A comparative multimodal perspective on the evolutionary origins of tool use and handedness
Bibliographic record
Abstract
Laterality and the evolution of handedness have been of significant scholarly investigation across a wide variety of disciplines, including animal behaviour, neurobiology, developmental psychology, archaeology, and language evolution. Despite the longstanding array of attention, there remains no clear consensus on how and why laterality, and by extension handedness, evolved. Here I review comparative research on handedness in nonhuman primates to draw attention to the leading theories in the evolution of laterality as they relate to tool use and language origins. In doing so, I aim to provide an overview of our current understanding of the factors influencing handedness and the potential insight further study of nonhuman primates, particularly wild great apes, could contribute to ongoing discussions. Moreover, drawing on recent studies in both human knapping and chimpanzee stone tool use behaviour, I advocate for a multimodal approach to investigations of handedness, one where sound is integrated into existing paradigms examining laterality in tool use behaviour. Such a perspective has the potential to reveal novel insights into the auditory information that may have aided our hominin ancestors at the advent of their lithic technical revolution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".