Assessing Children’s Implicit Attitudes Using the Affect Misattribution Procedure
Bibliographic record
Abstract
In the current research, we examined whether the Affect Misattribution Procedure (AMP) could be successfully adapted as an implicit measure of children’s attitudes. We tested this possibility in 3 studies with 5- to 10-year-old children. In Study 1, we found evidence that children misattribute affect elicited by attitudinally positive (e.g., cute animals) and negative (e.g., aggressive animals) primes to neutral stimuli (inkblots). In Study 2, we found that, as expected, children’s responses following flower and insect primes were moderated by gender. Girls (but not boys) were more likely to judge inkblots as pleasant when they followed flower primes. Children in Study 3 showed predicted affect misattribution following happy-face compared with sad-face primes. In addition, children’s responses on this child-friendly AMP predicted their self-reported empathy: The greater children’s spontaneous misattribution of affect following happy and sad primes, the more children reported feeling the joy and pain of others. These studies provide evidence that the AMP can be adapted as an implicit measure of children’s attitudes, and the results of Study 3 offer novel insight into individual differences in children’s affective responses to the emotional expressions of others.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 0.001 |
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".