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Record W576724027 · doi:10.1299/jsmewes.2009.69

1E2-6 An Empirical Investigation of Age-related Performance in Computer Interface Tasks

2009· article· en· W576724027 on OpenAlexaff
Xiaolei Zhou, Shengdong Zhao, Mark Chignell, Xiangshi Ren

Bibliographic record

VenueThe Proceedings of the JSME Symposium on Welfare Engineering · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceInterface (matter)CognitionThe InternetCognitive agingHuman–computer interactionAge groupsSimulationPsychologyWorld Wide WebOperating system

Abstract

fetched live from OpenAlex

As the computer and internet generations age there is an increasing need to develop appropriate interfaces for the elderly that can accommodate age-related changes in manual dexterity, visual acuity, and cognitive abilities. Assessment of age-related effects is typically a necessary first step in designing age-appropriate interfaces, but may be complicated in how the tradeoff between speed and accuracy is handled by different people. In order to assess the impact of a possible speed-accuracy tradeoff, performance was observed under three different instructional sets i.e., accuracy (A), neutral (N), and speed (S) when steering on a circular track. The elderly group performed significantly less accurately for all three instruction sets. The younger subjects were more influenced by instructions to perform faster, or with more accuracy. Implications for user interface design for older users, and for the evaluation of age effects in HCI generally, are discussed.

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)0.004
metaresearch head score (Gemma)0.039
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.010
GPT teacher head0.246
Teacher spread0.236 · 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
Published2009
Admission routes1
Has abstractyes

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Same venueThe Proceedings of the JSME Symposium on Welfare EngineeringSame topicTechnology Use by Older AdultsFrench-language works237,207