MétaCan
Menu
Back to cohort
Record W2157999315 · doi:10.3389/fpsyg.2010.00019

Healthy aging delays scalp EEG sensitivity to noise in a face discrimination task

2010· article· en· W2157999315 on OpenAlexfundno aff
Guillaume A. Rousselet

Bibliographic record

VenueFrontiers in Psychology · 2010
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsnot available
FundersEconomic and Social Research CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsScottish Funding Council
KeywordsPsychologyScalpTask (project management)ElectroencephalographyAudiologyNoise (video)Face (sociological concept)Sensitivity (control systems)Cognitive psychologyNeuroscienceArtificial intelligenceComputer scienceMedicine

Abstract

fetched live from OpenAlex

We used a single-trial ERP approach to quantify age-related changes in the time-course of noise sensitivity. A total of 62 healthy adults, aged between 19 and 98, performed a non-speeded discrimination task between two faces. Stimulus information was controlled by parametrically manipulating the phase spectrum of these faces. Behavioral 75% correct thresholds increased with age. This result may be explained by lower signal-to-noise ratios in older brains. ERP from each subject were entered into a single-trial general linear regression model to identify variations in neural activity statistically associated with changes in image structure. The fit of the model, indexed by R(2), was computed at multiple post-stimulus time points. The time-course of the R(2) function showed significantly delayed noise sensitivity in older observers. This age effect is reliable, as demonstrated by test-retest in 24 subjects, and started about 120 ms after stimulus onset. Our analyses suggest also a qualitative change from a young to an older pattern of brain activity at around 47 ± 4 years old.

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 score0.826
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.310
Teacher spread0.291 · 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

Citations54
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in PsychologySame topicNeural dynamics and brain functionFrench-language works237,207