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Record W1970533575 · doi:10.3200/jmbr.38.6.478-484

Numerosity and Rhythmicity in Stimulus-Response Compatibility

2006· article· en· W1970533575 on OpenAlexfundno aff
Stephen G. Atkins, Jeff Miller

Bibliographic record

VenueJournal of Motor Behavior · 2006
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
FundersDalhousie UniversityUniversity of Otago
KeywordsNumerosity adaptation effectStimulus–response compatibilityStimulus (psychology)PsychologyRhythmCognitive psychologyAudiologyCommunicationNeuroscienceCognitionMedicine

Abstract

fetched live from OpenAlex

When people must respond discriminatively to 1 or 2 stimuli by making 1 or 2 taps of a response key, they initiate the response more rapidly when the correct number of taps matches the number of stimuli (compatible condition) than when it mismatches (incompatible condition; J. O. Miller, S. G. Atkins, & F. Van Nes, 2005). Miller et al. sometimes found an effect of compatibility on response execution time, as reflected in the interresponse intervals between successive taps. The authors report 2 further experiments (N = 8 participants) in which they generalized the numerosity compatibility effects on response-initiation time and interresponse intervals to 2- versus 3-stimulus sequences. In addition, they varied gap length between stimuli to see whether the rhythm of the stimulus would influence that of the response. Weak rhythmicity effects were repeatedly found, but those were too small to suggest a plausible alternative explanation for the numerosity compatibility effect on response-initiation time.

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)0.004
metaresearch head score (Gemma)0.045
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.045
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.310
Teacher spread0.282 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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