Evolutionary and mechanistic drivers of laterality: A review and new synthesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it