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Record W2105709390 · doi:10.1037/0012-1649.36.6.731

Age differences in visual search for compound patterns: Long- versus short-range grouping.

2000· article· en· W2105709390 on OpenAlexaff
Jacob A. Burack, James T. Enns, Grace Iarocci, Beth Randolph

Bibliographic record

VenueDevelopmental Psychology · 2000
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsVisual searchPsychologyPerceptionContrast (vision)Range (aeronautics)Visual perceptionCognitive psychologyDevelopmental psychologyCommunicationArtificial intelligenceComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Visual search for compound patterns was examined in observers aged 6, 8, 10, and 22 years. The main question was whether age-related improvement in search rate (response time slope over number of items) was different for patterns defined by short- versus long-range spatial relations. Perceptual access to each type of relation was varied by using elements of same contrast (easy to access) or mixed contrast (hard to access). The results showed large improvements with age in search rate for long-range targets; search rate for short-range targets was fairly constant across age. This pattern held regardless of whether perceptual access to a target was easy or hard, supporting the hypothesis that different processes are involved in perceptual grouping at these two levels. The results also point to important links between ontogenic and microgenic change in perception (H. Werner, 1948, 1957).

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.209
GPT teacher head0.424
Teacher spread0.215 · 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

Citations44
Published2000
Admission routes1
Has abstractyes

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