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Record W4417360129 · doi:10.1177/10870547251394173

Inattention and Hyperactivity Symptom Dimensions of ADHD Differentially Moderate the Relationship Between Concurrent Attention States and Affective Valence

2025· article· en· W4417360129 on OpenAlexafffund
Yudhajit Ain, SN Rai, Andrew Galbraith, Jonas Buerkner, Jessica R. Andrews‐Hanna, Brandy L. Callahan, Julia W. Y. Kam

Bibliographic record

VenueJournal of Attention Disorders · 2025
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsValence (chemistry)Attention deficit hyperactivity disorderCognitionVisual attentionResponse inhibitionAttentional bias

Abstract

fetched live from OpenAlex

BACKGROUND: ADHD has been characterised by excessive mind wandering (MW), or thoughts unrelated to the task at hand, with recent findings indicating that ADHD is specifically associated with more unintentional, but not intentional, MW. These two types of MW are also differentially associated with affective well-being. Most existing studies in ADHD, however, mainly rely on retrospective reports of MW tendencies, which are susceptible to memory-related errors and biases. Further, most studies categorise participants based on overall levels of ADHD, instead of accounting for the spectrum and dimensional heterogeneity of ADHD, including inattention and hyperactivity symptom dimensions. Our study aimed to address the knowledge gap regarding the relationship between different types of MW and affective well-being, across different symptom dimensions of ADHD. METHODS: We used ecological momentary assessment to capture participants' momentary attention state (on-task, intentional MW, or unintentional MW) and affective valence, six times daily for 7 days. Using linear mixed-effects modelling to account for inter-individual variance, we tested whether inattention and hyperactivity symptom dimensions of ADHD differentially moderate the relationship between attention states and affective valence. RESULTS: We found that higher levels of inattention symptoms predicted more negative affect during intentional MW compared to on-task attention; in contrast, higher levels of hyperactivity symptoms predicted more positive affect during intentional MW compared to on-task attention. DISCUSSION: Together, our results indicate that intentional MW moderates opposing effects of inattention and hyperactivity ADHD symptoms on affective valence. Our findings suggest that intentional MW - and not just unintentional MW - may also play a role in affective or behavioural outcomes associated with ADHD symptomatology, and highlight the importance of considering the heterogeneity of ADHD symptomatology, as well as the distinction between intentional and unintentional MW, in future ADHD research.

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)0.001
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 score0.024
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.340
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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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