Stressing what is important: Orthographic cues and lexical stress assignment

Computational models of reading have typically focused on monosyllabic words. However extending those models to polysyllabic word reading can uncover critical points of distinction between competing models. We present a connectionist model of stress assignment that learned to map orthography onto stress position for English disyllabic words. We compared the performance of the connectionist model to Rastle and Coltheart's [(2000).] rule-based model of stress assignment for words and nonwords. The connectionist model performed well on predicting human performance in reading nonwords that both contained and did not contain affixes, whereas the Rastle and Coltheart model performed well only on nonwords with affixes. The connectionist model provides an important first step to simulating all aspects of polysyllabic word reading, and indicates that a probabilistic approach to stress assignment can reflect human performance on stress assignment for both words and nonwords.