Evaluating visual cue consistency in groups: The effects of cue numerosity, group formation, and cue type
Bibliographic record
Abstract
Humans often encounter a group of people looking in different directions.Little is known about how information from multiple visual cues, like gaze, which could be consistent or inconsistent, is evaluated in such group contexts.In the case of eye gaze, emerging research suggests that both the spatial consistency of eye gaze cues and group size play a role in determining which part of the group influences observers' behaviour.Recent research has found that in groups of three, a minority of consistent gaze cues (or 1/3) have been found to speed up observers' behavioural responses, whereas in groups of five, a majority of consistent gaze cues (or 3/5) were needed for the same behaviour.One explanation for this is that evaluation of gaze cue consistency may engage a quorum-like evaluation principle, which is seen across animal Contributions of Authors I, Jessica Savoie, was responsible for stimulus creation, experimental design, experiment programming and implementation, data collection, analyses and visualizations for all studies presented in this thesis (Chapters 1-3).I wrote this thesis with input and guidance from my supervisor Professor Jelena Ristic.
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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 | 0.007 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".