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Record W2341729749 · doi:10.1068/ic926

Perspective Modulates Temporal Synchrony Discrimination of Visual and Proprioceptive Information in Self-Generated Movements

2011· article· en· W2341729749 on OpenAlexaff
Adria E. N. Hoover, Laurence R. Harris

Bibliographic record

Venuei-Perception · 2011
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
Fundersnot available
KeywordsProprioceptionAsynchrony (computer programming)Perspective (graphical)EfferentMovement (music)PsychologyAfferentCommunicationSensory systemMultisensory integrationEfference copyCognitive psychologyComputer visionComputer scienceArtificial intelligenceNeuroscienceAsynchronous communication

Abstract

fetched live from OpenAlex

Temporally congruent sensory information during a movement and the visual perspective in which we see our movement provide cues for self-recognition. Here we measured threshold and sensitivity for delay detection between a self-generated finger movement (proprioceptive and efferent copy) and the visual image of that movement under differing perspectives. Asynchrony was detected more easily (45–60 ms) when the hand was viewed in an egocentric perspective, even if its image was mirrored so that it appeared to be the other hand. Significantly longer delays (80–100ms) were needed to detect asynchrony when the hand was viewed in an allocentric (or inverted) perspective. These effects were replicated when the movement was seen as if looking in a mirror and when the non-dominant hand was used. We conclude that the tolerance for temporally matching visual, proprioceptive and efferent copy information that informs about the perceived position of body parts depends on whether one is viewing one's own body or someone else's.

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)0.002
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.041
GPT teacher head0.303
Teacher spread0.262 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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