Classical Short-Delay Eyeblink Conditioning in One-Year-Old Children
Bibliographic record
Abstract
Classical eyeblink conditioning (EBC) refers to the learned association between a conditioned stimulus (an auditory tone) and an unconditioned stimulus (a puff of air to the cornea). Eyeblink conditioning is often used experimentally to detect abnormalities in cerebellar-dependent learning and memory that underlies this type of associative learning. While experiments in adults and older children are relatively simple to administer using commercial equipment, eyeblink conditioning in infants is more challenging due to their poor compliance, which makes correct positioning of the equipment difficult. To achieve conditioning in one-year-old infants, a custom-made or an adapted commercial system can be used to deliver the air puff to the infant's cornea. The main challenge lies in successfully detecting and classifying the behavioral responses. We report that automated blink detection methods are unreliable in this population, and that conditioning experiments should be analyzed using frame-by-frame analysis of supplementary video camera recordings. This method can be applied to study developmental changes in eyeblink conditioning and to examine whether this paradigm can detect children with neurological disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".