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Record W2058117894 · doi:10.1002/hbm.21457

Neural events leading to and associated with detection of sounds under high processing load

2011· article· en· W2058117894 on OpenAlexaff
Merav Sabri, Colin Humphries, Jeffrey R. Binder, Einat Liebenthal

Bibliographic record

VenueHuman Brain Mapping · 2011
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsUniversity of Manitoba
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institute on Deafness and Other Communication Disorders
KeywordsPsychologyFunctional magnetic resonance imagingNeuroscienceOddball paradigmThalamusBrain activity and meditationAudiologyHumEvent-related potentialDefault mode networkElectroencephalographyMedicine

Abstract

fetched live from OpenAlex

The neural events that lead to successful or failed detection of suprathreshold sounds are not well established. In this experiment, event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI) were recorded while participants performed two tasks: a primary difficult duration judgment task on a sequence of tones presented to one ear, and a secondary target detection task on an auditory oddball stream presented to the other ear. The paradigm was designed to elicit competition and variability in detection of auditory targets despite identical input. Successful detection of auditory targets was associated mainly with greater fMRI activity in superior parietal cortex and thalamus. In the ERPs, successful detection was linked with a larger fronto-central negativity at 200-400 ms, and a later centro-posterior positivity. Failure to detect targets was associated with greater fMRI signal in the default mode network, a significantly smaller electrical fronto-central negativity and no late positivity. These findings demonstrate that variability in auditory detection is related to modulation of activity in multimodal parietal and frontal networks active ∼ 200 ms after target onset. Results are consistent with a limited capacity and late selection view of attention.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.398

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 head0.101
GPT teacher head0.287
Teacher spread0.186 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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