Demystifying animal ‘personality’ (or not): why individual variation matters to experimental biologists
Bibliographic record
Abstract
Animal 'personality', defined as repeatable inter-individual differences in behaviour, is a concept in biology that faces intense controversy. Critics argue that the field is riddled with terminological and methodological inconsistencies and lacks a sound theoretical framework. Nevertheless, experimental biologists are increasingly studying individual differences in physiology and relating these to differences in behaviour, which can lead to fascinating insights. We encourage this trend, and in this Commentary we highlight some of the benefits of estimating variation in (and covariation among) phenotypic traits at the inter- and intra-individual levels. We focus on behaviour while drawing parallels with physiological and performance-related traits. First, we outline some of the confusion surrounding the terminology used to describe repeatable inter-individual differences in behaviour. Second, we argue that acknowledging individual behavioural differences can help researchers avoid sampling and experimental bias, increase explanatory power and, ultimately, understand how selection acts on physiological traits. Third, we summarize the latest methods to collect, analyse and present data on individual trait variation. We note that, while measuring the repeatability of phenotypic traits is informative in its own right, it is only the first step towards understanding how natural selection and genetic architecture shape intra-specific variation in complex, labile traits. Thus, understanding how and why behavioural traits evolve requires linking repeatable inter-individual behavioural differences with core aspects of physiology (e.g. neurophysiology, endocrinology, energy metabolism) and evolutionary biology (e.g. selection gradients, heritability).
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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 | 0.071 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.039 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".