Abstract


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Click to download PDF version Click to download BibTeX data Clik to view abstract A. Borji, L. Itti, Defending Yarbus: Eye movements reveal observers' task, Journal of Vision, Vol. 14, No. 3(29), pp. 1-22, Mar 2014. [2012 impact factor: 2.47]

Abstract: In a very influential yet anecdotal illustration, Yarbus suggested that human eye movement patterns are modulated top-down by different task demands. While the hypothesis that it is possible to decode the observer's task from eye movements has received some support (e.g., Iqbal & Bailey (2004); Henderson et al. (2013)), Greene et al. (2012) argued against it by reporting a failure. In this study, we perform a more systematic investigation of this problem, probing a larger number of experimental factors than previously. Our main goal is to determine the informativeness of eye movements for task and mental state decoding. We perform two experiments. In the first experiment, we re-analyze the data from a previous study by Greene et al. (2012) and contrary to their conclusion, we report that it is possible to decode the observer's task from aggregate eye movement features slightly but significantly above chance, using a Boosting classifier (34.12% correct vs. 25% chance-level; binomial test, p=1.0722e-04). In the second experiment, we repeat and extend Yarbus' original experiment by collecting eye movements of 21 observers viewing 15 natural scenes (including Yarbus' scene) under Yarbus' seven questions. We show that task decoding is possible, also moderately but significantly above chance (24.21% vs. 14.29% chance-level; binomial test, p=2.4535e-06). We thus conclude that Yarbus' idea is supported by our data and continues to be an inspiration for future computational and experimental eye movement research. From a broader perspective, we discuss techniques, features, limitations, societal and technological impacts, and future directions in task decoding from eye movements.

Themes: Model of Bottom-Up Saliency-Based Visual Attention, Scene Understanding, Human Eye-Tracking Research

 

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