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Cognitive load in Games for Health - A discussion

2020· article· en· W3087767647 on OpenAlexaff
Ravishankar Subramani Iyer, Rita Orji

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCognitive loadTask (project management)Computer scienceCognitionMeasure (data warehouse)Key (lock)Cognitive psychologyHuman–computer interactionPsychologyComputer securityEngineering

Abstract

fetched live from OpenAlex

Cognitive load refers to the mental effort required or invested to achieve the objectives of a task. Cognitive load plays vital role in various everyday tasks including security, management, video games, driving and learning. Since its inception in the 1980s, cognitive load has been used as a key parameter for measuring task performance using various tools and techniques. One of the most significant areas that cognitive load affects is the performance of game players. Depending on the performance and cognitive load of gamers, certain games can be regarded as either exciting or boring. This paper aims to discuss games for health and the need to measure cognitive load in these games. Finally, we suggest methods to measure cognitive load in games for using some existing games for health as examples.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.877
Threshold uncertainty score0.720

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)0.0010.000

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 head0.067
GPT teacher head0.416
Teacher spread0.348 · 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 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

Citations6
Published2020
Admission routes1
Has abstractyes

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