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Record W4407835162 · doi:10.1016/j.heliyon.2025.e42911

The effects of cannabis on mind-wandering

2025· article· en· W4407835162 on OpenAlexafffund
Adrian B. Safati, Wisam Almohamad Alkheder, Cassandra J. Lowe, Daniel Smilek

Bibliographic record

VenueHeliyon · 2025
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMind-wanderingCannabisPsychologyPsychotherapistPsychiatryCognition

Abstract

fetched live from OpenAlex

To examine the effects of cannabis on mind-wandering, regular cannabis smokers of legally purchased pre-rolls took part in a three-session remote study. In each 30-min session participants completed three blocks of an attention task in which they pressed the spacebar in time with a metronome tone (the Metronome Response Task), and intermittently reported their levels of spontaneous and deliberate mind-wandering. Performance on the Metronome Response Task was indexed through response time variability, with greater response variability indicating poorer performance. Following an initial 'baseline' block, participants were instructed to mind-wander either 20 % or 80 % of the time in the second and third blocks (counterbalanced). Critically, the first and third sessions were scheduled on days of planned abstention while the second session immediately followed the (planned) use of cannabis (an ABA design). In the baseline blocks we found that cannabis use is associated with a large increase in spontaneous mind-wandering, a smaller increase in deliberate mind-wandering, and impaired task performance. When participants were instructed to mind-wander 20 % or 80 % of the time, cannabis use reduced instruction-related changes in deliberate mind-wandering and task performance, suggesting an impairment of the regulation of mind-wandering.

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 score0.068
Threshold uncertainty score0.139

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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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