Inference by exclusion in lion-tailed macaques (Macaca silenus), a hamadryas baboon (Papio hamadryas), capuchins (Sapajus apella), and squirrel monkeys (Saimiri sciureus).
Bibliographic record
Abstract
Previous research has suggested that several primate species may be capable of reasoning by exclusion based on the finding that they can locate a hidden object when given information about where the object is not. The present research replicated and extended the literature by testing 2 Old World monkey species, lion-tailed macaques (Macaca silenus) and a hamadryas baboon (Papio hamadryas), and 2 New World species, capuchin monkeys (Sapajus apella) and squirrel monkeys (Saimiri sciureus). The New World monkeys were tested on the traditional 2-way object choice task, and all 4 species were also tested on a more complex 3-way object choice task. In addition, the squirrel monkeys were tested on a 2-way object choice task with auditory information. The results showed that, whereas the Old World species were able to infer by exclusion on the 3-object task, some of the capuchin monkeys had difficulty on each of the 2- and 3-cup tasks. All but 1 of the squirrel monkeys failed to infer successfully, and their strategies appeared to differ between the visual and auditory versions of the task. Taken together, this research suggests that the ability to succeed on this inference task may be present throughout Old World monkey species, but is fragile in the New World species tested thus far. (PsycINFO Database Record
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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 | 0.004 |
| 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 | 0.000 | 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".