Information Acquisition During Online Decision Making: A Model-Based Exploration Using Eye-Tracking Data

Background. Discrete choice experiments (DCEs) are increasingly used to elicit preferences for benefit-risk tradeoffs. The primary aim of this study was to explore how eye-tracking methods can be used to understand DCE respondents’ decision-making strategies. A secondary aim was to explore if the presentation and communication of risk affected respondents’ choices. Method. Two versions of a DCE were designed to understand the preferences of female members of the public for breast screening that varied in how risk attributes were presented. Risk was communicated as either 1) percentages or 2) icon arrays and percentages. Eye-tracking equipment recorded eye movements 1000 times a second. A debriefing survey collected sociodemographics and self-reported attribute nonattendance (ANA) data. A heteroskedastic conditional logit model analyzed DCE data. Eye-tracking data on pupil size, direction of motion, and total visual attention (dwell time) to predefined areas of interest were analyzed using ordinary least squares regressions. Results. Forty women completed the DCE with eye-tracking. There was no statistically significant difference in attention (fixations) to attributes between the risk communication formats. Respondents completing either version of the DCE with the alternatives presented in columns made more horizontal (left-right) saccades than vertical (up-down). Eye-tracking data confirmed self-reported ANA to the risk attributes with a 40% reduction in mean dwell time to the “probability of detecting a cancer” ( P = 0.001) and a 25% reduction to the “risk of unnecessary follow-up” ( P = 0.008). Conclusion. This study is one of the first to show how eye-tracking can be used to understand responses to a health care DCE and highlighted the potential impact of risk communication on respondents’ decision-making strategies. The results suggested self-reported ANA to cost attributes may not be reliable.

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Visual saliency model based on crowdsourcing eye tracking data and its application in visual design

Personal and Ubiquitous Computing ◽

10.1007/s00779-020-01463-7 ◽

2020 ◽

Author(s):

Shiwei Cheng ◽

Jing Fan ◽

Yilin Hu

Keyword(s):

Eye Tracking ◽

Visual Saliency ◽

Visual Design ◽

Tracking Data ◽

Model Based ◽

Saliency Model ◽

Visual Saliency Model

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Characterizing missed identifications and errors in latent fingerprint comparisons using eye-tracking data

PLoS ONE ◽

10.1371/journal.pone.0251674 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0251674

Author(s):

Thomas A. Busey ◽

Nicholas Heise ◽

R. Austin Hicklin ◽

Bradford T. Ulery ◽

JoAnn Buscaglia

Keyword(s):

Decision Making ◽

Eye Tracking ◽

No Value ◽

Eye Gaze ◽

Tracking Data ◽

Gaze Behavior ◽

Cognitive Capacities ◽

Latent Fingerprint ◽

The Gaze ◽

Decision Making Factors

Latent fingerprint examiners sometimes come to different conclusions when comparing fingerprints, and eye-gaze behavior may help explain these outcomes. missed identifications (missed IDs) are inconclusive, exclusion, or No Value determinations reached when the consensus of other examiners is an identification. To determine the relation between examiner behavior and missed IDs, we collected eye-gaze data from 121 latent print examiners as they completed a total 1444 difficult (latent-exemplar) comparisons. We extracted metrics from the gaze data that serve as proxies for underlying perceptual and cognitive capacities. We used these metrics to characterize potential mechanisms of missed IDs: Cursory Comparison and Mislocalization. We find that missed IDs are associated with shorter comparison times, fewer regions visited, and fewer attempted correspondences between the compared images. Latent print comparisons resulting in erroneous exclusions (a subset of missed IDs) are also more likely to have fixations in different regions and less accurate correspondence attempts than those comparisons resulting in identifications. We also use our derived metrics to describe one atypical examiner who made six erroneous identifications, four of which were on comparisons intended to be straightforward exclusions. The present work helps identify the degree to which missed IDs can be explained using eye-gaze behavior, and the extent to which missed IDs depend on cognitive and decision-making factors outside the domain of eye-tracking methodologies.

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Exploring the Potential of Online Webcam-based Eye Tracking in Decision-Making Research and Influence Factors on Data Quality

10.31234/osf.io/zm3us ◽

2021 ◽

Author(s):

Tim Schneegans ◽

Matthew D. Bachman ◽

Scott A. Huettel ◽

Hauke Heekeren

Keyword(s):

Decision Making ◽

Eye Tracking ◽

Data Quality ◽

Open Source ◽

Influence Factors ◽

Frame Rate ◽

Quality Data ◽

Tracking Data ◽

Chin Rest ◽

Negative Effect

Recent developments of open-source online eye-tracking algorithms suggests that they may be ready for use in online studies, thereby overcoming the limitations of in-lab eye-tracking studies. However, to date there have been limited tests of the efficacy of online eye-tracking for decision-making and cognitive psychology. In this online study, we explore the potential and the limitations of online eye-tracking tools for decision-making research using the webcam-based open-source library Webgazer (Papoutsaki et al., 2016). Our study had two aims. For our first aim we assessed different variables that might affect the quality of eye-tracking data. In our experiment (N = 210) we measured a within-subjects variable of adding a provisional chin rest and a between-subjects variable of corrected vs uncorrected vision. Contrary to our hypotheses, we found that the chin rest had a negative effect on data quality. In accordance with out hypotheses, we found lower quality data in participants who wore glasses. Other influence factors are discussed, such as the frame rate. For our second aim (N = 44) we attempted to replicate a decision-making paradigm where eye-tracking data was acquired using offline means (Amasino et al., 2019). We found some relations between choice behavior and eye-tracking measures, such as the last fixation and the distribution of gaze points at the moment right before the choice. However, several effects could not be reproduced, such as the overall distribution of gaze points or dynamic search strategies. Therefore, our hypotheses only find partial evidence. This study gives practical insights for the feasibility of online eye-tacking for decision making research as well as researchers from other disciplines.

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Positive Moods Can Encourage Inertial Decision Making: Evidence from Eye-Tracking Data

Information Systems and Neuroscience - Lecture Notes in Information Systems and Organisation ◽

10.1007/978-3-030-28144-1_25 ◽

2019 ◽

pp. 229-238

Author(s):

Yu-feng Huang ◽

Feng-yang Kuo

Keyword(s):

Decision Making ◽

Eye Tracking ◽

Tracking Data

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Making Sense of Mobile Eye-Tracking Data in the Real-World

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931213601362 ◽

2016 ◽

Vol 60 (1) ◽

pp. 1569-1573 ◽

Cited By ~ 4

Author(s):

Allan Fong ◽

Daniel Hoffman ◽

Raj M. Ratwani

Keyword(s):

Decision Making ◽

Eye Tracking ◽

Eye Movement ◽

Real World ◽

Interaction Mechanism ◽

Tracking Data ◽

The Real ◽

Human In The Loop ◽

Tracking Technology ◽

Mobile Eye Tracking

Stationary eye-tracking technology has been used extensively in human-computer interaction to both understand how humans interact with computers and as an interaction mechanism. Mobile eye-tracking technology is becoming more prevalent, yet the analysis and annotation of mobile eye-tracking data remains challenging. We present a novel human-in-the-loop approach for mobile eye-tracking data analysis that dramatically reduces resource requirements. This method incorporates human insight in a semi-automatic decision making process, leveraging both computational power and human decision making abilities. We demonstrate the accuracy of this approach with eye movement data from two real-world use cases. Average accuracy across the two environments is 82.3%. Our approach holds tremendous promise and has the potential to open the door to more robust eye movement studies in the real-world.

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