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Record W4312983521 · doi:10.1177/1071181322661481

Using Signal-to-Noise Ratio to Explore The Cognitive Cost of The Detection Response Task

2022· article· en· W4312983521 on OpenAlexaff
Prarthana Pillai, Balakumar Balasingam, Francesco Biondi

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2022
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTask (project management)CognitionCognitive loadComputer scienceSensitivity (control systems)Noise (video)Measure (data warehouse)Effects of sleep deprivation on cognitive performanceSignal-to-noise ratio (imaging)Pupil sizeElementary cognitive taskPupil diameterPupilPupillary responsePsychologyArtificial intelligenceElectronic engineeringEngineeringTelecommunicationsData miningImage (mathematics)

Abstract

fetched live from OpenAlex

The Detection Response Task (DRT) is a standardized measure of cognitive load requiring manual responses to intermittent stimuli. Given its simplicity, it is hypothesized that its completion will not interfere with the primary task. However, recent studies challenge this assumption showing a definite cost of DRT performance. In this study we adopt signal-to-noise ratio (SNR), a measure commonly used in communication engineering: 1) to explore the cognitive cost of DRT 2) to compare the sensitivity of DRT performance and pupil size in measuring cognitive load. SNR was calculated using the data from a study wherein DRT performance and pupil size were recorded while participants completed increasingly difficult mental tasks. We conclude that DRT completion interfered with the overall cognitive task demand and showed pupil size’s greater sensitivity to changes in cognitive load. Though exploratory, our study advances using SNR as a powerful tool for data integration in HF/E research.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
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 studies0.0020.000
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 head0.060
GPT teacher head0.330
Teacher spread0.270 · 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 designQualitative
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

Citations3
Published2022
Admission routes1
Has abstractyes

Explore more

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