Head and Foot Coordination in Head Scratching and Food Manipulation by Purple Swamp Hens ( Porphyrio porphyrio ): Rules for Minimizing the Computational Costs of Combining Movements from Multiple Parts of the Body
Bibliographic record
Abstract
Complex movements, such as placing food into the mouth, involve coordinating multiple limb segments. Given the degrees of freedom for one limb segment, the computational costs of such complex movements can be high. One way to reduce such costs is to limit the adjusting movements needed to achieve coordination of distal body parts to only one part of the body. For example, for scratching the head, the hand or foot needs to make contact with the head and this involves movements of the head, neck and torso, as well as those of the foot and leg, or hand and arm. In this situation, the foot or hand is raised to a specific location in space and then makes oscillatory movements, but it is movements by the head and neck that ensure appropriate contact is made with the head (Pellis, 2010). In this paper, whether such cost-saving rules apply across functional contexts is tested in the purple swamp hen by comparing head and foot coordination during head scratching and during food reaching and handling. This species uses its foot to grasp and hold a wide range of food items that are picked up in its bill. Comparison of hundreds of videotaped sequences revealed that, in both cases, the bird uses the same rule: that of making the accommodating movements with only one of those body parts, even when coordination requires movements of disparate parts of the body. These data show that there are likely common computational cost-saving rules that widely apply to movements occurring in many different functional contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".