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Record W4296281016 · doi:10.3389/fpsyt.2022.886692

Hyperfocus or flow? Attentional strengths in autism spectrum disorder

2022· article· en· W4296281016 on OpenAlexafffundabout
Annie Dupuis, Piyumi Mudiyanselage, Christie L. Burton, Paul Arnold, Jennifer Crosbie, Russell Schachar

Bibliographic record

VenueFrontiers in Psychiatry · 2022
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of CalgaryCentre for Addiction and Mental HealthHospital for Sick ChildrenPublic Health OntarioUniversity of Toronto
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsAutism spectrum disorderPsychologyAutismCognitive psychologyNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

The comorbidity of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) diagnoses is well established. An ASD diagnosis is associated with elevated ADHD traits and symptoms, as well as strengths in attention. In the ASD literature, attentional strengths have been described as maladaptive (e.g., hyperfocus), in contrast with positive portrayals in the typically developing population (e.g., flow). The objective of this study was to (1) compare profiles of attentional strengths and weaknesses in ASD and ADHD and (2) determine whether attentional strengths in ASD are associated with impairment, poorer cognitive flexibility, and perseveration/perfectionism. In a community sample of 5,744 children and youth, 131 children were reported as having a diagnosis of ASD (mean age 10.3 years) and 346 children were reported as having a diagnosis of ADHD (mean age 10.7 years). We used the Strengths and Weaknesses of Attention-Deficit/Hyperactivity-symptoms and Normal-behaviors (SWAN) rating scale to calculate attentional and hyperactive/impulse control strength and weakness counts and scores. The Autism-Spectrum Quotient Switching factor served as a measure of cognitive flexibility. Impairment was assessed with the Columbia Impairment Scale. We used the symmetry/ordering factor on the Toronto Obsessive-Compulsive Scale as a measure of perseveration/perfectionism. No differences were found between the ADHD and ASD groups in SWAN weakness scores, symptoms, or hyperactive/impulse control strengths; however, autistic children had higher rates of attentional strengths [odds ratio: 5.7, 95% CI (2.8, 11.6), p < 0.0001]. Post-hoc pairwise testing identified four attentional strengths with significantly higher rates in ASD than in ADHD. Attentional strength scores were not associated with impairment or poor cognitive flexibility, but predicted levels of perseveration/perfectionism. The effect of attentional strengths on impairment and cognitive flexibility did not differ between autistic and Control children, but the higher perseveration/perfectionism scores seen in ASD were not found in Control children. ASD is associated with a pattern of attentional strengths that is not found in ADHD Characterizing the full range of attentional abilities in autistic children may explain variability in outcomes such as quality-of-life indicators and identify protective factors, providing targets for strength-based behavioral interventions. The clinical and etiological implications of the subgroup of autistic children with attentional strengths require further investigation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.272
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.

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

Citations47
Published2022
Admission routes3
Has abstractyes

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