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Record W2340846364 · doi:10.3819/ccbr.2016.110003

Comparative Cognition Outside the Laboratory

2016· article· en· W2340846364 on OpenAlex
Suzanne E. MacDonald, Sarah Ritvo

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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueComparative Cognition & Behavior Reviews · 2016
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsYork University
Fundersnot available
KeywordsComparative cognitionAnimal cognitionAnimal behaviorCognitionComparative psychologyPsychologyCognitive scienceCognitive psychologyNeuroscienceBiologyZoology

Abstract

fetched live from OpenAlex

With its roots firmly planted in behaviorist and animal learning traditions, lab-based research is an enduring and pervasive characteristic of comparative cognition. In this review, we discuss progress in comparative cognition research in other experimental settings such as zoos, captive animal parks, and wild settings. Zoos provide access to a large array of species housed in seminatural environments that allow a reasonable degree of experimental control. Thanks to the advent of computer technology, a wide range of complex cognitive processes is increasingly being successfully studied in zoo environments. Further, cognitive research provides enrichment for captive animal participants, reducing anxiety and promoting psychological well-being. The results of cognitive research also benefit the welfare of captive animals through preference assessment, species-specific exhibit design, and behavioral management. Field settings also offer unique advantages and have allowed researchers to systematically study such diverse topics as spatial cognition, cultural transmission, problem solving, and preference. Not only does field research expand our understanding of the evolutionary and ecological drivers of animal cognition, but it also can directly inform conservation efforts. Although venturing out of the lab presents tangible challenges, including the restriction of testable hypotheses and conclusions that can be inferred from results, the benefits to be gained outweigh the costs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.007

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 head0.312
GPT teacher head0.440
Teacher spread0.128 · 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