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Record W2787310660 · doi:10.1177/0956797618761039

On the Clock: Evidence for the Rapid and Strategic Modulation of Mind Wandering

2018· article· en· W2787310660 on OpenAlexafffund
Paul Seli, Jonathan S. A. Carriere, Jeffrey D. Wammes, Evan F. Risko, Daniel L. Schacter, Daniel Smilek

Bibliographic record

VenuePsychological Science · 2018
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of WaterlooBishop's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMind-wanderingAnticipation (artificial intelligence)PsychologyTask (project management)Cognitive psychologyNeuroscienceCognitionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We examined the hypothesis that people can modulate their mind wandering on the basis of their expectations of upcoming challenges in a task. To this end, we developed a novel paradigm in which participants were presented with an analog clock, via a computer monitor, and asked to push a button every time the clock's hand was pointed at 12:00. Importantly, the time at which the clock's hand was pointed at 12:00 was completely predictable and occurred at 20-s intervals. During some of the 20-s intervals, we presented thought probes to index participants' rates of mind wandering. Results indicated that participants decreased their levels of mind wandering as they approached the predictable upcoming target. Critically, these results suggest that people can and do modulate their mind wandering in anticipation of changes in task demands.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.375
GPT teacher head0.430
Teacher spread0.054 · 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

Citations81
Published2018
Admission routes2
Has abstractyes

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