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Found 14 papers, 3 papers with code
Modeling Sentence Comprehension Deficits in Aphasia: A Computational Evaluation of the Direct-access Model of Retrieval
Several researchers have argued that sentence comprehension is mediated via a content-addressable retrieval mechanism that allows fast and direct access to memory items.
SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading
Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading.
When words collide: Bayesian meta-analyses of distractor and target properties in the picture-word interference paradigm
Finally, we found that distractor word frequency and target word frequency interact; the effect of distractor frequency decreases as the frequency of the target word increases.
A computational investigation of sources of variability in sentence comprehension difficulty in aphasia
We present a computational evaluation of three hypotheses about sources of deficit in sentence comprehension in aphasia: slowed processing, intermittent deficiency, and resource reduction.
Feature overwriting as a finite mixture process: Evidence from comprehension data
One explanation for this facilitation effect is the feature percolation account: the plural feature on cabinets percolates up to the head noun key, leading to the illusion.
Modelling dependency completion in sentence comprehension as a Bayesian hierarchical mixture process: A case study involving Chinese relative clauses
We present a case-study demonstrating the usefulness of Bayesian hierarchical mixture modelling for investigating cognitive processes.
Models of retrieval in sentence comprehension: A computational evaluation using Bayesian hierarchical modeling
We show that by introducing a modification of the activation model, i. e, by assuming that the accumulation of evidence for retrieval of incorrect items is not only slower but noisier (i. e., different variances for the correct and incorrect items), the model can provide a fit as good as the one of the direct access model.
Introduction: Cognitive Issues in Natural Language Processing
This special issue is dedicated to get a better picture of the relationships between computational linguistics and cognitive science.
Balancing Type I Error and Power in Linear Mixed Models
Linear mixed-effects models have increasingly replaced mixed-model analyses of variance for statistical inference in factorial psycholinguistic experiments.
Applications
Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists
With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in experimentally-driven areas like psychology, linguistics, and cognitive science.
Methodology
Parsimonious Mixed Models
The analysis of experimental data with mixed-effects models requires decisions about the specification of the appropriate random-effects structure.
Methodology
