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Neural Signatures of the Processing of Temporal Patterns in Sound

2018· article· en· W2949233563 on OpenAlexafffund
Björn Herrmann, Ingrid S. Johnsrude

Bibliographic record

VenueJournal of Neuroscience · 2018
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchCanada First Research Excellence Fund
KeywordsElectroencephalographyCategorizationPsychologyPerceptionBrain activity and meditationNeural activitySynchronization (alternating current)Coherence (philosophical gambling strategy)Speech recognitionTask (project management)Neural adaptationCommunicationDistractionCognitive psychologyComputer scienceAudiologyNeuroscienceAdaptation (eye)Artificial intelligenceMedicine

Abstract

fetched live from OpenAlex

The ability to detect regularities in sound (i.e., recurring structure) is critical for effective perception, enabling, for example, change detection and prediction. Two seemingly unconnected lines of research concern the neural operations involved in processing regularities: one investigates how neural activity synchronizes with temporal regularities (e.g., frequency modulation; FM) in sounds, whereas the other focuses on increases in sustained activity during stimulation with repeating tone-frequency patterns. In three electroencephalography studies with male and female human participants, we investigated whether neural synchronization and sustained neural activity are dissociable, or whether they are functionally interdependent. Experiment I demonstrated that neural activity synchronizes with temporal regularity (FM) in sounds, and that sustained activity increases concomitantly. In Experiment II, phase coherence of FM in sounds was parametrically varied. Although neural synchronization was more sensitive to changes in FM coherence, such changes led to a systematic modulation of both neural synchronization and sustained activity, with magnitude increasing as coherence increased. In Experiment III, participants either performed a duration categorization task on the sounds, or a visual object tracking task to distract attention. Neural synchronization was observed regardless of task, whereas the sustained response was observed only when attention was on the auditory task, not under (visual) distraction. The results suggest that neural synchronization and sustained activity levels are functionally linked: both are sensitive to regularities in sounds. However, neural synchronization might reflect a more sensory-driven response to regularity, compared with sustained activity which may be influenced by attentional, contextual, or other experiential factors. <b>SIGNIFICANCE STATEMENT</b> Optimal perception requires that the auditory system detects regularities in sounds. Synchronized neural activity and increases in sustained neural activity both appear to index the detection of a regularity, but the functional interrelation of these two neural signatures is unknown. In three electroencephalography experiments, we measured both signatures concomitantly while listeners were presented with sounds containing frequency modulations that differed in their regularity. We observed that both neural signatures are sensitive to temporal regularity in sounds, although they functionally decouple when a listener is distracted by a demanding visual task. Our data suggest that neural synchronization reflects a more automatic response to regularity compared with sustained activity, which may be influenced by attentional, contextual, or other experiential factors.

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 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 score0.516
Threshold uncertainty score0.210

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 communication0.0000.000
Open science0.0010.000
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 head0.039
GPT teacher head0.291
Teacher spread0.252 · 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

Citations58
Published2018
Admission routes2
Has abstractyes

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