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Record W2898236089 · doi:10.1037/xlm0000660

Examining the hierarchical nature of scene representations in memory.

2018· article· en· W2898236089 on OpenAlexafffund
Monica S. Castelhano, Jordan E. Theriault

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2018
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsQueen's University
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceArtificial intelligenceScene statisticsRepresentation (politics)Set (abstract data type)Block (permutation group theory)HierarchyHierarchical database modelComputer visionNatural language processingPsychologyMathematicsDatabasePerception

Abstract

fetched live from OpenAlex

How are scene representations stored in memory? Researchers have often posited that scene representations have a hierarchical structure with background elements providing a scaffold for more detailed foreground elements. To further investigate scene representation and the role of background and foreground information, we introduced a new stimulus set: chimera scenes, which have the central block of objects belonging to one scene category (foreground), and the surrounding structure belonging to another (background). We used a contextual cueing paradigm and emphasized the relative importance of each by having the target placed on either the background or foreground. In a transfer block, we found that though changes to the background were highly detrimental to search performance for background targets, search performance was only slightly affected by changes to either the foreground or the background for foreground targets. These results indicate that rather than a fixed hierarchy, the structure of scene representations are more aptly captured by a parallel model that stores information flexibly. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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)0.006
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.374
Teacher spread0.334 · 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

Citations14
Published2018
Admission routes2
Has abstractyes

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