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Record W2028735896 · doi:10.1097/wnr.0000000000000070

Endogenous auditory frequency-based attention modulates electroencephalogram-based measures of obligatory sensory activity in humans

2013· article· en· W2028735896 on OpenAlexaff
Caroline M. Sheedy, Alan J. Power, Richard B. Reilly, Michael J. Crosse, Gerard M. Loughnane, Edmund C. Lalor

Bibliographic record

VenueNeuroreport · 2013
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsTrinity College
FundersIrish Research CouncilScience Foundation Ireland
KeywordsSensory systemSensory processingPsychologyNeuroscienceElectroencephalographyAuditory perceptionSelective attentionAuditory stimuliPerceptionCognitive psychologyAudiologyCognitionMedicine

Abstract

fetched live from OpenAlex

Auditory selective attention is the ability to enhance the processing of a single sound source, while simultaneously suppressing the processing of other competing sound sources. Recent research has addressed a long-running debate by showing that endogenous attention produces effects on obligatory sensory responses to continuous and competing auditory stimuli. However, until now, this result has only been shown under conditions where the competing stimuli differed in both their frequency characteristics and, importantly, their spatial location. Thus, it is unknown whether endogenous selective attention based only on nonspatial features modulates obligatory sensory processing. Here, we investigate this issue using a diotic paradigm, such that competing auditory stimuli differ in frequency, but had no separation in space. We find a significant effect of attention on electroencephalogram-based measures of obligatory sensory processing at several poststimulus latencies. We discuss these results in terms of previous research on feature-based attention and by comparing our findings with the previous work using stimuli that differed both in terms of spatial and frequency-based characteristics.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.317
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.087
GPT teacher head0.272
Teacher spread0.185 · 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.

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

Citations3
Published2013
Admission routes1
Has abstractyes

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