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- W49847216 abstract "Word recognition refers to a component process of language. Word recognition transforms written and spoken forms of words into linguistic representations. Historically, word recognition also referred to lexical decision performance. In lexical decision, participants judge whether individually presented letter-strings actually spell words (with respect to a target language). Linear statistical methods were applied to ‘word’ response time data to isolate component effects and thereby individuate word recognition. Component effects would be revealed in additive interactions among factors that affect ‘word’ response times. But empirical evidence has not resolved the nature of word recognition. The effort is frustrated by ubiquitous, nonadditive, interactions among word factors. Recurrent connectionist models are conducive to nonadditive interactions. Recurrent connectionist models are nonlinear dynamical systems in which each component interacts directly or indirectly with every other component. Because the models are nonlinear, small changes in parameters that control the interaction may induce large or qualitative changes (nonadditive interactions) in the performance of a model. Consequently, empirical tests of nonlinear dynamical systems focus on the stability of the interaction among system components (not component effects). Conventional methods are inadequate for this task. Advances in nonlinear modeling await advances in methodology before cognitive experiments may adequately test recurrent connectionist models." @default.
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- W49847216 date "2001-01-01" @default.
- W49847216 modified "2023-10-02" @default.
- W49847216 title "Word Recognition, Cognitive Psychology of" @default.
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- W49847216 doi "https://doi.org/10.1016/b0-08-043076-7/01553-9" @default.
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