Do young children believe that robots possess a Theory of Mind?
Bibliographic record
Abstract
This study investigated children’s anthropomorphism using direct and indirect tasks, as well as a parental report measure of Theory of Mind (ToM) skills. Given that much of the prior research has examined biological aspects of animate and inanimate agents, the present work examines the psychological properties of agents (e.g., whether children will attribute mental states to inanimate agents). To capture children’s anthropomorphic tendencies, subsets (Epistemic, Intentions and Desires) of the Attribution of Mental States Questionnaire (Manzi et al., 2020) were administered to 4-year-old children. A new subset of questions that assessed False Beliefs was also created as part of the Interview task. Additionally, children completed the Wellman and Liu Theory of Mind Scale (Wellman and Liu, 2004), which featured either a human or a robot protagonist. Parents also completed the Children’s Social Understanding Scale (CSUS, Tahiroglu et al., 2014), which measures children’s ToM skills. Findings showed that regardless of condition, children performed similarly on the Interview Questions and the Wellman and Liu Scale. However, differences were observed across conditions for some of the subsets of questions. Children in the Human Condition scored higher on the Epistemic subset of interview questions (e.g., “Do you think this robot/person can learn?”) compared to the children in the Robot Condition. No correlations between either task (Wellman and Liu Scale, Interview questions) and the CSUS was found. Overall, we conclude that 4-yer-old children anthropomorphize a humanoid robot.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".