Eye-tracking investigation of infants' understanding of social versus nonsocial goals
Bibliographic record
Abstract
A wealth of research in the cognitive sciences have demonstrated that human's ability to mentalize emerge early in infancy; for example, infants and toddlers represent others' goals (e.g., Woodward, 1998; Cannon & Woodward, 2012). However, failed replications of seminal findings (e.g., Ganglmayer et al., 2019) have brought into question the strength of infants' capacity to mentalize. Given the high resource demand of mentalizing, one possibility is that infants may be more likely to mentalize in contexts that are more relevant to themselves, such as socially evaluative contexts (Woo et al., 2023). Indeed, whether an agent prefers object A versus object B is a trivial matter to the infant, and thus not "worth" expending resources to mentalize; on the other hand, whether an agent prefers agent A or agent B might have consequences for infants; for example, on their own downstream social affiliations. The present work seeks to follow up on this possibility by investigating one of the most basic forms of mentalizing: Goal attribution. Specifically, this project will examine whether infants at 11 months of age, the earliest age at which goal attribution has been demonstrated via anticipatory eye-gaze (Cannon & Woodward, 2012), will be more likely to predictive eye movements suggestive of goal attribution when the goals in question are social versus when they are nonsocial in nature. Closely adapting the methodology of Cannon & Woodward (2012), 11-month-olds will first watch three trials in which a human protagonist approaches target A over target B; then the targets will switch places, and infants watch one test trial in which the protagonist moves ambiguously towards both targets, but never approaches either. Critically, the targets in question will either be social (i.e., other human agents) or nonsocial (i.e. physical objects). Given that 11-month-olds have not consistently shown goal-directed anticipatory looking (Ganglmayer et al., 2019), we predict that infants in our sample will show more anticipatory looking in the social condition than the nonsocial condition. Specifically, we predict that infants in the social condition will be more likely to predictively look at the original target during the test trial compared to the nonsocial condition (results as in Cannon & Woodward, 2012).
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".