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Record W4252582341 · doi:10.22215/etd/2014-10461

Socioeconomic Neurogradients of Attention: An ERP Study

2014· dissertation· en· W4252582341 on OpenAlexaff
Kylie Schibli

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsSocioeconomic statusPsychologyDevelopmental psychologyOddball paradigmNeuroimagingEvent-related potentialEarly childhoodElectroencephalographyDemographyNeurosciencePopulation

Abstract

fetched live from OpenAlex

This study examined children's socioeconomic neurogradients of selective attention.Indices of contextual socioeconomic family status (SES) and early development experience, the Early Development Instrument (EDI), were related to behavioural and neural correlates of an auditory oddball task in a sample including 52 preadolescent children (Aged 12-14 years) stratified according to SES: High (n = 14), Middle (n = 20), and Low (n = 18).Event-related potentials (ERPs) were recorded while children were asked to emit or withhold response to a series of rare/frequent and target/distracter tones.Despite SES-dependent differences in midline ERPs, children from the High-and Low-SES showed similar behavioural patterns with high performance.However, the Mid-SES group showed a markedly different performance pattern than their High-SES counterpart, correlated with differences in both ERP and EDI measurements.Confirming the joint role of SES and early childhood, these findings further our understanding of neuroimaging data across diverse experiential developmental contexts.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0020.000

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 head0.121
GPT teacher head0.412
Teacher spread0.290 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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