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Record W2078018431 · doi:10.1068/p5023

Search of Jumping Items: Visual Marking and Discrete Motion

2003· article· en· W2078018431 on OpenAlexaff
Elizabeth S. Olds, C. Meghan McMurtry

Bibliographic record

VenuePerception · 2003
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsJumpingMotion (physics)PsychologyComputer scienceArtificial intelligenceComputer visionCognitive psychologyGeology

Abstract

fetched live from OpenAlex

Watson and Humphreys (1997 Psychological Review 104 90-122) showed that when searching for a target, observers can ignore a previewed set of distractors (other items), effectively decreasing the number of relevant items in a difficult search display and thus speeding performance ('visual marking'). Other researchers have more recently investigated visual marking for continuously moving items, finding that shared features, and preserved inter-item spatial relationships, are helpful. Here, we tested whether visual marking occurs for a set of initial items that moves in one discrete jump (preserving shared features and inter-item spatial relationships). Marking did not occur in these displays, and we interpret this result in the context of previous research on visual marking.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.906
Threshold uncertainty score0.185

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.031
GPT teacher head0.332
Teacher spread0.302 · 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 designSimulation or modeling
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

Citations2
Published2003
Admission routes1
Has abstractyes

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