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The advances discussed so far come largely from research on reading in alphabetic writing systems, mainly English. Indeed, the two routes of the DRC model were intended to capture an orthographic property of English – its inconsistent mappings between letters and phonemes.² Reading science needed to address reading more broadly and a step in that direction came from the comparative analysis of orthographies by Katz and Frost (1992). “Orthographic depth” orders orthographies according to the tradeoff they make between coding speech components and meaning. Thus, among alphabetic writing systems, Welsh and Finnish are shallow (consistent mappings to phonemes), Czech and Italian only slightly less so, with English at the deep end. Moving beyond alphabetic writing toward a more universal perspective, orthographic depth was extended to nonalphabetic writing, for example, the consonant‐based Abjad system and morpho‐syllabic Chinese.

The single scale of orthographic depth, however, fails to reflect the design principles that separate other systems from alphabets. Explicit attention to these principles was the basis of the Universal Phonological Principle (Perfetti et al., 1992; Perfetti, 2003) that reading words universally involved phonology at the lowest level allowed by the writing system and the psycholinguistic grain size hypothesis (Ziegler & Goswami, 2005), which focused on where the writing system makes its connection within the phonological hierarchy. Where this connection is made – phoneme, syllable, word – has consequences for reading development. Despite increasing recognition of writing system differences, Share (2008) correctly argued that the dominant role of English in reading research had resulted in research questions and models of reading that might not apply to other systems.

More recent progress from research across languages and writing systems was the focus of two volumes on learning to read (Verhoeven & Perfetti, 2017a) and dyslexia (Verhoeven et al., 2019) The conclusions include universals across 17 languages in learning to read, along with specific features of languages, writing systems, and instruction (see chapters by Caravolas, McBride et al., and Nag, this volume).

Table 1.2 Examples of adaptations of writing systems to language features

Language	Adaptations of the writing system to features of the language
Chinese	Small number of syllables with tones. Extensive syllable homophony makes alphabets and syllabaries less adaptive. Characters map onto syllable morphemes and can distinguish between homophones.
Japanese	Agglutinative language. Many multisyllabic words and small number of syllables with open structure. Japanese syllabaries (Kana) are adaptive to these factors, but historical borrowing of Chinese supports dominant Kanji character system.
Finnish	Relatively small number of phonemes and long words of several syllables. Complex inflectional morphology. Highly consistent alphabetic orthography supports decoding of multisyllabic, multimorpheme words
English	Phonological complexity and many syllables make an alphabet efficient. Simple inflectional morphology favors morphophonemes and morpheme spellings. A mismatched letter‐to‐phoneme ratio keeps phonological consistency low.

Cross‐language comparisons suggest that writing systems show accommodation to the properties of the language they represent (Frost, 2012; Perfetti & Harris, 2013; Seidenberg, 2011). Illustrating this possibility, Table 1.2 summarizes four of the orthographies reviewed in Verhoeven and Perfetti (2017a). Two examples of alphabetic writing suggest accommodation to phoneme inventories, syllable structures, and morphology. The Chinese writing system suggests adaptation to its relatively few syllables, which create many meaning mappings for any given syllable. For example, it would be inefficient to simply represent the phonological properties of spoken Mandarin because it contains homophones with different meanings. Hence, the Chinese orthography uses semantic radicals to represent the meanings of words directly. Thus, whereas using an alphabetic writing system (there is such a system, Pinyin) would result in a huge number of homophones, the character system usually identifies a particular morpheme. In contrast, English seems a poor match to a syllabary because its phonological complexity would create large numbers of syllables, and thus less efficiency than an alphabet.

The variations across language and writing systems have important implications for reading science. Perfetti and Verhoeven (2017, Table 19.1) present an extended summary of reading development across languages. Some conclusions are specific to writing systems and languages (e.g., phoneme awareness is more important for learners of alphabetic than nonalphabetic writing); some are applicable broadly within a writing system (e.g., phoneme awareness in alphabetic orthographies is not dependent on mapping consistency); some apply across all writing systems (e.g., children’s linguistic awareness emerges first at the syllable level).

One consequence of variation in mapping principles is variation in visual complexity. The number of graphs (the basic visual symbols of writing) depends on the number of linguistic units at the level where mapping occurs. In turn, the number of graphs determines their visual complexity: More graphs, more average complexity because the graphic features sufficient to distinguish among few graphs cannot distinguish among many graphs. The result is that abjads and alphabets, which typically have fewer than 40 graphs (letters), have less visual complexity than syllabaries and alpha‐syllabaries, which typically have more than 400 graphs. All systems are visually simpler than the Chinese basic morpho‐syllabary of more than 3 000 graphs (characters). In a study of graphs from different writing systems, Chang et al. (2017) reported that simple perceptual judgments of graphs vary with their complexity. Thus, visual complexity cannot be ignored in considering the challenges of learning to read. The long learning course required for Chinese and the many South Asian alpha‐syllabaries (Nag, 2017) is partly a reflection of the number of graphs and the resulting visual complexity of these orthographies.

Comparative research has also stimulated the extension of models of alphabetic reading to nonalphabetic reading. Li and Pollatsek (2020) presented an integrated model of word reading and eye movement control for Chinese, applying the Interactive Activation model (McClelland & Rumelhart, 1981) for word identification while also implementing word segmentation. Segmentation is needed because spaces separate characters but not words. PDP models have also been extended to reading Chinese (Yang et al., 2009; Zevin, 2019) and to morphological effects in Hebrew (Plaut & Gonnerman, 2000).