Technical and conceptual considerations for using animated stimuli in studies of animal behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rapid technical advances in the field of computer animation (CA) and virtual reality (VR) have opened new avenues in animal behavior research. Animated stimuli are powerful tools as they offer standardization, repeatability, and complete control over the stimulus presented, thereby "reducing" and "replacing" the animals used, and "refining" the experimental design in line with the 3Rs. However, appropriate use of these technologies raises conceptual and technical questions. In this review, we offer guidelines for common technical and conceptual considerations related to the use of animated stimuli in animal behavior research. Following the steps required to create an animated stimulus, we discuss (I) the creation, (II) the presentation, and (III) the validation of CAs and VRs. Although our review is geared toward computer-graphically designed stimuli, considerations on presentation and validation also apply to video playbacks. CA and VR allow both new behavioral questions to be addressed and existing questions to be addressed in new ways, thus we expect a rich future for these methods in both ultimate and proximate studies of animal behavior.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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