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Record W2111490631 · doi:10.1162/jocn_a_00375

Mind Wandering and the Adaptive Control of Attentional Resources

2013· article· en· W2111490631 on OpenAlexaff
Julia W. Y. Kam, Elizabeth Dao, M Stanciulescu, Hamish Tildesley, Todd C. Handy

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMind-wanderingPsychologyNeurocognitiveCognitive psychologyAttentional controlDistractionInhibition of returnDecoupling (probability)CognitionNeuroscienceVisual attention

Abstract

fetched live from OpenAlex

Mind wandering is a natural, transient state wherein our neurocognitive systems become temporarily decoupled from the external sensory environment as our thoughts drift away from the current task at hand. Yet despite the ubiquity of mind wandering in everyday human life, we rarely seem impaired in our ability to adaptively respond to the external environment when mind wandering. This suggests that despite widespread neurocognitive decoupling during mind wandering states, we may nevertheless retain some capacity to attentionally monitor external events. But what specific capacities? In Experiment 1, using traditional performance measures, we found that both volitional and automatic forms of visual-spatial attentional orienting were significantly attenuated when mind wandering. In Experiment 2, however, ERPs revealed that, during mind wandering states, there was a relative preservation of sensitivity to deviant or unexpected sensory events, as measured via the auditory N1 component. Taken together, our findings suggest that, although some selective attentional processes may be subject to down-regulation during mind wandering, we may adaptively compensate for these neurocognitively decoupled states by maintaining automatic deviance-detection functions.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.032
GPT teacher head0.252
Teacher spread0.221 · 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

Citations49
Published2013
Admission routes1
Has abstractyes

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