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Record W2131051240 · doi:10.1080/09541440600890680

Planning keypress and reaching responses: Manipulating number of effectors and preparation interval

2006· article· en· W2131051240 on OpenAlexaff
Jos J. Adam, Bettine Taminiau, Jay Pratt

Bibliographic record

VenueThe European Journal of Cognitive Psychology · 2006
Typearticle
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEffectorPsychologyTask (project management)Selection (genetic algorithm)Set (abstract data type)Interval (graph theory)Social psychologyCommunicationDevelopmental psychologyCognitive psychologyArtificial intelligenceBiologyComputer scienceImmunologyMathematics

Abstract

fetched live from OpenAlex

This study tested the hypothesis that separate mechanisms mediate the planning of reaching and keypress responses. Participants performed a spatial precueing task with two preparation intervals (100 ms and 1000 ms) and three response sets: (a) pressing one of four response keys; (b) reaching with one of two hands; and (c) reaching with one hand. Reaction time results showed a pattern of precueing effects that strongly depended on the number of effectors in the response set and on the preparation interval. This outcome was interpreted as evidence that distinct mechanisms mediate the planning of multiple-effector and single-effector actions, with effector selection being relevant in the former but not in the latter. The theoretical implications of this conclusion are discussed.

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)0.001
metaresearch head score (Gemma)0.013
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.055
GPT teacher head0.359
Teacher spread0.304 · 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
Published2006
Admission routes1
Has abstractyes

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