Using Drift Diffusion Modeling to Understand Inattentive Behavior in Preterm and Term-Born Children

Jenny Retzler, Chris Retzler, Madeleine Groom, Samantha Johnson, Lucy Cragg

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Objective: Children born very preterm are at increased risk of inattention, but it remains unclear whether the underlying processes are the same as in their term-born peers. Drift diffusion modeling (DDM) may better characterize the cognitive processes underlying inattention than standard reaction time (RT) measures. This study used DDM to compare the processes related to inattentive behavior in preterm and term-born children. Method: Performance on a cued continuous performance task was compared between 33 children born very preterm (VP; ≤32 weeks' gestation) and 32 term-born peers (≥37 weeks' gestation), aged 8-11 years. Both groups included children with a wide spectrum of parent-rated inattention (above average attention to severe inattention). Performance was defined using standard measures (RT, RT variability and accuracy) and modeled using a DDM. A hierarchical regression assessed the extent to which standard or DDM measures explained variance in parent-rated inattention and whether these relationships differed between VP and term-born children. Results: There were no group differences in performance on standard or DDM measures of task performance. Parent-rated inattention correlated significantly with hit rate, RT variability, and drift rate (a DDM estimate of processing efficiency) in one or both groups. Regression analysis revealed that drift rate was the best predictor of parent-rated inattention. This relationship did not differ significantly between groups. Conclusions: Findings suggest that less efficient information processing is a common mechanism underlying inattention in both VP and term-born children. This study demonstrates the benefits of using DDM to better characterize atypical cognitive processing in clinical samples.

Original languageEnglish
Pages (from-to)77-87
Number of pages11
Issue number1
Early online date3 Oct 2019
Publication statusPublished - 1 Jan 2020


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