MétaCan
Menu
Back to cohort
Record W2044925743 · doi:10.1027/1618-3169/a000182

A Mixture Distribution of Spatial Attention

2012· article· en· W2044925743 on OpenAlex
Jing Feng, Ian Spence

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExperimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · 2012
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of TorontoBaycrest Hospital
Fundersnot available
KeywordsCued speechPsychologyFocus (optics)Visual attentionCognitive psychologySelective attentionPerceptionCognitionNeurosciencePhysics

Abstract

fetched live from OpenAlex

Although it may seem paradoxical, the unified-focus and multiple-foci theories of spatial selective attention are both well supported by experimental evidence. However, the apparent contradiction is illusory and the two competing views may be reconciled by a closer examination of the spatial mechanisms involved. We propose that the deployment of attention may be modeled as a mixture of individual distributions of attention and we tested this hypothesis in two experiments. Participants had to identify targets among distractors, with the targets presented at various distances from the cued locations. Experiment 1 confirmed that the distribution of attention may be described by a mixture of individual distributions, each centered at a cued location. Experiment 2 showed that cue separation is an important determinant of whether spatial attention is divided or not.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.070
GPT teacher head0.418
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