An empirical test of 2-dimensional signal detection theory applied to Batesian mimicry
Bibliographic record
Abstract
Signal detection theory (SDT) has been invoked to help explain why imperfect mimics of particularly unprofitable or abundant models might experience no further selection to improve their mimicry. However, most tests of SDT have focused on single dimensions of mimetic phenotypes, or used multivariate techniques to compress many dimensions of phenotype into a single scale. Here, we explicitly tested SDT in both one and two dimensions by asking human subjects to discriminate computer-generated mimics and models that varied continuously in both size and/or color. We arrived at two major conclusions. First, although subjects can use prey size or color to help discriminate profitable and unprofitable prey that vary in only one dimension, responses of subjects to prey that vary in two dimensions are poorly represented by multidimensional SDT. Second, because different individuals within groups may use different strategies, the behavior of groups is often better fit by more complex models. In general, humans give more weight to color when making discriminations than is optimal. This bias may indicate that they believe that color has higher relative validity than size. More studies on the behavior of natural predators when foraging on multidimensional prey are urgently needed.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".