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Record W4285027693 · doi:10.6084/m9.figshare.c.4153862

Supplementary material from "The repeatability of cognitive performance: a meta-analysis"

2018· other· en· W4285027693 on OpenAlexafffund
Maxime Cauchoix, Pizza Ka Yee Chow, J O van Horik, Cristina M. Atance, Emmanuel J. Barbeau, Gladys Barragan‐Jason, P. Bize, Annika Boussard, S D Buechel, Amélie Cabirol, Laure Cauchard, Nicolas Claidière, S. Dalesman, Jean‐Marc Devaud, Mira Didic, Blandine Doligez, Joël Fagot, Claudia Fichtel, Johanna Henke‐von der Malsburg, E. Hermer, Liam Huber, Franziska Huebner, Peter M. Kappeler, Simon Klein, Jan Langbein, Ellis Langley, S. E. G. Lea, Mathieu Lihoreau, Hanne Løvlie, Louis D. Matzel, Shinichi Nakagawa, Christian Nawroth, Susann Oesterwind, Bruno Sauce, Elizabeth Smith, Enrico Sorato, Sabine Tebbich, Lisa Wallis, Mark Whiteside, Anna Wilkinson, Alexis S. Chaine, Julie Morand‐Ferron

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2018
Typeother
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversité de MontréalUniversity of Ottawa
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsRepeatabilityCognitionMeta-analysisPsychologyCognitive psychologyComputer scienceMathematicsStatisticsMedicineNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

Behavioural and cognitive processes play important roles in mediating an individual's interactions with its environment. Yet, while there is a vast literature on repeatable individual differences in behaviour, relatively little is known about the repeatability of cognitive performance. To further our understanding of the evolution of cognition, we gathered 44 studies on individual performance of 25 species across six animal classes and used meta-analysis to assess whether cognitive performance is repeatable. We compared repeatability (<i>R</i>) in performance (1) on the same task presented at different times (temporal repeatability), and (2) on different tasks that measured the same putative cognitive ability (contextual repeatability). We also addressed whether <i>R</i> estimates were influenced by seven extrinsic factors (moderators): type of cognitive performance measurement, type of cognitive task, delay between tests, origin of the subjects, experimental context, taxonomic class and publication status. We found support for both temporal and contextual repeatability of cognitive performance, with mean <i>R</i> estimates ranging between 0.15 and 0.28. Repeatability estimates were mostly influenced by the type of cognitive performance measures and publication status. Our findings highlight the widespread occurrence of consistent inter-individual variation in cognition across a range of taxa which, like behaviour, may be associated with fitness outcomes.This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive ability’.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.2170.000

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 head0.041
GPT teacher head0.288
Teacher spread0.247 · 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
GenreOther

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

Citations11
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueHAL (Le Centre pour la Communication Scientifique Directe)Same topicCognitive Abilities and TestingFrench-language works237,207