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Record W7062561606

Troubling Inconsistencies in the Mind-Wandering Literature: A Comparison of the Impacts of Reporting Techniques and Types of Mind-Wandering in Driving and N-back Tasks

2023· dissertation· en· W7062561606 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Atrium (University of Guelph) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTask (project management)Affect (linguistics)InterruptMeasure (data warehouse)Task analysis
DOInot available

Abstract

fetched live from OpenAlex

Many individuals have had the experience of “coming to” in the middle of a task and realizing that they aren’t paying attention. This shift in attention away from a primary task is known as mind-wandering. There has been an increasing amount of research on mind-wandering, but results are inconsistent. In this thesis, we investigate two sources of inconsistency: differences in the way mind-wandering is measured (thought-probes, post-task reports), and differences in the type of mind-wandering experienced (intentional, unintentional) under different task conditions. Because there is a danger that mind-wandering may also vary depending on the task, we investigated these issues in two primary tasks: a simulated driving task, and the N-back task (a working memory task used in studies of mind-wandering). In Experiments 1 and 2, we manipulated how mind-wandering was measured (thought-probes, post-task reports) and examined effects on rates of mind-wandering changes in task performance, finding that measurement type had an effect on both. Although post-task reports may be less sensitive and capture less mind-wandering, thought-probes interrupt the flow of the task, changing performance and influencing the way mind-wandering varies with time on task. This suggest that some discrepancies emerge because the two report types do not measure the same thing and can affect how other variables influence performance. Our second line of research investigated the prevalence of intentional versus unintentional mind-wandering (assessed by thought-probes), as it as it varies by task difficulty (Experiment 3: driving, Experiment 4: N-back task). Here we found that unintentional mind-wandering was more prevalent than intentional and more detrimental to primary task performance—especially when the task was difficult. Across all experiments, we also monitored the relationship between mind-wandering and differences in working memory (measured by the Operation Span) and sustained attention (measured by the Sustained Attention to Response Task), finding that effects varied across studies. Overall, this research challenges the assumption that mind-wandering is the same regardless of context (primary task), suggesting that researchers should be cautions in generalizing across primary tasks when studying mind-wandering. These findings have ramifications for theory and methodology in the mind-wandering literature, as well as implications for driver safety.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.266
Teacher spread0.240 · 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