Embodying cognitive ethology
Bibliographic record
Abstract
Cognitive psychology considers the environment as providing information, not affecting fundamental information processes. Thus, cognitive psychology's traditional paradigms study responses to precisely timed stimuli in controlled environments. However, new research demonstrates the environment does influence cognitive processes and offers cognitive psychology new methods. The authors examine one such proposal: cognitive ethology. Cognitive ethology improves cognitive psychology's ecological validity through first drawing inspiration from robust phenomena in the real world, then moving into the lab to test those phenomena. To support such methods, cognitive ethologists appeal to embodied cognition, or 4E cognition, for its rich relationships between agents and environments. However, the authors note while cognitive ethology focuses on new methods (epistemology) inspired by embodied cognition, it preserves most traditional assumptions about cognitive processes (ontology). But embodied cognition-particularly its radical variants-also provides strong ontological challenges to cognitive psychology, which work against cognitive ethology. The authors argue cognitive ethology should align with the ontology of less radical embodied cognition, which produces epistemological implications, offering alternative methodologies. For example, cognitive ethology can explore differences between real-world and lab studies to fully understand how cognition depends on environments.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.028 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".