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Effects of prior stimulus and prior perception on neural correlates of auditory stream segregation

2009· article· en· W2163104424 on OpenAlexaff
Joel S. Snyder, W. Trent Holder, David M. Weintraub, Olivia Carter, Claude Alain

Bibliographic record

VenuePsychophysiology · 2009
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsBaycrest Hospital
Fundersnot available
KeywordsPsychologyStimulus (psychology)PerceptionEvent-related potentialAudiologyNeural correlates of consciousnessN100Auditory perceptionElectroencephalographyCognitive psychologyDevelopmental psychologyNeuroscienceCognition

Abstract

fetched live from OpenAlex

We examined whether effects of prior experience are mediated by distinct brain processes from those processing current stimulus features. We recorded event-related potentials (ERPs) during an auditory stream segregation task that presented an adaptation sequence with a small, intermediate, or large frequency separation between low and high tones (Deltaf), followed by a test sequence with intermediate Deltaf. Perception of two streams during the test was facilitated by small prior Deltaf and by prior perception of two streams and was accompanied by more positive ERPs. The scalp topography of these perception-related changes in ERPs was different from that observed for ERP modulations due to increasing the current Deltaf. These results reveal complex interactions between stimulus-driven activity and temporal-context-based processes and suggest a complex set of brain areas involved in modulating perception based on current and previous experience.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.280
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), 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

Citations58
Published2009
Admission routes1
Has abstractyes

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