Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, LMU Munich, Germany; Department of Psychology and Cognitive Sciences, University of Trento, Italy.
Department of Psychology and Cognitive Sciences, University of Trento, Italy; Department of Developmental Psychology & Socialisation, University of Padova, Italy.
Cortex. 2022 Jul;152:1-20. doi: 10.1016/j.cortex.2022.03.014. Epub 2022 Apr 7.
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.
正字法的复杂性（描述印刷到语音规则的多字母图形-音素规则的数量）和不可预测性（即使对规则有最高的了解，也无法正确读出的单词的数量）各不相同。为了评估这些结构如何影响阅读习得，我们使用了一种人工正字法学习范例，其中参与者学习阅读用陌生符号书写的假词，然后用相同的符号朗读新单词（泛化）。在三个实验中（第三个实验预先注册），我们操纵了符号到声音映射的一致性：在第一个不一致条件下，元音的发音取决于后续字母（条件复杂性），在第二个不一致条件下，元音的发音无法从上下文预测（条件不可预测性）。在所有实验中，我们发现，与具有一致映射的假词相比，具有不一致映射的假词更难学习，无论不一致是由于复杂性还是不可预测性引起的。从数值上看，学习具有不可预测对应关系的正字法的参与者似乎不太可能形成规则，无论是对于简单的对应关系还是复杂的对应关系。我们提出，在阅读习得过程中，规则提取和分布学习是同时发生的：在一个数学模型中，我们表明，对于不可预测性较高的正字法，分布学习可能会导致比规则提取更完整的知识。
