title: The role of textual semantic constraints in knowledge-based inference generation during reading comprehension: A computational approach.
abstract: The present research adopted a computational approach to explore the extent to which the semantic content of texts constrains the activation of knowledge-based inferences. Specifically, we examined whether textual semantic constraints (TSC) can explain (1) the activation of predictive inferences, (2) the activation of bridging inferences and (3) the higher prevalence of the activation of bridging inferences compared to predictive inferences. To examine these hypotheses, we computed the strength of semantic associations between texts and probe items as presented to human readers in previous behavioural studies, using the Latent Semantic Analysis (LSA) algorithm. We tested whether stronger semantic associations are observed for inferred items compared to control items. Our results show that in 15 out of 17 planned comparisons, the computed strength of semantic associations successfully simulated the activation of inferences. These findings suggest that TSC play a central role in the activation of knowledge-based inferences.
authors.affiliation: a School of Education , Bar Ilan University , Ramat-Gan , Israel.|b Department of Education , Leiden University , Leiden , the Netherlands.
authors.forename: Menahem
authors.initials: M
authors.lastname: Yeari
authors.affiliation: b Department of Education , Leiden University , Leiden , the Netherlands.
authors.forename: Paul
authors.initials: P
authors.lastname: van den Broek
mesh_terms: D032882:Comprehension; D003198:Computer Simulation; D003210:Concept Formation; D006801:Humans; D019359:Knowledge; D007807:Language Tests; D008960:Models, Psychological; D011930:Reaction Time; D011932:Reading; D012660:Semantics
keywords: Associative and strategic processes; Bridging and predictive inferences; Knowledge-based inferences; Latent semantic analysis; Semantic associations; Textual semantic constraints