | 0.343 | 0.268 | 0.170 | 0.074 | 1.584 |
School level | 1.085 | 0.295 | 0.763 | 0.637 | 1.849 |
IQ | 0.939 | 0.036 | 0.096 | 0.872 | 1.011 |
Sex | 0.238 | 0.200 | 0.088 | 0.046 | 1.238 |
Metrical task | 0.641 | 0.124 | 0.439 | 0.938 |
City | 1.386 | 1.137 | 0.690 | 0.278 | 6.920 |
School level | 1.081 | 0.329 | 0.797 | 0.595 | 1.964 |
IQ | 0.937 | 0.0368 | 0.099 | 0.868 | 1.012 |
Sex | 0.871 | 0.708 | 0.865 | 0.177 | 4.283 |
Rhythm Reproduction | 0.429 | 0.163 | 0.203 | 0.903 |
City | 0.626 | 0.519 | 0.572 | 0.124 | 3.173 |
School level | 0.658 | 0.189 | 0.146 | 0.375 | 1.156 |
IQ | 0.968 | 0.039 | 0.418 | 0.895 | 1.047 |
Sex | 2.050 | 1.773 | 0.407 | 0.376 | 11.170 |
Mother School Level | 6.371 | 4.277 | 1.709 | 23.748 |
City | 24.179 | 47.008 | 0.101 | 0.535 | 1092.288 |
School level | 5.789 | 5.443 | 0.062 | 0.917 | 36.550 |
IQ | 0.830 | 0.079 | 0.052 | 0.688 | 1.002 |
Sex | 3.764 | 6.777 | 0.462 | 0.110 | 128.281 |
Metrical Task | 0.2698 | 0.165 | 0.081 | 0.893 |
Analyses of the MT text reading test point to the meter perception task as a good predictor of reading accuracy (
Analyses of the word reading test point to the mother school level as a good predictor of reading accuracy (
Analyses of the pseudoword reading test point to the rhythm reproduction test as a good predictor of reading accuracy (
This study explored whether and to what extent different levels of temporal processing are associated to reading and phonological abilities.
We found that rhythm reproduction were strongly associated with most reading outcome measures and phonological awareness. Furthermore, tapping tasks correlated with some aspects of language and rise time correlated with text reading, in accordance with previously published studies (Goswami et al.,
Intriguingly, the factor analysis identified three significant factors: the first grouping reading tests and rise time thresholds; the second spanning broad auditory temporal processing, including pseudoword repetition and verbal short term memory; the third describing phonological processing but also including rhythm reproduction.
Last but not least, the logistic regression analyses indicated the meter perception task as a good predictor of text reading accuracy and word reading speed, while rhythm reproduction was the best predictor of pseudoword reading accuracy. Finally, maternal formal education level was also a good predictor of word reading accuracy.
We will first discuss the results of these complementary analyses, bridging temporal processing skills on one side and phonological awareness and literacy on the other. We will then present some considerations on the different temporal scales that are addressed by our tasks and by other tasks and models described in the literature. Finally, we will consider the use of music training as a possible rehabilitation of developmental dyslexia and give some tentative recommendations.
Correlations between the temporal processing tasks, phonology measures, and literacy confirm previously published data in the literature (Anvari et al.,
The perceptual metrical tasks also require grouping events in chunks on the basis of a metrical hierarchy (e.g., strong-weak-weak). The independent variable was the duration of the strong beat which was sometimes lengthened by 100 ms. This is somewhat related to the two psychoacoustic tests measuring rise time and temporal anisochrony thresholds because lengthening the strong beat produces both a change in the temporal envelope of the note—like in the rise time task—and a change in the temporal relation with the preceding and following notes—like in the temporal anisochrony task. Interestingly, the temporal anisochrony task did not correlate with any phonological or literacy measures. By contrast, both the metrical and rise time tasks correlated with some literacy measures (word and text reading) pointing to a greater role of temporal envelope compared to temporal isochrony.
