Long-term stability of chimpanzee personality: comparing trait ratings and behaviour codings over a quarter of a century
Bibliographic record
Abstract
There is a lack of research examining whether trait ratings and behaviour codings yield similar measures of personality in nonhuman animals and whether these measures are stable over the long term. Here, we compare personality assessments in the same group of zoo-housed chimpanzees over a 25-year period, offering a rare opportunity for the analysis of long-term temporal stability of personality in this species. We attempt to disentangle the effects of time and measure through an analysis of similarities and differences at a group and individual level. The first wave of the study rated 59 chimpanzees’ personality traits on an adapted version of the Madingley Questionnaire. The trait principal component analysis revealed five components: Submissiveness, Extraversion, Neuroticism, Agreeableness and Openness. A separate principal component analysis identified five components based on behavioural codings: Grooming, Play, Sociability, Aggression and Responsibility. The second wave, with 19 individuals, identified three components arising from behavioural codings: Popularity, Sociability and Influence. When comparing across time for the 11 chimpanzees common to both waves, our primary hypothesis, that trait ratings from the first wave would not correlate with behaviour codings from the second because they are measuring different axes, was largely supported. Our second hypothesis that the behaviour codings carried out during the two waves would correlate because they were measuring the same thing, was supported, both at the component level and the individual behaviour level, thus providing evidence of stability of behaviour codings over time, particularly those reflecting sociability measures. Personality trait ratings were different from behavioural codings and included aspects not captured through those codings, including intelligence, apprehension, excitability and gentleness; thus, we argue that they represent a more comprehensive characterization of individuality. • Evidence of stability of personality over a 25-year period in chimpanzees. • Ratings and codings measure different constructs so only sometimes correlate. • Cross-sectional measurement of personality traits and behaviour codings correlate. • Some trait items capture unique aspects of character. • Grooming, Responsibility and Popularity identified as new personality components.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".