Endothermy, neuron counts, and other issues: Further remarks on neurocognitive evolution in fossil vertebrates
Bibliographic record
Abstract
Last year, we challenged the view that large-bodied theropod dinosaurs such as Tyrannosaurus rex resembled primates in cognition and behavior, a proposition made by Herculano-Houzel in 2023. More recently, Jensen et al. have criticized our work on this topic, raising methodological and conceptual issues. Central to their argument is the assumption that tachymetabolic endotherms should be expected to converge in neurocognitive traits, which follows the recently proposed endothermic brain hypothesis. We here respond to their critique, address critical misconceptions, and argue that none of the points raised by Jensen et al. challenge the conclusions we have drawn. We show that the endothermic brain hypothesis lacks robust support from the fossil record. As of now, no compelling evidence suggests that endothermy coevolved with enlarged brains or elevated neuron densities in either the avian or mammalian lineage. Various fossil groups containing endothermic taxa retain plesiomorphic endocast traits and do not converge with birds and mammals in the relative size and proportions of their brains. Furthermore, we elaborate on our discussion on (forebrain) neuron counts as correlates of cognitive performance and highlight that neuron numbers evolve in tandem with body mass in birds and mammals, suggesting that comparatively high neuron number estimates for some Mesozoic dinosaurs do not require explanations that orbit around exceptional cognitive abilities. Despite these disagreements, we identify significant overlap in opinion between Jensen et al. and ourselves, including in the position that neuron count estimates for Mesozoic dinosaurs will remain unreliable and are unsuitable for inferring cognitive complexity.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".