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Record W4394713651 · doi:10.1038/s44271-024-00077-6

Defining key concepts for mental state attribution

2024· article· en· W4394713651 on OpenAlex
François Quesque, Ian A. Apperly, Renée Baillargeon, Simon Baron‐Cohen, Cristina Becchio, Harold Bekkering, Daniel M. Bernstein, Maxime Bertoux, Geoffrey Bird, Henryk Bukowski, Pascal Burgmer, Peter Carruthers, Caroline Catmur, Isabel Dziobek, Nicholas Epley, Thorsten M. Erle, Chris Frith, Uta Frith, Carl Michael Galang, Vittorio Gallese, Delphine Grynberg, Francesca Happé, Masahiro Hirai, Sara D. Hodges, Philipp Kanske, Mariska E. Kret, Claus Lamm, Jean‐Louis Nandrino, Sukhvinder S. Obhi, Sally Olderbak, Josef Perner, Yves Rossetti, Dana Schneider, Matthias Schurz, Tobias Schuwerk, Natalie Sebanz, Simone Shamay‐Tsoory, Giorgia Silani, Shannon Spaulding, Andrew R. Todd, Evan Westra, Dan Zahavi, Marcel Braß

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

VenueCommunications Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsMcMaster UniversityKwantlen Polytechnic University
Fundersnot available
KeywordsTerminologyAttributionKey (lock)Consistency (knowledge bases)State (computer science)Set (abstract data type)Mental stateComputer scienceMental healthData scienceManagement scienceKnowledge managementPsychologyCognitive scienceSocial psychologyArtificial intelligenceComputer securityEngineeringLinguisticsPsychotherapistAlgorithm

Abstract

fetched live from OpenAlex

The terminology used in discussions on mental state attribution is extensive and lacks consistency. In the current paper, experts from various disciplines collaborate to introduce a shared set of concepts and make recommendations regarding future use.

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)0.000
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: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.132
GPT teacher head0.535
Teacher spread0.403 · 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