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Record W2056430302 · doi:10.1121/1.4786401

Speeded detection of sound signals based on temporal differences

2006· article· en· W2056430302 on OpenAlexaff
Clara Suied, Laurent Pruvost, Patrick Susini, Nicolas Misdariis, Sabine Langlois, Stephen McAdams

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2006
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsExpectancy theorySalientEvent (particle physics)Task (project management)Computer scienceCognitive psychologyPerceptionOutlierPsychologySpeech recognitionArtificial intelligenceSocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

Previous research has identified acoustic parameters modulating the perceived urgency of alarms. Such approaches have focused mainly on the subjective relationship between characteristics of the alarms and the perception of urgency. We suggest that an objective measurement will ensure with more certainty the effectiveness of the acoustic parameters and will help us to understand more precisely the underlying processes. We have to the improve the ability of warning signals to increase the probability of a faster reaction under emergency conditions. Thus, we performed two experiments using a speeded reaction-time (RT) paradigm, under two tracking task conditions: motor and visual. Experiment 1 studied the influence of the tempo of the sequences. RT decreased as tempo became faster. Experiment 2 studied the influence of temporal irregularity. Outlier analyses show that RT decreased with the irregularity of the sequences. A scaled judgment of urgency was also given by subjects. Two underlying processes seem to be involved: experiment 1 highlights expectancy effects (temporal regularities provide a temporal reference frame), whereas experiment 2 emphasizes capture effects (a temporally perturbed event is more salient than a temporally expected one) [Jones, M.R., ‘‘Attention and timing,’’ in Ecological Psyhoacoustics, edited by J. Neuhoff (Elsevier Acadamic Press, San Diego, 2004) pp. 49–88 (2004)]. Moreover, comparisons between RT measurements and subjective judgments improve the validity of our approach.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.334
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 designSimulation or modeling
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

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

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