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Record W4411937686 · doi:10.31234/osf.io/mvche_v2

Flexible behavior or flexible methods? A cross-taxon review of experimental designs in reversal learning

2025· review· en· W4411937686 on OpenAlex
Nicolás Alessandroni, Rachael Miller, Drew Altschul, Lisa P. Barrett, Marina Bazhydai, Mahmoud Medhat Elsherif, Julia Espinosa, Biljana Gjoneska, Yseult Héjja‐Brichard, Valeria Mazza, Annika Paukner, Ekaterina Pronizius, Michael J. Proulx, Muhammad A. J. Qadri, Olivia T. Reilly, Raoul Schwing, Carla Sebastián‐Enesco, Vedrana Šlipogor, Alexandra A. de Sousa, Ingmar Visser, Justin Yeager, Martin Zettersten, Krista Byers‐Heinlein, Josep Call, Ludwig Huber, Lars Chıttka, Laurent Prétôt

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typereview
Languageen
FieldPsychology
TopicPsychological and Educational Research Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaAustrian Science FundSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversité Catholique de LouvainEuropean CommissionLeverhulme TrustOpen Philanthropy ProjectNational Science Foundation
KeywordsTaxonComputer sciencePsychologyData scienceArtificial intelligencePaleontologyBiology

Abstract

fetched live from OpenAlex

Behavioral flexibility—the ability to adapt behavior in response to changing conditions—is widely recognized as a key feature of animal cognition. It is often measured using reversal learning tasks, where individuals must inhibit a previously rewarded response and adopt a new one after contingencies shift. Despite its widespread use, the comparability of these tasks across species remains unclear. We conducted a systematic review of 206 empirical studies (2014–2023) spanning eight major taxonomic groups: invertebrates, fishes, amphibians and reptiles, birds, rodents, other mammals, non-human primates, and humans. For each study, we extracted variables related to taxon coverage, sampling, learning and reversal criteria, cue types, and outcome measures. Analyses included nonparametric tests to assess group-level differences, linear discriminant analyses to explore multivariate structure, and model-based robustness checks. Our findings reveal three fundamental obstacles to reliable cross-species inference. First, research effort is highly imbalanced: birds, rodents, and humans accounted for over half of all study cells, while most animal diversity—especially invertebrates and amphibians and reptiles—remains virtually untested, with less than 1% of described species included per taxon. Second, research is taxonomically siloed: 99% of studies focus on a single group, limiting opportunities for direct comparison. Third, and most critically, methodological standards diverge dramatically across taxa. Humans were consistently held to the strictest learning criteria (median threshold 90%), while birds, invertebrates, and fishes most often used lower thresholds (80–84%). Overtraining was implemented in two-thirds of amphibian and reptile studies but was rare (less than 30%) elsewhere. The number of reversal phases differed more than threefold among groups. Nearly all studies of amphibians, reptiles, fishes, and invertebrates used single-reversal designs, whereas multi-reversal protocols were much more common in humans and non-human primates. Sample sizes—both per cell and per study—, evaluation window lengths, cue types, and outcome metrics also displayed taxon-specific patterns. These systematic differences in experimental design introduce structural asymmetries that complicate cross-taxon comparisons, blurring the line between true cognitive variation and methodological artifacts. Although research to date has advanced our understanding, further progress will depend on greater methodological coordination and broader taxonomic coverage. Emerging large-scale collaborations are beginning to address these gaps, offering a promising path toward a more robust and equitable science of behavioral flexibility.

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)0.003
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0250.000

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 head0.467
GPT teacher head0.657
Teacher spread0.190 · 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