Validation of 2D‐animated pictures as an investigative tool in the behavioural sciences: A case study with a West African cichlid fish,<i>Pelvicachromis pulcher</i>
Bibliographic record
Abstract
Abstract Virtual stimuli represent an increasingly popular tool in the study of animal behaviour. Modern techniques have the potential to simplify and improve traditional experiments using live stimuli. However, the increasing availability of diverse techniques is associated with problems and limitations. Although many new methods have been developed, their validation remains largely untested. In the present study, we therefore performed two experiments to test whether 2‐D animations of predators and conspecifics elicit biologically appropriate behavioural responses in male rainbow kribs,Pelvicachromis pulcher. Individual responses towards a sympatric natural fish predator,Parachanna obscura, were tested using live predators and still colour photographs, animated using PowerPoint©. Compared to control trials (empty aquarium and white computer screen, respectively), individuals decreased their activity in response to both live and animated predators. We found no difference in activity between live and animation trials. Further, we tested individual aggression (frequency of aggressive behaviours) exhibited towards live and animated conspecifics. Individual aggressive behaviours shown towards live and animated conspecifics were positively correlated. Moreover, an individual's mean distance towards the opponent was a suitable proxy for individual aggression permitting the facilitation and standardisation of an individual's aggression through the use of a tracking software compared with the more laborious, traditional manual assessment. Our results show that simple, inexpensive animation techniques have the potential to provide an easy‐to‐apply and useful technological advance in animal behaviour research.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".