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Record W4377288949 · doi:10.1038/s42003-023-04942-8

Ultra-high field fMRI identifies an action-observation network in the common marmoset

2023· article· en· W4377288949 on OpenAlexafffund
Alessandro Zanini, Audrey Dureux, Janahan Selvanayagam, Stefan Everling

Bibliographic record

VenueCommunications Biology · 2023
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsMarmosetMacaquePremotor cortexNeuroscienceCallithrixPrefrontal cortexPsychologyImitationAction (physics)CognitionPrimateCognitive psychologyBiologyAnatomyDorsum

Abstract

fetched live from OpenAlex

The observation of others' actions activates a network of temporal, parietal and premotor/prefrontal areas in macaque monkeys and humans. This action-observation network (AON) has been shown to play important roles in social action monitoring, learning by imitation, and social cognition in both species. It is unclear whether a similar network exists in New-World primates, which separated from Old-Word primates ~35 million years ago. Here we used ultra-high field fMRI at 9.4 T in awake common marmosets (Callithrix jacchus) while they watched videos depicting goal-directed (grasping food) or non-goal-directed actions. The observation of goal-directed actions activates a temporo-parieto-frontal network, including areas 6 and 45 in premotor/prefrontal cortices, areas PGa-IPa, FST and TE in occipito-temporal region and areas V6A, MIP, LIP and PG in the occipito-parietal cortex. These results show overlap with the humans and macaques' AON, demonstrating the existence of an evolutionarily conserved network that likely predates the separation of Old and New-World primates.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.172
GPT teacher head0.420
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2023
Admission routes2
Has abstractyes

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