A scalable method for characterizing visual search strategies: A novel application of time-series clustering to raw eye-tracking data
- DOI
- 10.3758/s13428-026-03154-2
- Published
- 2026-09-09
- Container
- Behavior Research Methods
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3758/s13428-026-03154-2,
title = {A scalable method for characterizing visual search strategies: A novel application of time-series clustering to raw eye-tracking data},
author = {Thomas Seacrist and Elizabeth E. Walshe and David Grethlein and Megan S. Ryerson and Flaura K. Winston},
year = {2026},
journal = {Behavior Research Methods},
doi = {10.3758/s13428-026-03154-2},
url = {https://doi.org/10.3758/s13428-026-03154-2}
}RIS
TY - JOUR TI - A scalable method for characterizing visual search strategies: A novel application of time-series clustering to raw eye-tracking data AU - Thomas Seacrist AU - Elizabeth E. Walshe AU - David Grethlein AU - Megan S. Ryerson AU - Flaura K. Winston PY - 2026 JO - Behavior Research Methods DO - 10.3758/s13428-026-03154-2 UR - https://doi.org/10.3758/s13428-026-03154-2 ER -
APA
Seacrist, T., Walshe, E. E., Grethlein, D., Ryerson, M. S., & Winston, F. K. (2026). A scalable method for characterizing visual search strategies: A novel application of time-series clustering to raw eye-tracking data. Behavior Research Methods. https://doi.org/10.3758/s13428-026-03154-2
Source records
- crossref · retrieved 2026-11-10T11:46:31.033Z