Eye movement monitoring as a process tracing methodology in decision making research.
Bibliographic record
Abstract
Over the past half century, research on human decision making has expanded from a purely behaviorist approach that focuses on decision outcomes, to include a more cognitive approach that focuses on the decision processes that occur prior to the response. This newer approach, known as process tracing, has employed various methods, such as verbal protocols, information search displays, and eye movement monitoring, to identify and track psychological events that occur prior to the response (such as cognitive states, stages, or processes). In the present article, we review empirical studies that have employed eye movement monitoring as a process tracing method in decision making research, and we examine the potential of eye movement monitoring as a process tracing methodology. We also present an experiment that further illustrates the experimental manipulations and analysis techniques that are possible with modern eye tracking technology. In this experiment, a gaze-contingent display was used to manipulate stimulus exposure during decision making, which allowed us to test a specific hypothesis about the role of eye movements in preference decisions (the Gaze Cascade model; Shimojo, Simion, Shimojo, & Scheier, 2003). The results of the experiment did not confirm the predictions of the Gaze Cascade model, but instead support the idea that eye movements in these decisions reflect the screening and evaluation of decision alternatives. In summary, we argue that eye movement monitoring is a valuable tool for capturing decision makers’ information search behaviors, and that modern eye tracking technology is highly compatible with other process tracing methods such as retrospective verbal protocols and neuroimaging techniques, and hence it is poised to be an integral part of the next wave of decision research.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".