The influence of motion quality on responses towards video playback stimuli
Bibliographic record
Abstract
Visual motion, a critical cue in communication, can be manipulated and studied using video playback methods. A primary concern for the video playback researcher is the degree to which objects presented on video appear natural to the non-human subject. Here we argue that the quality of motion cues on video, as determined by the video's image presentation rate (IPR), are of particular importance in determining a subject's social response behaviour. We present an experiment testing the effect of variations in IPR on pigeon (Columbia livia) response behaviour towards video images of courting opposite sex partners. Male and female pigeons were presented with three video playback stimuli, each containing a different social partner. Each stimulus was then modified to appear at one of three IPRs: 15, 30 or 60 progressive (p) frames per second. The results showed that courtship behaviour became significantly longer in duration as IPR increased. This finding implies that the IPR significantly affects the perceived quality of motion cues impacting social behaviour. In males we found that the duration of courtship also depended on the social partner viewed and that this effect interacted with the effects of IPR on behaviour. Specifically, the effect of social partner reached statistical significance only when the stimuli were displayed at 60 p, demonstrating the potential for erroneous results when insufficient IPRs are used. In addition to demonstrating the importance of IPR in video playback experiments, these findings help to highlight and describe the role of visual motion processing in communication behaviour.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".