People Do Not Always Know Best: Preschoolers’ Trust in Social Robots
Bibliographic record
Abstract
In this paper, we investigated whether Canadian preschoolers prefer to learn from a competent robot over an incompetent human using the classic trust paradigm. An adapted Naive Biology task was also administered to assess children’s perception of robots. In Study 1, 3-year-olds and 5-year-olds were presented with two informants; A social, humanoid robot (Nao) who labeled familiar objects correctly, while a human informant labeled them incorrectly. Both informants then labeled unfamiliar objects with novel labels. It was found that 3-year-old children equally endorsed the labels provided by the robot and the human, but 5-year-old children learned significantly more from the competent robot. Interestingly, 5-year-olds endorsed Nao’s labels even though they accurately categorized the robot as having mechanical insides. In contrast, 3-year-old children associated Nao with biological or mechanical insides equally. In Study 2, new samples of 3-year-olds and 5-year-olds were tested to determine whether the human-like appearance of the robot informant impacted children’s trust judgments. The procedure was identical to that of Study 1, except that a non-humanoid robot, Cozmo, replaced Nao. It was found that 3-year-old children still trusted the robot and the human equally and that 5-year-olds preferred to learn new labels from the robot, suggesting that the robot’s morphology does not play a key role in their selective trust strategies. It is concluded that by 5 years of age, preschoolers show a robust sensitivity to epistemic characteristics (e.g., competency), but that younger children’s decisions are equally driven by the animacy of the informant.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".