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Record W2555456333 · doi:10.1139/cjpp-2016-0498

Caffeine and theanine exert opposite effects on attention under emotional arousal

2016· article· en· W2555456333 on OpenAlexvenueno aff
Grace E. Giles, Caroline R. Mahoney, Tad T. Brunyé, Holly A. Taylor, Robin B. Kanarek

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2016
Typearticle
Languageen
FieldMedicine
TopicTea Polyphenols and Effects
Canadian institutionsnot available
Fundersnot available
KeywordsCaffeineTheanineArousalPsychologyMoodPlaceboCognitionDevelopmental psychologyPharmacologyGreen teaMedicineChemistryNeuroscienceFood scienceClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Tea is perceived as more relaxing than coffee, even though both contain caffeine. L-theanine in tea may account for the difference. Consumed together, caffeine and theanine exert similar cognitive effects to that of caffeine alone, but exert opposite effects on arousal, in that caffeine accentuates and theanine mitigates physiological and felt stress responses. We evaluated whether caffeine and theanine influenced cognition under emotional arousal. Using a double-blind, repeated-measures design, 36 participants received 4 treatments (200 mg caffeine + 0 mg theanine, 0 mg caffeine + 200 mg theanine, 200 mg caffeine + 200 mg theanine, 0 mg caffeine + 0 mg theanine) on separate days. Emotional arousal was induced by highly arousing negative film clips and pictures. Mood, salivary cortisol, and visual attention were evaluated. Caffeine accentuated global processing of visual attention on the hierarchical shape task (p < 0.05), theanine accentuated local processing (p < 0.05), and the combination did not differ from placebo. Caffeine reduced flanker conflict difference scores on the Attention Network Test (p < 0.05), theanine increased difference scores (p < 0.05), and the combination did not differ from placebo. Thus, under emotional arousal, caffeine and theanine exert opposite effects on certain attentional processes, but when consumed together, they counteract the effects of each other.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.481

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 head0.011
GPT teacher head0.266
Teacher spread0.255 · 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

Citations27
Published2016
Admission routes1
Has abstractyes

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