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Record W4382918346 · doi:10.1177/17470218231187892

Examining the effect of expected test format and test difficulty on the frequency and mnemonic costs of mind wandering

2023· article· en· W4382918346 on OpenAlexafffund
Skylar J. Laursen, Jeffrey D. Wammes, Chris M. Fiacconi

Bibliographic record

VenueQuarterly Journal of Experimental Psychology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsQueen's UniversityUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMnemonicTest (biology)PsychologyCognitive psychologyMind-wanderingCognitionNeuroscience

Abstract

fetched live from OpenAlex

Mind wandering, generally defined as task-unrelated thought, has been shown to constitute between 30% and 50% of individuals' thoughts during almost every activity in which they are engaged. Critically, however, previous research has shown that the demands of a given task can lead to either the up- or down-regulation of mind wandering and that engagement in mind wandering may be differentially detrimental to future memory performance depending on learning conditions. The goal of the current research was to gain a better understanding of how the circumstances surrounding a learning episode affect the frequency with which individuals engage in off-task thought, and the extent to which these differences differentially affect memory performance across different test formats. Specifically, while prior work has manipulated the conditions of encoding, we focused on the anticipated characteristics of the retrieval task, thereby examining whether the anticipation of later demands imposed by the expected test format/difficulty would influence the frequency or performance costs of mind wandering during encoding. Across three experiments, we demonstrate that the anticipation of future test demands, as modelled by expected test format/difficulty, does not affect rates of mind wandering. However, the costs associated with mind wandering do appear to scale with the difficulty of the test. These findings provide important new insights into the impact of off-task thought on future memory performance and constrain our understanding of the strategic regulation of inattention in the context of learning and memory.

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 score0.100
Threshold uncertainty score0.286

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 communication0.0000.000
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 head0.044
GPT teacher head0.324
Teacher spread0.280 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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