Octopus arm ‘slap’ in situ: A syntactic analysis of a complex motor action
Bibliographic record
Abstract
Abstract An octopus, Abdopus sp., can use rotation and translation of its hydrostatic arms, and combine these kinematic behaviors serially and in parallel, on different arms, to ‘slap’ at fish in the wild. Different motor programs may be used in multiple arms producing complex actions. The movements analyzed in this work show how complex the movements of the octopus, in situ, can be, furthering knowledge of this animal’s behavior, as well as furthering understanding of the structure of animal motor control. Stiffening of the flexible muscular hydrostatic arms was found to be important to both primitives of translation and rotation. By combining these kinematic primitives, the octopus is able to maintain flexibility while controlling only a few factors, or degrees of freedom, a concept we term ‘flexible rigidity’. The slapping action of the octopus of interest, Abdopus sp., therefore, gives support for Flash and Hochner’s embodied organization view of motor behavior, as well as their idea that motor primitives can combine syntactically to form a complex action. Our results suggest that the octopus’s ability to use sensory feedback from the position of a moving fish target, along with the feed-forward motor primitives, allows for the building of complex actions at dynamic equilibrium with the environment. Overall, these findings lead to a more realistic view of how a complex behavior allows an animal to coordinate with its environment.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".