Presentation - Using Computer Mouse Tracking for Stress Measurement? An Online Study
Author(s) / Creator(s)
Freihaut, Paul
Göritz, Anja
Rockstroh, Christoph
Blum, Johannes
Abstract / Description
Computer mouse tracking offers a simple and cost-efficient way to gather continuous behavioral data and has mostly been utilized in psychological science to study cognitive processes. The present study extends the potential applicability of computer mouse tracking and investigates the feasibility of using computer mouse tracking for stress measurement. First empirical results and theoretical considerations suggests that stress affects sensorimotor processes involved in mouse usage. The underlying processes, however, are complex and the empirical evidence sparse. Therefore, our research is exploratory with the goal of finding a meaningful relationship between stress and computer mouse usage.
We present data from a between-participant field experiment with N = 994 participants. The online setting allowed capturing mouse usage in a natural setting, while the experimental design fostered internal validity. In the experiment, participants worked on four different prototypical mouse usage tasks (point-and-click task, drag-and-drop task, slider task and target-following task) in a high-stress or low-stress condition. Stress was manipulated with a self-developed stress manipulation task as well as a threatening versus neutral framing of the study’s purpose. In the manipulation check, participants in the two conditions reported small, but consistently different stress levels on multiple self-report measures.
The relationship between stress and mouse usage was explored with multiple analysis: (1) We calculated 17 different mouse usage features representing temporal, spatial and task specific mouse usage information in each mouse usage task and compared each feature between the stress conditions using frequentist data analysis. (2) We used the mouse usage features to predict a participant’s experimental condition using a machine learning classification approach. (3) We collapsed the two groups and used the mouse usage features to predict participant’s stress using a machine learning regression approach. (4) We transformed the raw mouse usage data into images of the mouse usage during each task and used the images as the input for machine learning classification of the stress condition and regression on reported stress.
None of these analytical approaches revealed a clear and systematic relationship between stress and mouse usage. These findings question the feasibility of using straightforward computer mouse tracking for generalized stress measurement. More generally, our research discloses challenges but also points out promises when working with unobtrusive sensor data to capture feeling states.
Persistent Identifier
Date of first publication
2021-05-18
Is part of
Research Synthesis & Big Data, 2021, online
Publisher
ZPID (Leibniz Institute for Psychology)
Citation
Freihaut, P., Göritz, A., Rockstroh, C., & Blum, J. (2021). Presentation - Using Computer Mouse Tracking for Stress Measurement? An Online Study. ZPID (Leibniz Institute for Psychology). https://doi.org/10.23668/PSYCHARCHIVES.4810
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ResynnBigDataConfPresentation-Using_Computer_Mouse_Tracking_for_Stress_Measurement-Freihaut_et_al.pdfAdobe PDF - 391.69KBMD5: d78e5d37fb15561330bbcbfb63f7cb4bDescription: Conference Paper Slides
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Author(s) / Creator(s)Freihaut, Paul
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Author(s) / Creator(s)Göritz, Anja
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Author(s) / Creator(s)Rockstroh, Christoph
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Author(s) / Creator(s)Blum, Johannes
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PsychArchives acquisition timestamp2021-05-07T10:15:36Z
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Made available on2021-05-07T10:15:36Z
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Date of first publication2021-05-18
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Abstract / DescriptionComputer mouse tracking offers a simple and cost-efficient way to gather continuous behavioral data and has mostly been utilized in psychological science to study cognitive processes. The present study extends the potential applicability of computer mouse tracking and investigates the feasibility of using computer mouse tracking for stress measurement. First empirical results and theoretical considerations suggests that stress affects sensorimotor processes involved in mouse usage. The underlying processes, however, are complex and the empirical evidence sparse. Therefore, our research is exploratory with the goal of finding a meaningful relationship between stress and computer mouse usage. We present data from a between-participant field experiment with N = 994 participants. The online setting allowed capturing mouse usage in a natural setting, while the experimental design fostered internal validity. In the experiment, participants worked on four different prototypical mouse usage tasks (point-and-click task, drag-and-drop task, slider task and target-following task) in a high-stress or low-stress condition. Stress was manipulated with a self-developed stress manipulation task as well as a threatening versus neutral framing of the study’s purpose. In the manipulation check, participants in the two conditions reported small, but consistently different stress levels on multiple self-report measures. The relationship between stress and mouse usage was explored with multiple analysis: (1) We calculated 17 different mouse usage features representing temporal, spatial and task specific mouse usage information in each mouse usage task and compared each feature between the stress conditions using frequentist data analysis. (2) We used the mouse usage features to predict a participant’s experimental condition using a machine learning classification approach. (3) We collapsed the two groups and used the mouse usage features to predict participant’s stress using a machine learning regression approach. (4) We transformed the raw mouse usage data into images of the mouse usage during each task and used the images as the input for machine learning classification of the stress condition and regression on reported stress. None of these analytical approaches revealed a clear and systematic relationship between stress and mouse usage. These findings question the feasibility of using straightforward computer mouse tracking for generalized stress measurement. More generally, our research discloses challenges but also points out promises when working with unobtrusive sensor data to capture feeling states.en
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Publication statusunknownen
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Review statusunknownen
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CitationFreihaut, P., Göritz, A., Rockstroh, C., & Blum, J. (2021). Presentation - Using Computer Mouse Tracking for Stress Measurement? An Online Study. ZPID (Leibniz Institute for Psychology). https://doi.org/10.23668/PSYCHARCHIVES.4810en
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/4247
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.4810
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Language of contenteng
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PublisherZPID (Leibniz Institute for Psychology)en
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Is part ofResearch Synthesis & Big Data, 2021, onlineen
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Dewey Decimal Classification number(s)150
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TitlePresentation - Using Computer Mouse Tracking for Stress Measurement? An Online Studyen
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DRO typeconferenceObjecten
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Leibniz institute name(s) / abbreviation(s)ZPID
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Visible tag(s)ZPID Conferences and Workshops