Evaluating Word-Reading Models at the Item Level: Matching the Grain of Theory and Data

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Abstract
Spieler and Balota (1997) showed that connectionist models of reading account for relatively little item-specific variance. In assessing this finding, it is important to recognize two factors that limit how much variance such models can possibly explain. First, item means are affected by several factors that are not addressed in existing models, including processes involved in recognizing letters and producing articulatory output. These limitations point to important areas for future research but have little bearing on existing theoretical claims. Second, the item data include a substantial amount of error variance that would be inappropriate to model. Issues concerning comparisons between simulation data and human performance are discussed with an emphasis on the importance of evaluating models at a level of specificity (“grain”) appropriate to the theoretical issues being addressed.