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Record W4233127322 · doi:10.1121/1.3592377

Combining auditory and tactile inputs to create a sense of auditory space.

2011· article· en· W4233127322 on OpenAlexaff
Ross W. Deas, Rob Adamson, Philip P. Garland, Manohar Bance, Jeremy Brown

Bibliographic record

VenueProceedings of meetings on acoustics · 2011
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsDalhousie University
Fundersnot available
KeywordsBinaural recordingSound localizationComputer scienceAuditory systemAcousticsAuditory scene analysisAuditory maskingAzimuthMicrophoneSensitivity (control systems)Speech recognitionAudiologyLoudspeakerMathematicsPerceptionEngineeringPsychologyOctave (electronics)PhysicsElectronic engineering

Abstract

fetched live from OpenAlex

To localize a sound, the auditory system uses multiple cues, including binaural differences in timing and level that arise from the separation of the ears by the solid mass of the head. It has repeatedly been shown that the ability to utilize these cues is plastic and experience-based. Vibrotactile input shares many common features with auditory signals, and there is some overlap between the frequency range of the sensitivity of the ear and skin. In this study, we examine whether the auditory system is capable of combining auditory and tactile inputs to localize sounds using a multi-speaker array. To induce deficits in azimuthal localization, one ear was plugged. To examine cross-modal localization, the input level to the plugged ear was recorded via microphone, and a vibratory signal that was perceptually equal in intensity was presented to the shoulder on the same side as the plugged ear. The participant's ability to localize low-pass, band-pass, high-pass, and broadband sounds was measured. Results showed that relative to baseline (plugged) conditions, localization performance improved, suggesting that listeners can combine auditory and tactile information to create a sense of auditory space.

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)0.003
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 score0.044
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 head0.038
GPT teacher head0.255
Teacher spread0.217 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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