Large scale modeling of single word reading and recognition




Sibley, Daragh E.

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The study of word reading and recognition has been strongly influenced by computational cognitive modeling. These models facilitate theorizing about the mechanisms that underlie word reading and recognition (e.g., Morton, 1970; McClelland & Rumelhart, 1981; Seidenberg & McClelland, 1989; Plaut, McClelland, Seidenberg, and Patterson, 1996; Harm & Seidenberg, 1999; Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001; Perry, Ziegler, & Zorzi, 2007). However, the preeminent models in this field only process monosyllabic words. This results from difficulties inherent in representing the orthography and phonology of multisyllabic words. To address this issue Sibley, Kello, Plaut, & Elman (in Press) created a connectionist architecture named the Sequence Encoder. The present work utilizes representations from a Sequence Encoder to build models that address an order of magnitude more data than previous models. A second goal of this work is to explore the possibility of hypothesizing fewer mechanisms in models of the reading system. The three preeminent models of reading all implement two distinct pathways from orthography to phonology. A sublexical route encodes statistical relationships between letters and phonemes, while a lexical route encodes whole word information. This dissertation explores whether each of these pathways are necessary for word reading and recognition. We present three models trained on 60,000 mono- and multisyllabic English words. Simulation 1 maps from orthography to phonology using a single sublexical route. It demonstrates substantial naming capacities, but is incapable of addressing lexical decision data. Simulation 2 utilizes only a lexical route, where reading is achieved by an inductive process that utilizes whole word information stored in a lexicon. This model addresses naming and lexical decision data on an unprecedented scale. Simulation 3 integrates sublexical and lexical routes from the previous models, but exhibits negligible capacities beyond Simulation 2. Finally, we examine each simulation’s sensitivity to stimuli characteristics that impact behavioral latencies. Our simulations mimicked the effects of all examined variables on participants’ latencies. These simulations demonstrate that models can be scaled up without incorporating new mechanisms specifically to address phenomena of multisyllabic word reading, such as stress assignment. We conclude that a single lexical pathway from orthography to phonology is sufficient to simulate word reading and recognition.



Language, Cognitive Modeling, Reading, Neural networks, Large–scale