Do Chimpanzees Predict Others’ Behavior by Simulating Their Beliefs?
Bibliographic record
Abstract
Recent studies have shown that great apes predict that other agents will search for objects of interest where the agents believe the objects are hidden. Little is understood about the cognitive process that apes undergo to make such predictions. According to prevailing models, great apes make such predictions by metarepresenting others’ beliefs or perceptual states. We investigated the simpler simulation model. In this model, apes predict where other agents will search for objects of interest by simulating believing what another agent believes about the location of the object. The simulation model predicts that simulating what another believes should manifest in altercentric biasing effects, such as behaving as if one shares another’s belief in cases where the other’s belief is false. We tested this by giving chimpanzees a novel search paradigm embedded in a change-of-location false-belief test and measured where they searched for a grape that they witnessed moved from its original location to a new location. In true-belief trials, chimpanzees were presented with an agent who knew (as they did) that the grape was hidden in the new location; in false-belief trials, the agent falsely believed the grape was still hidden in the original location while the chimpanzee knew it was hidden in the new location. As predicted by the simulation model, chimpanzees searched for the grape closer to its original location than to its new location in significantly more false-belief trials than true-belief trials. Results suggest that chimpanzees show a signature altercentric biasing effect of simulating believing what others believe and may use simulation, rather than metarepresentation, to predict where others will search for objects of interest.
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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 | 0.007 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".