The impact of learning opportunities on the development of learning and decision-making: an experiment with passerine birds
Bibliographic record
Abstract
Developmental context has been shown to influence learning abilities later in life, namely through experiments with nutritional and/or environmental constraints (i.e. lack of enrichment). However, little is known about the extent to which opportunities for learning affect the development of animal cognition, even though 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) colour cues reliably predicted the presence of food (associative learning), (ii) a combination of two-colour cues reliably predicted the presence of food (conditional learning), or (iii) colour cues were non-informative (control). After conducting two different discrimination tasks, our results showed that experience with predictive cues can cause increased choice accuracy and decision-making speed. Our first learning task showed that individuals in the associative learning treatment outperformed the control treatment, while task 2 showed that individuals in the conditional learning treatment had shorter latencies when making choices compared with 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 versus non-predictive cues during development on the cognition of adult animals. This article is part of the theme issue ‘Life history and learning: how childhood, caregiving and old age shape cognition and culture in humans and other 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".