A Rose in Any Other Context: Context Alters the Responses of Both Birds and Rodents to Novel Objects
Bibliographic record
Abstract
INTRODUCTION: The detection of novelty is a cognitive ability that is fundamental to survival. Following detection, a decision must be made to either approach (neophilia) or avoid (neophobia) the novel stimulus. The tendency to choose one strategy over the other is referred to as an animal's neotic preference. To date, the bulk of research reports that mammals are neophilic, while birds tend to be neophobic. These data, however, are differentiated not only by the class of animal (i.e., Mammalia vs. Aves), but also by the testing methods used, namely the context in which testing occurs. METHOD: To disentangle these factors, we assessed the reaction to novelty in two commonly used domesticated species, rats and pigeons, within two different contexts, a novel testing arena (common for mammals) and within the home cage (common for birds). RESULTS: Here, we show that both rats and pigeons show neophobia in the home cage and neophilia in a testing arena, demonstrating that some degree of the differences previously reported are likely due to testing protocols. Moreover, individual scores in one testing protocol did not predict testing scores in the other. CONCLUSION: These results limit the ability to: (a) compare findings across these paradigms and (b) conceive of neotic preference as a single stable trait across multiple (especially novel) contexts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".