Developing an understanding of others’ emotional states: Relations among affective theory of mind and empathy measures in early childhood
Bibliographic record
Abstract
Theory of mind (ToM) consists of cognitive and affective components; however, few studies have evaluated the coherence of affective ToM measures and their associations with empathy. This research examined the relations among affective ToM tasks and assessments of empathy, measured directly and via parent reports in 4- to 6-year-olds. Children (N = 117) completed: an Appearance-Reality Emotion Task, an adapted Reading the Mind in the Eyes Test, an Affective Stories Task, and an empathy task. Parents reported on children's ToM and empathy, and language was assessed using a Picture Vocabulary Test. Controlling for language, no relationships were found among affective ToM measures and children's age was only related to the Affective Stories Task. Further, controlling for age, only parent-reported empathy was associated with the Appearance-Reality Emotion Task. Once vocabulary and age were controlled, measures of affective ToM are unrelated and different developmental patterns emerged across measures. Statement of contribution What is already known on this subject Affective theory of mind is a distinct subcomponent of theory of mind that corresponds to an independent developmental mechanism. However, little research has been conducted on affective ToM, its developmental trajectory during the preschool years, its assessment, and its relationship with related constructs, such as empathy. What the present study adds Children's performance on affective ToM tasks was unrelated once age and language abilities were accounted for. Thus, there is a need to examine affective ToM and its measurement more extensively to ensure we are effectively capturing this construct. This study was the first to establish a Preschool Reading the Mind in the Eyes Task that uses images of children and pictorial response options and an Affective Stories Task that captures age-related changes in affective ToM beyond language skills.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".