Semantic Processing and Individual Variation: Experimental and Modeling Evidence from Quantifier Scope

This study investigates the real-time processing of quantifier scope ambiguity using self-paced reading, comparing surface and inverse interpretations of a–every sentences. Reaction time data revealed greater processing difficulty for inverse scope, supporting an account grounded in semantic computation rather than purely heuristic-based parsing. Offline interpretation results further indicate that abstract structural operations are engaged during scope computation. Surprisal estimates derived from a pre-trained autoregressive language model GPT-2 small successfully predicted both the locus and direction of scope effects observed in the human data, with partial convergence in effect magnitude. When surprisal was not considered, language experience, but not working memory, more robustly accounted for variability in complex scope interpretations. Crucially, incorporating individual differences into surprisal-based analyses showed that both language experience and working memory capacity modulate surprisal effects in scope processing: higher proficiency and greater working memory are associated with increased sensitivity to surprisal, whereas lower levels show reduced or even reversed effects, suggesting weaker engagement in expectation-based processing. Together, these findings highlight the interplay among structural complexity, cognitive resources, language experience, and expectation-based mechanisms in shaping real-time semantic processing.