Assessing cognitive performance in nature: brain size and personality correlates of novel object recognition in nest-guarding male pumpkinseed sunfish
Bibliographic record
Abstract
Studying the factors that determine cognitive performance in animals is challenging under natural conditions but necessary to ensure that the laboratory test results are relevant to wild populations. We took advantage of nest fidelity in parental male pumpkinseed sunfish ( Lepomis gibbosus ) to conduct a novel object recognition (NOR) assay in the field. We assessed consistent individual differences in behaviour across the object familiarisation and test phases of the NOR assay and collected anatomical measurements, including brain size, after assay completion. We hypothesised that brain size would influence pumpkinseed cognitive ability and predict NOR performance after accounting for individual behavioural differences. Parental males showed repeatable reactions to nest disturbance and to the presence of objects at the nest periphery between assay phases, as well as correlated object investigation behaviours. We found evidence of novel object recognition memory at the population level, although it varied widely among individuals. Individual differences in object interactivity did not influence NOR performance, but relative brain mass (corrected for body length) did. Parental male pumpkinseed with relatively larger brains performed more poorly than males with relatively smaller brains. We interpret this negative relationship between brain size and NOR performance in the context of severe energy limitations faced by parental males during reproduction. Specifically, males maintaining energetically costly larger brains are likely operating near their upper energetic limit, with little or no spare resources available for investment in demanding learning processes. If this is the case, our findings emphasise that relationships between brain size and cognitive test performance may depend on energy availability and expenditure rates.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".