The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Eye tracking and pupillometry are indicators of dissociable latent decision processes.
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Delorme, A., & Makeig, S. (2004) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2003.10.009 · Cited by 24,371
How do you feel? Interoception: The sense of the physiological condition of the body
Craig, A. D. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn894 · Cited by 6,006
Ratcliff, R. (1978) · Psychological Review · doi:10.1037/0033-295x.85.2.59 · Cited by 4,513
(2001) · doi:10.1017/cbo9780511546396 · Cited by 4,482
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
Contributions of anterior cingulate cortex to behaviour
Devinsky, O., Morrell, M. J., & Vogt, B. A. (1995) · Brain · doi:10.1093/brain/118.1.279 · Cited by 3,377
Personality: A Psychological Interpretation
Jenkins, J. G., & Allport, G. W. (1938) · The American Journal of Psychology · doi:10.2307/1415717 · Cited by 3,215
The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks
Ratcliff, R., & McKoon, G. (2008) · Neural Computation · doi:10.1162/neco.2008.12-06-420 · Cited by 3,059
The time course of perceptual choice: The leaky, competing accumulator model.
Usher, M., & McClelland, J. L. (2001) · Psychological Review · doi:10.1037/0033-295x.108.3.550 · Cited by 2,152
The integration of negative affect, pain and cognitive control in the cingulate cortex
Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J., & Davidson, R. J. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn2994 · Cited by 2,082
Research works printed but not linked: 2
- Ratcliff R McKoon G The diffusion decision model: theory and data for two-choice decision tasks Neural computation 2008b 20 4 873 922 doi:10.1162/neco.2008.12-06-420 18085991 PMC2474742 · DOI · Paper page
- Wiecki Thomas V Sofer I Frank MJ HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python Frontiers in Neuroinformatics 2013 7 August 1 10 doi:10.3389/fninf.2013.00014 23935581 PMC3731670 · DOI · Paper page
Cited by
Sorted by citations
Diffusion decision model: Current issues and history
Ratcliff, R., Smith, P. L., Brown, S. D., & McKoon, G. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.01.007 · Cited by 1,630
“Relationships have been established between diffusion model analyses and behavioral measures such as eye tracking [128,137] and pupil dilation [142].”
BackgroundTen simple rules for the computational modeling of behavioral data
Wilson, R. C., & Collins, A. G. E. (2019) · eLife · doi:10.7554/elife.49547 · Cited by 794
Best practices in eye tracking research
Carter, B. T., & Luke, S. G. (2020) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2020.05.010 · Cited by 659
Pupil-linked arousal is driven by decision uncertainty and alters serial choice bias
Urai, A. E., Braun, A., & Donner, T. H. (2017) · Nature Communications · doi:10.1038/ncomms14637 · Cited by 466
A systematic review of Bayesian articles in psychology: The last 25 years.
van de Schoot, R., Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M. A. J., & Depaoli, S. A. (2017) · Psychological Methods · doi:10.1037/met0000100 · Cited by 401
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 ›
Are you an author, or do you see a problem with this page? Request a review
