Context is key: A comment on Herczeg et al. 2019
Bibliographic record
Abstract
In the last several years, there has been a surge in the number of studies addressing the causes and consequences of among-individual variation in cognitive ability and behavioural plasticity. Here, we use a recent publication by Herczeg et al. (2019: 32(3), 218-226) to highlight three shortcomings common to this newly emerging field. In their study, Herczeg et al. attempted to link variation in cognitive ability and behavioural plasticity by testing whether selection lines of guppies (Poecilia reticulata) that differ in relative brain size also differ in behavioural plasticity, as might be expected if the costs to plasticity are predominantly derived from the cost of developing large brains. First, residual brain size may not be a suitable proxy for 'cognitive ability'. Recent work has shown that intraspecific variation in cognitive ability can be better understood by considering variation in the specific brain areas responsible for the relevant behaviours as opposed to whole-brain mass. Second, the measure of behavioural plasticity, habituation, is unlikely to fulfil the assumptions that plasticity is both adaptive and costly. Finally, we point out several misconceptions regarding animal personality that continue to contribute to the choice of traits that are not well aligned with study objectives. Elucidating the mechanisms underlying among-individual variation in cognition and behavioural plasticity within populations requires integration between behavioural ecology and comparative cognition, and the study system developed by Herczeg et al. has the potential to provide important mechanistic insights. We hope that by articulating and critically appraising the underlying assumptions that are common in these traditionally separate disciplines, a strong foundation can emerge to allow for more fruitful integration of these fields.
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.069 | 0.075 |
| Insufficient payload (model declined to judge) | 0.018 | 0.018 |
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".