MétaCan
Menu
Back to cohort

Rapid Resumption of Interrupted Visual Search

2005· article· en· W2058612431 on OpenAlexaff
Alejandro Lleras, Ronald A. Rensink, James T. Enns

Bibliographic record

VenuePsychological Science · 2005
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVisual searchTask (project management)PsychologyPerceptionVisual perceptionCognitive psychologySensory systemCommunicationArtificial intelligenceComputer visionComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

A modified visual search task demonstrates that humans are very good at resuming a search after it has been momentarily interrupted. This is shown by exceptionally rapid response time to a display that reappears after a brief interruption, even when an entirely different visual display is seen during the interruption and two different visual searches are performed simultaneously. This rapid resumption depends on the stability of the visual scene and is not due to display or response anticipations. These results are consistent with the existence of an iterative hypothesis-testing mechanism that compares information stored in short-term memory (the perceptual hypothesis) with information about the display (the sensory pattern). In this view, rapid resumption occurs because a hypothesis based on a previous glance of the scene can be tested very rapidly in a subsequent glance, given that the initial hypothesis-generation step has already been performed.

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)0.009
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.384
GPT teacher head0.522
Teacher spread0.138 · 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

Citations62
Published2005
Admission routes1
Has abstractyes

Explore more

Same venuePsychological ScienceSame topicNeural and Behavioral Psychology StudiesFrench-language works237,207