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Record W4378172604 · doi:10.1186/s41235-023-00483-0

When there is noise on Sherlock Holmes: mind wandering increases with perceptual processing difficulty during reading and listening

2023· article· en· W4378172604 on OpenAlexaff
Lena Steindorf, Sebastian Pink, Jan Rummel, Jonathan Smallwood

Bibliographic record

VenueCognitive Research Principles and Implications · 2023
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsQueen's University
FundersUniversität HeidelbergDeutsche Forschungsgemeinschaft
KeywordsActive listeningMind-wanderingPerceptionReading (process)PsychologyPresentation (obstetrics)NovellaCognitive psychologyComprehensionNoise (video)CommunicationLinguisticsComputer scienceCognitionArtificial intelligence

Abstract

fetched live from OpenAlex

We investigated whether increased perceptual processing difficulty during reading or listening to a Sherlock Holmes novella impacts mind wandering as well as text comprehension. We presented 175 participants with a novella in either a visual or an auditory presentation format and probed their thoughts and motivational states from time to time during reading/listening. For half of the participants in each presentation-format condition (visual or auditory), the story was superimposed by Gaussian noise. For both presentation formats, the participants who were exposed to noise while processing the story mind-wandered more and performed worse in a later comprehension test than the participants who processed the story without added noise. These negative effects of increased perceptual processing difficulty on task focus and comprehension were partly driven by motivational factors: reading/listening motivation mediated the relationship between perceptual processing difficulty and mind wandering.

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)0.000
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.186
GPT teacher head0.389
Teacher spread0.204 · 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 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

Citations8
Published2023
Admission routes1
Has abstractyes

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