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Record W2782936485 · doi:10.31234/osf.io/q7s8u

What does (and should) "mind wandering" mean?

2017· preprint· en· W2782936485 on OpenAlexaff
Paul Seli, David Maillet, Daniel L. Schacter, Michael J. Kane, Jonathan Smallwood, Jonathan W. Schooler, Daniel Smilek

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMind-wanderingCLARITYVariety (cybernetics)PsychologyContext (archaeology)Construct (python library)Cognitive psychologyEpistemologyField (mathematics)Cognitive scienceComputer scienceCognitionArtificial intelligenceHistoryPhilosophy

Abstract

fetched live from OpenAlex

In recent years, there has been a tremendous increase in the number of studies examining mind wandering, and research on the topic has spread widely across various domains of psychological research. As research on the topic of mind wandering has accelerated, the defining features of this conscious state have expanded, and researchers have begun to define mind wandering in conceptually and operationally different ways between – and sometimes even within – studies. Yet, despite clear differences in the definitions adopted, ‘mind wandering’ is often discussed in broad terms, and inferences drawn by researchers are rarely constrained to their specific operational definitions. This practice produces a lack of clarity in our understanding of mind wandering, and it can lead to illusory inconsistencies in the literature. To minimize these problems, we propose that researchers adopt a family-resemblances approach to the investigation of mind wandering, which entails (a) treating mind wandering as a heterogeneous construct and (b) more clearly measuring and describing the specific aspects of the variety of mind wandering that researchers are attempting to investigate. To help move the field forward, we delineate a prototypical case of mind wandering in the broader context of related forms of thought, which should guide the use of the term in future research.

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 categoriesScholarly communication
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 score0.681
Threshold uncertainty score0.999

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 communication0.0020.000
Open science0.0000.001
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 head0.122
GPT teacher head0.339
Teacher spread0.217 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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