Individual pupil size changes as a robust indicator of cognitive familiarity differences
Bibliographic record
Abstract
Cognitive psychology has a long history of using physiological measures, such as pupillometry. However, their susceptibility to confounds introduced by stimulus properties, such as color and luminance, has limited their application. Pupil size measurements, in particular, require sophisticated experimental designs to dissociate relatively small changes in pupil diameter due to cognitive responses from larger ones elicited by changes in stimulus properties or the experimental environment. Here, building on previous research, we present a pupillometry paradigm that adapts the pupil to stimulus properties during the baseline period without revealing stimulus meaning or context by using a pixel-scrambled image mask around an intact image. We demonstrate its robustness in the context of pupillary responses to branded product familiarity. Results show larger average and peak pupil dilation for passively viewed familiar product images and an extended later temporal component representing differences in familiarity across participants (starting around 1400 ms post-stimulus onset). These amplitude differences are present for almost all participants at the single-participant level, and vary somewhat by product category. However, amplitude differences were absent during the baseline period. These findings demonstrate that involuntary pupil size measurements combined with the presented paradigm are successful in dissociating cognitive effects of familiarity from physical stimulus confounds.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".