by Fumiko Hoeft MD PhD, Peggy McCardle PhD MPH, and Kenneth Pugh PhD
Developmental dyslexia (aka reading disorder or specific reading disability) is a neurodevelopmental, often heritable, condition in which learning to read is disrupted by problems with the phonological components of reading. How does the manifestation of dyslexia vary across writing systems? If dyslexia has a single underlying cause, then one might expect dyslexia to have a common neurobiological basis and occur in all countries relatively homogenously. If, on the other hand, multiple factors contribute to dyslexia, and the manifestation is influenced by environmental and cultural factors such as writing systems, then one might expect the prevalence rate and the neurobiological basis to vary across writing systems. It is important to understand the neural representations of different writing systems because increased globalization and immigration are turning today’s world into a multilingual society with more multilinguals than monolinguals (European Commission Special Eurobarometer, 2006). Such understanding will provide us with hints as to how various writing systems interact in multilinguals.
Historically, learning multiple languages has sometimes been associated with disadvantages such as smaller vocabulary in each language (Ben-Zeev, 1977); at the same time, though disputed, there appears to be compelling evidence of cognitive advantages [(McCardle, 2015); (for advantages, see Bialystok, Craik, & Luk, 2012; Kroll & Bialystok, 2013; Marian & Shook, 2012); (for dispute, see Paap & Greenberg, 2013)]. These disadvantages and advantages may partially depend on the combination of languages and factors, such as age of acquisition and relative usage patterns for each language over the course of development. As a result of globalization, more of our children with dyslexia are also becoming multilingual, yet they are often discouraged from learning a foreign language (See http://eida.org/at-risk-students-and-the-study-of-foreign-language-in-school/). Does this mean that our children with dyslexia are missing out on opportunities to benefit from the putative cognitive advantages? While the jury is still out, and more research is warranted on these issues, we do know that the story is quite complex.
The current article is intended to provide the latest update on these important issues by taking a close look at how dyslexia is represented in different writing systems. In future issues, we hope to cover the topics of the multilingual brain and cognitive advantage in multilinguals, with implications for whether and how we should encourage children with dyslexia to learn foreign languages.
Neurocognitive patterns in different writing systems
In order to understand how dyslexia differs (or is similar) across writing systems, we start by addressing the cognitive and neurobiological mechanisms associated with typical reading in different writing systems and orthographies. Writing systems are different from orthographies and reflect fundamental variations in writing-language relationships (e.g., alphabetic—English, syllabic—Japanese Kana, and logographic—Chinese writing systems) (Perfetti, Liu, & Tan, 2005). Orthographies, on the other hand, represent written languages (e.g., the alphabetic system) that differ in the complexity, consistency, or transparency of the grapheme-to-sound correspondence. For example, within alphabetic languages, English and French are opaque and orthographically deep languages with many variations in grapheme-to-sound correspondence, whereas Spanish and Finnish are transparent and orthographically shallow languages with fewer variations in their grapheme-to-sound correspondence. Other non-alphabetic writing systems can also map onto orthographic depth; for example, Hebrew and Chinese are considered to be opaque orthographies.
The now classic “Orthographic Depth Hypothesis” argues that in shallow orthographies (e.g., Spanish and Finnish), where spelling-sound correspondence is more direct, phonology is generated directly from print; hence, these languages are easier to learn. On the other hand, in deep orthographies (e.g., English, French, and Hebrew), phonology is derived from the internal lexicon because there is a complex relationship between graphemes and phonemes; the relationship is less direct, leading to greater difficulty learning to read words (R. Frost, Katz, & Bentin, 1987; Seymour, Aro, & Erskine, 2003). Differences in writing systems also result in substantial variability in the level of detail with which phonology is represented in the orthography (known as grain size of lexical representation and the “Grain Size Theory”) (Ziegler & Goswami, 2005); this grain size is thought to critically affect the speed of learning to read (Kyle, Kujala, Richardson, Lyytinen, & Goswami, 2013).
Recent theories, however, have also proposed that there are higher-order cognitive operations that are invariant across writing systems; these invariant properties are known as “reading universals” (Ram Frost, 2012). All writing systems, despite their differences, have evolved to convey meaning through written forms, and there should be fundamental properties that are similar across writing systems. Consistent with this theory of reading universals, learning to read, regardless of the orthographic depth in the language, is predicted by phonemic awareness and rapid naming (Caravolas, Lervåg, Defior, Seidlová Málková, & Hulme, 2013; Lei et al., 2011). This is particularly surprising as Chinese is a language with an orthography that, at its surface, appears to require little insight at the phoneme level.
Reflecting these paradoxes in behavioral research, there is variability in the claims made by neuroimaging studies. Some suggest that the same core network underlies reading in different orthographies with subtle differences in the relative weighting of the network’s components between orthographies (i.e., invariance in the reading network) (Bolger, Perfetti, & Schneider, 2005; Nakamura et al., 2012). Others contest language invariance, and suggest that certain components of the reading network are language specific (Tan et al., 2001). Although more recent trends seem to suggest that similarities vastly outweigh differences, thus lending some support for the neurobiological underpinning of reading universals (summarized in Richlan, 2014), there appears to be little agreement.
Prevalence rate of dyslexia in different writing systems?
