Predicting an observer's task using multi-fixation pattern analysis
Abstract
Since Yarbus's seminal work in 1965, vision scientists have argued that people's eye movement patterns differ depending upon their task. This suggests that we may be able to infer a person's task (or mental state) from their eye movements alone. Recently, this was attempted by Greene et al. [2012] in a Yarbus-like replication study; however, they were unable to successfully predict the task given to their observer. We reanalyze their data, and show that by using more powerful algorithms it is possible to predict the observer's task. We also used our algorithms to infer the image being viewed by an observer and their identity. More generally, we show how off-the-shelf algorithms from machine learning can be used to make inferences from an observer's eye movements, using an approach we call Multi-Fixation Pattern Analysis (MFPA).
Additional Information
2014 Copyright held by the Owner/Author. C.K. was affilitated with UC San Diego when this work was completed. This project was supported in part by NSF REU Site grant SMA-1005256 as well as NSF grant and SMA 1041755 to the Temporal Dynamics of Learning Center.Attached Files
Published - p287-kanan.pdf
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Additional details
- Eprint ID
- 71916
- Resolver ID
- CaltechAUTHORS:20161110-132855891
- SMA 1005256
- NSF
- SBE 0542013
- NSF
- SMA 1041755
- NSF
- Created
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2016-11-10Created from EPrint's datestamp field
- Updated
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2021-11-11Created from EPrint's last_modified field