Bibliographic record
Abstract
-concept learning, including same/different and matching-to-sample concept learning, provides the basis for many other forms of "higher" cognition. The issue of which species can learn abstract concepts and the extent to which abstract-concept learning is expressed across species is discussed. Definitive answers to this issue are argued to depend on the subjects' learning strategy (e.g., a relational-learning strategy) and the particular procedures used to test for abstract-concept learning. Some critical procedures that we have identified are: How to present the items to-be-compared (e.g., in pairs), a high criterion for claiming abstract-concept learning (e.g., transfer performance equivalent to baseline performance), and systematic manipulation of the training set (e.g., increases in the number of rule exemplars when transfer is less than baseline performance). The research covered in this article on the recent advancements in abstract-concept learning show this basic ability in higher-order cognitive processing is common to many animal species and that "uniqueness" may be limited more to how quickly new abstract concepts are learned rather than to the ability itself.
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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.026 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".