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Record W2061531210 · doi:10.1037/cjep2006012

Parametric exploration of the Simon effect across visual space.

2006· article· en· W2061531210 on OpenAlexafffund
Raymond M. Klein, Mary. E. Dove, Jason Ivanoff, Gail A. Eskes

Bibliographic record

VenueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsSimon effectGeneralizability theoryParametric statisticsStimulus (psychology)ConcentricPsychologyDiagonalMathematicsCognitive psychologyGeometryStatisticsCognitionNeuroscience

Abstract

fetched live from OpenAlex

The Simon effect refers to the performance advantage for responding to the nonspatial identity of the target when the target's irrelevant location corresponds with the relative location of the response. The present study is a parametric examination of the magnitude of the Simon effect across visual space. Response keys were arranged along vertical, horizontal, and two diagonal axes, and stimuli were arranged in two concentric circles (near and far from fixation) along the same axes. The results show that the Simon effect is of similar magnitude regardless of stimulus-response axis. In contrast to findings from stimulus-response compatibility paradigms, there was no evidence in this study for the presence of an orthogonal compatibility effect or left-right prevalence effect, suggesting that these effects only arise when response location is relevant. The results demonstrate the robust generalizability of the Simon effect under different spatial conditions and thus broaden the relevance of the Simon effect to a variety of applications.

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)0.017
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
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.002
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 head0.103
GPT teacher head0.388
Teacher spread0.285 · 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

Citations11
Published2006
Admission routes2
Has abstractyes

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