Bibliographic record
Abstract
The measurement of pupil diameter in psychology (in short, "pupillometry") has just celebrated 50 years. The method established itself after the appearance of three seminal studies (Hess & Polt, 1960, 1964; Kahneman & Beatty, 1966). Since then, the method has continued to play a significant role within the field, and pupillary responses have been successfully used to provide an estimate of the "intensity" of mental activity and of changes in mental states, particularly changes in the allocation of attention and the consolidation of perception. Remarkably, pupillary responses provide a continuous measure regardless of whether the participant is aware of such changes. More recently, research in neuroscience has revealed a tight correlation between the activity of the locus coeruleus (i.e., the "hub" of the noradrenergic system) and pupillary dilation. As we discuss in this short review, these neurophysiological findings provide new important insights to the meaning of pupillary responses for mental activity. Finally, given that pupillary responses can be easily measured in a noninvasive manner, occur from birth, and can occur in the absence of voluntary, conscious processes, they constitute a very promising tool for the study of preverbal (e.g., infants) or nonverbal participants (e.g., animals, neurological patients).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.025 |
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".