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Record W2950375092 · doi:10.1101/192880

Done in 100 ms: Path-dependent visuomotor transformation in the human upper limb

2017· preprint· en· W2950375092 on OpenAlexafffund
Chao Gu, J. Andrew Pruszynski, Paul L. Gribble, Brian D. Corneil

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2017
Typepreprint
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsRobarts Clinical TrialsWestern University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsStimulus (psychology)Computer scienceLatency (audio)Reference frameNeuroscienceComputer visionArtificial intelligencePsychologyCognitive psychologyFrame (networking)

Abstract

fetched live from OpenAlex

ABSTRACT A core assumption underlying mental chronometry is that more complex tasks increase cortical processing, prolonging reaction times. Here we show that increases in task complexity alter the magnitude, rather than the latency, of the output for a circuit that rapidly transforms visual information into motor actions. We quantified visual stimulus-locked responses (SLRs), which are changes in upper limb muscle recruitment that evolve at a fixed latency ∼100 ms after novel visual stimulus onset. First, we studied the underlying reference frame of the SLR, by dissociating initial eye and hand position. Despite its quick latency, we found that the SLR was expressed in a hand-centric reference frame, suggesting that the circuit mediating the SLR integrated retinotopic visual information with body configuration. Next, we studied the influence of planned movement trajectory, requiring participants to prepare and generate curved or straight reaches in the presence of obstacles to attain the same visual stimulus. We found that SLR magnitude reflected the initial planned movement trajectory, regardless of the ensuing movement curvature. Based on these results, we suggest that the circuit mediating the SLR lies in parallel to other well-studied corticospinal pathways. Although the fixed latency of the SLR precludes extensive cortical processing, inputs conveying information relating to task complexity, such as body configuration and planned movement trajectory, can pre-set nodes within the circuit underlying to the SLR to modulate its magnitude. SIGNIFICANCE STATEMENT A core assumption underlying mental chronometry is that more complex tasks increase cortical processing, prolonging reaction times. Here, we showed that increases in task complexity altered the magnitude, rather than the latency, of a circuit that rapidly transforms visual information into motor actions. We focus on stimulus-locked responses (SLRs), which are changes in upper limb muscle recruitment that evolve at a fixed latency ∼100 ms after novel visual stimulus onset. We showed that despite is quick latency, the circuitry mediating the SLR transformed a retinotopic visual signal into a hand-centric motor command suitable to contribute to the initial movement trajectory. We suggest that this circuit lies in parallel to other well-studied corticospinal pathways.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.031
GPT teacher head0.256
Teacher spread0.225 · 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 designBench or experimental
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

Citations1
Published2017
Admission routes2
Has abstractyes

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