Examination of prior contest experience and the retention of winner and loser effects
Bibliographic record
Abstract
In many animal taxa, prior contest experience affects future performance such that winning increases the chances of winning in the future (winner effect) and losing increases the chances of losing in the future (loser effect). It is, however, not clear whether this pattern typically arises from experience effects on actual or perceived fighting ability (or both). In this study, we looked at winner and loser effects in the jumping spider Phidippus clarus. We assigned winning or losing experience to spiders and tested them against opponents of similar fighting ability in subsequent contests at 1-, 2-, 5-, and 24-h intervals. We examined the strength of winner and loser effects, how long effects persist, as well as how experience affected perceived and actual fighting ability. Our results demonstrate that winner and loser effects are of approximately the same magnitude, although loser effects last longer than winner effects. Our results also demonstrate that previous experience alters actual fighting ability because both the assessment and escalation periods were affected by experience. We suggest that the retention time of experience effects depends on expected encounter rates as well as other behavioral and ecological factors. In systems with short breeding seasons and/or rapidly fluctuating populations, context-dependent retention of experience effects may allow males to track their status relative to the fluctuating fighting ability of local competitors without paying the costs necessary to recall or assess individual competitors.
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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 | 0.002 |
| 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 | 0.001 |
| 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".