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Record W2110334077 · doi:10.1518/hfes.46.4.738.56809

The Apparent and Effective Dimensionality of Representations of Objects

2004· article· en· W2110334077 on OpenAlexafffund
Ian Spence

Bibliographic record

VenueHuman Factors The Journal of the Human Factors and Ergonomics Society · 2004
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDimension (graph theory)Variation (astronomy)Curse of dimensionalityPerceptionRelation (database)Dimensionality reductionComputer sciencePsychophysicsArtificial intelligenceMathematicsPattern recognition (psychology)StatisticsPsychologyData miningCombinatoricsPhysics

Abstract

fetched live from OpenAlex

Information displays commonly use 2-D and 3-D objects even though the numbers represented are 1-D. This practice may be problematic because the psychophysical relation between perceived and physical magnitudes is generally nonlinear for areas and volumes. Nonetheless, this research shows that apparent 2-D and 3-D objects can produce linear psychophysical functions if only one dimension shows variation. Processing time increases with the number of dimensions in the objects that show variation, not with the apparent dimensionality. Indeed, when only one dimension showed variation, apparent 3-D objects were judged more quickly than were apparent 2-D or 1-D objects. These results present a challenge for computational models of size perception and have implications for the design of information displays. Actual or potential applications of this research include the design and use of statistical graphs and information displays; objects that display variation in more than one dimension should not be used to represent single (1-D) numerical variables if they are to be judged accurately and rapidly.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.320
Teacher spread0.268 · 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 teacher head, not a consensus.

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

Citations18
Published2004
Admission routes2
Has abstractyes

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