Effects of complexity and unpredictability on the learning of an artificial orthography

Abstract

Orthographies vary in complexity (the number of multi-letter grapheme-phoneme rules describing print-to-speech regularities) and unpredictability (the number of words which cannot be read correctly, even with at-ceiling knowledge of the rules). To assess how these constructs affect reading acquisition, we used an artificial orthography learning paradigm, where participants learn to read pseudowords written in unfamiliar symbols, and subsequently read aloud novel words written in the same symbols (generalisation). In three experiments (third experiment pre-registered), we manipulated the consistency of symbol-to-sound mappings: in the first inconsistent condition, vowel pronunciation depended on the subsequent letter (condition complexity), and in the second inconsistent condition, vowel pronunciation was unpredictable from the context (condition unpredictability). Across experiments, we found that pseudowords with inconsistent mappings are more difficult to learn than pseudowords with consistent mappings only, regardless of whether the inconsistency is due to complexity or unpredictability. Numerically, participants learning orthographies containing unpredictable correspondences seem to be less likely to form rules, either for simple or for complex correspondences. We propose that rule extraction and distributional learning happens simultaneously during reading acquisition: in a mathematical model, we show that distributional learning may lead to more complete knowledge than rule extraction for orthographies that are high in unpredictability.

Keywords: Artificial orthography; Learning; Orthographic depth; Sublexical processing.