The impact of learning opportunities on the development of learning and decision making: an experiment with passerine birds
Bibliographic record
Abstract
Learning abilities have been shown to be influenced by the developmental context, namely through experiments that imposed severe nutritional and/or environmental constraints (i.e. lack of enrichment). In contrast, we know little on the impact of opportunities for learning on the development of cognition in animals, despite that such opportunities are known to influence human cognitive development. We exposed young zebra finches (Taenopygia guttata) (n=26) to one of three experimental conditions, i.e. an environment where (i) the presence of food was predicted by one colour cue (associative learning), (ii) a combination of two-colour cues (conditional learning), or (iii) colour cues were non-informative (control). After conducting two different discrimination tasks, our results show that experience with predictive cues can cause increased choice accuracy and decision-making speed. Our first learning task showed that individuals in the associative treatment outperformed the control treatment, while task 2 showed that individuals in the conditional treatment shad shorter latencies when making choices compared to the control treatment. We found no support for a speed-accuracy trade-off. This dataset provides a rare longitudinal and experimental examination of the effect of predictive vs non-predictive cues during development on the cognition of adult animals.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".