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Record W4249750559 · doi:10.1145/503436.503438

Cognitive cubes

2002· article· en· W4249750559 on OpenAlex
Ehud Sharlin, Yuichi Itoh, Benjamin Watson, Yoshifumi Kitamura, Steve Sutphen, Lili Liu

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceCognitionCognitive flexibilityConsistency (knowledge bases)Flexibility (engineering)Reliability (semiconductor)Pencil (optics)Spatial abilityCognitive psychologyArtificial intelligencePsychologyMathematicsEngineering

Abstract

fetched live from OpenAlex

Assessments of spatial, constructional ability are used widely in cognitive research and in clinical diagnosis of disease or injury. Some believe that three-dimensional (3D) forms of these assessments would be particularly sensitive, but difficulties with consistency in administration and scoring have limited their use. We describe Cognitive Cubes, a novel computerized tool for 3D constructional assessment that increases consistency and promises improvements in flexibility, reliability, sensitivity and control. Cognitive Cubes makes use of ActiveCube, a novel tangible user interface for describing 3D shape. In testing, Cognitive Cubes was sensitive to differences in cognitive ability and task, and correlated well to a standard paper-and-pencil 3D spatial assessment

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
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)0.0000.002

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 head0.042
GPT teacher head0.264
Teacher spread0.222 · 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

Quick stats

Citations3
Published2002
Admission routes1
Has abstractyes

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