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Record W2266797869 · doi:10.1093/geronb/55.1.p27

Aging and the development of automaticity in conjunction search

2000· article· en· W2266797869 on OpenAlexaff
Charles T. Scialfa, Lisa Jenkins, Eleanor Hamaluk, Petra Skaloud

Bibliographic record

VenueThe Journals of Gerontology Series B · 2000
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Calgary
FundersNational Institute on Aging
KeywordsConjunction (astronomy)LuminanceVisual searchPsychologyContrast (vision)AutomaticityOrientation (vector space)Latency (audio)Feature (linguistics)Computer scienceArtificial intelligenceMathematicsCognitionCognitive psychologyPhysicsNeuroscienceTelecommunicationsGeometry

Abstract

fetched live from OpenAlex

In two experiments, younger and older observers carried out feature searches for targets defined by their luminance contrast and orientation. Additionally, they received consistent-mapping (CM) training in luminance contrast by orientation conjunction search, followed by a brief exposure to conjunction search under reversal conditions. In Experiment 1, display size effects on reaction time suggested that both younger and older observers were conducting a parallel search in all conditions and showed equivalent disruption at reversal. Experiment 2 was a substantive replication of the first using more difficult conjunction search displays. In addition to latency, we measured the number, duration, and feature-based selectivity of fixations made during conjunction search. Display size effects were larger than in Experiment 2 and were of equivalent magnitude in younger and older people. There were no age differences in improvement in conjunction search and minimal age differences in disruption following reversal. Both age groups demonstrated early in training that they could select items possessing target features (i.e., the color white), and both age groups demonstrated that they could not completely reverse this selectivity when these features no longer defined the target. These experiments have several implications for models of visual attention and age differences therein.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.124
GPT teacher head0.372
Teacher spread0.248 · 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 designBench or experimental
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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