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Record W4382585517 · doi:10.1007/s00227-023-04243-y

A syntactic analysis of a complex motor action: the octopus arm ‘slap’

2023· article· en· W4382585517 on OpenAlexaff
Jean Alupay, Jennifer A. Mather, Khalil Iskarous

Bibliographic record

VenueMarine Biology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsUniversity of Lethbridge
FundersUniversity of Southern CaliforniaNational Science Foundation
Keywordsoctopus (software)KinematicsBiologyAction (physics)Movement (music)Flexibility (engineering)Computer scienceCommunicationMathematicsPhysicsPsychology

Abstract

fetched live from OpenAlex

Abstract The analysis of a natural motor action is always difficult, especially when different motor programs are combined within the same interaction with the environment. We analyzed the behavior of an octopus, Abdopus sp., filmed in tidal pools in Okinawa, Japan, which used the kinematic primitives of rotation and translation of its hydrostatic arms, and combined these kinematic behaviors serially and in parallel to ‘slap’ at fish in the wild. In total, 19 slaps were analyzed. The kinematics of arm movement were measured in both external and animal-centered reference frames, while the octopus was slapping at the fish. By combining these primitives, the octopus is able to maintain flexibility while controlling only a few degrees of freedom, a concept we term ‘flexible rigidity’. This slapping action supports 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. 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. Over all, these findings lead to a more realistic view of how a complex behavior allows an animal to coordinate with its environment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.066
GPT teacher head0.296
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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