Bibliographic record
Abstract
Abstract Since the question “Do chimpanzees have a theory of mind?” was raised in 1978, scientists have attempted to answer it, and philosophers have attempted to clarify what the question means and whether it has been, or could be, answered. Mindreading (a term used mostly by philosophers) or theory of mind (a term preferred by scientists) refers to the ability to attribute mental states to other individuals. Some versions of the question focus on whether chimpanzees engage in belief reasoning or can think about false belief, and chimpanzees have been given nonverbal versions of the false belief moved‐object task (also known as the Sally–Anne task). Other versions of the question focus on whether chimpanzees understand what others can see, and chimpanzees can pass those tests. From this data, some claim that chimpanzees know something about perceptions, but nothing about belief. Others claim that chimpanzees do not understand belief or perceptions, because the data fails to overcome the “logical problem,” and permits an alternative, non‐mentalistic interpretation. I will argue that neither view is warranted. Belief reasoning in chimpanzees has focused on examining false belief in a moved object scenario, but has largely ignored other functions of belief. The first part of the paper is an argument for how to best understand belief reasoning and offers suggestion for future investigation. The second part of the paper addresses and diffuses the “logical problem.” I conclude that chimpanzees may reason about belief, but that there is already compelling evidence that they reason about perceptions.
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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 | 0.005 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".