Collective Behavior in Medaka Fish Depends on Discrete Kinematic States of Swimming Behavior
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Complex collective behaviors such as schooling are believed to emerge from simple, individual-level computations that translate incoming information from conspecifics into actions. Recently, it has been proposed that discrete behavioral modes, or internal states, may modulate these computations, affecting the resulting collective behaviors. Direct evidence for such hierarchical control remains limited due to challenges in inferring hidden perception-action computations and uncovering discrete behavioral modes from continuous behaviors. To address this, we analyzed swimming behaviors of Medaka fish ( Oryzias latipes ) throughout development. At the group level, Medaka exhibit synchronized swimming formations that develop early, emerging around two weeks of age and stabilizing within one month. Unlike many teleost species that use burst-and-coast swim patterns, Medaka exhibit continuous tail and body undulations. We show that this continuous behavior can be segmented into three distinct kinematic states: acceleration, deceleration, and prolonged constant speed swimming. Using state-dependent computational models, we tested how Medaka translate social information from neighbors into actions across these kinematic states. The models revealed distinct computations governing social information processing and decision making in each state. Moreover, social responsiveness varied significantly between states—it was strongest during constant-speed epochs, intermediate during accelerations, and lowest during decelerations. Compared to similarly-sized zebrafish that employ burst-and-coast kinematics, Medaka exhibited greater diversity in state-dependent social interaction computations, ultimately resulting in stronger coordinated swimming. These findings highlight discrete behavioral modes as key modulators of social interaction computations underlying collective behavior.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 it