The use of machine learning to understand the role of visual attention in multi-attribute choice.

Whether eye movements (as a measure of visual attention) contribute to the understanding of how multi-attribute decisions are made, is still a matter of debate. In this study, we show how machine learning methods can be used to separate the effects of the information presented, eye movement patterns, and attention to specific information. We also show how to deal with data from a relatively small sample of participants, often found in eye tracking studies that require in-lab testing. We make use of a dataset of 30 females who decided whether or not to accept screening for Chlamydia in 21 different scenarios. For this dataset, we find that eye movements did not add to the prediction of choice beyond the information presented to participants. Future studies should determine whether the same conclusion holds for other eye tracking datasets.