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Eye tracking and pupillometry are indicators of dissociable latent decision processes.
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Can you predict what people are going to do just by watching them? This is certainly difficult: it would require a clear mapping between observable indicators and unobservable cognitive states. In this report, we demonstrate how this is possible by monitoring eye gaze and pupil dilation, which predict dissociable biases during decision making. We quantified decision making using the drift diffusion model (DDM), which provides an algorithmic account of how evidence accumulation and response caution contribute to decisions through separate latent parameters of drift rate and decision threshold, respectively. We used a hierarchical Bayesian estimation approach to assess the single trial influence of observable physiological signals on these latent DDM parameters. Increased eye gaze dwell time specifically predicted an increased drift rate toward the fixated option, irrespective of the value of the option. In contrast, greater pupil dilation specifically predicted an increase in decision threshold during difficult decisions. These findings suggest that eye tracking and pupillometry reflect the operations of dissociated latent decision processes.
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“Beyond user reliance, eye tracking and pupillometry have been used to gauge the attentional, cognitive, and emotional state of a user [12,18]; for instance, pupil diameter, eyelid closure, and gaze entropy [71] are highly correlated with a person's cognitive load [84].”
From Understanding User Reliance on AI in Assisted Decision-Making (2022) · cites this paper as [12]
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Metadata
- Authors
- Cavanagh, J. F., Wiecki, T. V., Kochar, A., & Frank, M. J.
- Affiliations
- Brown University (US)
- Venue
- Journal of Experimental Psychology: General
- Published
- 2014
- Volume
- 143
- Issue
- 4
- Pages
- 1476-1488
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- PMC
- PMC4114997
- OpenAlex
- W2132578666
- Cited by
- 289
- References
- 58
- Citations by year
- 2026: 23 · 2025: 24 · 2024: 22 · 2023: 22 · 2022: 27 · 2021: 36 · 2020: 31 · 2019: 29 · 2018: 21 · 2017: 23 · 2016: 20 · 2015: 7 · 2014: 4
- FWCI
- 16.42
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Experimental Psychology General (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5T32MH019118-21 and RO1 MH080066-01)
- National Science Foundation (1125788)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- eye tracking, pupillometry, decision-making, drift-diffusion model, drift rate, decision threshold, response caution, evidence accumulation, pupil dilation, hierarchical Bayesian estimation
- MeSH terms
- Attention, Computer Simulation, Conflict, Psychological, Decision Making, Eye Movements, Female, Humans, Male, Models, Psychological, Pupil, Eye Movement Measurements, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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