August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Measuring visual attention to online videos with a mouse cursor window paradigm: considerations for large scale data collection
Author Affiliations & Notes
  • Karissa Payne
    Kansas State University
  • Brian Howatt
    Kansas State University
  • Sahand Shaghaghi
    University of Waterloo
  • Lester Loschky
    Kansas State University
  • Footnotes
    Acknowledgements  This work was supported by the Cognitive and Neurobiological Approaches to Plasticity (CNAP) Center of Biomedical Research Excellence (COBRE) of the NIH under grant number P20GM113109.
Journal of Vision August 2023, Vol.23, 5974. doi:
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      Karissa Payne, Brian Howatt, Sahand Shaghaghi, Lester Loschky; Measuring visual attention to online videos with a mouse cursor window paradigm: considerations for large scale data collection. Journal of Vision 2023;23(9):5974.

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      © ARVO (1962-2015); The Authors (2016-present)

  • Supplements

Previous research has allowed for large scale datasets to be created from mouse-based measures of attention to images (e.g., SALICON). However, there has not been a similar, easy-to-use solution for large scale data collection of attention to video stimuli. Here, we demonstrate our novel mouse-based measure of attention that can be used with videos in online experiments. Our results show similar performance between this paradigm and eye tracking in terms of the attended regions of interest in video. This paradigm tracks the user’s mouse location as they use their mouse to move a window of high resolution around an otherwise blurred screen. To view video content in more detail, the user moves their mouse window to that location. This results in a robust measure of visual attention that can be used to identify regions of video content participants find most salient or informative. Our research has compared eye movements from the DIEM dataset to mouse movements collected from online participants watching videos with a mouse-contingent bi-resolution display. To test the settings of the mouse-based method, participants experienced large, medium, and small window sizes and blur levels, in a 3x3 within-subjects factorial design. New results show that the motions made between these methodologies differ, but they result in visits to similar regions of interest in video. This suggests that mouse-based methods may not replicate effects that require the speed and ease of eye movements, but can replicate effects regarding attention to salient, informative, and preferred regions of content. These findings further support the validity of this method in large scale data collection on users’ attention to video stimuli–valuable to both experimental research, and the training and testing of video saliency models. With this presentation, we will discuss plans to make our methodology available for use with online experiment software.


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