Induction of Social Behavior in Zebrafish: Live Versus Computer Animated Fish as Stimuli
Bibliographic record
Abstract
The zebrafish offers an excellent compromise between system complexity and practical simplicity and has been suggested as a translational research tool for the analysis of human brain disorders associated with abnormalities of social behavior. Unlike laboratory rodents zebrafish are diurnal, thus visual cues may be easily utilized in the analysis of their behavior and brain function. Visual cues, including the sight of conspecifics, have been employed to induce social behavior in zebrafish. However, the method of presentation of these cues and the question of whether computer animated images versus live stimulus fish have differential effects have not been systematically analyzed. Here, we compare the effects of five stimulus presentation types: live conspecifics in the experimental tank or outside the tank, playback of video-recorded live conspecifics, computer animated images of conspecifics presented by two software applications, the previously employed General Fish Animator, and a new application Zebrafish Presenter. We report that all stimuli were equally effective and induced a robust social response (shoaling) manifesting as reduced distance between stimulus and experimental fish. We conclude that presentation of live stimulus fish, or 3D images, is not required and 2D computer animated images are sufficient to induce robust and consistent social behavioral responses in zebrafish.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".