Abstract-concept learning in two species of new world corvids, pinyon jays (Gymnorhinus Cyanocephalus) and California scrub jays (Aphelocoma Californica).
Bibliographic record
Abstract
) chosen as they belong to a different clade than nutcrackers and magpies, were examined using the same set-size expansion procedure of the same/different task (the task used with nutcrackers and magpies) to evaluate whether this trait is common across the Corvidae lineage. During this task, concept learning is assessed with novel images after training. Results from the current study showed that when presented with novel stimuli after training with an 8-image set, discrimination accuracy did not differ significantly from chance for pinyon jays and California scrub jays, unlike the magpies and nutcrackers from previous studies that showed partial transfer at that stage. However, concept learning improved with each set-size expansion, and the jays reached full concept learning with a 128-image set. This performance is similar to the other corvids and monkeys tested, all of which outperform pigeons. Results from the current study show a qualitative similarity in full abstract-concept learning in all species tested with a quantitative difference in the set-size functions, highlighting the shared survival importance of mechanisms supporting abstract-concept learning for corvids and primates. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".