MétaCan
Menu
Back to cohort

Distribution of visual attention within a cued area: evidence based on temporal order judgments

2011· article· en· W2059746245 on OpenAlexaff
Mikael Cavallet, César Galera, Michael W. von Grünau, Afroditi Panagopoulos

Bibliographic record

VenuePsicologia Reflexão e Crítica · 2011
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsConcordia University
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCued speechPerceptionFrame (networking)Orientation (vector space)Cognitive psychologyComputer scienceVisual attentionVisual fieldPsychologyVisual perceptionReference frameComputer visionArtificial intelligenceCommunicationMathematicsGeometryNeuroscienceTelecommunications

Abstract

fetched live from OpenAlex

Three experiments were performed to investigate the distribution of attention across the visual field and the possibility of attentional resources to be more concentrated inside an abrupt onset frame (cue). The participants performed a temporal order judgment task of two letters presented in sequence; one letter presented inside and the other outside the frame. The results showed that the information presented inside the frame had its perceptual latency shortened in relation to the information presented outside the frame in experimental conditions where the frame orientation, the distance between the two letters and the cue onset time were manipulated. The advantage of the information presented inside the frame was attributed to the displacement of attention to the area delimited by the frame. The results contribute to the understanding of visual perception, showing that attentional resources may be redistributed inside the borders of a geometric figure.

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)0.002
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.213
GPT teacher head0.376
Teacher spread0.164 · 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 designBench or experimental
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

Citations2
Published2011
Admission routes1
Has abstractyes

Explore more

Same venuePsicologia Reflexão e CríticaSame topicVisual perception and processing mechanismsFrench-language works237,207