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Record W2063590032 · doi:10.1167/10.13.7

The default allocation of attention is broadly ahead of smooth pursuit

2010· article· en· W2063590032 on OpenAlexafffund
Aarlenne Z. Khan, Philippe Lefèvre, Stephen Heinen, Gunnar Blohm

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsSmooth pursuitSaccadeVisual attentionEye movementGoal pursuitPsychologyCognitive psychologyVisual fieldComputer scienceNeurosciencePerceptionSocial psychology

Abstract

fetched live from OpenAlex

When moving through our environment, it is vital to preferentially process positions on our future path in order to react quickly to critical situations. During smooth pursuit, attention may be directed ahead with either a focused locus or a broad bias. We examined the 2D spatial extent of attention during a smooth pursuit task using both saccade (SRT) and manual (MRT) reaction times as measures of attentional allocation. Targets were flashed at various locations around current eye position while subjects pursued a moving target. Subjects made a saccade or pressed a button as soon as they perceived the target. Both SRTs and MRTs were shortest to targets flashed ahead of compared to behind the direction of pursuit across half of the visual field ahead of pursuit direction. Furthermore, we found an increase specific to SRTs at small target eccentricities directly ahead of pursuit, which may be related to an additional saccade trigger strategy; small saccades take longer to execute if smooth pursuit brings the eyes close to the target. In summary, both SRTs and MRTs revealed that attention is by default broadly allocated in the visual hemi-field ahead of the line of sight during smooth pursuit eye movements. This attentional bias may serve a predictive purpose for facilitating the processing of upcoming events.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.051
Threshold uncertainty score0.159

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.036
GPT teacher head0.356
Teacher spread0.320 · 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 teacher head, 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

Citations41
Published2010
Admission routes2
Has abstractyes

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