Untitled

Collings, Raymond D.

doi:10.1023/a:1023525007441

Cited by 43 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…He found that ADHD-C made significantly more omission errors than ADHD-I and controls. In contrast to the results found by Collings (2003), Epstein et al (2011) did not find significant differences between DSM-defined ADHD presentations; neither in the number of omission and commission errors nor in the mean RTs. Both studies relied on a CPT with only visual trials and with only blocks of rare go trials.…”

Section: Generalizecontrasting

confidence: 99%

“…A few studies (e.g., Carr et al, 2010;Collings, 2003;O'Driscoll, 2005;Pritchard, Neumann, & Rucklidge, 2008) have already examined differences among ADHD presentations.…”

Section: Introductionmentioning

confidence: 99%

“…A few studies (e.g., Carr et al, 2010;Collings, 2003;O'Driscoll et al, 2005;Pritchard et al, 2008) have already examined differences among ADHD presentations. For instance, Carr et al (2010) found that children with ADHD-I detected significantly more target letters in a stream of letters than those with ADHD-C though only if they were instructed to divide attention between two types of targets (i.e., type and color of target letters).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Characterizing Underlying Cognitive Components of ADHD Presentations and Co-morbid Diagnoses: A Diffusion Decision Model Analysis

et al. 2021

View full text Add to dashboard Cite

Objective: To Explore whether subtypes and comorbidities of attention-deficit hyperactivity disorder (ADHD) induce distinct biases in cognitive components involved in information processing. Method: Performance on the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT) was compared between 150 children (aged 7 to 10) with ADHD, grouped by DSM-5 presentation (ADHD-C, ADHD-I) or co-morbid diagnoses (anxiety, oppositional defiant disorder [ODD], both, neither), and 60 children without ADHD. Diffusion decision modeling decomposed performance into cognitive components. Results: Children with ADHD had poorer information integration than controls. Children with ADHD-C were more sensitive to changes in presentation modality (auditory/visual) than those with ADHD-I and controls. Above and beyond these results, children with ADHD+anxiety+ODD had larger increases in response biases when targets became frequent than children with ADHD-only or with ADHD and one comorbidity. Conclusion: ADHD presentations and comorbidities have distinct cognitive characteristics quantifiable using DDM and IVA-CPT. We discuss implications for tailored cognitive-behavioral therapy.

show abstract

Section: Generalizecontrasting

confidence: 99%

“…A few studies (e.g., Carr et al, 2010;Collings, 2003;O'Driscoll, 2005;Pritchard, Neumann, & Rucklidge, 2008) have already examined differences among ADHD presentations.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Characterizing Underlying Cognitive Components of ADHD Presentations and Co-morbid Diagnoses: A Diffusion Decision Model Analysis

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Although the current sample was derived from a tertiary care university hospital that provides services to the general population, this fact limits our ability to generalize our results to different populations. Also, there is limited information about participants' demographic, cognitive, and clinical characteristics, such as IQ level and ADHD subtypes, which can be associated with CPT performance (Mahone et al, 2002;Collings, 2003). Given our limited information about participants' cognitive and personal characteristics, there was no way to rule out the possibility that some participants were not motivated to optimize their CPT performance.…”

Section: Discussionmentioning

confidence: 99%

A Machine-Based Prediction Model of ADHD Using CPT Data

Slobodin

Yahav

Berger

2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learningbased model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6-12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD.

show abstract

“…Many of the studies that examine the link between observable problem behaviors and CPT performance do so in the context of investigating performance characteristics that differentiate children with certain diagnoses (e.g., Attention Deficit/Hyperactivity Disorder [ADHD]; Corkum, Schachar, & Siegel, 1996; Collings, 2003). These studies inform the use of CPT measures in applied settings; however, they do not necessarily provide information about the underlying behavioral and cognitive constructs that are reflected in CPT performance parameters.…”

mentioning

confidence: 99%

Relations between response trajectories on the continuous performance test and teacher-rated problem behaviors in preschoolers.

Allan¹,

Lonigan²

2015

Psychological Assessment

View full text Add to dashboard Cite

Although both the Continuous Performance Test (CPT) and behavior rating scales are used in both practice and research to assess inattentive and hyperactive/impulsive behaviors, the correlations between performance on the CPT and teachers' ratings are typically only small-to-moderate. This study examined trajectories of performance on a low target-frequency visual CPT in a sample of preschool children and how these trajectories were associated with teacher-ratings of problem behaviors (i.e., inattention, hyperactivity/impulsivity [H/I], and oppositional/defiant behavior). Participants included 399 preschool children (Mean age = 56 months; 49.4% female; 73.7% White/Caucasian). An ADHD-rating scale was completed by teachers, and the CPT was completed by the preschoolers. Results showed that children's performance across four temporal blocks on the CPT was not stable across the duration of the task, with error rates generally increasing from initial to later blocks. The predictive relations of teacher-rated problem behaviors to performance trajectories on the CPT were examined using growth curve models. Higher rates of teacher-reported inattention and H/I were uniquely associated with higher rates of initial omission errors and initial commission errors, respectively. Higher rates of teacher-reported overall problem behaviors were associated with increasing rates of omission but not commission errors during the CPT; however, the relation was not specific to one type of problem behavior. The results of this study indicate that the pattern of errors on the CPT in preschool samples is complex and may be determined by multiple behavioral factors. These findings have implications for the interpretation of CPT performance in young children. KeywordsContinuous Performance Test; Teacher-Ratings; Inattention; Hyperactivity/Impulsivity; Oppositional Defiant Behaviors Behavioral rating forms and cognitive tasks, such as continuous performance tests (CPTs), are two methods that are used in both research and applied settings for assessing children's Inquiries concerning this work can be sent to either Darcey Allan (dallan@psy.fsu.edu) or Christopher J. Lonigan (lonigan@psy.fsu.edu), Department of Psychology, Florida State University, 1107 W. Call Street, Tallahassee FL 32306-4301. HHS Public Access Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript inattention and hyperactivity/impulsivity (H/I). Although these methods presumably measure the same underlying constructs, the correlations between behavior ratings and CPT performance are typically small (Egeland, Johansen, & Ueland, 2009;McGee, Clark, & Symons, 2000). The reasons for these weak relations are unclear. Research explicating the specific ways in which direct measures and behavior-ratings of problem behaviors do and do not converge is needed to provide a stronger foundation for the use of these measures to operationalize inattention and H/I. Such research may be used to guide the valid use of the CPT in both clinical practice and research.To d...

show abstract

Untitled

Cited by 43 publications

References 33 publications

Characterizing Underlying Cognitive Components of ADHD Presentations and Co-morbid Diagnoses: A Diffusion Decision Model Analysis

Characterizing Underlying Cognitive Components of ADHD Presentations and Co-morbid Diagnoses: A Diffusion Decision Model Analysis

A Machine-Based Prediction Model of ADHD Using CPT Data

Relations between response trajectories on the continuous performance test and teacher-rated problem behaviors in preschoolers.

Contact Info

Product

Resources

About