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

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.

Comparing and validating methods of reading instruction using behavioural and neural findings in an artificial orthography.

“…However, to provide an initial investigation into the impact of teaching method on neural mechanisms for reading, we used an artificial language approach with adults. There has been a surge of interest in recent years in using these approaches to model the acquisition of different types of linguistic information ( Bowers, Davis, & Hanley, 2005 ; Clay, Bowers, Davis, & Hanley, 2007 ; Fitch & Friederici, 2012 ; Gaskell & Dumay, 2003 ; Hirshorn & Fiez, 2014 ; Tamminen, Davis, & Rastle, 2015 ; Taylor, Plunkett, & Nation, 2011 ). In contrast to studying children learning their first language (who vary in their prior experience with both spoken and written forms), artificial language approaches provide total control over participants’ prior knowledge of a new language or writing system.…”

Fusiform Gyrus Laterality in Writing Systems with Different Mapping Principles: An Artificial Orthography Training Study

“…However, it goes beyond the prior work through the use of an artificial orthography in which a corpus of 375 syllable-grapheme mappings can be used to represent any spoken English word. Face images are used as the component graphemes in the system, which is termed "Faceabary" (Hirshorn & Fiez, 2014). The Faceabary system can regarded as either a syllabic system, because each of the 375 face graphs is mapped onto a single English syllable, or as an alphasyllabic system, because both consonant and vowel information can be identified within the graphs.…”

The effect of orthographic systems on the developing reading system: Typological and computational analyses.

“…Constructing artificial corpora ensures that relations both within and between representational domains are controlled (see Hirshorn and Fiez (2014) and Plaut et al (1996)…”