False recognition through semantic amplification


This paper describes a computational model to explain a variety of results in false recognition. The processing mechanism in the model is built around a co-occurrence representation of lexical semantics, affording an account of both structure and process. We show that this model can naturally account for levels of false recognition that are seen in studies using the DRM paradigm, including item-level effects, reaction times, and event-related brain potentials.

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