Two complementary models and an experimental test of how receivers respond to multicomponent visual signals
Bibliographic record
Abstract
Animals often communicate using elaborate displays containing multiple components, but it is unclear why these complex signals have evolved when one component might be sufficient to inform the receiver.In this thesis, I first briefly review this burgeoning field.I then evaluate how human receivers respond when faced with a signaller displaying a two-component signal, when each component differs in its probability of being associated with a binary outcome (desirable/undesirable).These tests were conducted under a broad range of conditions under which neither, one or both multicomponent signals were predicted to be followed according to a simple signal detection model.Signal detection theory identifies an optimal response once the receiver's learning is complete.However, I also considered a complementary modelling approach that predicts the same long-term response but uses explorationexploitation theory to identify the optimal tradeoff between learning more about the nature of the signaller and using current information to reject it.The best supported statistical models for my data generally included both signal elements as significant predictors of acceptance.Indeed, receivers frequently attended to both forms of signal even under conditions when they are not predicted to do so by the signal detection model.The primary reason for this departure was that receiver learning was influential in shaping the response strategy of the volunteers.The exploration-exploitation model which makes assumptions about receiver learning was more successful in accounting for the observed behaviour and may therefore provide a promising starting point for future work on the study of multicomponent signals.A great deal of gratitude goes to my supervisor Dr. Tom Sherratt for inviting me to take part in his research program and for providing helpful input and continued encouragement throughout my Master's degree.I am truly thankful for the opportunity to work under someone with extensive experience and who still shows great patience when guiding new researchers.I would also like
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".