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Record W2134317949 · doi:10.1080/00222895.2012.746283

The Gunslinger Effect: Why Are Movements Made Faster When Responding to Versus Initiating an Action?

2013· article· en· W2134317949 on OpenAlexaff
Nicholas J. La Delfa, Daniel Bl Garcia, Jessica Cappelletto, Alison C. McDonald, Timothy D. Lee

Bibliographic record

VenueJournal of Motor Behavior · 2013
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMovement (music)PsychologyAction (physics)Cognitive psychologyCommunicationPhysical medicine and rehabilitationPhysicsMedicineAcoustics

Abstract

fetched live from OpenAlex

The authors replicated and extended results from the gunfight paradigm (A. Welchman, J. Stanley, M. Schomers, R. Miall, & H. Bulthoff, 2010a) in which participants moved faster when reacting to the perceived initiation of an opponent compared to initiating an action themselves. In addition to replicating these movement time effects, the authors found that time to peak velocity, peak velocity, and movement-endpoint dispersions were similarly impacted. The findings are discussed in terms of a triggering mechanism involved in ballistic and internally generated movements.

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)0.003
metaresearch head score (Gemma)0.018
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.086
GPT teacher head0.380
Teacher spread0.295 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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