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Record W2090473473 · doi:10.1163/1568568041920186

Varied-mapping conjunction search: Evidence for rule-based learning

2004· article· en· W2090473473 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSpatial Vision · 2004
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConjunction (astronomy)AutomaticityVisual searchOrientation (vector space)Contrast (vision)Polarity (international relations)PsychologyArtificial intelligenceComputer sciencePattern recognition (psychology)CognitionNeuroscienceMathematicsBiologyPhysics

Abstract

fetched live from OpenAlex

Five experiments were carried out to examine whether top-down processes can aid search, even when targets and distractors are variably mapped. Experiments 1a and 1b determined that effortless VM search can be obtained in Contrast Polarity X Orientation and Color X Orientation conjunction search when one feature dimension remains consistently mapped across blocks. Experiment 2 showed that efficient VM search is possible when both dimensions are variably mapped. In Experiment 3, efficient VM search was found when target-distractor reversals occurred on a trial-wise basis. Experiments 4 and 5 found that VM search deteriorates when target identity is not known prior to display onset. These studies demonstrate the role of top-down mechanisms in the development of efficient VM search and present several challenges to strength-theoretic views on the mechanisms underlying automaticity.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.677
Threshold uncertainty score0.999

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)0.0020.001

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 head0.187
GPT teacher head0.429
Teacher spread0.242 · 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