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Record W1505902925 · doi:10.1111/brv.12174

Studying the evolutionary ecology of cognition in the wild: a review of practical and conceptual challenges

2015· review· en· W1505902925 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiological reviews/Biological reviews of the Cambridge Philosophical Society · 2015
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Ottawa
FundersNatural Environment Research CouncilSight Research UK
KeywordsCognitionForagingEcologyNatural selectionSelection (genetic algorithm)Animal cognitionEvolutionary psychologyEvolutionary ecologyPsychologyCognitive psychologySexual selectionBiologyEvolutionary biologyComputer scienceNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

Cognition is defined as the processes by which animals collect, retain and use information from their environment to guide their behaviour. Thus cognition is essential in a wide range of behaviours, including foraging, avoiding predators and mating. Despite this pivotal role, the evolutionary processes shaping variation in cognitive performance among individuals in wild populations remain very poorly understood. Selection experiments in captivity suggest that cognitive traits can have substantial heritability and can undergo rapid evolution. However only a handful of studies have attempted to explore how cognition influences life-history variation and fitness in the wild, and direct evidence for the action of natural or sexual selection on cognition is still lacking, reasons for which are diverse. Here we review the current literature with a view to: (i) highlighting the key practical and conceptual challenges faced by the field; (ii) describing how to define and measure cognitive traits in natural populations, and suggesting which species, populations and cognitive traits might be examined to greatest effect; emphasis is placed on selecting traits that are linked to functional behaviour; (iii) discussing how to deal with confounding factors such as personality and motivation in field as well as captive studies; (iv) describing how to measure and interpret relationships between cognitive performance, functional behaviour and fitness, offering some suggestions as to when and what kind of selection might be predicted; and (v) showing how an evolutionary ecological framework, more generally, along with innovative technologies has the potential to revolutionise the study of cognition in the wild. We conclude that the evolutionary ecology of cognition in wild populations is a rapidly expanding interdisciplinary field providing many opportunities for advancing the understanding of how cognitive abilities have evolved.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.410
GPT teacher head0.387
Teacher spread0.023 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it