A Computational and Empirical Investigation of Graphemes in Reading

It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full‐blown model of multi‐syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading.     

Phonological information in immediate and delayed sentence recall

Potter and Lombardi (1990) state in their conceptual regeneration hypothesis that immediate sentence recall is only based on conceptual and lexical information; phonological information does not contribute. As experimental evidence for this hypothesis, they reported that if a sentence is followed by a word list that included a lure word similar to one of the content words of the sentence (target word), the lure word frequently intrudes into sentence recall. We demonstrated that Potter and Lombardi did not observe any influence of phonological information because list presentation followed sentence presentation, and phonological information was discarded. We observed that phonological information influenced the intrusion rate if recall was not delayed by the subsequent presentation of a word list. With immediate recall, the lure intrusion effect disappeared in auditorily presented sentences. This shows that, if available, phonological information contributes to sentence recall.