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Record W2403304187 · doi:10.1123/mc.2015-0079

Gunslinger Effect and Müller-Lyer Illusion: Examining Early Visual Information Processing for Late Limb-Target Control

2016· article· en· W2403304187 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

VenueMotor Control · 2016
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIllusionFeed forwardImpulse (physics)Computer scienceInformation processingMovement controlAccelerationMovement (music)Control (management)Visual controlPhysical medicine and rehabilitationComputer visionPsychologyArtificial intelligenceCommunicationCognitive psychologyPhysicsMedicineAcousticsEngineeringControl engineering

Abstract

fetched live from OpenAlex

The multiple process model contends that there are two forms of online control for manual aiming: impulse regulation and limb-target control. This study examined the impact of visual information processing for limb-target control. We amalgamated the Gunslinger protocol (i.e., faster movements following a reaction to an external trigger compared with the spontaneous initiation of movement) and Müller-Lyer target configurations into the same aiming protocol. The results showed the Gunslinger effect was isolated at the early portions of the movement (peak acceleration and peak velocity). Reacted aims reached a longer displacement at peak deceleration, but no differences for movement termination. The target configurations manifested terminal biases consistent with the illusion. We suggest the visual information processing demands imposed by reacted aims can be adapted by integrating early feedforward information for limb-target control.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.239
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