Bibliographic record
Abstract
▪ Abstract This review focuses on five key evolutionary issues pertaining to animal cognition, defined as the neuronal processes concerned with the acquisition, retention, and use of information. Whereas the use of information, or decision making, has been relatively well examined by students of behavior, evolutionary aspects of other cognitive traits that affect behavior, including perception, learning, memory, and attention, are less well understood. First, there is ample evidence for genetically based individual variation in cognitive traits, although much of the information for some traits comes from humans. Second, several studies documented positive association between cognitive abilities and performance measures linked to fitness. Third, information on the evolution of cognitive traits is available primarily for color vision and decision making. Fourth, much of the data on plasticity of cognitive traits appears to reflect nonadaptive phenotypic plasticity, perhaps because few evolutionary analyses of cognitive plasticity have been carried out. Nonetheless, several studies suggest that cognitive traits show adaptive plasticity, and at least one study documented genetically based individual variation in plasticity. Fifth, whereas assertions that cognition has played a central role in animal evolution are not supported by currently available data, theoretical considerations indicate that cognition may either increase or decrease the rate of evolutionary change.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 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".