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Record W2078042008 · doi:10.1093/cercor/bhv059

The Attentional Fields of Visual Search in Simultanagnosia and Healthy Individuals: How Object and Space Attention Interact

2015· article· en· W2078042008 on OpenAlexaff
Aarlenne Z. Khan, Myriam Prost-Lefebvre, Roméo Salemme, Gunnar Blohm, Yves Rossetti, Caroline Tilikete, Laure Pisella

Bibliographic record

VenueCerebral Cortex · 2015
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsQueen's UniversityUniversité de Montréal
FundersAgence Nationale de la Recherche
KeywordsVisual searchObject (grammar)Cognitive psychologyPerceptionGazeVisual fieldPsychologyTask (project management)Eye movementComputer scienceComputer visionArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Simultanagnosia is a deficit in which patients are unable to perceive multiple objects simultaneously. To date, it remains disputed whether this deficit results from disrupted object or space perception. We asked both healthy participants as well as a patient with simultanagnosia to perform different visual search tasks of variable difficulty. We also modulated the number of objects (target and distracters) presented. For healthy participants, we found that each visual search task was performed with a specific "attentional field" depending on the difficulty of visual object processing but not on the number of objects falling within this "working space." This was demonstrated by measuring the cost in reaction times using different gaze-contingent visible window sizes. We found that bilateral damage to the superior parietal lobule impairs the spatial integration of separable features (within-object processing), shrinking the attentional field in which a target can be detected, but causing no deficit in processing multiple objects per se.

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)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
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 head0.073
GPT teacher head0.359
Teacher spread0.287 · 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

Citations25
Published2015
Admission routes1
Has abstractyes

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