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Cue‐ versus response‐locked processes in backward inhibition: Evidence from ERPs

2007· article· en· W2166282462 on OpenAlexaff
Marco Sinai, Philippe Goffaux, Natalie A. Phillips

Bibliographic record

VenuePsychophysiology · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsConcordia UniversityJewish General Hospital
Fundersnot available
KeywordsPsychologyResponse inhibitionTask (project management)Negativity effectEvent-related potentialSet (abstract data type)Cognitive psychologyElectrophysiologySelective attentionTask switchingInhibitory controlElectroencephalographyNeuroscienceCognitionComputer science

Abstract

fetched live from OpenAlex

A task set may need to be inhibited to facilitate the switch to another task. This event-related potential (ERP) study determined (1) whether backward inhibition (BI) is exerted preferentially in high interference environments, and (2) whether ERPs locked to critical time points reflect BI during cue preparation and/or response stages. High interference (HI) and low interference (LI) were created by manipulating task difficulty. A reaction time (RT) BI effect (i.e., BI>control trials) was shown only during HI tasks. Cue-locked ERPs on LI tasks suggest increased attentional resources were allocated during the reactivation of a recently inhibited task. For HI tasks, BI versus control trial differences were reflected in a response-locked ERP negativity only after response selection (indexed by the response-locked lateralized readiness potential), indicating that BI is a lateral inhibition mechanism exerted during response preparation.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.225
GPT teacher head0.426
Teacher spread0.201 · 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

Citations31
Published2007
Admission routes1
Has abstractyes

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