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Record W1746659866

Auditory spatial attention in a complex acoustic environment while walking: Investigation of dual-task performance

2012· article· en· W1746659866 on OpenAlexafffundvenueabout
Sin Tung Lau, Jacob Maracle, Dario Coletta, Gurjit Singh, Jennifer L. Campos, M. Kathleen Pichora‐Fuller

Bibliographic record

VenueCanadian acoustics · 2012
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsActive listeningTask (project management)AudiologyPsychologySpeech recognitionComputer scienceCommunicationEngineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

Auditory spatial attention in a complex acoustic environment while walking was investigated to find the dual-task performance. Three undergraduate students, ages 19 to 26 years, with normal pure-tone air-conducted hearing thresholds for frequencies from 0.25 to 8 kHz, performed a word identification task in two experimental conditions. Testing was conducted in StreetLab in the Challenging Environment Assessment Laboratory (CEAL) at the Toronto Rehabilitation Institute. The stimuli for the listening task were all sentences recorded by four male talkers for the Coordinated Response Measure (CRM). All participants completed 8 sessions in each of two conditions; standing and walking. The standing and walking conditions differed in terms of whether or not there was a secondary task during listening. The high degree of similarity in listening task performance between the standing and walking conditions suggest that the listening abilities in multi-talker environments of these participants were not affected by the inclusion of a walking component.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.959
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.036
GPT teacher head0.227
Teacher spread0.192 · 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 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

Citations3
Published2012
Admission routes4
Has abstractyes

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