A computational implementation of the Swinging Lexical Network model of language production

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Behavioral studies on semantic effects in language production have observed apparently contradictory context effects: In come studies, the presence of semantically related distractors (such as the word "LION" when participants have to name the picture of a tiger) has resulted in facilitation (i.e., faster responses), while others have observed interference (i.e., slower responses). In order to explain these effects in a unified, comprehensive model, Abdel Rahman and Melinger (2009, 2019) have proposed the Swinging Lexical Network Model of language production. This model relies on two core assumptions: (1) it assumes priming at the conceptual-semantic level as a consequence of spreading activation (leading to faster responses in the presence of semantically related context words) but competition during word selection at the lexical level (since only one word is to be selected for production), and (2) it assumes that the amount of priming and competition does not only result from the activation of the target and context, but is also influenced by a co-activated cohort - other concepts that are mutually activated by the target, the context word, and by each other during processing (such as, in our example, "leopard" or "cat"). However, as acknowledged by the authors, the fact that this model is currently a purely verbal theory and not yet computationally implemented makes it very difficult to rigorously assess ist actual explanatory power and empirical validity. The aim of the current project is to provide this computational implementation and empirical evaluation. This model consists of the following components: (I) We will employ distributional semantic models/word embeddings as a state-of-the-art computational model of semantic memory, and (II) apply Kintsch’s (1988) construction-integration algorithm to model the mutual co-activation of the cohort. We start from very simplistic assumptions about (III) activation spread between the semantic and lexical level and (IV) selection at the lexical level. The first work package focuses on implementing this model and making the implemented model accessible to the research community. The second work package focuses on estimating the free parameters of the model from already existing and published studies on semantic context effects in language processing. Finally, the third work package focuses on empirically validating the model in experimental studies, generating new item material for which specific context effects would be expected according to the model predictions.