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Record W2046844216 · doi:10.1177/1087054713510351

A Double Dissociation Between Inattentive and Impulsive Traits, on Tasks of Visual Processing and Emotion Regulation

2013· article· en· W2046844216 on OpenAlexaff
Dorit Ben Shalom, Ziv Ronel, Yifat Faran, Gal Meiri, Lidia V. Gabis, Kimberly A. Kerns

Bibliographic record

VenueJournal of Attention Disorders · 2013
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsImpulsivityPsychologyDissociation (chemistry)Confirmatory factor analysisTask (project management)Developmental psychologyPopulationCognitive psychologyStructural equation modeling

Abstract

fetched live from OpenAlex

OBJECTIVE: To dissociate between inattentive and impulsive traits common in attention deficit hyperactivity disorder (ADHD) using a non-dichotomous measurment of these traits. METHOD: 120 university students who completed the Conner's adult ADHD rating scales (CAARS) were also tested on the Microgenesis task which requires visual attention and on the Cyber Cruiser task which requires emotion regulation. RESULTS: Results show that a measure of inattention was specifically related to a measure of effortful visual processing condition. In addition, a measure of impulsivity was specifically related to the tendency to fail in refueling one's car on time, although this relation was opposite to the predicted direction. Furthermore, by using exploratory and confirmatory factor analyses, the CAARS' factor structure was confirmed to be relevant to an Israeli population. CONCLUSION: The current experiment supports the idea that visual attention may play a part in inattentive symptoms, and that emotion regulation may play a part in impulsivity symptoms.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score0.532

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 communication0.0000.001
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 head0.018
GPT teacher head0.315
Teacher spread0.297 · 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

Citations9
Published2013
Admission routes1
Has abstractyes

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