Infants’ Ability to Detect Emotional Incongruency: Deep or Shallow?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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 it