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Record W2912273413 · doi:10.1037/pas0000701

Using ecological momentary assessments to evaluate extant measures of mind wandering.

2019· article· en· W2912273413 on OpenAlexafffund
Dragana Ostojic‐Aitkens, Brianne Brooker, Carlin J. Miller

Bibliographic record

VenuePsychological Assessment · 2019
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Windsor
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMind-wanderingPsycINFOPsychologyExperience sampling methodEcological validityExtant taxonTask (project management)Developmental psychologyMEDLINECognitionSocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Mind wandering is a commonly experienced phenomenon. Although self-report measures are available to assess these attentional lapses, examination of their correspondence with the reported frequency of these episodes in daily life is warranted. Using ecological momentary assessments (EMAs), the present study aimed to validate 3 mind-wandering measures: the Mind-Wandering Questionnaire (MWQ) and the Mind Wandering-Spontaneous (MW-S) and Mind Wandering-Deliberate (MW-D) measures in university students (N = 100). Participants completed a series of questionnaires in an in-lab session. Using time-based EMA sampling, participants received 6 prompts via text message daily for 7 days. Each prompt asked students to report if their current thoughts were about something other than what they were doing using a scale ranging from completely on-task to completely on unrelated concerns. Self-report data collected via EMA indicated that reporting more mind-wandering episodes was associated with higher MWQ and MW-S scores but was not significantly correlated with the MW-D score. Results highlight the utility of EMA in validating measures designed to capture mind-wandering episodes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.757
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.306
GPT teacher head0.475
Teacher spread0.169 · 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.

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

Citations19
Published2019
Admission routes2
Has abstractyes

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