International society for developmental psychobiology
Bibliographic record
Abstract
Motor planning can be studied by examining goal-directed behavior, particularly for sequential tasks where multiple actions are required to solve a problem.We presented spider monkeys (N ¼ 5) and adults (N ¼ 8) with the common problem of needing to get something that is just beyond reach.An object was placed inside a bowl suspended on chain-link out of reach of the hands.Spider monkeys were chosen for this paradigm because they have a tail capable of grasping.In lieu of a tail, adults were given a reach-extender claw.Prior to testing, we measured handedness in all subjects to determine whether laterality affected problem-solving strategies.Strikingly, monkeys were exclusively lateralized in their tail use to pull the bowl (tail to left or right of midline), and the direction of tail preference matched their individual hand preferences.Once the tail brought the bowl close, monkeys used their hands to obtain the object; hand use for this second step varied.Similar to monkeys, adults exclusively used their preferred hand to operate their simulated tail.Notably, only half of the adults pulled the bowl closer and then used the opposite hand to retrieve the object.The adults that did not break hand preference executed movements that ended in awkward arm positions with the claw and longer latencies or multiple attempts to solve the task.While preference may have facilitated the first step of this sequence in both groups, failure to later inhibit preference may have hindered efficient planning in adults.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.175 | 0.080 |
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".