Most problems can only be solved if one knows what causes that particular problem. A disease such as pellagra, also called the disease of the four D’s — dermatitis, diarrhea, dementia and death — took the lives of thousands in the Southern states of America during the early part of the twentieth century. Data is sketchy, but by 1912, the State of California alone reported 30,000 cases and a mortality rate of 40%. Today, pellagra is virtually unknown because we know that it is caused by a vitamin B3 deficiency. A viable point of departure would thus be to ask: what causes dyslexia?
Clinicians have known for a long time that dyslexia runs in families. Soon after developmental dyslexia was first described by Pringle-Morgan (1896) and Kerr (1897), several reports of familial aggregation appeared (Hinshelwood, 1907, 1917; Stephenson, 1907; Thomas, 1905).
DeFries and colleagues (1978) initiated a large family study of dyslexia. They recruited a sample of 133 children who were identified by teachers as having significant reading difficulty and then tested them in the laboratory with an extensive battery to confirm that they had significant reading and possibly related cognitive disabilities. An age and gender matched sample of 125 children with no reading problems was also identified by teachers and tested in the laboratory. The parents and siblings of the children with dyslexia and of the normal children were tested on the same measures. The main result of the DeFries et al. family study was clear: There was strong evidence for the familial transmission of dyslexia. The relatives of the children ascertained with dyslexia were significantly more likely to also have reading problems, compared to the relatives of children with normal-range reading abilities.
The relative contributions of genetic influences and shared family environment can be dissected in twin studies. It has been shown robustly that concordance for a qualitative diagnosis of dyslexia is significantly higher in identical tiwns, who have a virtually identical genetic makeup, than it is in non-dentical twins who (like ordinary siblings) share about half of their segregating alleles. A large-scale study of twins with dyslexia yielded a concordance rate of 68% in identical twins, as compared with 38% in non-identical twins, indicating a substantial genetic component (DeFries & Alarcón, 1996).
Although some causes of dyslexia have a genetic origin, and environmental factors play an important role, cognition mediates brain-behavior relationships and therefore offers a sufficient level of explanation for the development of principled interventions. We thus need to understand the cognitive difficulties that underpin reading failure, regardless whether their origin is constitutional or environmental (Elliott & Grigorenko, 2014).
Research into dyslexia has been dominated by the quest for a single cognitive deficit that is necessary and sufficient to cause all behavioral characteristics of the disorder. Until the 1950s, the belief was that dyslexia is attributable to visual processing problems, perhaps also including motor skill problems. But in 1957 Noam Chomsky published his seminal book, Syntactic Structures, which suggested that humans are genetically endowed with an “encapsulated linguistic processor” which mediates a “Universal Grammar” that underlies all languages. These ideas quickly transformed the study of language and with it, reading. Dyslexia became attributed to a fault in Chomsky’s linguistic processor, and any role for visual processing was abandoned. Dyslexia became a linguistic, phonological problem, not a visual one. In an inﬂuential book, Dyslexia: Theory and Research, Vellutino (1979) argued that many of the apparent visual problems could actually be attributed to language difﬁculties — especially to deficient phonological awareness.
Phonological deficit theory
The phonological deficit theory became the most well-developed and supported of the theories of dyslexia. The U.S. researchers have united in adopting the phonological deficit hypothesis since the early 1980s, and this united front has led to the investment of more than $15 million annually by the US government, via the National Institute for Child Health and Human Development (NICHD) (Fawcett, 2001). This research program into the causes and remediation of reading disabilities continues until the present day.
Phonological awareness (PA) refers to an individual’s awareness of the phonological structure, or sound structure, of language. It is a listening skill that includes the ability to distinguish units of speech, such as rhymes, syllables in words, and individual phonemes in syllables. PA is often confused with phonics, but it is different. Phonics requires students to know and match letters or letter patterns with sounds, learn the rules of spelling, and use this information to decode (read) and encode (write) words. PA relates only to speech sounds, not to alphabet letters or sound-spellings, so it is not necessary for students to have alphabet knowledge in order to develop a basic phonological awareness of language. Phonemic awareness is a subset of PA that focuses on recognizing and manipulating phonemes, the smallest units of sound. The two most important phonemic awareness skills are segmenting and blending.
The ability to segment and blend phonemes is said to be critical for the development of reading skills, including decoding and fluency, and even that it predicts reading ability (Edwards & Taub, 2016). It is also claimed that PA training can prevent and correct reading difficulties (Kilpatrick, 2016, p. 13). Moustafa, however, points out that correlation does not establish causation. “In statistics, the word predicts means nothing more than that there is a high correlation between two phenomena” (Moustafa, 2001, p. 248).
