NE03
Ex-Gaussian Simulations of Response Speed Distributions and Memory Acquisition in MS

Thursday, May 25, 2017
B2 (New Orleans Convention Center)
Joshua Sandry, PhD , Psychology, Montclair State University, Montclair, NJ
Jessica Rothberg, BA , Psychology, Montclair State University, Montclair, NJ
Mark Zuppichini, BS , Psychology, Montclair State University, Montclair, NJ
John DeLuca, Ph.D. , Neuropsychology and Neuroscience, Kessler Foundation, West Orange, NJ
Joshua Sandry, PhD , Psychology, Montclair State University, Montclair, NJ



Background: Slowed information processing speed and impairments in learning and memory are common cognitive symptoms of MS. There is some evidence that MS participants with slowed information processing speed may experience greater difficulty acquiring new information.

Objectives: The aim of this exploratory study was to investigate the relationship between memory acquisition (two sub-processes of encoding & consolidation) and simple and complex processing speed (modeled with ex-Gaussian components).

Methods: 24 MS and 15 healthy control participants completed a series of verbal list learning trials (acquisition: memory encoding & memory consolidation) and a same-different choice response speed task (simple and complex processing speed). Ex-Gaussian response speed distributions were deconvolved into three parametric estimates for their underlying normal and exponential components.

Results: There were no correlations between the two measures of acquisition and the deconvolved components of simple and complex processing speed (all p’s >.12). In subsequent analysis we simulated full ex-Gaussian distributions using the point estimates from the deconvolved scores for the healthy control and MS groups and present descriptive comparisons of whole distributions.

Conclusions: MS participants were generally slower than healthy control participants at the response speed tasks. We found no relationship between measures of memory acquisition and processing speed in either group. The simulations provide preliminary evidence that whole distribution analyses may be a promising future direction to understand information processing speed differences in MS. The 3-parameter approach may be more powerful at identifying differences that are masked by more traditional methods using central tendency.