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Record W2010488513 · doi:10.3389/fpsyg.2013.00892

The way we encounter reading material influences how frequently we mind wander

2013· article· en· W2010488513 on OpenAlexafffund
Trish L. Varao-Sousa, Jonathan S. A. Carriere, Daniel Smilek

Bibliographic record

VenueFrontiers in Psychology · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMind-wanderingReading (process)Active listeningPsychologyReading aloudCognitive psychologyCommunicationLinguisticsCognition

Abstract

fetched live from OpenAlex

We examined whether different encounters of reading material influence the likelihood of mind wandering, memory for the material, and the ratings of interest in the material. In a within-subjects design participants experienced three different reading encounters: (1) reading a passage aloud, (2) listening to a passage being read to them, and (3) reading a passage silently. Throughout each reading encounter probes were given in order to identify mind wandering. After finishing the passage participants also rated how interesting it was and completed a content recognition test. Results showed that reading aloud led to the least amount of mind wandering, while listening to the passage led to the most mind wandering. Listening to the passage was also associated with the poorest memory performance and the least interest in the material. Finally, within the silent reading and listening encounters we observed negative relations between mind wandering and both memory performance and interest in the material, replicating previous findings. Taken together, the present findings improve our understanding of the nature of mind wandering while reading, and have potentially important implications for readers seeking to take advantage of the convenience of audiobooks and podcasts.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.415

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 head0.028
GPT teacher head0.288
Teacher spread0.260 · 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 designNot applicable
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

Citations73
Published2013
Admission routes2
Has abstractyes

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