How Granularity of Orthography-Phonology Mappings Affect Reading Development: Evidence from a Computational Model of English Word Reading and Spelling
Authors: Alfred Lim, Beth A. O'Brien, Luca Onnis (Nanyang Technological University, Università di Genova, 2020)
Topic: Lexical and Semantic Resources and Analysis

Abstract
It is widely held that children implicitly learn the structure of their writing system through statistical learning of spelling-to-sound mappings. Yet an unresolved question is how to sequence reading experience so that children can ‘pick up’ the structure optimally. We tackle this question here using a computational model of encoding and decoding. The order of presentation of words was manipulated so that they exhibited two distinct progressions of granularity of spelling-to-sound mappings. We found that under a training regime that introduced written words progressively from small-to-large granularity, the network exhibited an early advantage in reading acquisition as compared to a regime introducing written words from large-to-small granularity. Our results thus provide support for the grain size theory AUTHOR and demonstrate that the order of learning can influence learning trajectories of literacy skills.

Introduction
Reading science provides evidence of the developmental path to acquiring reading for alphabetic languages AUTHOR. From parsing the speech stream into words in infancy AUTHOR, to familiarizing with print in the preschool years AUTHOR --- these activities lead to the accrual of key knowledge for learning to read. Knowledge about the language’s phonotactic and graphotactic properties and symbolic representations with abstract letter units is necessary for the forthcoming insight that print represents spoken language (the alphabetic principle). Subsequent to this insight, children are ready to take on the process of learning the precise mapping of print-to-speech. At its basis, learning to read involves learning to decode a script into oral language representations. The question arises as to the optimal input for learning this orthography-to-phonology mapping in an alphabetic system, especially for languages that have deep orthographies, such as English. Shallow orthographies (e.g., Finnish, Spanish) have a more precise match between letters and sounds; whereas deep orthographies match phonemes to graphemes (one or more letters) in an inconsistent way --- with multiple spellings per phoneme, or multiple pronunciations per grapheme --- and, thus, have a greater number of GPCs (grapheme-phoneme correspondences). Therefore, reading acquisition is found to occur at a comparatively slower rate for readers in deep as compared to shallow orthographies AUTHOR. The deep orthographic complexity of English also partly results from variation in the functional units of the writing system --- graphemes –-- which may consist of a single letter (e.g., a), or multiple letters (e.g., ay, aye). While skilled adult readers have unitized these subword patterns AUTHOR, beginning readers need to acquire these patterns of graphemes and their mappings to phonemes. Here we consider this mapping problem along two dimensions: (1) the granularity of the units of analysis to be picked up at any given time; and (2) the ordering of learning such units and types. A fruitful approach to examining the GPC learning process is through computational modelling AUTHOR. Specifically, connectionist models are sensitive to the timing and ordering of learning events, in that they learn incrementally. This feature is particularly apt for modeling reading development, as it affords simulating the incremental nature of a child learning to read new words daily, as schooling progresses. Order effects as well as frequency trajectory effects have been documented in previous connectionist models AUTHOR, and here we are interested in comparing learning trajectories for particular training orderings for reading development. To this end, we present connectionist networks with small batches of words, which we test regularly for accuracy until a given criterion across the batch is achieved --- in essence, an adaptive training regime. Using this approach, we can address long-standing issues in the area of reading education with a more systematic approach to understanding how print-to-speech mappings are learned AUTHOR. Below we briefly review why print-to-speech decoding can be a hard problem, both for learners and for researchers trying to understand its mechanisms. Then, we discuss dimensions of granularity derived from the literature, and offer a first set of connectionist simulations of the order of reading acquisition of American English.

Conclusion
As the process of learning to read requires picking up and internalizing representational units of print associated with sound, the ordering of training input to the reading system becomes paramount. How best to order input and maximize learning efficiency has been debated in the literacy education field. This study capitalizes on a computational modelling approach to this issue, using a highly controlled context without the ethical concerns of human learning studies. Directly contrasting the effects of two literacy training regimes differing in granularity order, the simulation results support better learning with smaller, less complex orthographic units, as predicted from corpus-based research (Vousden, 2008). At training stages comprising of 3015 words, we found that the model initially trained with words of smaller granularity performed and generalized to pseudo-words better than the model trained with larger granularity. The LS model did require significantly more training epochs to reach the same performance as the SL model. Essentially, when children learn to read, they must navigate the structure of their language and its writing system. Granularity and consistency are important aspects of this structure, and both impact reading performance. Adult readers are slower to identify letters within a multi-letter grapheme (Smith and Monaghan, 2011; Rey et al., 2000), suggesting that graphemes are functional reading units. Furthermore, Rastle and Coltheart (1998) found that naming latencies were slower for pseudo-words with, as compared to without, multi-letter graphemes. Adult word naming and lexical decision are also faster for consistent words (Andrews, 1982; Jared, 1997; Jared, 2002), and consistent words are more accurately read and spelled by children (Alegria and Mousty, 1996; Lété et al., 2008; Weekes et al., 2006). Granularity and consistency have been regarded to be associated (Treiman et al., 1995), and our corpus analysis revealed this as well — monosyllabic English words of smaller granularity tend to be more consistent than words with larger granularity. This relationship indicates that granularity and consistency may not be entirely disentangled, at least for English. With this in mind, the SL model was first exposed to words of smaller granularity that were also more consistent in their GPC and PGC (phoneme-grapheme correspondence) mappings. Thus consistency and granularity may be two sides of the same coin, and when manipulated they could lead to faster or slower rates of convergence. Importantly, the current model included bidirectional links between orthographic and phonological units, simulating the real-world scenario that children acquire decoding and encoding skills simultaneously. These findings have implications for educational planning for early literacy. In particular, our pilot simulation provides preliminary evidence on the potential utility of manipulating the order of training in terms of word granularity to unveil facilitative effects on literacy acquisition. Reading instruction can consider the early acquisition of words with smaller granularity, or more consistency. However, we note that the present findings are based on the analysis of monosyllabic words only and should not be generalized to multisyllabic words directly. Future work can consider using models that are capable of reading multisyllabic words (Perry et al., 2010), or explore the link between granularity and consistency across languages that are either less or more orthographically transparent.