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Record W3006712940 · doi:10.1093/geronb/gbaa031

Mind-Wandering Across the Age Gap: Age-Related Differences in Mind-Wandering Are Partially Attributable to Age-Related Differences in Motivation

2020· article· en· W3006712940 on OpenAlexafffund
Paul Seli, Kevin O’Neill, Jonathan S. A. Carriere, Daniel Smilek, Roger E. Beaty, Daniel L. Schacter

Bibliographic record

VenueThe Journals of Gerontology Series B · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of WaterlooBishop's University
FundersNational Institute of Mental HealthNational Institute on AgingNatural Sciences and Engineering Research Council of Canada
KeywordsMind-wanderingPsychologyContext (archaeology)Developmental psychologyTask (project management)Age groupsCognitive psychologyTheory of mindYoung adultCognitionDemographyNeuroscience

Abstract

fetched live from OpenAlex

OBJECTIVES: A common finding in the mind-wandering literature is that older adults (OAs) tend to mind-wander less frequently than young adults (YAs). Here, we sought to determine whether this age-related difference in mind-wandering is attributable to age-related differences in motivation. METHOD: YAs and OAs completed an attention task during which they responded to thought probes that assessed rates of mind-wandering, and they provided self-reports of task-based motivation before and after completion of the attention task. RESULTS: Age-related differences in mind-wandering are partially explained by differences in motivation, and motivating YAs via incentive diminishes mind-wandering differences across these groups. DISCUSSION: We consider these results in the context of theories on age-related differences in mind wandering, with a specific focus on their relevance to the recently proposed motivational account of such age-related differences.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.610
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.204
GPT teacher head0.324
Teacher spread0.120 · 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 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

Citations26
Published2020
Admission routes2
Has abstractyes

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