Detecting inclusive OR in lexical decision latencies with NDL and GAM by Harald Baayen, Michael Ramscar and Stefanie Wolf

Generalized additive models (GAMs, Wood 2006) provide a powerful extension to the standard multiple regression toolkit. In my presentation, I will illustrate this point for two lexical decision datasets which we are currently analysing using naive discrimination learning (NDL, Baayen et al., Ramscar et al., 2010). NDL provides corpus-derived estimates of how well a word's meaning is learned given the distributional properties of the words' letter unigrams and bigrams and meanings. Discrimination learning appears to be largely a function of the striatum in the brain (Schultz, 2006), and this dynamic process yields varying degrees support for multiple responses in context. The detection of any conflict between these responses, and the selection of a context appropriate response is a function of anterior cingulate (ACC) and prefrontal cortex (Botvinick, Cohen & Carter, 2004). For modeling the impact of the ACC/PFC circuit on lexical decision RTs, GAMs turn out to be indispensible. Tensor products are required to model decision surfaces that reflect acceptance of a lexicality decision under inclusive OR. These findings raise some interesting questions about the ecological validity of taking the lexical decision task as a straightforward measure of normal lexical processing.

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