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Record W2038979217 · doi:10.1167/7.9.1079

Selection and distribution of attention across the visualfield

2010· article· en· W2038979217 on OpenAlexaff
A. B. Roggeveen, Lawrence M. Ward

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFixation (population genetics)Cued speechMeridian (astronomy)Cognitive psychologyPsychologyVisual fieldStimulus (psychology)Computer scienceCommunicationArtificial intelligenceNeuroscienceSociologyPhysicsPopulation

Abstract

fetched live from OpenAlex

Visual attention has been conceptualized as being a mechanism of selecting a target location or object (e.g. Intriligator & Cavanagh, 2001), and as a medium that can be distributed across a span of the visual field (e.g. Eriksen & Hoffman, 1974). These two approaches to understanding attention, however, have typically been investigated separately. We asked whether the effects of selecting a location in the visual field would be mediated by the way attention was distributed across possible target locations. To answer this question, subjects performed a letter identification task while orienting their attention endogenously to one of several squares on a computer screen. In Experiment 1, eight squares were presented in a line, four on either side of fixation, increasing in size toward the periphery to account for cortical magnification. The line of squares could be oriented on the vertical or horizontal meridian, or at intercardinal locations halfway between the cardinal meridians. If invalidly cued (30% of trials), the target would always appear on the opposite side of fixation, in order to require subjects to distribute their attention across the entire display to the extent needed to maintain high levels of performance. In Experiment 2, the same stimulus configuration was used, but rather than a fixation cross at center, there remained the possibility that a target might appear in a centrally-located box at fixation. Results showed that maintaining attention at fixation, as in Experiment 2, facilitated performance closer to fixation to a greater extent than in Experiment 1, revealing an attentional gradient anchored at fixation. Across the two experiments, attentional facilitation was anisotropic across the visual field, especially in the right visual field on the horizontal meridian. Our findings reveal an intriguing interplay between distribution of attention across possible target locations and the ability to attentionally select a target location.

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.029
GPT teacher head0.377
Teacher spread0.348 · 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
Published2010
Admission routes1
Has abstractyes

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