Not all studies support phonological and phonemic awareness training (Pape-Neumann, van Ermingen-Marbach, Grande, Willmes, & Heim, 2015; Krashen, 1999a; Krashen 1999b). Blomert and Willems (2010) investigated children at familial risk for dyslexia in kindergarten and first grade. The familial risk was genuine; 40% developed reading deficits in first grade. However, they did not find any relationship between a PA or other phonological processing deficits in kindergarten and reading deficits in first grade. In a study by Daigle et al. (2016), inefficiency of phonological processing could not explain the spelling delay in a group of French children with dyslexia. Taylor (1998) points out that while children’s early cognition develops from concrete experiences to abstract understandings, phonemic awareness training begins with abstract exercises. Stein (2018) concludes that the phonological theory does not provide a helpful explanation for dyslexic reading problems because it is set at too high a cognitive level.
Some findings indicate that phoneme awareness may develop as a consequence of exposure to reading and writing, while other support an intermediate view, “that phonological awareness and alphabetic literacy learning influence each other reciprocally” (Manolitsis & Tafa, 2011, p. 31). Some researchers claim that phonological factors may be less important than is commonly accepted (Byrne, 2011). Not all children with reading disabilities demonstrate a phonological deﬁcit, and Catts and Adlof (2011) point out that children with poor phonological abilities can nevertheless develop good reading skills. In addition, a single cognitive deficit model cannot account for comorbidity. Dyslexia co-occurs more often than would be expected by chance with other developmental disorders, such as ADHD and specific language impairment.
Given that a single phonological deﬁcit is not necessary or sufﬁcient to cause a reading disability, current thinking sees this as one of multiple deﬁcits that are likely to interact to cause reading disability (Pennington, 2006; Peterson & Pennington, 2012). Van Bergen et al (2014) summarize Pennington’s multiple deficit model as follows:
In his model, multiple genetic and environmental risk factors operate probabilistically by increasing the liability to a disorder; conversely, protective factors decrease the liability. These etiological factors produce the behavioral symptoms of developmental disorders by influencing the development of relevant neural systems and cognitive processes. Importantly, there is no single etiological or cognitive factor that is sufficient to cause a disorder. Instead, multiple cognitive deficits (each due to multiple etiological factors) need to be present to produce a disorder at the behavioural level. Some of the etiological and cognitive risk factors are shared with other disorders. As a result, comorbidity among developmental disorders is to be expected, rather than something that requires additional explanations. Finally, from Pennington’s multiple deficit model (MBM) it follows that “the liability distribution for a given disease is often continuous and quantitative, rather than being discrete and categorical” (Pennington, 2006, p 404).
In addition to phononological awareness, cognitive psychology has now linked many brain-based skills to dyslexia:
- verbal fluency (Moura, Simões & Pereira, 2015);
- attention and executive functions (Menghini et al., 2010);
- visual attention, i.e. our ability to rapidly select the most relevant visual information ranges when we are engaged in various reading tasks (Elliott, 2015; Valdois, Bosse & Tainturier, 2004);
- visuospatial abilities (Giovagnoli, Vicari, Tomassetti & Menghini, 2016; Menghini et al., 2010; Helland & AsbjØrnsen, 2003);
- processing speed (Moura, Simões & Pereira, 2015; Stoodley & Stein, 2006);
- short-term memory (Cowan et al., 2017; Majerus & Cowan, 2016);
- auditory working memory (Vender, 2017; Weiss, Granot & Ahissar, 2014);
- visual and visual sequential memory (Talepasand, Eskandaripour & Taghinezhad, 2018; Guthrie & Goldberg, 1972);
- visual long-term memory (Binamé, Danzio & Poncelet, 2015), especially for details (Huestegge, Rohrßen, van Ermingen-Marbach, Pape-Neumann & Heim, 2014);
- verbal long-term memory (Helland & Morken, 2015); and
- rapid naming (Brizzolara, 2006; Denckla & Rudel, 1976).
Video: Dyslexia intervention based on the multiple deficit model
Rapid naming refers to the speed with which the names of symbols (letters, numbers, colors, or pictured objects) can be retrieved from long-term memory. This process is often termed rapid automatized naming (RAN), and people with dyslexia typically score poorer on RAN assessments than normal readers. Deficits in rapid naming is often viewed as part of the phonological deficit in poor readers. Wolf and Bowers (1999), however, claim that it constitutes a separate construct that is related to reading independently. According to the double deficit hypothesis model, people with dyslexia can be subdivided into three groups: those with PA difficulties but with average RAN ability, those with a RAN deficit but average PA skills, and those with both PA and RAN difficulties. According to this model, those with the double deficit would be likely to have the most severe form of reading difficulties.
In a longitudinal study, Landerl et al. (2018) examined 1,120 children acquiring one of five alphabetic orthographies with different degrees of orthographic complexity (English, French, German, Dutch, and Greek). While RAN was a universal predictor of reading in five alphabetic orthographies varying in consistency, no consistent pattern appeared for the PA–reading relationship. The researchers conclude that phonological awareness’s direct contribution to reading development might be less causal than is generally assumed. They speculate that instead of being a prerequisite for learning to read, PA may function as a corequisite skill for typical reading development. Ziegler et al. (2010), however, examined the influence of phonemic awareness and RAN across five languages lying at different positions along a transparency continuum (Finnish, Hungarian, Dutch, Portuguese, and French). They found phonological awareness to be the main factor associated with reading performance in each language; its impact was stronger in less transparent orthographies. The influence of RAN was rather weak and limited to reading and decoding speed.
