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Record W2144356555 · doi:10.3389/fpsyg.2013.00477

Driver of discontent or escape vehicle: the affective consequences of mindwandering

2013· article· en· W2144356555 on OpenAlexaff
Malia F. Mason, Kevin Brown, Raymond A. Mar, Jonathan Smallwood

Bibliographic record

VenueFrontiers in Psychology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsYork University
Fundersnot available
KeywordsPsychologyPerceptionCognitive psychologyNeuroscience

Abstract

fetched live from OpenAlex

An emerging body of evidence suggests that our penchant for entertaining thoughts that are unrelated to ongoing activities might be a detriment to our emotional wellbeing. In light of this evidence, researchers have posited that mindwandering is a cause rather than a manifestation of discontent. We review the evidence in support of this viewpoint. We then consider this evidence in a broader context-with regards to mindwandering's antecedents, respecting the observation that people frequently find pleasure in their off-task moments, and in light of the lay beliefs people hold about its causes. We report data from two studies that speak to the potential challenges of establishing a definitive causal link between mindwandering and wellbeing. First, to advance the idea that mindwandering can convey affective benefits, in spite of negative feelings about mental disengagement, we examined cortical responses in a unique individual who presents with a long history of excessive-but enjoyable-task-irrelevant thinking. Second, to explore the idea that lay beliefs about mindwandering may substantially color the affective responses people have to a mindwandering episode, we surveyed people's beliefs about mindwandering's antecedents and related them to the affective reactions people anticipated to off-task moments. Our hope is to provide a nuanced evaluation of the available evidence for the assertion that mindwandering causes unhappiness, and to provide a clear direction forward to better evaluate this possibility.

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 score0.244
Threshold uncertainty score0.237

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 studies0.0000.001
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 head0.040
GPT teacher head0.301
Teacher spread0.261 · 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

Citations45
Published2013
Admission routes1
Has abstractyes

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