Developmental Versus Individual Differences In Post-Error Slowing In ADHD: A Drift Diffusion Model Analysis
Open Access
- Author:
- Warner, Tyler
- Graduate Program:
- Psychology
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- June 09, 2020
- Committee Members:
- Cynthia L Huang-Pollock, Thesis Advisor/Co-Advisor
Koraly Elisa Perez-Edgar, Committee Member
Stephen Jeffrey Wilson, Committee Member
Kristin Ann Buss, Program Head/Chair - Keywords:
- ADHD
Posterror Slowing
Diffusion Model
Developmental
Cognitive Control - Abstract:
- Post-error slowing (PES), or a slowing of reaction time on trials immediately following errors in cognitive tasks, has been used as a measure of increased response caution due to an increase in cognitive control. Because of this, changes in mean reaction time (MRT) following errors have been used to assess the use of cognitive control in cognitive tasks. However, research comparing groups that should demonstrate differences in cognitive control, including adults versus children, or children with or without ADHD, have shown mixed results. Inconsistencies may result from MRT being a composite measure of many different processes that is unable to directly test increase in response caution. Using the diffusion model (DM), this study operationalizes response caution as boundary separation to test if it increases post-error. DM was applied to a categorization learning paradigm, in which 37 college-attending adults, 60 schoolaged children with ADHD, and 50 school-aged children without ADHD, sorted stimuli into categories without being explicitly informed of the sorting paradigm. All groups demonstrated increased drift rate post-error, which represented information processing leaking through the inter-stimulus interval into the next trial. Only adults, however, demonstrated increased boundary separation post error, suggesting that they were the only group to show increased response caution. The implications of this finding, as well as the potential importance of reinforcement learning in PES task interpretation, is discussed.