Abstract
It is a well-accepted view that the prior semantic (general) knowledge that readers possess plays a central role in reading comprehension. Nevertheless, computational models of reading comprehension have not integrated the simulation of semantic knowledge and online comprehension processes under a unified mathematical algorithm. The present article introduces a computational model that integrates the landscape model of comprehension processes with latent semantic analysis representation of semantic knowledge. In three sets of simulations of previous behavioral findings, the integrated model successfully simulated the activation and attenuation of predictive and bridging inferences during reading, as well as centrality estimations and recall of textual information after reading. Analyses of the computational results revealed new theoretical insights regarding the underlying mechanisms of the various comprehension phenomena.
| Original language | English |
|---|---|
| Pages (from-to) | 880-896 |
| Number of pages | 17 |
| Journal | Behavior Research Methods |
| Volume | 48 |
| Issue number | 3 |
| DOIs | |
| State | Published - 1 Sep 2016 |
Bibliographical note
Funding Information:This research was supported by Grant No. PIEF-GA-2009-254607-IEF from the European Union to M.Y. Facilities for conducting the research were provided by the Brain and Education Lab, the Leiden Institute for Brain and Cognition, and the Department of Education at Leiden University. We thank Eva Leusink, Sanne Rovers, and Adi Avramovich for help setting up and running the simulations.
Publisher Copyright:
© 2016, Psychonomic Society, Inc.
Keywords
- Computational modeling
- Inference generation
- Information centrality
- Landscape model
- Latent semantic analysis
- Reading comprehension
- Semantic knowledge
- Text recall