Using pupillometry to investigate predictive processes in infancy
Bibliographic record
Abstract
Prediction, a prospective cognitive process, is increasingly believed to be crucial for adult cognition and learning. Despite decades of targeted research on prediction in adults, methodological limitations still exist for investigating prediction in infancy. In this article, we argue that pupillometry, or the measurement of pupil size, is an effective method to examine predictive processing in infants and will expand on existing methods (namely looking time and anticipatory eye movements). In particular, we argue that there are three specific features of pupillometry that make it particularly useful for augmenting the investigation of prediction in infancy. First, pupillometry has excellent temporal resolution that will facilitate the differentiation of prediction subcomponents. Second, pupillometry is highly continuous across the life span, allowing researchers to directly compare responses between infants and adults using an identical paradigm. Third, pupillometry can be used in conjunction with other behavioral measures, allowing for different yet complementary results. In addition, we review relevant adult and infant pupillometry studies that will facilitate infancy researchers to adopt this technique. Overall, pupillometry is particularly useful in investigating prediction in infancy and opens up several avenues for developmental 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".