Bibliographic record
Abstract
Studies have shown that using robot pets in dementia care contributes to a reduction in loneliness and anxiety, and other benefits. Studies also show that, even when people know they are dealing with robots, they often treat the robot as though it is a real pet with genuine emotions. This disconnect between beliefs and behavior occurs not just for people living with dementia, but with cognitively healthy adults, including those who are knowledgeable about how robots work. One possible explanation is that robot pets prompt contradictory beliefs, and so the use of robot pets encourages self-deception. Sparrow argues that this makes the use of robot pets in dementia care morally objectionable. We disagree. We argue that Gendler's concept of alief offers a better explanation of the belief-behavior disconnect observed when people interact with robot pets. An alief is a mental state composed of an automatic, arational, emotional, and behavioral response to representational input. Aliefs are not beliefs and are not subject to truth norms. Thus, on our view, harms associated with the use of robot pets in dementia care are not likely to include the self-deceptions that Sparrow suggests. It might seem like philosophical hair-splitting to claim that deception has not occurred because discordant aliefs rather than false beliefs have been formed, but this distinction matters. We argue that aliefs carry their own risks. These risks are important to consider when using robot pets in dementia care.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".