Great Tits Chosen for Greatness Makes Them Representative: A commentary on Farrar et al.'s "Replications, Comparisons, Sampling and the Problem of Representativeness in Animal Cognition Research"
Bibliographic record
Abstract
Studies of animal cognition struggle frequently with the question of how representative results from small samples are for a species. A recent article by Farrar et al. (2021), in this journal, highlights some of the major problems and suggests some solutions to these with cautionary examples drawn from the animal cognition literature. One such example comes from a study of inhibitory control in the great tit, Parus major, by the authors of this commentary. Although we recognize, and agree, that there are issues regarding representativeness in studies of animal cognition, we disagree with the use of our inhibitory control study as a cautionary example. Here, we explain why we think that our study is representative of Great tit inhibitory control. In fact, some of our arguments as to why our study is representative are in agreement with suggestions by Farrar et al (2021), e.g., comparing individuals with different levels of previous experiences in the cognitive paradigm under investigation. Moreover, we also add to Farrar et al.’s (2021) conclusion on how to approach studies with ambiguous representativeness by highlighting the importance of recognizing and discussing methodological differences in studies of cognitive ability. In summary, we do not argue against the valid points laid out by Farrar et al (2021), but discuss important nuances of the representativeness issue to also consider and, most importantly, add an additional point of scrutiny to account for in comparative animal cognition research.
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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 | 0.053 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.012 | 0.005 |
| Research integrity | 0.061 | 0.073 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".