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Record W7133091423

A Wandering Mind is a Pondering Mind: Lapses in Sustained Attention Can Be Beneficial

2025· dissertation· W7133091423 on OpenAlexaff
Michael Paul Dubois

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMind-wanderingSet (abstract data type)CognitionSelective attentionFalse memoryImplicit learning
DOInot available

Abstract

fetched live from OpenAlex

Attention is a foundational set of processes that dictate what information undergoes additional cognitive processing, and what information is ignored. Nonetheless, attention cannot be sustained indefinitely—given sufficient time, lapses in sustained attention will occur. So what are the consequences for lapses in attention for learning? The conventional understanding is that lapses impair learning, however, my dissertation indicates a more nuanced relationship, which enhances learning. In chapter two I leveraged recent improvements in tracking sustained attention during a correlated flanker task. I found that participants with more lapses in attention were able to learn more about seemingly irrelevant distractor information, and critically, this finding was driven by greater learning during the moments when attention lapsed. These findings suggest that lapses in attention can enhance learning for seemingly-irrelevant information. In chapter three I tested children aged four to eleven years with the same correlated flanker task. I again found that lapses in attention were correlated with greater learning (with a marginally larger benefit during momentary lapses). This finding is an important complement to individual-differences research in developmental psychology; they provide clear support for the role of sustained attention specifically, without influence from extraneous variables that may differ between age groups. Finally, in chapter four I used the same reaction time based approach to track attention during a category learning paradigm. Here I found tentative evidence that lapses in attention boosted learning for category-level information, and that this learning is driven by the induction of abstract rules rather than memory for specific stimuli. These findings are consistent with the hypothesis that lapses in attention allow for forgetting of non-diagnostic information. Overall, I contend that lapses are not necessarily “suboptimal” for learning—an overly simplistic description—but rather, provide an opportunity to take advantage of alternative learning opportunities. Clearly outlining the unexpected learning benefits from lapses in attention is critical for attaining a more balanced understanding of sustained attention. Lapses in sustained attention ultimately have a nuanced impact on human cognition, and this should be reflected in the laboratory and the real world.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.039
GPT teacher head0.339
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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