A bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts
- 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 conditionsIn 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.…
Author details: | Maximilian Michael RabeORCiDGND, Johan ChandraORCiDGND, André KrügelORCiDGND, Stefan A. SeeligORCiDGND, Shravan VasishthORCiDGND, Ralf EngbertORCiDGND |
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DOI: | https://doi.org/10.1037/rev0000268 |
ISSN: | 0033-295X |
ISSN: | 1939-1471 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/33983783 |
Title of parent work (English): | Psychological Review |
Publisher: | American Psychological Association |
Place of publishing: | Washington |
Publication type: | Article |
Language: | English |
Year of first publication: | 2021 |
Publication year: | 2021 |
Release date: | 2022/08/31 |
Tag: | Bayesian inference; control; dynamical models; individual differences; oculomotor; reading eye movements |
Volume: | 128 |
Issue: | 5 |
Number of pages: | 21 |
First page: | 803 |
Last Page: | 823 |
Funding institution: | Deutsche Forschungsgemeinschaft via Collaborative Research Center [(SFB) 1287, 317633480, SFB 1294, 318763901]; Norddeutscher Verbund fur Hoch-und Hochstleistungsrechnen (HLRN) [bbx00001] |
Organizational units: | Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Linguistik |
Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie | |
DDC classification: | 1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie |
Peer review: | Referiert |