Simulating the N400 ERP component as semantic network error: Insights from a feature-based connectionist attractor model of word meaning

Abstract

The N400 ERP component is widely used in research on language and semantics, but the specific underlying mechanisms are currently unclear. We explored the mechanisms underlying the N400 by examining how a connectionist semantic network’s performance measures covary with N400 amplitudes. We simulated six N400 effects obtained in empirical research. Network error was consistently in the same direction as N400 amplitudes, namely smaller for high frequency words, words with few semantic features, semantically related targets and repeated words. Furthermore, the repetition-induced decrease was stronger for low frequency words and words with many features. In contrast, semantic activation corresponded less well with the N400, and instead seemed related to lexical decision performance. Our results suggest an interesting relation between N400 amplitudes and semantic network error. In psychological terms, network error has been conceptualized as implicit prediction error. Thus, N400 amplitudes may reflect implicit prediction error in semantic memory (McClelland, 1994).


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