Cognitive ecology of surprise in predator–prey interactions
Bibliographic record
Abstract
Abstract In this review, we relate theoretical work on the importance of surprise in cognition to empirical research relevant to surprise in predator–prey interactions. There have been multiple proposals as to how surprise should be defined and quantified in the context of animal cognition, including contributions from associative learning, information theory, Bayesian inference and the recent framework of active inference. We argue that active inference provides a novel and powerful approach to quantifying surprise and advances the field by revealing how proactive behaviour on the part of predators relates to reducing surprise. The active inference framework encompasses both proximate (e.g. neurobiological) and ultimate (evolutionary) aspects of surprise and brings new insights into key aspects of prey defences that exploit predator surprise. We focus on surprise in defences that involve a sudden change in prey appearance (such as deimatic displays), and in defences that increase prey unpredictability (such as variation in chemical defences). We review literature that have investigated these phenomena and connect them to active inference. We also consider how multiple prey defences impact surprise in predators. Finally, we consider the implications of active inference for future studies of predator–prey interactions, illustrate how this approach can be used to quantify surprise in prey defences and predict predator behaviour, and outline key questions that can be addressed within this framework. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".