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Record W4401287712 · doi:10.1016/j.concog.2024.103729

Examining the effects of caffeine during an auditory attention task

2024· article· en· W4401287712 on OpenAlexafffund
Tyler B. Kruger, Mike J. Dixon, Jonathan M. Oakman, Daniel Smilek

Bibliographic record

VenueConsciousness and Cognition · 2024
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyCaffeinePlaceboBoredomAudiologyArousalMind-wanderingFeelingAffect (linguistics)Task (project management)MoodChewing gumSession (web analytics)CognitionDevelopmental psychologyClinical psychologyPsychiatrySocial psychologyMedicineCommunication

Abstract

fetched live from OpenAlex

Participants completed two sessions of an auditory attention task and intermittently responded to thought probes asking about their level of mind-wandering. After the first session one group received 200 mg of caffeinated chewing gum (n = 61) and another group received regular (placebo) chewing gum (n = 66). The gum was chewed for 20-minutes and then disposed of before beginning the second session. Participants who received caffeine showed a performance benefit as well as reported being more on task and fewer instances of spontaneous mind-wandering compared to those in the placebo group. Participants who received caffeine also reported greater positive affect and arousal, as well as less feelings of boredom, sleepiness, and mental effort required to stay on task compared to those who received placebo. These results suggest that caffeine may benefit attentional engagement as well as performance during a sustained attention task.

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 score0.112
Threshold uncertainty score0.226

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 head0.018
GPT teacher head0.242
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

Citations3
Published2024
Admission routes2
Has abstractyes

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