Bibliographic record
Abstract
UNLABELLED: Pupillometry is the study of changes in the diameter of the pupil as a function of cognitive processing. This review paper provides a brief historical overview of the study of pupillometry in cognitive science. The physiology of pupillary responses is introduced, leading to an outline of early pupillometry work, which began with the seminal work of Hess and Polt in the 1960s. The paper then presents a broad review of contemporary research in cognitive sciences that relies on pupillometry. This review is organized around five general domains, namely perception, language processing, memory and decision making, emotion and cognition, and cognitive development. In order to illustrate the nature of the method, and the challenges of analysis, the next section of the review details the process of compiling, processing, and analyzing data from a simple, typical pupillometry study. WIREs Cogn Sci 2014, 5:679-692. doi: 10.1002/wcs.1323 For further resources related to this article, please visit the WIREs website. CONFLICT OF INTEREST: The authors have declared no conflicts of interest for this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".