Is exploration a metric for information gathering? Attraction to novelty and plasticity in black‐capped chickadees
Bibliographic record
Abstract
Abstract Animals can learn about the value of resources and predation risk by exploring novel environments or exploring novel stimuli in their regular environments. Still, there is a disconnect in the way that exploration has been defined and measured; exploration is defined in terms of information acquisition, while measured in terms of movement speed and diversity of contacted items in a novel environment. If exploration is indeed a measurement of information gathering, fast explorers should seek to reduce uncertainty about their environment more than slow explorers. Exploration speed has also been linked to behavioral plasticity, where fast explorers move fast but collect less detailed information, thereby forming routines and expressing less plasticity than slow explorers. We test these two hypotheses by comparing exploration in a novel environment to individuals' attraction to novelty and behavioral plasticity. Our results support the view that exploration is a measurement of information‐gathering tendencies as fast explorers were more likely to collect novel information, which should reduce uncertainty further than sampling familiar information sources, compared with slower explorers. Furthermore, faster explorers switched to sampling novel information more quickly than slow explorers when the value of the familiar option decreased, opposing the widely held view that faster explorers present more routine‐like behavior. By providing familiar and novel foraging options in close spatial contiguity, we demonstrate an attraction to novelty in faster explorers that cannot be confounded by activity rate, thereby suggesting that these individuals seek to reduce uncertainty. In conclusion, our results support the biological validity of the term “exploration” through its association with information gathering.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".