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Record W2981688151 · doi:10.1177/0956797620953842

Does Posture Influence the Stroop Effect?

2020· article· en· W2981688151 on OpenAlexaff
Emilie E. Caron, Michael Reynolds, Brandon C. W. Ralph, Jonathan S. A. Carriere, Derek Besner, Daniel Smilek

Bibliographic record

VenuePsychological Science · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsBishop's UniversityTrent UniversityUniversity of Waterloo
Fundersnot available
KeywordsStroop effectPsychologySittingAudiologyColor termHueCognitive psychologyDevelopmental psychologyCognitionPsychiatryMedicineLinguistics

Abstract

fetched live from OpenAlex

Rosenbaum, Mama, and Algom (2017) reported that participants who completed the Stroop task (i.e., name the hue of a color word when the hue and word meaning are congruent or incongruent) showed a smaller Stroop effect (i.e., the difference in response times between congruent and incongruent trials) when they performed the task standing than when sitting. We report five attempted replications (analyzed sample sizes: N = 108, N = 108, N = 98, N = 78, and N = 51, respectively) of Rosenbaum et al.’s findings, which were conducted in two institutions. All experiments yielded the standard Stroop effect, but we failed to detect any consistent effect of posture (sitting vs. standing) on the magnitude of the Stroop effect. Taken together, the results suggest that posture does not influence the magnitude of the Stroop effect to the extent that was previously suggested.

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 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)0.000
metaresearch head score (Gemma)0.002
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 score0.554
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.345
Teacher spread0.305 · 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 teacher head, 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

Citations24
Published2020
Admission routes1
Has abstractyes

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