Results of the factor analysis confirm and extend results of the correlation matrix. Interestingly, all temporal tasks except the rise time task appear in the same factor, which also includes the auditory attention and verbal working memory (digit span) tasks. This raises the issue of the relation between attention and working memory on one side, and temporal skills on the other side. More precisely, in the case of the metrical and rhythmic reproduction tasks (but it is also the case in the text reading task), children need a global representation of the stimuli, while a serial and local representation of stimulus parts necessarily produces a poor performance. This global representation possibly needs an attentional window spanning approximately 2 s. This is also the case of the psychoacoustic task because the change to be detected was embedded in a five-note sequence for the temporal anisochrony. In the case of tapping, the temporal window is shorter when considering the interval between successive taps, but this shorter window possibly engenders a larger temporal windows, due to the emergence of a metrical structure, yielding a more global percept of several taps. In other words, when tapping along a metronome, the child will group taps together in series of two, three of four (the latter being the most likely here), with the first tap of each group being perceived as the most relevant. The third factor of the analysis shows the rhythmic task together with the phonological awareness tasks. Thus, while an attentional and memory component may indeed play a role, there seems to be a cognitive process in the rhythm reproduction task that is independent of selective attention and verbal working memory processes and that is strongly related to phonological processing. While the tapping does not appear in the third factor, this is due to the thresholding criterion we used (eigenvalue ≤ 0.4), but the tight relationship between the rhythmic task and tapping is visible in the high correlation values between these two variables.
Another interesting result of the factor analysis is the presence of the rise time task together with all reading measures. In speech, amplitude modulations in the temporal envelope (rise time) are one of the critical acoustic features underlying syllable rate and speech rhythm, and allow to distinguish between stressed and unstressed syllables (Leong et al.,
Correlation and factor analyses do not take into account certain sources of covariance such as age, sex, IQ and so on. However, the sources of correlation due to these variables can be controlled in regression analyses such as the logistic regression use here. In the logistic regression the dependent variables (e.g., text reading accuracy) are categorized into two categories corresponding to a severe or moderate level of dyslexia. Thus, after controlling for the effects of variables city, school-level, QI and sex, the model tests whether there is still one or more (continuous) independent variables that constitute a significant predictor of the reading outcome category. Interestingly the two measures that best predict reading outcomes are not the phonological awareness, attention or working memory tasks but the two tasks that present a greater temporal complexity, the rhythm reproduction and the metrical perceptual task. Both tasks measure a rather global level of temporal processing, including amplitude modulation, grouping events into chunks and applying a metrical hierarchy.
Although it was not the main aim of the present work, an interesting result is that mother school level was a good predictor of word reading abilities. This is probably linked to the fact that word recognition is influenced by the lexical/vocabulary development of the child (Sénéchal et al.,
One aim of the present work was to compare how different temporal skills relate to phonological and reading abilities. In doing this we had to choose a limited number of tasks, each testing a different aspect of temporal processing. We will try here to discuss how there different levels relate to each other, and how they may possibly be linked to reading disabilities in developmental dyslexia.
The smallest temporal scale is at the millisecond level. Hornickel and Kraus (
In her rapid auditory processing theory, Tallal (
We have already discussed of the temporal sampling deficit framework suggested by Goswami (
Both the meter perception and rhythm reproduction tasks also require building a longer temporal structure wherein the different inter-stimuli intervals are categorized in terms of relative durations (typically simple fractions: 1/2, 1/3, 1/4 or their reciprocal) and grouped together in larger units. The temporal scale here is longer, below 2 Hz, because these larger units may contain several notes. This would correspond in speech to word segmentation (several syllables) and prosodic phrasal boundaries (several words). Moreover, these grouping phenomena give rise to the emergence of the metrical structure, the alternation of strong and weak beats which typically corresponds to the a musical bar and falls again in a rather slow temporal window (below 2 Hz). An interesting theoretical account of the perception of musical meter is given in terms of continuous attentional modulations that would be coupled via entrainment to the metrical structure of the musical stimulus (Large and Jones,
The last temporal scale that we would like to address is of a somewhat different quality and not specific to the auditory domain. It concerns the ability to predict events in time. This is a more general cognitive mechanism, sometimes referred to as Bayesian inference. For instance, making a good guess by prior probabilities (i.e., our experience of the world as we know it) about which words are most likely to be heard or seen. This is especially true when the environment is “noisy” and the choice of the signal representation is ambiguous, which is the case in natural speech but also in reading (due to time pressure and competition between similar words) and even more so in children with developmental dyslexia (Norris,
To conclude this section, one should keep in mind that all the different time scales that we presented above are strongly inter-related, and that the serial presentation from short to long time scale does not mean that the levels are serial or independent from each other or that embedding of one level into another only takes place in one direction.
