Continuous Measures of Decision-Difficulty Captured Remotely: II. Webcam eye-tracking reveals early decision processing
Bibliographic record
Abstract
Abstract As decisions require the gathering of relevant information, eye-tracking measures that capture the way visual information is typically acquired offer powerful indices of the dynamic decision-making process. This study is the second of a pair of studies that explore continuous measures of decision-making using remote, online tools in naturalistic settings. While cursor-tracking, used in the companion paper (Ouellette Zuk et al., 2023), enabled access to dynamic decision processes expressed during movement, in the present study, we now employ webcam eye-tracking to examine the dynamics of information gathering during decision making prior to movement initiation. Using three previously published binary choice tasks, we explored indices of decision difficulty in the gaze dynamics that would complement the motor measures in our companion paper. We find that harder choices elicit more eye dwells and longer final dwells, reflecting a decision resolution process that Ouellette Zuk et al. index during the final choice movement. Beyond this, we identify distinct gaze patterns uniquely employed in each task, revealing the utility and sensitivity of gaze metrics in illuminating the early difficulty-independent information gathering processes at play. Together, this paper series demonstrates the power of remote, online methods as tools for deeply understanding the complete, dynamic and continuous decision process, from the first glance to the final response.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".