Evolutionary and mechanistic drivers of laterality: A review and new synthesis
Bibliographic record
Abstract
Laterality, best understood as asymmetries of bilateral structures or biases in behaviour, has been demonstrated in species from all major vertebrate classes, and in many invertebrates, showing a large degree of evolutionary conservation across vertebrate groups. Despite the establishment of this phenomenon in so many species, however, the evolutionary and mechanistic study of laterality is uneven with numerous areas in this field requiring greater attention. Here, I present a partial review of how far the study of laterality has come, outlining previous pioneering work, I discuss the hypothesized costs and benefits of a lateralized brain and the suggested path of the evolution of laterality for populations and individuals. I propose an expansion of laterality research into areas that have been touched upon in the past but require stronger evidence from which the field will greatly benefit. Namely, I suggest a continuation of the phylogenetic approach to investigating laterality to better understand its evolutionary path; and a further focus on mechanistic drivers, with special attention to genetic and environmental effects. Putting together the puzzle of laterality using as many pieces as possible will provide a stronger understanding of this field, allowing us to continue to expand the field in novel ways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".