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Modeling the Structure and Dynamics of Semantic Processing
Author(s) -
Rotaru Armand S.,
Vigliocco Gabriella,
Frank Stefan L.
Publication year - 2018
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12690
Subject(s) - concreteness , computer science , semantic similarity , semantic memory , natural language processing , semantics (computer science) , connectionism , distributional semantics , artificial intelligence , semantic computing , semantic compression , semantic network , similarity (geometry) , lexical decision task , semantic technology , cognition , cognitive psychology , artificial neural network , psychology , semantic web , neuroscience , image (mathematics) , programming language
The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co‐occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure.