Elsevier

Journal of Neurolinguistics

Volume 31, September 2014, Pages 69-85
Journal of Neurolinguistics

Review
Using artificial orthographies for studying cross-linguistic differences in the cognitive and neural profiles of reading

https://doi.org/10.1016/j.jneuroling.2014.06.006Get rights and content

Highlights

  • Writing systems have tradeoffs between visual and decoding demands.

  • Such tradeoffs could have greater effects on certain readers.

  • “Grain size” can mean: mapping principle, unit of instruction, unit for decoding.

  • Artificial orthographies help advance the study of writing systems and grain size.

Abstract

Reading and writing are cultural inventions that have become vital skills to master in modern society. Unfortunately, writing systems are not equally learnable and many individuals struggle to become proficient readers. Languages and their writing systems often have co-varying characteristics, due to both psycholinguistic and socio-cultural forces. This makes it difficult to determine the source of cross-linguistic differences in reading and writing. Nonetheless, it is important to make progress on this issue: a more precise understanding of the factors that affect reading disparities should improve reading instruction theory and practice, and the diagnosis and treatment of reading disorders. In this review, we consider the value of artificial orthographies as a tool for unpacking the factors that create cognitive and neural differences in reading acquisition and skill. We do so by focusing on one dimension that differs among writing systems: grain size. Grain size, or the unit of spoken language that is mapped onto a visual graph, is thought to affect learning, but its impact is still not well understood. We review relevant literature about cross-linguistic writing system differences, the benefits of using artificial orthographies as a research tool, and our recent work with an artificial alphasyllabic writing system for English. We conclude that artificial orthographies can be used to elucidate cross-linguistic principles that affect reading and writing.

Introduction

In the timeline of human evolution, reading and writing are relatively new inventions, but they have become vital skills to master in modern society. Unfortunately, writing systems were not created equal and many people struggle to become proficient readers. Furthermore, the cognitive manifestations of reading disorders and their neural signatures also vary by writing system. In this review we will discuss the diversity of the world's writing systems, some of the factors that may account for differences in the learnability of these systems, and experimental approaches for investigating the role of these factors in reading acquisition and skill. Cross-linguistic comparisons are a logical way to study the diversity of the world's writing systems. We will discuss the strengths and weaknesses of using cross-linguistic comparisons, and highlight the use of artificial orthographies as a more recent methodology that can help advance the field.

There is mounting evidence that writing systems are not equally easy to learn. One of the clearest demonstrations of this finding has come from the comparison of alphabetic writing systems that vary in their phonological/orthographic consistency. Phonological consistency refers to the degree to which each symbol has one and only one phonological mapping. It has been primarily studied in the context of alphabetic writing systems, though this definition can be applied to non-alphabetic systems as well (Lee et al., 2005, Yang et al., 2006). Other terms that are used interchangeably to refer to phonological consistency are ‘shallow or transparent orthography’ for phonologically consistent writing systems and ‘opaque or deep orthography’ for phonologically inconsistent ones. Serbo-Croatian is an example of a perfectly consistent alphabetic orthography, with one-to-one grapheme to phoneme correspondences. In contrast, English is at the other end of the spectrum, with complex mapping patterns: most letters can represent more than one sound (the letter g makes a different sound in ‘gem’ ‘game’ and ‘tough’), and most sounds can be represented in more than one way (the vowel sound/u/is represented differently in the words ‘view’, ‘to’, ‘through’, ‘shoot’, and ‘lute’).

There has been extensive research showing that phonological consistency affects learning rates and the acquisition of phonological awareness (Ellis and Hooper, 2001, Patel et al., 2004, Spencer and Hanley, 2003, Ziegler et al., 2001). For example, children learning English struggle to reach 90% accuracy on nonword reading after 4–5 years of instruction. In comparison, children learning Finnish, a phonologically consistent alphabetic system, reach 90% after 10 weeks of instruction (Goswami et al., 1998, Ziegler and Goswami, 2005). Furthermore, dyslexia is manifested differently and at higher rates in readers of inconsistent versus consistent alphabetic systems (Everatt and Elbeheri, 2008, Goulandris, 2003, Ziegler and Goswami, 2005). For example, in English slow and inaccurate nonword reading is a hallmark symptom of dyslexia. In more consistent writing systems, like Italian, slow word reading and poor performance on rapid naming tasks are more likely to serve as diagnostic markers. Phonological consistency has also been shown to modulate neural networks involved in reading. For example, readers of Italian, a phonologically consistent language, show greater activation in a left superior temporal region associated with speech-based phonological processing during word reading, as compared to readers of English (Bolger et al., 2005, Paulesu et al., 2000).

A second demonstration that writing systems are not equally easy to learn comes from comparisons of orthographies that differ in their fundamental mapping principle (see Fig. 1). Different languages map differently-sized units of spoken language onto visual graphs. Alphabets use a phonemic grain size, such that in a perfectly consistent alphabet each letter represents a single phoneme. In syllabaries, like Kana used in Japan, a syllable unit is mapped onto each visual unit. Somewhere in between are alphasyllabaries, like those used to represent many languages in India and Ethiopia. For these systems, each visual unit represents a whole syllable but phonemic sub-components are visually identifiable within the graphs. Lastly, in logographic systems, a whole word or morpheme is mapped onto a visual unit, such as a character in the Chinese writing system.

