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Temporal dynamics of eye movements are related to differences in scene complexity and clutter
- Source :
- Wu, D W, Anderson, N C C, Bischof, W F & Kingstone, A 2014, ' Temporal dynamics of eye movements are related to differences in scene complexity and clutter ', Journal of Vision, vol. 14, no. 9 . https://doi.org/10.1167/14.9.8, Journal of Vision, 14(9). Association for Research in Vision and Ophthalmology Inc.
- Publication Year :
- 2014
-
Abstract
- Recent research has begun to explore not just the spatial distribution of eye fixations but also the temporal dynamics of how we look at the world. In this investigation, we assess how scene characteristics contribute to these fixation dynamics. In a free-viewing task, participants viewed three scene types: fractal, landscape, and social scenes. We used a relatively new method, recurrence quantification analysis (RQA), to quantify eye movement dynamics. RQA revealed that eye movement dynamics were dependent on the scene type viewed. To understand the underlying cause for these differences we applied a technique known as fractal analysis and discovered that complexity and clutter are two scene characteristics that affect fixation dynamics, but only in scenes with meaningful content. Critically, scene primitives-revealed by saliency analysis-had no impact on performance. In addition, we explored how RQA differs from the first half of the trial to the second half, as well as the potential to investigate the precision of fixation targeting by changing RQA radius values. Collectively, our results suggest that eye movement dynamics result from topdown viewing strategies that vary according to the meaning of a scene and its associated visual complexity and clutter. © 2014 ARVO.
- Subjects :
- Communication
Eye Movements
business.industry
Computer science
Eye movement
SDG 10 - Reduced Inequalities
Fixation, Ocular
Fixation (psychology)
Fractal analysis
Sensory Systems
Visual complexity
Ophthalmology
Fractal
Fractals
Pattern Recognition, Visual
Salience (neuroscience)
Recurrence quantification analysis
Clutter
Humans
Computer vision
Artificial intelligence
business
Subjects
Details
- ISSN :
- 15347362
- Volume :
- 14
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- Journal of vision
- Accession number :
- edsair.doi.dedup.....8b63a385872bcec40df2df1620e84601
- Full Text :
- https://doi.org/10.1167/14.9.8