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Record W2138067124 · doi:10.1037/0096-1523.31.6.1171

On the Role of Object Representations in Substitution Masking.

2005· article· en· W2138067124 on OpenAlexaff
Cathleen M. Moore, Alejandro Lleras

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2005
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsObject (grammar)Computer visionPerceptionMasking (illustration)Computer scienceArtificial intelligenceStimulus (psychology)Substitution (logic)CommunicationPsychologyArtCognitive psychologyVisual arts

Abstract

fetched live from OpenAlex

Three experiments were conducted to investigate the role of object representations in object substitution masking (OSM). OSM occurs when a very sparse mask is presented simultaneously with a target stimulus and the target offsets first, leaving the mask to linger in the display for some time. Results confirm earlier claims that there is an isolatable object-level component to OSM and indicate that a target can be protected from OSM if, prior to offsetting, it can be represented as a distinct object from the mask. When the mask was presented as sliding past the target (Experiment 1), as jiggling independently of the target (Experiment 2), or in a different color from the target (Experiment 3), OSM was reduced or eliminated. This suggests that OSM reflects basic updating processes that allow the perception of continuity of object identity over change.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.061
GPT teacher head0.380
Teacher spread0.319 · 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 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

Citations84
Published2005
Admission routes1
Has abstractyes

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