One of the most reliable and often-quoted associated characteristics of developmental dyslexia is an inefficiency in short-term memory (STM) which, together with rapid naming, has been mainly interpreted within the phonological core deficit hypothesis. Verbal STM capacity, measured by digit span or non-word repetition tasks, is typically reduced in children with dyslexia, and this reduction is still present in adults with a history of dyslexia. Ramus and Szenkovits (2008), however, raised the question whether STM deficits in dyslexia are perhaps a basic impairment, rather than being accounted for by phonological processing difficulties.
In an attempt to answer this question, Trecy et al. (2013) distinguished between item and order retention processes. While STM for item information has been shown to depend on the quality of underlying phonological representations, and hence should be impaired in dyslexia, STM for order information is considered to reflect core STM processes independent from language processing. In their study 30 adults with dyslexia and 30 control participants were matched for age, education, vocabulary and IQ, and presented with STM tasks that distinguished item and order STM capacities. The researchers observed not only impaired order STM in adults with dyslexia, but this impairment was independent of item STM impairment. This study shows that adults with dyslexia present a deficit in core verbal STM processes, a deficit which cannot be accounted for by the language processing difficulties that characterize dyslexia. These results support theoretical accounts considering independent order STM and item STM processes, with a potentially causal involvement of order STM processes in reading acquisition.
Verbal and nonverbal IQ
Researchers have also found a link between dyslexia and verbal and nonverbal IQ. Van Bergen et al. (2014) assessed four-year-olds (N = 212) with and without familial risk for dyslexia on ten IQ subtests. Reading and arithmetic skills were measured four years later, at the end of Grade 2. Relative to the controls, the at-risk group without dyslexia had subtle impairments only in the verbal domain, while the at-risk group with dyslexia lagged behind across IQ tasks. Nonverbal IQ was associated with both reading and arithmetic, whereas verbal IQ was uniquely related to later reading. The children who went on to develop dyslexia performed relatively poorly in both verbal and nonverbal abilities at age four, which lends credence to the multiple deficit model.
There is old but famous evidence consistent with the idea that there is something wrong in the brain of some persons who have persistent unexpected difficulty in learning to read. Specifically, Galaburda and colleagues reported a number of anatomical anomalies in the brains of a few persons with reading difficulties. Although those findings are intriguing, they are far from decisive. The samples were extremely small (a total of 4 men and 3 women) and included participants with evidence for neurological or psychiatric conditions and participants with impairments not limited to written language, who should have been excluded from a study purporting to examine brain correlates of dyslexia. The control group was also very small, effectively precluding reliable estimation of the specificity of any findings.
As technology advanced, neuroscience contributed more and more to dyslexia research. Unfortunately, most studies have too small samples to permit reliable conclusions to be drawn, and many results are inconsistent (Protopapas & Parrila, 2018). In a meta-analysis of functional neuro-imaging studies of dyslexia, Martin et al. (2016) list studies in which differences between groups with and without dyslexia were found in specific brain regions. The most consistent findings concerned the left occipitotemporal cortex, which includes the so-called “visual word form area,” though to be critical for reading. Skilled readers draw on word representations from the “visual word form area” (Glezer et al., 2016).
Neuroscientists at Georgetown University Medical Center discovered that skilled readers can recognize words at lightning fast speed when they read because the word has been placed in a sort of visual dictionary. This part of the brain is known as the visual word form area, and functions separately from an area that processes the sounds of written words.
Glezer and her coauthors tested word recognition in 27 volunteers in two different experiments using fMRI. They were able to see that words that were different, but sound the same, like ‘hare’ and ‘hair’ activate different neurons, akin to accessing different entries in a dictionary’s catalogue. If the sounds of the word had influence in this part of the brain we would expect to see that they activate the same or similar neurons, but this was not the case — ‘hair’ and ‘hare’ looked just as different as ‘hair’ and ‘soup’. In addition, the researchers found a different distinct region that was sensitive to the sounds, where ‘hair’ and ‘hare’ did look the same. The researchers thus showed that the brain has regions that specialize in doing each of the components of reading: one region is doing the visual piece and the other is doing the sound piece. The part of the brain that does not work well then in the case of a child with orthographic dyslexia, is most likely the region that is doing the visual piece, which is called the visual word form area.
This negates the dual-access theory which suggests that visual word recognition in skilled readers is not based on visual processing alone, but that we access both the phonology and the visual perception of a word (Frost, 1998).
Video: Dyslexia case study
Meet Susan, Vivienne’s mom. Vivienne was adopted from China at age 5. This video is about Susan helping her 11-year-old daughter catch up on development delays, including dyslexia. They started with the Edublox program 13 weeks ago. Click here to follow their journey to learning success.
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