Readers of non-alphabetic systems will generally need more time to learn the visual graphs of their writing systems, because the graph inventory will increase as the grain size of the writing system increases. For example, it takes readers of Chinese years to learn the foundational 3000 characters that are needed to become a proficient reader (Carson, 1992, Cheung and Ng, 2003). Readers of alphasyllabaries, like Kanada or Marathi, similarly take years to master all of the visual-phonological mappings within their writing systems. The visual-phonological mappings of alphabets, with 25–35 graphs are typically learned pre-literacy (Ehri, 1999). In a highly consistent alphabetic system, this knowledge can allow beginning readers to sound out, or “decode,” a large number of words within an initial year of formal reading instruction. On the other hand, readers of less consistent alphabetic systems still require years of practice to become highly fluent at visual word recognition.

The frequency and profile of reading disabilities also varies across writing systems that use different grain sizes for mapping between orthography and phonology (see Fig. 2). The predominant view in the literature is that the core deficit in dyslexic readers of alphabetic languages has to do with phonological awareness (Bruck, 1992, Snowling, 1998, Snowling, 1981). Phonemic awareness more specifically has been shown to be a good predictor of reading skill (Hulme et al., 2002, Snowling, 1981). Phonological deficits are also thought to be a primary deficit in reading disorders of alphasyllabic writing systems (Nag & Snowling, 2011), but children with poorer visual processing skills are also at heightened risk for poor reading achievement. The contrast is even more marked in the Chinese writing system, which uses a logographic mapping principle. In Chinese, the primary deficits in dyslexia are thought to reflect visual/orthographic (Ho et al., 2004, Ho et al., 2002) and morphological (Shu, McBride-Chang, Wu, & Liu, 2006) risk factors, rather than a fundamental deficit in phonological awareness. In addition, writing deficits (Tan, Spinks, Eden, Perfetti, & Siok, 2005) are thought to play a larger role in Chinese reading impairments.

Lastly, writing systems that use different grain sizes for mapping between orthography and phonology have also been associated with differences in the neural networks for reading. In their meta-analysis, Bolger and colleagues found greater left superior temporal gyrus activation in English/Western European alphabetic writing systems and greater right fusiform gyrus activation in logographic Chinese (Bolger et al., 2005), although this finding remains a topic of debate (Mei et al., 2012, Tan et al., 2005a, Tan et al., 2001). Alphasyllabaries, with approximately 37 active scripts used by over 100 different languages (omniglot.com), were not included in these prior reviews. Thus, it remains largely unknown whether they impose a distinctive character on the neural networks for reading, although recent findings suggest that alphasyllabary reading engages typical cortical areas involved in both alphabetic and syllabic writing systems, such as left superior temporal gyrus and left inferior and superior parietal gyri (Das et al., 2011, Das et al., 2009).

Past research has made substantial progress in describing the many dimensions, or design principles, by which writing systems vary (Perfetti & Harris, 2013). In addition to phonological consistency and grain size, writing systems vary along many other dimensions that have been shown to affect learning. For instance, it is harder to learn writing systems that are more visually complex, less semantically transparent, or that represent a more syllabically complex spoken language (Frost, 2012, Pelli et al., 2006, Seymour et al., 2003). Because each of the world's writing systems represents a unique position in a complex multi-dimensional space, it is challenging to determine the impact of a given dimension on learnability. However, the impact of some dimensions are inherently easier to study than others using cross-linguistic comparisons (Katz and Frost, 1992, Seymour et al., 2003). For example, it is easier to study a dimension if it is possible to isolate it by comparing two or more languages that are minimally different (i.e., they only differ in one dimension). With that in mind, studying phonological consistency using cross-linguistic comparisons has been quite productive because there are many languages that are minimally different with the exception of phonological consistency (e.g., English and Welsh).

In contrast, it is more difficult to cleanly compare writing systems that have different mapping principles because many dimensions co-vary with grain size. For example, writing systems that use larger grain sizes for orthographic-phonological (O–P) mapping tend to be more visually complex than those that use a smaller grain size (Changizi and Shimojo, 2005, Pelli et al., 2006), and they tend to represent spoken languages with predominantly simple (e.g., consonant-vowel) syllabic inventories (Perfetti & Harris, 2013). Many reading scholars have described in depth the complexity involved in cross-linguistic comparisons of writing systems (Katz and Frost, 1992, Perfetti and Harris, 2013, Wydell, 2012, Wydell and Butterworth, 1999, Ziegler and Goswami, 2005, Ziegler and Goswami, 2006), all making similar observations that grain size and other factors are not orthogonally related. Another issue that complicates any cross-linguistic comparison is that it is quite difficult to control for sociocultural differences across languages, such as terms of instruction, teacher quality, availability of resources, and student motivation (Ziegler & Goswami, 2006). All of these factors certainly affect learning outcomes, independent of how they may interact with the characteristics of a given writing system and the spoken language it represents.

Section snippets

The value of artificial orthographies in understanding effects of grain size

A handful of investigators have turned to artificial orthographies – in which specially created or “borrowed” graphs from an unknown writing system are mapped onto a naturally occurring spoken language – as a tool for studying writing system differences in laboratory settings. Artificial orthographies allow researchers to minimize problems of covariance, inherent to any cross-linguistic study, by being able to control overall statistics and characteristics of each design dimension, and thus

Theoretical advancement

Some scholars propose that each writing system has evolved and settled on the most efficient combination of design features or dimensions for a given spoken language. As should be clear by now, typical cross-linguistic descriptive or even experimental comparisons are wrought with the covariance problem. Recent debates regarding whether or not writing systems are optimal or not (e.g., see Frost, 2012, Seidenberg, 2011) could benefit from using artificial orthographies. Do all languages get the

Summary

In closing, despite great gains in understanding how writing systems affect learning to read through cross-linguistic comparison, we argue that artificial orthographies can provide a tool for further advancement. Considering the distinction and interaction between the grain size of the mapping principle, of instruction, and of internal representations for decoding can also help guide future research. Lastly, gaining a deeper understanding of the complex interactions between design principles

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