Folk Psychological Attributions of Consciousness to Large Language Models
Bibliographic record
Abstract
Technological advances raise new puzzles and challenges for cognitive science and thestudy of how humans think about and interact with artificial intelligence (AI). For example, theadvent of Large Language Models and their human-like linguistic abilities has raised substantialdebate regarding whether or not AI could be conscious. Here we consider the question ofwhether AI could have subjective experiences such as feelings and sensations(“phenomenological consciousness”). While experts from many fields have weighed in on thisissue in academic and public discourse, it remains unknown how the general populationattributes phenomenology to AI. We surveyed a sample of US residents (N=300) and found thata majority of participants were willing to attribute phenomenological consciousness to LLMs.These attributions were robust, as they predicted attributions of mental states typically associatedwith phenomenology – but also flexible, as they were sensitive to individual differences such asusage frequency. Overall, these results show how folk intuitions about AI consciousness candiverge from expert intuitions – with important implications for the legal and ethical status of AI.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".