Mind the Robot! Variation in Attributions of Mind to a Wide Set of Real and Fictional Robots
Bibliographic record
Abstract
The rapid rise of computing power has prompted the desire to develop more social, human-like robots. Quantitatively comparing different computing systems on their ability to simulate human qualities has been a major technical challenge. A recent framework put forth by Gray et al. (Science 315(5812):619, 2007. https://doi.org/10.1126/science.1134475 ) provides promise as a new means for comparing robots. While the framework has been validated for assessing individual robots with different descriptions, or different behaviours, the framework has not been applied to a wider landscape of robots and machines situated amongst other characters. The present study sought to investigate attributions of mind towards a wide range of real and fictional robots. We asked participants to rate the agency (the ability “to do”) and experience (the ability “to feel”) of 24 characters made up of humans, robots, inanimate objects, and animals. Although robots were collectively rated lower than humans on agency and experience, there was significant variation among robots—even when fictional robots were omitted. The results of this investigation suggest that building robots that are perceived to feel is a fruitful avenue for future development as people are more open to perceiving aspects of mind in a wider range of robots than previously established. Our results also indicate that age is a critical factor in people’s attributions of mind to robots, suggesting that there may be a generational shift towards greater acceptance of robots’ ability to both do and feel.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".