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Record W2073034897 · doi:10.1080/00140130701811933

Designing information fusion for the encoding of visual–spatial information

2008· article· en· W2073034897 on OpenAlexaff
Samuel Waldron, John Patrick, Geoffrey B. Duggan, Simon Banbury, Andrew Howes

Bibliographic record

VenueErgonomics · 2008
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsProfessional Engineers Ontario
Fundersnot available
KeywordsEncoding (memory)Computer scienceContext (archaeology)LimitingTask (project management)Spatial contextual awarenessInterface (matter)Human–computer interactionInformation fusionArtificial intelligenceSimulationComputer visionEngineeringSystems engineering

Abstract

fetched live from OpenAlex

In a simulated aircraft navigation task, a fusion technique known as triangulation was used to improve the accuracy and onscreen availability of location information from two separate radars. Three experiments investigated whether the reduced cognitive processing required to extract information from the fused environment led to impoverished retention of visual-spatial information. Experienced pilots and students completed various simulated flight missions and were required to make a number of location estimates. Following a retention interval, memory for locations was assessed. Experiment 1 demonstrated, in an applied setting, that the retention of fused information was problematic and Experiment 2 replicated this finding under laboratory conditions. Experiment 3 successfully improved the retention of fused information by limiting its availability within the interface, which it is argued, shifted participants' strategies from over-reliance on the display as an external memory source to more memory-dependent interaction. These results are discussed within the context of intelligent interface design and effective human-machine interaction.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.549

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 communication0.0000.001
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 head0.031
GPT teacher head0.323
Teacher spread0.292 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations11
Published2008
Admission routes1
Has abstractyes

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