A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts.

doi:10.31234/osf.io/nw2pb

Paper

A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts.

Published Dec 11, 2019 · M. M. Rabe, Johan Chandra, A. Krügel

Psychological review

Q1 SJR score

30

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0

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Abstract

In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777-813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between-subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Non-RCT

Study Snapshot

Key takeawayThe Bayesian approach to eye-movement control during reading allows for more accurate statistical inference and modeling of interindividual differences.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Paper

A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts.

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References

Citations

Explaining the Sentence Superiority Effect and N400s Elicited by Words and Short Sentences with OB1-Reader

OB1-reader simulates reading behavior and brain activity, including the sentence superiority effect and N400 component, accurately predicting human reading performance and EEG activity.

Integrating large language models and active inference to understand eye movements in reading and dyslexia

Our model effectively simulates reading and eye movements, aiding in understanding cognitive processes and potential dyslexia treatment approaches.

Synthetic predictabilities from large language models explain reading eye movements

Synthetic predictabilities from large language models can serve as a substitute for empirical cloze predictabilities in explaining reading eye movements.

Eyettention: An Attention-based Dual-Sequence Model for Predicting Human Scanpaths during Reading

Eyettention, a dual-sequence model, effectively predicts human scanpaths during reading by aligning word and fixation sequences, outperforming state-of-the-art models across different languages.

SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading

The SEAM model successfully integrates eye-movement control and sentence processing, providing a complete mathematical model of natural language comprehension in reading.