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Aging and electrocortical response to error feedback during a spatial learning task

2008· article· en· W2067161666 on OpenAlexaff
Karen J. Mathewson, Jane Dywan, Peter J. Snyder, William J. Tays, Sidney J. Segalowitz

Bibliographic record

VenuePsychophysiology · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychologyAnterior cingulate cortexTask (project management)AudiologyError-related negativityYoung adultNegative feedbackDevelopmental psychologyElectroencephalographyExecutive functionsNeural activityCognitive psychologyNeuroscienceCognition

Abstract

fetched live from OpenAlex

Event-related potentials were collected as older and younger adults responded to error feedback in an adaptation of the Groton Maze Learning Test, an age-sensitive measure of spatial learning and executive skills expected to maximally involve anterior cingulate cortex (ACC). Older adults made more errors and produced smaller feedback-related negativities (FRNs) than young controls. LORETA source localization revealed that, for young adults, neural activation associated with the FRN was focused in ACC and was stronger to negative feedback. Older adults responded with less intense and less differentiated ACC activation, but FRN amplitudes did relate to error rate for the most difficult mazes. The feedback P3 was sensitive to negative feedback but played no role in the prediction of error for either group. These data reflect the selective age-related decline of ACC response but also its continued contribution to performance monitoring in aging.

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)0.002
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.069
GPT teacher head0.353
Teacher spread0.284 · 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

Citations53
Published2008
Admission routes1
Has abstractyes

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