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Record W4405807666 · doi:10.1016/j.isci.2024.111691

Performance modulations phase-locked to action depend on internal state

2024· article· en· W4405807666 on OpenAlexaff
Tommaso Tosato, Guillaume Dumas, Gustavo Rohenkohl, Pascal Fries

Bibliographic record

VenueiScience · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsUniversité de MontréalMila - Quebec Artificial Intelligence InstituteCentre Hospitalier Universitaire Sainte-Justine
FundersFundação de Amparo à Pesquisa do Estado de São PauloDeutsche Forschungsgemeinschaft
KeywordsAction (physics)Phase (matter)ChemistryPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Previous studies have shown that perceptual performance can be modulated at specific frequencies phase-locked to self-paced motor actions, but findings have been inconsistent. To investigate this effect at the population level, we tested 50 participants who performed a self-paced button press followed by a threshold-level detection task, using both fixed- and random-effects analyses. Contrary to expectations, the aggregated data showed no significant action-related modulation. However, when accounting for internal states, we found that trials during periods of low performance or following a missed detection exhibited significant modulation at approximately 17 Hz. Additionally, participants with no false alarms showed similar modulation. These effects were significant in random effects tests, suggesting that they generalize to the population. Our findings indicate that action-related perceptual modulations are not always detectable but may emerge under specific internal conditions, such as lower attentional engagement or higher decision criteria, particularly in the beta-frequency range.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
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)0.0020.000

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 head0.333
Teacher spread0.275 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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