Learning from Peers’ Eye Movements in the Absence of Expert Guidance: a Proof of Concept Using Laboratory Stock Trading, Eye Tracking, and Machine Learning

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Abstract

Existing research shows that people can improve their decision skills by learning what experts 23 paid attention to when faced with the same problem. However, in domains like financial 24 education, effective instruction requires frequent, personalized feedback given at the point of 25 decision, which makes it time-consuming for experts to provide and thus prohibitively costly. 26 We address this by demonstrating an automated feedback mechanism that allows amateur 27 decision-makers to learn what information to attend to from one another, rather than from an 28 expert. In the first experiment, eye-movements of N=100 subjects were recorded while they 29 repeatedly performed a standard behavioral finance investment task. Consistent with previous 30 studies, we found that a significant proportion of subjects were affected by decision bias. In the 31 second experiment, a different group of N=100 subjects faced the same task but, after each 32 choice, they received individual, machine-learning-generated feedback on whether their pre-33 decision eye-movements resembled those made by Experiment 1 subjects prior to good 34 decisions. As a result, Experiment 2 subjects learned to analyze information similarly to their 35 successful peers, which in turn reduced their decision bias. Furthermore, subjects with low 36 Cognitive Reflection Test scores gained more from the proposed form of process feedback than 37 from standard behavioral feedback based on decision outcomes.

Bibliographical metadata

Original languageEnglish
JournalCognitive Science
Volume43
Issue number2
Early online date25 Feb 2019
DOIs
Publication statusPublished - 25 Feb 2019

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