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Record W2400151226

Effects of Task-Modality Fit on User Performance.

2000· article· en· W2400151226 on OpenAlexaff
Hyunkyu Lee, Kil-Soo Suh, Izak Benbasat

Bibliographic record

VenueJournal of the Association for Information Systems · 2000
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsModality (human–computer interaction)ModalitiesComputer scienceTask (project management)Stimulus modalityAuditory displayHuman–computer interactionTask analysisRepresentation (politics)Cognitive psychologyPsychologySensory system
DOInot available

Abstract

fetched live from OpenAlex

Abstract Research on visual and auditory modalities in human–computer interfaces has been aimed at making the interface similar to the process through which people naturally acquire information. The objective of this study is to compare the effectiveness of visual, auditory, and multi-modalities for representing information in different problem domains. The results of this study indicate that visual and auditory modalities were effective in different problem domains. Visual modality was generally appropriate for representing static events, while auditory modality was appropriate for the representation of changing events. Multi-modality interfaces led to significantly better performance than either an auditory or a visual modality in a high-attention task. No statistically significant differences were observed for the low-attention task.

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)0.054
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.054
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.302
Teacher spread0.289 · 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
Published2000
Admission routes1
Has abstractyes

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