Do Infants Recognize Engagement in Social Interactions? The Case of Face‐to‐Face Conversation
Bibliographic record
Abstract
This study explores 12‐month‐olds' understanding of face‐to‐face conversation, a key contextual structure associated with engagement in a social interaction. Using a violation‐of‐expectations paradigm, we habituated infants to a “face‐to‐face” conversation, and in a test phase compared their looking times between “back‐to‐back” (conceptually novel) and “face‐to‐face” (conceptually familiar) conversations, while simultaneously manipulating perceptual familiarity in a 2 × 2 factorial design. We also analyzed dynamic changes in pupil dilation, which are considered a reliable measure of cognitive load that may index processing of social interactions. Infants looked relatively longer at perceptual changes (new speaker positions) but not at conceptual change (back‐to‐back conversation), suggesting that face‐to‐face conversation may not elicit particular expectations, and so may not carry any particular conceptual significance. Moreover, on the first test trial, larger pupil dilation was observed for familiar conditions, suggesting that familiarity with perceptual features could enhance processing of conversations. Thus, this study undermines assertions regarding infants' conceptual understanding of the social signals underlying engagement. Infants may rather recognize such signals through their perceptual familiarity and associated positive feelings. This may then increase their engagement when observing and participating in others' collaborative activities, in turn allowing for the development of knowledge regarding others' intentions.
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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 | 0.007 |
| 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".