Ordered Recall in Rhesus Macaques (Macaca Mulatta): Can Monkeys Recall the Correct Order of Sequentially Presented Images?
Bibliographic record
Abstract
Previous research has demonstrated that rhesus monkeys (Macaca mulatta) are capable of identifying sequentially shown images in any order among distractors (i.e. images not part of the list sequence). We investigated ordered recall in rhesus monkeys in which subjects were expected to recognize the correct order of images during a “test” phase (simultaneous presentation of images) after they had seen the images presented sequentially in a “presentation” phase (sequential presentation of images). If subjects were successfully able to execute the ordered recall task, the first trial accuracy data would appear close to 100% accuracy and it would only take one day to learn the list since the lists used were short, 3-item lists. While this study did not conclusively demonstrate monkeys are capable of ordered recall of sequentially presented, trial unique images (i.e. a list sequence presented only once per session), the data suggests that when the stimuli are not trial unique the monkeys treat each sequence as a simultaneous chaining task. A simultaneous chaining paradigm entails simultaneous presentation of all items without any previous sequential presentation of the images. It is unclear whether results resembling simultaneous chaining are seen because these animals have previous experience with simultaneous chaining, if the training procedure needs to be modified for the monkeys to understand the task, or if the task is beyond their cognitive abilities. Further research with serial learning will clarify this finding and also seek to prove whether rhesus monkeys are in fact capable of such ordered recall tasks.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".