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Record W2054810255 · doi:10.1080/13875868.2010.487963

Monitoring Object Orientation: Effects of Layout Complexity, Viewpoint Changes, and Object Function

2010· article· en· W2054810255 on OpenAlexfundno aff
Catherine Mello, David Waller

Bibliographic record

VenueSpatial Cognition and Computation · 2010
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOrientation (vector space)Object (grammar)Computer scienceUnitary stateArtificial intelligenceObject-orientationComputer visionCognitive psychologyPsychologyObject-oriented programmingMathematicsGeometry

Abstract

fetched live from OpenAlex

Studies of spatial representations have typically limited their analysis to memory for object location. Three experiments examined whether another spatial feature, object orientation, could be monitored and represented in a similar fashion. In Experiment 1, an adaptation of the change detection paradigm of Simons and Wang (1998) Simons, D. J. and Wang, R. F. 1998. Perceiving real-world viewpoint changes. Psychological Science, 9: 315–320. [Crossref], [Web of Science ®] , [Google Scholar], we found that, whereas unitary location or identity changes were readily noticed, generalized orientation changes were not. Experiment 2 showed that orientation monitoring is strongly affected by layout complexity, viewpoint changes, and the extent of array modifications. Finally, Experiment 3 suggested that an object's behavioral relevance may selectively enhance its orientation processing.

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)0.008
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
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 science0.0000.000
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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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