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Record W2018414977 · doi:10.4103/1463-1741.107160

Mental arithmetic and non-speech office noise: An exploration of interference-by-content

2013· article· en· W2018414977 on OpenAlexaff
Nick Perham, Helen M. Hodgetts, Simon Banbury

Bibliographic record

VenueNoise and Health · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsProfessional Engineers Ontario
FundersEconomic and Social Research Council
KeywordsQUIETTask (project management)Noise (video)RecallDistractionArithmeticMental arithmeticInterference (communication)PsychologySpeech recognitionCognitive psychologyComputer scienceAudiologyMathematicsTelecommunicationsArtificial intelligenceEngineeringMedicine

Abstract

fetched live from OpenAlex

An interference-by-content account of auditory distraction - in which the impairment to task performance derives from the similarity of what is being recalled and what is being ignored - was explored concerning mental arithmetic performance. Participants completed both a serial recall and a mental arithmetic task in the presence of quiet, office noise with speech (OS) and office noise without speech (ONS). Both tasks revealed that the two office noise condition's significantly impaired performance. That the ONS produced this deficit suggests that an interference-by-content account cannot explain impairment to mental arithmetic performance by background sound.

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)0.004
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.168
GPT teacher head0.343
Teacher spread0.175 · 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

Citations24
Published2013
Admission routes1
Has abstractyes

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