Food for thought: Rosy-faced lovebirds (Agapornis roseicollis) are capable of associative symbol learning and inference-based quantity discrimination
Bibliographic record
Abstract
Cognitive capacity for quantity discrimination is highly adaptive in various ecological contexts and subject to convergent evolution across diverse animal species, yet the underlying mechanism involved is not fully understood. Discrimination accuracy generally increases with the ratio between two quantities; however, this ability is expected to differ across ratio ranges. To test this we presented a novel symbol system to 28 rosy-faced lovebirds (Agapornis roseicollis), associating additive tally marks with symbols representing a one-to-one correspondence with different food quantities. Trained lovebirds could spontaneously infer the relative food quantities represented by other symbols. Lovebirds proved capable of (1) associating symbols (i.e., object-file symbolism); with (2) "more-less" quantity inference, by deducing food quantities based on their knowledge of this symbol-quantity association; and (3) enhancing their performance in relation to disparity ratio (conforming to Weber's law) and absolute difference. Furthermore (4), the influence of food ratio and absolute difference varied with different ratio ranges. Within a small ratio range (<= 3) discrimination performance improved with increments of ratio or absolute difference, whereas within a higher ratio range (> 3), the effect of these factors diminished. We conclude that rosy-faced lovebirds are capable of advanced numeracy and quantity discrimination, similar to larger parrot species.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".