A guideline for integrating dynamic areas of interests in existing set-up for capturing eye movement: Looking at moving aircraft

Friedrich, Maik; Rußwinkel, Nele; Möhlenbrink, Christoph

doi:10.3758/s13428-016-0745-x

Cited by 22 publications

(5 citation statements)

References 26 publications

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“…Although our estimates are provided ex-post, the results in this study can lead to the design of other platforms in which real-time eye-tracking and neural measurements can provide triggers to users, alerting them with regard to their attention level while also controlling for specific individual socio-behavioral traits. This would act as a platform for real-time monitoring of similar performancedependent tasks (Friedrich et al 2017). In the future, we aim to develop experiments in which subjects can calibrate and improve their decisions by monitoring the previous eye movements of experts in such tasks by also using machine-learning algorithms, a methodology also explored by Król and Król (2019b).…”

Section: Future Directionsmentioning

confidence: 99%

Gazing through the bubble: an experimental investigation into financial risk-taking using eye-tracking

et al. 2023

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Eye tracking can facilitate understanding irrational decision-making in contexts such as financial risk-taking. For this purpose, we develop an experimental framework in which participants trade a risky asset in a simulated bubble market to maximize individual returns while their eye movements are recorded. Returns are sensitive to eye movement dynamics, depending on the presented visual stimuli. Using eye-tracking data, we investigated the effects of arousal, attention, and disengagement on individual payoffs using linear and nonlinear approaches. By estimating a nonlinear model using attention as a threshold variable, our results suggest that arousal positively influences trading returns, but its effect becomes smaller when attention exceeds a certain threshold, whereas disengagement has a higher negative impact on reduced attention levels and becomes almost irrelevant when attention increases. Hence, we provide a neurobehavioral metric as a function of attention that predicts financial gains in boom-and-bust scenarios. This study serves as a proof-of-concept for developing future psychometric measures to enhance decision-making.

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Section: Future Directionsmentioning

confidence: 99%

Gazing through the bubble: an experimental investigation into financial risk-taking using eye-tracking

et al. 2023

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“…Other methods have been proposed based on determining dynamic areas of interest (Papenmeier and Huff, 2010;Friedrich et al, 2017). These papers focus on precisely specifying the spatial regions in which gaze corresponds to tracking a particular object.…”

Section: Related Workmentioning

confidence: 99%

A hidden Markov model for analyzing eye-tracking of moving objects

et al. 2020

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Eye-tracking provides an opportunity to generate and analyze high-density data relevant to understanding cognition. However, while events in the real world are often dynamic, eye-tracking paradigms are typically limited to assessing gaze toward static objects. In this study, we propose a generative framework, based on a hidden Markov model (HMM), for using eye-tracking data to analyze behavior in the context of multiple moving objects of interest. We apply this framework to analyze data from a recent visual object tracking task paradigm, TrackIt, for studying selective sustained attention in children. Within this paradigm, we present two validation experiments to show that the HMM provides a viable approach to studying eye-tracking data with moving stimuli, and to illustrate the benefits of the HMM approach over some more naive possible approaches. The first experiment utilizes a novel 'supervised' variant of TrackIt, while the second compares directly with judgments made by human coders using data from the original TrackIt task. Our results suggest that the HMM-based method provides a robust analysis of eye-tracking data with moving stimuli, both for adults and for children as young as 3.5-6 years old.

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“…In the same vein, the decision screens we used were stylized and the same for all subjects, which would not be the case in reality. Nevertheless, our eye‐tracking area of interest specification was extremely simple, while recent advances in computer vision enable real‐time tracking of AOI's even when their position on the screen is not fixed (see e.g., Friedrich, Rußwinkel, & Möhlenbrink, ). In other words, a machine learning algorithm could work out what a person is looking at before deciding if she does it “in the right way.”…”

Section: Discussionmentioning

confidence: 99%

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

Król

2019

Cognitive Science

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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.

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A guideline for integrating dynamic areas of interests in existing set-up for capturing eye movement: Looking at moving aircraft

Cited by 22 publications

References 26 publications

Gazing through the bubble: an experimental investigation into financial risk-taking using eye-tracking

Gazing through the bubble: an experimental investigation into financial risk-taking using eye-tracking

A hidden Markov model for analyzing eye-tracking of moving objects

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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