Rijpstra, T. J. (2017) Comparing webcam-based eyetracking with normal eyetracking in a value-based decision-making task. Master's Thesis / Essay, Human-Machine Communication.
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Abstract
Everyday we are presented with simple choices such as the choice between an apple or a candy-bar as a snack. Value-based decision-making research tries to investigate which factors contribute to the decision-making process for such choices. Previous studies found that visual attention plays an important part in value-based decision-making, and that this process can be quantitatively and accurately modeled using computational models such as the Drift-Diffusion Model. Using eye-gaze data, the internal process of the value comparison of options can be accurately modeled for both binary and trinary choices. However, these studies all rely on usage of an expensive standalone eyetracker for collecting eye-gaze data and are therefore conducted in a clinical setting, limiting the applications of this type of research. In this thesis a methodology was developed for using a commodity webcam to extract information about eye-gaze using the OpenFace framework and inferring the intrinsic value of options in a value-based decision-making task. An experiment was conducted ($n = 17$) in which binary simple choice was investigated using Hierarchical Drift-Diffusion Models augmented with gaze information simultaneously collected from a webcam and eyetracker. By exposing the connection between gaze and intrinsic value, predictions can be made about upcoming decisions using only information collected through the webcam, thus providing an intrinsic measure of item preference using commonly available hardware. We concluded that value-based decision-making research is possible using a webcam instead of an eyetracker, and that similar conclusions are reached irregardless of the gaze collection method. The methodology developed in this thesis enables researchers to conduct value-based decision-making research through online questionnaires (provided that a webcam is available) allowing for larger sample sizes while maintaining low research costs.
| Item Type: | Thesis (Master's Thesis / Essay) |
|---|---|
| Degree programme: | Human-Machine Communication |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 08:31 |
| Last Modified: | 15 Feb 2018 08:31 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/15645 |
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