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Record W4292466935 · doi:10.1016/j.ynirp.2022.100123

Brain network for small-scale features in active touch

2022· article· en· W4292466935 on OpenAlex

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

VenueNeuroimage Reports · 2022
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsMcGill University
FundersEuropean Commission
KeywordsPerceptionTactile perceptionComputer scienceNeuroscienceCognitionContrast (vision)Resting state fMRIFunctional connectivityArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

An important tactile function is the active detection of small-scale features, such as edges or asperities, which depends on fine hand motor control. Using a resting-state fMRI paradigm, we sought to identify the functional connectivity of the brain network engaged in mapping tactile inputs to and from regions engaged in motor preparation and planning during active touch. Human participants actively located small-scale tactile features that were rendered by a computer-controlled tactile display. To induce rapid perceptual learning, the contrast between the target and the surround was reduced whenever a criterion level of success was achieved, thereby raising the task difficulty. Multiple cortical and subcortical neural connections within a parietal-cerebellar-frontal network were identified by correlating behavioral performance with changes in functional connectivity. These cortical areas reflected perceptual, cognitive, and attention-based processes required to detect and use small-scale tactile features for hand dexterity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.749

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.282
Teacher spread0.254 · 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