The issue raised here between the lines is whether and how music can help children with developmental dyslexia to restore a normal developmental trajectory of reading abilities. While there is not yet a clear cut answer to these questions, our data, together with other previously published results strongly suggest that music should have a positive effect on reading abilities. The reasons of this benefit are probably multiple and are still debated and will thus require further research in the years to come.
From a perspective on music and rehabilitation, it is interesting to consider the OPERA hypothesis proposed by Patel (
From a more precise perspective on music and rehabilitation of developmental dyslexia, several authors have hypothesized a rehabilitation centered on rhythm, capable of developing several temporal skills that may in turn transfer to reading skills (Overy et al.,
Some authors suggest to work at a global level on rhythm and meter, both in perception and production (Goswami,
Putting together our results with the general framework of music and language rehabilitation suggested by Patel and the more specific frameworks suggested for developmental dyslexia we will give some tentative but scientifically grounded recommendations when considering a music intervention with this population.
Our first recommendation (R1) is to use a group setting rather than an individual setting. This will possibly boost the playful and positive emotional aspects of the training and will possibly maximize rhythmic entrainment. Indeed, Kirschner and Tomasello (
Our second recommendation (R2) is to use a fully active setting with music making and active musical games wherein music, body movements, emotions, and intentionality influence each other in a complex dynamical process (Maes et al.,
Our third recommendation (R3) is to focus on rhythm rather than on pitch accuracy as it is often the case in classical music pedagogy. This can be easily associated to movement and dance and, despite the idea that music has to be perfectly in tune, there are a plethora of musical games or even styles that are not too demanding on pitch accuracy, such as beat boxing, body tapping, rap and so on. This type of rhythmic activity seems to us to be the most appropriate in the rehabilitation of developmental dyslexia. On one side it will improve global temporal skills (meters and rhythm processing, sequencing, temporal prediction). On the other side, the lack or limitation of pitch and tonality will force the music teacher to make a larger use of the spectral dimension, by using different timbres produced with the mouth, body or different percussive instruments which may in turn facilitate fast temporal processing of speech sounds.
Our last recommendation (R4) is to keep variety high. While repetition is intrinsic to musical structure, the music teacher, by contrast to the computer game, can propose an almost infinite number of befittingly variations of a given game/exercise/song, that will possibly emerge in the musical interaction between the teacher and the children or the children themselves. This high variety is important in our view, to capture children attention but also to maximize the chances of a generalization process and thus a transfer to language and reading.
In this study we investigated the link between different levels of temporal processing and reading skills in developmental dyslexia. We confirmed and extended previous findings describing a strong relation between timing and reading abilities. However, due to time constraints of the testing session we could not assess all temporal processing levels (for instance the fine structure level, important for phonetic discrimination). Moreover while the three statistical analyses point into a similar direction, results are only partially concordant, possibly due to the intrinsic heterogeneity of a population of dyslexic children.
Despite these limitations, our results show a strong association between reading skills and meter perception and rhythm processing. These two measures of temporal processing do not only involve timing mechanisms, but also other competences that are notoriously poor in children with developmental dyslexia, such as auditory attention (Facoetti et al.,
The next step should be to develop interventions based on musical training for children with developmental dyslexia, and to test their efficacy through randomized controlled trials, although sufficient numerosity to allow adequate statistical power to detect treatment effects may be difficult to achieve due to the high cost and risk of drop out. A multicenter study may overcome these obstacles. To conclude, the literature review literature and our findings suggest that music training, focused on rhythm, could be beneficial for children with dyslexia, or maybe even for children identified earlier as at risk based on low phonological abilities.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This work was funded by the Mariani Foundation, grant no. R-11-85. We wish to thank Giorgio Tamburlini for helpful comments on this manuscript and all the families and children for their patience.