The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Viewing task influences eye movement control during active scene perception
Open accessAbstract
Expanding on the seminal work of G. Buswell (1935) and I. A. Yarbus (1967), we investigated how task instruction influences specific parameters of eye movement control. In the present study, 20 participants viewed color photographs of natural scenes under two instruction sets: visual search and memorization. Results showed that task influenced a number of eye movement measures including the number of fixations and gaze duration on specific objects. Additional analyses revealed that the areas fixated were qualitatively different between the two tasks. However, other measures such as average saccade amplitude and individual fixation durations remained constant across the viewing of the scene and across tasks. The present study demonstrates that viewing task biases the selection of scene regions and aggregate measures of fixation time on those regions but does not influence other measures, such as the duration of individual fixations.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Eye movements in reading and information processing: 20 years of research
Rayner, K. (1998) · Psychological Bulletin · doi:10.1037/0033-2909.124.3.372 · Cited by 7,979
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Contextual Cueing: Implicit Learning and Memory of Visual Context Guides Spatial Attention
Chun, M. M., & Jiang, Y. (1998) · Cognitive Psychology · doi:10.1006/cogp.1998.0681 · Cited by 1,959
From Blobs to Boundary Edges: Evidence for Time- and Spatial-Scale-Dependent Scene Recognition
Schyns, P. G., & Oliva, A. (1994) · Psychological Science · doi:10.1111/j.1467-9280.1994.tb00500.x · Cited by 715
Prioritizing new objects for eye fixation in real-world scenes: Effects of object–scene consistency
Brockmole, J. R., & Henderson, J. M. (2008) · Visual Cognition · doi:10.1080/13506280701453623 · Cited by 69
Self-citation
Cited by
Best practices in eye tracking research
Carter, B. T., & Luke, S. G. (2020) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2020.05.010 · Cited by 659
A review of gaze entropy as a measure of visual scanning efficiency
Shiferaw, B., Downey, L., & Crewther, D. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.12.007 · Cited by 203
Introducing a Bayesian model of selective attention based on active inference
Mirza, M. B., Adams, R. A., Friston, K., & Parr, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-50138-8 · Cited by 83
Does it look safe? An eye tracking study into the visual aspects of fear of crime
Crosby, F., & Hermens, F. (2019) · Quarterly Journal of Experimental Psychology · doi:10.1177/1747021818769203 · Cited by 35
Metadata
- Authors
- Castelhano, M. S., Mack, M. L., & Henderson, J. M.
- Venue
- Journal of Vision
- Published
- 2009-03-01
- Volume
- 9
- Issue
- 3
- Pages
- 6-6
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2135682029
- Cited by
- 378
- References
- 5
- FWCI
- 22.35
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.