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Record W4411239377 · doi:10.1037/com0000423

Does comparative cognition have a WEIRD problem?

2025· article· en· W4411239377 on OpenAlexafffund
Kristin Andrews, Susana Monsó

Bibliographic record

VenueJournal of comparative psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of CanadaTempleton World Charity Foundation
KeywordsCognitionCognitive scienceComparative cognitionComputer scienceCognitive psychologyPsychologyNeuroscience

Abstract

fetched live from OpenAlex

We describe an as yet unidentified bias relevant to comparative cognition research: WEIRD-centrism. This bias leads us to take as the gold standard the practices, capacities, or concepts of WEIRD (Western, Educated, Industrialized, Rich, and Democratic) humans, that is, humans who grew up in WEIRD societies and whose behavior has been shaped by the influence of WEIRD cultural norms and practices. We identify how the bias impacts the study of practices, capacities, and concepts, and offer two suggestions for mitigating the bias. The first is to use what we are calling a multibaseline approach, which involves starting with constructs that come not from our experiences as humans, but from our growing understanding of other species. The second is to make use of philosophical analysis and conceptual engineering, which includes identifying minimal concepts of psychological capacities as well as a dimensional approach that depicts the many ways in which a capacity can be instantiated. We hope that these tools will allow us to better understand the similarities and differences both within and between species. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.559
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.417
Teacher spread0.359 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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