Learning From Peers' Eye Movements in the Absence of Expert Guidance: A Proof of Concept Using Laboratory Stock Trading, Eye Tracking, and Machine Learning.

Existing research shows that people can improve their decision skills by learning what experts paid attention to when faced with the same problem. However, in domains like financial education, effective instruction requires frequent, personalized feedback given at the point of decision, which makes it time-consuming for experts to provide and thus, prohibitively costly. We address this by demonstrating an automated feedback mechanism that allows amateur decision-makers to learn what information to attend to from one another, rather than from an expert. In the first experiment, eye movements of N = 100 subjects were recorded while they repeatedly performed a standard behavioral finance investment task. Consistent with previous studies, we found that a significant proportion of subjects were affected by decision bias. In the second experiment, a different group of N = 100 subjects faced the same task but, after each choice, they received individual, machine learning-generated feedback on whether their pre-decision eye movements resembled those made by Experiment 1 subjects prior to good decisions. As a result, Experiment 2 subjects learned to analyze information similarly to their successful peers, which in turn reduced their decision bias. Furthermore, subjects with low Cognitive Reflection Test scores gained more from the proposed form of process feedback than from standard behavioral feedback based on decision outcomes.