MétaCan
Menu
Back to cohort
Record W2144860625 · doi:10.1111/1467-9280.01413

Long-Term Inhibition of Return of Attention

2003· article· en· W2144860625 on OpenAlexaff
Steven P. Tipper, Sarah Grison, Klaus Kessler

Bibliographic record

VenuePsychological Science · 2003
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity College of the North
Fundersnot available
KeywordsInhibition of returnPsychologyCognitive psychologyTerm (time)Information processingRight hemisphereVisual searchNeuroscienceVisual attentionCognition

Abstract

fetched live from OpenAlex

During search of the environment, the inhibition of the return (IOR) of attention to already-examined information ensures that the target will ultimately be detected. Until now, inhibition was assumed to support search of information during one processing episode. However, in some situations search may have to be completed long after it was begun. We therefore propose that inhibition can be associated with an episode encoded into memory such that later retrieval reinstates inhibitory processing and encourages examination of new information. In two experiments in which attention was drawn to face stimuli with an exogenous cue, we demonstrated for the first time the existence of long-term IOR. Interestingly. this was the case only for faces in the left visual field, perhaps because more efficient processing of faces in the right hemisphere than the left hemisphere results in richer, more retrievable memory representations.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.106
GPT teacher head0.381
Teacher spread0.275 · 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

Citations106
Published2003
Admission routes1
Has abstractyes

Explore more

Same venuePsychological ScienceSame topicFace Recognition and PerceptionFrench-language works237,207