Data and models about recognition and recall of words and nonwords are unified using a real-time network processing theory. Lexical decision and word frequency effect data are analysed in terms of the same theoretical concepts that have unified data about development of circular reactions, imitation of novel sounds, matching phonetic to articulatory requirements, serial and paired associate verbal learning, free recall, unitization, categorical perception, selective adaptation, auditory contrast, and word superiority effects. The theory, called adaptive resonance theory, arose from an analysis of how a language system self-organizes in real-time in response to its complex input environment. Such an approach emphasizes the moment-by-moment dynamical interactions that control language development, learning, and stability. Properties of language performance emerge from an analysis of the system constraints that govern stable language learning. Concepts such as logogens, verification, automatic activation, interactive activation, limited-capacity processing, conscious attention, serial search, processing stages, speed-accuracy trade-off, situational frequency, familiarity, and encoding specificity are revised and developed using this analysis. Concepts such as adaptive resonance, resonant equilibration of short term memory, bottom-up adaptive filtering, top-down adaptive template matching, competitive masking field, unitized list representation, temporal order information over item representations, attentional priming, attentional gain control, and list-item error trade-off are applied.
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