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Record W2070900525 · doi:10.1097/wnr.0b013e3283505ac6

Neural correlates of auditory distraction revealed in theta-band EEG

2012· article· en· W2070900525 on OpenAlexaff
Karla D. Ponjavic-Conte, Jarrod Robert Dowdall, Dillon A. Hambrook, Artur Luczak, Matthew S. Tata

Bibliographic record

VenueNeuroreport · 2012
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsDistractionElectroencephalographyPsychologyAudiologyStimulus (psychology)Mismatch negativitySensory systemPerceptionElectrophysiologyNeuroscienceEvent-related potentialCommunicationCognitive psychologyMedicine

Abstract

fetched live from OpenAlex

Selective attention involves the exclusion of irrelevant information in order to optimize perception of a single source of sensory input; failure to do so often results in the familiar phenomenon of distraction. The term 'distraction' broadly refers to a perceptual phenomenon. In the present study we attempted to find the electrophysiological correlates of distraction using an auditory discrimination task. EEG and event-related potential responses to identical stimuli were compared under two levels of distraction (continuous broad-band noise or continuous speech). Relative to broad-band noise, the presence of a continuous speech signal in the unattended ear impaired task performance and also attenuated the N1 peak evoked by nontarget stimuli in the attended ear. As the magnitude of a peak in the event-related potential waveform can be modulated by differences in intertrial power but also by differences in the stability of EEG phase across trials, we sought to characterize the effect of distraction on intertrial power and intertrial phase locking around the latency of the N1. The presence of continuous speech resulted in a prominent reduction of theta EEG band intertrial phase locking around the latency of the N1. This suggests that distraction may act not only to disrupt a sensory gain mechanism but also to disrupt the temporal fidelity with which the brain responds to stimulus events.

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 score0.302
Threshold uncertainty score0.423

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 head0.035
GPT teacher head0.286
Teacher spread0.251 · 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

Citations32
Published2012
Admission routes1
Has abstractyes

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