But that’s possible! Infants, pupils, and impossible events
Bibliographic record
Abstract
Infants' expectations of the world around them have been extensively assessed through the violation of expectation paradigm and related habituation tasks. Typically, in these tasks, longer looking to impossible events following familiarisation with possible equivalents is taken to reflect surprise at their occurrence, thus revealing infants' knowledge. In this study, the role of learning during the task itself is explored by switching the archetypal approach on its head and familiarising infants to impossible events. In a partial replication of Jackson and Sirois (2009), nine-month-old infants were presented with short video clips of toy trains moving around a circular track. A tunnel over a short section of the track meant trains were briefly occluded as they completed a circuit. In impossible versions of events, the train switched colours while occluded by the tunnel. Both looking times and pupil dilation were used as dependent measures. Using a factorial design in which perceptual (novelty-familiarity) and conceptual (possible-impossible) variables were independently and jointly analysed, we show that infants showed greater responding to possible events than to impossible events following familiarisation. Pupil dilation data successfully allowed for more precise interpretation of infants' perception of events than could have been achieved through looking times alone. These findings suggest a central role for learning in violation of expectation tasks, and also further support the use of pupil dilation as a dependent measure in infancy work.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".