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Record W2112266536 · doi:10.1086/665048

Is Noise Always Bad? Exploring the Effects of Ambient Noise on Creative Cognition

2012· article· en· W2112266536 on OpenAlexaff
Ravi Mehta, Rui Zhu, Amar Cheema

Bibliographic record

VenueJournal of Consumer Research · 2012
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNoise (video)CognitionPsychologyComputer scienceArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

This paper examines how ambient noise, an important environmental variable, can affect creativity. Results from five experiments demonstrate that a moderate (70 dB) versus low (50 dB) level of ambient noise enhances performance on creative tasks and increases the buying likelihood of innovative products. A high level of noise (85 dB), on the other hand, hurts creativity. Process measures reveal that a moderate (vs. low) level of noise increases processing difficulty, inducing a higher construal level and thus promoting abstract processing, which subsequently leads to higher creativity. A high level of noise, however, reduces the extent of information processing and thus impairs creativity.

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)0.003
metaresearch head score (Gemma)0.015
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.239
GPT teacher head0.450
Teacher spread0.212 · 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

Citations329
Published2012
Admission routes1
Has abstractyes

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