Prenatal exposure to predation affects predator recognition learning via lateralization plasticity
Bibliographic record
Abstract
Prey with cerebral lateralization often shows a bias in escape direction and asymmetrical use of eyes for scanning. Such asymmetries are likely to cause ecological disadvantages when, for example, predators attack from the side in which the prey is more susceptible. However, lateralized individuals are diffuse in many species and, paradoxically, their frequency increases via developmental plasticity in environments with high-predation risk. Using wood frog tadpoles, Lithobates sylvaticus, we tested the hypothesis that cerebral lateralization enhances predator recognition learning and thus overcomes the costs of behavioral asymmetries in high predation risk environments. In the first experiment, we found tadpoles exposed to risk as embryos developed more intense lateralization in a rotational test compared to predator-naive controls. Risk exposure led to the more frequent development of clockwise swimming preference. In the second experiment, we found that tadpoles exhibiting no behavioral lateralization and tadpoles with marked clockwise swimming preference learned to recognize the novel predator odor, with the latter showing a better performance as predicted. Tadpoles with anticlockwise swimming preference did not learn to associate the predator with risk. Exposure to a high-risk environment during early ontogeny appears to favor the development of either a lateralization phenotype with refined predator recognition learning skills, or, to a lesser extent, a lateralization phenotype with poor predator recognition learning skills. Such individuals likely cope with predation using mechanisms other than learning.
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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 | 0.002 |
| 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 | 0.001 |
| 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".