Developing a Phonotaxis Performance Index to Uncover Signal Selectivity in Walking Phonotaxis
Bibliographic record
Abstract
Any sensory strategies that prey take to avoid eavesdropping predators will depend on the behavioural decisions of eavesdroppers. As these decisions are guided by the sensory processing of communication signals, accurate measurements of sensorimotor output will provide insights into signal preferences, parameters evaluated for signal recognition, and the perceptual and cognitive capacity of receivers. A number of techniques have been proposed for measuring walking phonotaxis (and taxis behaviour more generally). Consistent limitations of such measures are (1) that some animals cannot discriminate alternative signals when they occur simultaneously (i.e. overlapping in the spectral and temporal domain), or (2) some animals respond with low selectivity to stimuli presented in isolation, and (3) identifying appropriate dimensions of response variability is not straightforward. Here we document an approach to develop a sensitive phonotaxis performance index to quantify pulse rate selectivity in two distinct populations of the acoustic parasitoid fly Ormia ochracea. Using a spherical treadmill to measure tethered walking phonotaxis, we examined the ability of flies to track a switch in the broadcast location of test songs with varying pulse-rates. By applying an information-theoretic approach, we identified a set of response parameters that best predict a previously described pulse-rate preference. These parameters were incorporated into an index to describe temporal pattern selectivity during walking phonotaxis. Our study also revealed that in Floridian Ormia ochracea, the pulse rate preference function is not affected by the locomotor mode (walking vs flying) used in phonotaxis. Furthermore, we describe for the first time, pulse rate selectivity in Californian Ormia ochracea. Both populations have pulse rate preference functions with peak selectivity between 50 to 60 pps. Previous studies demonstrating natural differences in host song preferences (Floridian O. ochracea preferring Gryllus rubens and Californian O. ochracea preferring Gryllus lineaticeps calling songs) may be based on other temporal parameters aside from pulse rate. Finally, we discuss the advantages and limitations of our approach in quantifying signal selectivity. This approach can be applied broadly to study signal preferences in other acoustic parasitoid flies and potentially other eavesdroppers that exhibit taxis behaviours in response to the communication signals of prey.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".