Testing the habituation assumption underlying models of parasitoid foraging behavior
Bibliographic record
Abstract
BACKGROUND: Habituation, a form of non-associative learning, has several well-defined characteristics that apply to a wide range of physiological and behavioral responses in many organisms. In classic patch time allocation models, habituation is considered to be a major mechanistic component of parasitoid behavioral strategies. However, parasitoid behavioral responses to host cues have not previously been tested for the known, specific characteristics of habituation. METHODS: ) chemical contact cues (footprints), in particular: (i) a training interval-dependent decline in response intensity, and (ii) a training interval-dependent recovery of the response. RESULTS: As would be expected of a habituated response, wasps trained at higher frequencies decreased their behavioral response to host footprints more quickly and to a greater degree than those trained at low frequencies, and subsequently showed a more rapid, although partial, recovery of their behavioral response to host footprints. This putative habituation learning could not be blocked by cold anesthesia, ingestion of an ATPase inhibitor, or ingestion of a protein synthesis inhibitor. DISCUSSION: Our study provides support for the assumption that diminishing responses of parasitoids to chemical indicators of host presence constitutes habituation as opposed to sensory fatigue, and provides a preliminary basis for exploring the underlying mechanisms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".