One Process, Not Two, in Reading Aloud: Lexical Analogies Do the Work of Non-Lexical Rules
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The presence in critical non-words of morphemes pronounced consistently or inconsistently with the biased pronunciations significantly affected biasing makes the case for lexical analogy theory even stronger.
Abstract
It is widely held that there are two (non-semantic) processes by which oral reading may be achieved: (a) by known words visually addressing lexical storage of their complete orthography and phonology; (b) by parsing a letter string into graphemes which are translated by rule into phonemes. Irregular words (HAVE) rely on the former, new and non-words rely on the latter. Recent evidence casts doubt on this view; to meet some of this data a revised version is presented. An alternative view is that the phonology of both words and non-words, at each encounter, is retrieved by analogy with all known words having matching segments. In a mixed list of words and non-words, presented singly for pronunciation, phonologically ambiguous non-words (NOUCH) were preceded critically by words with the same ambiguous segments, either pronounced regularly (COUCH) or irregularly (TOUCH). Standard (and revised) dual-process theory predicts that preceding words will not affect pronunciation of non-words; analogy theory predicts that they will. Significant biasing effects, compared to control conditions, support analogy theory, but a further modification to dual-process theory enables it to deal with these results. However the presence in critical non-words of morphemes pronounced consistently or inconsistently with the biased pronunciations significantly affected biasing. This makes the case for lexical analogy theory even stronger. Formal knowledge (descriptive spelling-sound rules) may be used consciously, but does not reflect tacit processes in oral reading, which are better described by a single-process lexical analogy model.
