A Computational Model of Normal and Impaired Lexical Decision: Graded Semantic Effects

Abstract

Lexical decision is an important paradigm in studies of visual word recognition yet the underlying mechanisms supporting the activity are not well understood. While most models of visual word recognition focus on orthographic processing as the primary locus of the lexical decision, a number of behavioural studies have suggested a flexible role for semantic processing regulated by the similarity of the nonword foil to real words. Here we developed a computational model that interactively combines visual-orthographic, phonological and semantic processing to perform lexical decisions. Importantly, the model was able to differentiate words from nonwords by dynamically integrating measures of polarity across the key processing layers. The model was more reliant on semantic information when nonword foils were pseudowords as opposed to consonant strings. Moreover, the model was able to capture a range of standard reading effects in lexical decision. Damage to the model also resulted in reading patterns observed in patients with pure alexia, phonological dyslexia, and semantic dementia, demonstrating for the first time that both normal and neurologically-impaired lexical decision can be addressed in a connectionist computational model of reading.