Bibliographic record
Abstract
We propose an expansion of neuroecological comparisons to include the capabilities of brainless and non-neural organisms. We begin this enterprise by conducting a systematic search for studies on learning in echinoderms. Echinodermata are marine invertebrates comprising starfish, brittle stars, sea cucumbers, sea urchins, and sea lilies. Animals in this phylum lack any centralized brain and instead possess diffuse neural networks known as nerve nets. The learning abilities of these animals are of particular interest as, within the bilaterian clade, they are close evolutionary neighbors to chordates, a phylum whose members exhibit complex feats in learning and contain highly specialized brains. The learning capacities and limitations of echinoderms can inform the evolution of nervous systems and learning in Bilateria. We find evidence of both non-associative and associative learning (in the form of classical conditioning) in echinoderms, which was primarily focused on starfish. Additional evidence of learning is documented in brittle stars, sand dollars, and sea urchins. We then discuss the evolutionary significance of learning capabilities without a brain, the presence of embodied cognition across multiple groups, and compare the learning present in echinoderms with the impressive cognitive abilities documented in the oldest linage group within vertebrates (the major group within the phylum of chordates), fish.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".