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Record W2991324499 · doi:10.1037/cep0000196

Direct and indirect effects of semantic priming in motor planning and control.

2019· article· en· W2991324499 on OpenAlexfundno aff
Mehreen Nadeem, Peter Dixon

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2019
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPriming (agriculture)Cognitive psychologyPsychologyPerceptionControl (management)Semantic relationResponse primingRepresentation (politics)Word (group theory)CommunicationComputer scienceArtificial intelligenceLexical decision taskNeuroscienceLinguisticsCognition

Abstract

fetched live from OpenAlex

shortly before, during, or after the brief presentation of a line segment and measured whether this semantic prime affected the extent of a movement to the line's end point. Priming was observed with only early presentation of the word. A 2nd experiment demonstrated that little priming was observed when the apparent line length was not relevant to the required movements. Finally, a 3rd experiment found that substantial priming was found under comparable presentation conditions when participants merely made perceptual length judgments. These results suggest that semantic priming primarily affects the representation of the line length, not the planning or control of the movement. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.025
GPT teacher head0.302
Teacher spread0.276 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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