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Record W2330292826 · doi:10.1037/a0035031

The effects of numerical magnitude, size, and color saturation on perceived interval duration.

2013· article· en· W2330292826 on OpenAlexafffund
Doug Alards-Tomalin, Jason P. Leboe-McGowan, Joshua Shaw, Launa C. Leboe-McGowan

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2013
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMagnitude (astronomy)MathematicsStatisticsDuration (music)Intensity (physics)Interval (graph theory)Contrast (vision)Absolute magnitudePhysicsOpticsCombinatoricsAcoustics

Abstract

fetched live from OpenAlex

The relative magnitude (or intensity) of an event can have direct implications on timing estimation. Previous studies have found that greater magnitude stimuli are often reported as longer in duration than lesser magnitudes, including Arabic digits (Xuan, Zhang, He, & Chen, 2007). One explanation for these findings is that different quantitative dimensions (size, intensity, number) are processed and represented according to a common analog magnitude system (Walsh, 2003). In the current study, we examined whether there were commonalities in how people judge the intervals of time occurring between discrete stimuli of different magnitudes across a variety of quantitative dimensions, which included number, size, and color saturation. It was found that duration judgments increased systematically as the overall magnitude difference between sequentially presented stimuli increased. This finding was robust against manipulations to the direction of the sequence, or whether the sequence followed ordered (continuous) or nonordered (discontinuous) pattern trajectories.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.319
Teacher spread0.303 · 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

Citations27
Published2013
Admission routes2
Has abstractyes

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