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Record W2084703098 · doi:10.1145/1979742.1979591

Brain and body interfaces

2011· article· en· W2084703098 on OpenAlexaff
Stephen Fairclough, Kiel Gilleade, Lennart E. Nacke, Regan L. Mandryk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHuman–computer interactionComputer scienceControl (management)Field (mathematics)CognitionUser interfacePsychologyNeuroscienceArtificial intelligence

Abstract

fetched live from OpenAlex

The brain and body provide a wealth of information about the physiological, cognitive and emotional state of the user. There is increased opportunity to use these data in computerised systems as forms of input control. As entry level physiological sensors become more widespread, physiological interfaces are liable to become more pervasive in our society (e.g., through mobile phones). While these signals offer new and exciting mechanisms for the control of interactive systems, the issue of whether these physiological interfaces are appropriate for application and offer the user a meaningful level of interaction remains relatively unexplored. This workshop sets out to bring together researchers working in the field of psychophysiological interaction to discuss the issue of how to design physiological interactions that are meaningful for users.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.493

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)0.0000.000

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 head0.050
GPT teacher head0.274
Teacher spread0.224 · 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 designBench or experimental
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

Citations13
Published2011
Admission routes1
Has abstractyes

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