A dynamical scan-path model for task-dependence during scene viewing
- In real-world scene perception, human observers generate sequences of fixations to move image patches into the high-acuity center of the visual field. Models of visual attention developed over the last 25 years aim to predict two-dimensional probabilities of gaze positions for a given image via saliency maps. Recently, progress has been made on models for the generation of scan paths under the constraints of saliency as well as attentional and oculomotor restrictions. Experimental research demonstrated that task constraints can have a strong impact on viewing behavior. Here, we propose a scan-path model for both fixation positions and fixation durations, which include influences of task instructions and interindividual differences. Based on an eye-movement experiment with four different task conditions, we estimated model parameters for each individual observer and task condition using a fully Bayesian dynamical modeling framework using a joint spatial-temporal likelihood approach with sequential estimation. Resulting parameter valuesIn real-world scene perception, human observers generate sequences of fixations to move image patches into the high-acuity center of the visual field. Models of visual attention developed over the last 25 years aim to predict two-dimensional probabilities of gaze positions for a given image via saliency maps. Recently, progress has been made on models for the generation of scan paths under the constraints of saliency as well as attentional and oculomotor restrictions. Experimental research demonstrated that task constraints can have a strong impact on viewing behavior. Here, we propose a scan-path model for both fixation positions and fixation durations, which include influences of task instructions and interindividual differences. Based on an eye-movement experiment with four different task conditions, we estimated model parameters for each individual observer and task condition using a fully Bayesian dynamical modeling framework using a joint spatial-temporal likelihood approach with sequential estimation. Resulting parameter values demonstrate that model properties such as the attentional span are adjusted to task requirements. Posterior predictive checks indicate that our dynamical model can reproduce task differences in scan-path statistics across individual observers.…
Author details: | Lisa SchwetlickORCiDGND, Daniel BackhausORCiD, Ralf EngbertORCiDGND |
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DOI: | https://doi.org/10.1037/rev0000379 |
ISSN: | 0033-295X |
ISSN: | 1939-1471 |
Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/36190753 |
Title of parent work (English): | Psychological review |
Publisher: | American Psychological Association |
Place of publishing: | Washington |
Publication type: | Article |
Language: | English |
Year of first publication: | 2023 |
Publication year: | 2023 |
Release date: | 2024/07/03 |
Tag: | Bayesian inference; eye movements; individual differences;; scene viewing; task dependence |
Volume: | 130 |
Issue: | 3 |
Number of pages: | 34 |
First page: | 807 |
Last Page: | 840 |
Funding institution: | Deutsche Forschungsgemeinschaft (DFG) [(SFB) 1294, 318763901]; DFG [EN; 471/16-1] |
Organizational units: | Humanwissenschaftliche Fakultät / Strukturbereich Kognitionswissenschaften / Department Psychologie |
DDC classification: | 1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie |
Peer review: | Referiert |