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Record W3210496126 · doi:10.5281/zenodo.3583091

fMRI dataset for: Cognitive control of orofacial motor and vocal responses in the ventrolateral and dorsomedial human frontal cortex

2019· dataset· en· W3210496126 on OpenAlexaff
Kep Kee Loh, Emmanuel Procyk, Rémi Neveu, Franck Lamberton, William D. Hopkins, Michael Petrides, Céline Amiez

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typedataset
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsFrontal cortexCognitionNeurosciencePsychologyMotor cortexVentrolateral prefrontal cortexAudiologyCognitive psychologyMedicinePrefrontal cortex

Abstract

fetched live from OpenAlex

STUDY OVERVIEW 19 subjects underwent three functional magnetic resonance imaging (fMRI) sessions in which they performed a visuo-motor conditional associative learning task and the appropriate control task with different motor responses: orofacial acts (mouth movements), vocal acts, i.e. both nonspeech and speech vocalizations, and -as a control- manual acts (button presses). In each learning task block, subjects first learnt the correct conditional relations between three different motor responses and visual stimuli based on the nonspeech vocal or speech vocal feedback provided (learning phase), and subsequently executed the learnt associations (post-learning phase). In each control task block, subjects performed an instructed response to three possible visual stimuli, i.e. the visual and motor aspects of the task were identical to those in the conditional task, but critically, no cognitive selection based on pre-learned cognitive if-then rules, or feedback-driven adaptation were required in the control task. DATA ORGANIZATION The BrocaMCC data (data.zip) is organised by subjects. Each subject folder contains: An “anat" folder which contains the raw anatomical T1 scans (.nii). An “func” folder which contains three subfolders (manual, vocal, orofacial) corresponding to three experimental sessions. The functional MRI data (4-6runs;.nii format) associated with each session can be found in the respective condition subfolder. A “behavior” folder that contains three subfolders (manual, vocal, orofacial) corresponding to three experimental sessions. The behavioural data, in the form log files that are generated by Presentation, can be found in the respective session folders. Each behavioural log file is associated with an experimental run (eg. XX_manualrun1.xlsx is associated with the fmri data of subject XX in run 1 of the manual condition). Each experimental run starts off with a short motor-mapping task followed directly by the experimental task (Please refer to the associated paper (https://doi.org/10.1073/pnas.1916459117) for more information on the task structure). Behavioral Data The subject initial, experimental session, and run number are captured in the filename of each logfile (e.g. XX_manual_run1.xls). The column “Code” informs of the event and the associated onset time can be found in the “Time” column. The various codes and their associated events are as follows: MRI pulses - “255” Motor-mapping task event onsets - “fixation”, “hand_movements”, “saccade_movements”, “mouth_movements”, “tongue_movements”, “vocal_responses”, “verbal_responses” Instruction screen indicating the start of a control block (visuomotormapping task) - “transM”, “transL” or “transR”. Instruction screen indicating the start of a learning block (conditional associative learning task) - “transCL”. Stimulus presentation - “question_XX" Sound feedback presentation Verbal (Speech) feedback - “fbcorrect_verbal”,"fberror_verbal”, “correctY”, “errorY”. Vocal (NonSpeech) feedback - “fbcorrect_vocal”,”fberror_vocal”, “correctR”, “errorR”. For more details about the data, please refer to: https://doi.org/10.1073/pnas.1916459117 or contact the authors.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.065
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0650.051

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 head0.044
GPT teacher head0.296
Teacher spread0.252 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2019
Admission routes1
Has abstractyes

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