How do differences in writing systems influence the development of dyslexia? Based on differences in each language’s writing system, including orthographic depth, one may expect differences in prevalence rates. In the 1980s, when systematic cross-linguistic comparisons were conducted for the first time, dyslexia was reported to have a surprisingly higher prevalence rate than what had been previously believed, not only in the US but also in countries such as Japan and China (Stevenson et al., 1982). Research in the past two decades has indicated that in Japanese speakers, the prevalence rate has been generally lower than the typical English rate of 5 to 10% (Katusic, Colligan, Barbaresi, Schaid, & Jacobsen, 2001; Landerl & Moll, 2010). When Japanese readers were assessed using the syllabic Kana writing system, the prevalence was estimated to be 2 to 3 %—because of the shallow orthography and transparent grapheme-sound correspondence. In contrast, when these readers were assessed using the logographic system, Kanji, the prevalence was 5 to 6 % (Wydell, 2012). Further, the prevalence of dyslexia in Chinese speakers has been thought to be around 3.9 % (Sun et al., 2013), a rate similar to the prevalence for dyslexia in orthographically shallow languages (e.g., 3.1 to 3.2 % for Italian: Barbiero et al., 2012).
It is important to note, however, that there are currently no biological tests for diagnosis of dyslexia, so the definition of dyslexia is based primarily on reading performance and test scores. Consequently, the prevalence rate is highly sensitive to the criteria used for diagnosis of dyslexia. For example, in a large sample of German children, prevalence was between 1.9 to 2.6 % when the criteria used was a reading score of 1.5 to 1 standard deviations below the norm AND average performance in at least one other cognitive measure (Moll, Kunze, Neuhoff, Bruder, & Schulte-Körne, 2014). This is consistent with the idea that the prevalence rate is lower for orthographically shallow languages as mentioned above. The German prevalence rate jumps up to a range of 7.1 to 15.6 %, however, if only the reading score of 1.5 to 1 standard deviations below the norm is used. Based only on this criterion of reading performance, the prevalence rate of dyslexia in this orthographically shallow language (German) is as high as the rate for a language with a deep orthography (e.g., English). This is not a trivial difference; therefore, a more biological evidence-based set of identification criteria for diagnosis of dyslexia is highly desirable (Tanaka et al., 2011).
Together, recent research on prevalence rates suggests that dyslexia exists in all languages at a higher rate than once suggested. Whether the prevalence rate varies with orthographic depth, with deeper languages having a higher prevalence rate, has not been well established and will require further research.
Neurocognitive manifestation of dyslexia in different writing systems
We began this article with the central question of whether underlying brain patterns in dyslexia are unique to each writing system. Initial evidence that many of us in the field remember came from the seminal study that suggested common effects of dyslexia in alphabetic orthographies with varying orthographic depth (i.e., English vs. French vs. Italian) (Paulesu et al., 2001). The assertion in that study would make sense based on the reading universals we discussed above. However, when researchers looked beyond alphabetic languages, this assertion came into question. A comparison between individuals with dyslexia from alphabetic (e.g., English) and logographic (e.g., Chinese) writing systems showed clear dissociations (Siok, Niu, Jin, Perfetti, & Tan, 2008; Siok, Perfetti, Jin, & Tan, 2004). Despite findings from past behavioral studies that showed similar cognitive profiles in dyslexia across languages, this dissociation was attributed to the superficial differences in aspects such as visuo-spatial processing and memory. More recent research suggests that when all confounding variables are well controlled for, there are remarkably few brain activity differences between writing systems such as Chinese and English (Hu et al., 2010). The current thinking is that there are core similarities in the neural representation of dyslexia, with small differences in weighting and recruitment of additional networks depending on the writing system (e.g., recent findings in Japanese people with dyslexia: Kita et al., 2013) (Richlan, 2014).
What researchers have learned so far is that writing systems vary extensively not only in their visual appearance (e.g., alphabetic vs. logographic and visual complexity) but also in many other factors such as orthographic depth and morphological complexity (the latter not discussed in the current article). Prevalence rates of dyslexia may vary somewhat across writing systems, perhaps influenced by these differences. It is important to note, however, that prevalence rate is highly sensitive to the criteria used to define dyslexia; some research groups have found essentially identical prevalence rates regardless of orthographic depth, depending on the criteria used. We have also learned that, despite these more ‘superficial’ differences, there seems to be a fundamental universal principle that dictates reading networks in different writing systems that is consistent with the goal of reading (i.e., to convey meaning). Finally, in terms of dyslexia, there appears to be a core dysfunction present across writing systems that reflects the phonological deficit hypothesis and may be driven by more subtle sensory processing deficits (Goswami, 2015). This would also make sense given the high heritability estimates of the disorder (Grigorenko, 2004).
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Fumiko Hoeft, MD, Ph.D. is Associate Professor of Child & Adolescent Psychiatry, Director of Laboratory for Educational Neuroscience (brainLENS.org), Board Member of the Dyslexia Center at the University of California, San Francisco (UCSF), and Research Scientist at Haskins Laboratories. She was the 2014 Geschwind Memorial Lecturer for the IDA. LENS is interested in how cognitive science can inform educational and clinical practices, with specific interests in understanding the neurobiological cause of dyslexia, early identification and prediction, and the emotional resilience necessary to succeed.
Peggy McCardle, Ph.D., MPH, is President of Peggy McCardle Consulting, LLC and a scientific research affiliate at Haskins Laboratories. She was formerly Chief of the Child Development and Behavior Branch, and Director of Language, Bilingualism, and Biliteracy Program at the Eunice Kennedy Shriver National Institute for Child Health and Human Development, NIH. She has been a classroom teacher, served as faculty at various universities, and worked as a speech language pathologist. She currently consults, writes, and edits on topics related to literacy, language development, education, and grant writing.
Kenneth Pugh, Ph.D. is President and Director of Research at Haskins Laboratories, Professor of Psychology at University of Connecticut, and Associate Professor of Linguistics and Diagnostic Radiology at Yale University. Haskins Laboratories is a private, non-profit research institute with a primary focus on speech, language, and reading, and their biological basis. His research primarily focuses on the neurocognitive basis of literacy acquisition. He was the 2011 Norman Geschwind Memorial Lecturer for the IDA.
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