MétaCan
Menu
Back to cohort
Record W2050782877 · doi:10.1167/11.9.3

Head roll influences perceived hand position

2011· article· en· W2050782877 on OpenAlexafffund
Joseph Burns, Joseph Y. Nashed, Gunnar Blohm

Bibliographic record

VenueJournal of Vision · 2011
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProprioceptionReference framePerceptionHead (geology)Sensory systemComputer visionArtificial intelligenceFrame of referenceOrientation (vector space)Computer scienceTask (project management)Position (finance)Hand positionCommunicationFrame (networking)PsychologyMathematicsCognitive psychologyGeometryPhysicsNeuroscienceEngineeringBiology

Abstract

fetched live from OpenAlex

Visual and proprioceptive sensory inputs are naturally coded in different reference frames, i.e., eye-centered and body-centered, respectively. To use these signals in conjunction for motor planning or perception ultimately requires converting them into a common frame of reference using estimates of the relative orientation of the eyes, head, and body. Here, we examine whether extraretinal signals-specifically head roll-alter multisensory perception through noisy reference frame transformations. To do so, we examine the accuracy of visual localization relative to proprioceptive hand position for different head roll orientations. Subjects were required to judge whether a visual target was located closer or further and left or right (4-alternative forced-choice task) from their unseen hand. This was done for three different head roll rotations (-30, 0, and 30 deg). We show that eccentric head roll increased the variability in the subjects' ability to discriminate target location relative to the fingertip. We conclude that sensory perception is sensitive to body-geometry-dependent noise affecting the coordinate matching transformations of sensory data.

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 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 score0.224
Threshold uncertainty score0.540

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 communication0.0000.001
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 head0.088
GPT teacher head0.373
Teacher spread0.286 · 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 teacher head, 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

Citations23
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of VisionSame topicVisual perception and processing mechanismsFrench-language works237,207