Memory Retrieval in Online Sentence Parsing: Empirical Evidence, Computational Modelling, and Simulations

This paper reports two experiments (Experiments 1 and 2) and computational simulations designed to investigate and model memory retrieval processes during real-time sentence processing. Central to this study is the hypothesis that linguistic information serves as a cue to retrieve target representations from memory during dependency formation. The basis for this cue-based memory retrieval stems from research showing that non-target representations that match a set of retrieval cues interfere with target retrieval. The susceptibility to this similarity-based interference has been debated in the sentence processing literature, and various hypotheses and models have been formulated and developed. This issue is addressed empirically in Experiments 1 and 2, which investigated similarity-based interference in sentences with a floating quantifier. Bayesian linear mixed models and Bayes factor analyses suggested similarity-based interference. However, the patterns of interference were not consistent with existing theories and models. To reconcile these findings within the framework of cue-based memory retrieval, this paper implements the Revision Integrated Cue-Based (RICB) model based on the ACT-R architecture. This model assumes that structural information is heavily weighted and incorporates the notions of initial retrieval and revision. The results of the simulations indicate that the RICB model successfully predicts the observed data, highlighting the central role of structural information and revision in memory retrieval during real-time sentence processing.