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Record W2472796880 · doi:10.3389/fnhum.2016.00344

The Role of Cognitive and Perceptual Loads in Inattentional Deafness

2016· article· en· W2472796880 on OpenAlexfundno aff
Mickaël Causse, Jean-Paul Imbert, Louise Giraudet, Christophe Jouffrais, Sébastien Tremblay

Bibliographic record

VenueFrontiers in Human Neuroscience · 2016
Typearticle
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInattentional blindnessCognitive loadPerceptionCognitionPsychologyStimulus (psychology)AudiologyCognitive psychologyMedicineNeuroscience

Abstract

fetched live from OpenAlex

The current study examines the role of cognitive and perceptual loads in inattentional deafness (the failure to perceive an auditory stimulus) and the possibility to predict this phenomenon with ocular measurements. Twenty participants performed Air Traffic Control (ATC) scenarios-in the Laby ATC-like microworld-guiding one (low cognitive load) or two (high cognitive load) aircraft while responding to visual notifications related to 7 (low perceptual load) or 21 (high perceptual load) peripheral aircraft. At the same time, participants were played standard tones which they had to ignore (probability = 0.80), or deviant tones (probability = 0.20) which they had to report. Behavioral results showed that 28.76% of alarms were not reported in the low cognitive load condition and up to 46.21% in the high cognitive load condition. On the contrary, perceptual load had no impact on the inattentional deafness rate. Finally, the mean pupil diameter of the fixations that preceded the target tones was significantly lower in the trials in which the participants did not report the tones, likely showing a momentary lapse of sustained attention, which in turn was associated to the occurrence of inattentional deafness.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.239
Teacher spread0.229 · 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

Citations36
Published2016
Admission routes1
Has abstractyes

Explore more

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