Infants’ Ability to Detect Emotional Incongruency: Deep or Shallow?
Bibliographic record
Abstract
Infants can detect individuals who demonstrate emotions that are incongruent with an event and are less likely to trust them. However, the nature of the mechanisms underlying this selectivity is currently subject to controversy. The objective of this study was to examine whether infants' socio-cognitive and associative learning skills are linked to their selective trust. A total of 102 14-month-olds were exposed to a person who demonstrated congruent or incongruent emotional referencing (e.g., happy when looking inside an empty box), and were tested on their willingness to follow the emoter's gaze. Knowledge inference and associative learning tasks were also administered. It was hypothesized that infants would be less likely to trust the incongruent emoter and that this selectivity would be related to their associative learning skills, and not their socio-cognitive skills. The results revealed that infants were not only able to detect the incongruent emoter, but were subsequently less likely to follow her gaze toward an object invisible to them. More importantly, infants who demonstrated superior performance on the knowledge inference task, but not the associative learning task, were better able to detect the person's emotional incongruency. These findings provide additional support for the rich interpretation of infants' selective trust.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".