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 (Koenig & Harris, 2004). 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.
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.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 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; both teacher heads agree on what is shown here.
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".