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Record W4410133415 · doi:10.3389/fcogn.2025.1547295

Inattention over time-on-task: the role of motivation in mitigating temporal increases in media multitasking

2025· article· en· W4410133415 on OpenAlexafffund
Allison C. Drody, Effie J. Pereira, James Danckert, Daniel Smilek

Bibliographic record

VenueFrontiers in Cognition · 2025
Typearticle
Languageen
FieldNeuroscience
TopicMind wandering and attention
Canadian institutionsQueen's UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada First Research Excellence Fund
KeywordsHuman multitaskingTask (project management)PsychologyComputer scienceCognitive psychologyHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

Introduction: Numerous studies have demonstrated that attention and performance decline with time-on-task. In modern contexts, this gradual fading of attention can manifest as increases in media multitasking over time. Across two studies, we investigated whether increasing individuals' motivation to perform well on a task mitigates temporal increases in media multitasking. Method: Study 1 re-analyzed data from a previously published study which provided participants with standard or motivating instructions before having them complete a sustained attention task with the option to media multitask. Study 2 extended this work by critically assessing in-the-moment motivation through thought probes throughout the task. Results: In both studies, media multitasking and corresponding decreases in task performance over time were attenuated as a function of increased motivation. Moreover, results from Study 2 revealed that motivation decreased with time-on-task for both groups; however, this decline was more gradual in the motivated group. Discussion: Our findings suggest that increasing the value individuals assign to attending to their current task may aid in prolonging sustained attention. These findings align with recent theories of vigilance that attribute temporal decrements in attention and performance to varying cost-benefit analyses rather than a depletion of resources over time.

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)0.001
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 score0.108
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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 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

Citations2
Published2025
Admission routes2
Has abstractyes

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