Analysis and interpretation of serial position data

Abstract

The representation of serial position in sequences is an important topic in a variety of cognitive areas including the domains of language, memory, and motor control. In the neuropsychological literature, serial position data have often been normalized across different lengths, and an improved procedure for this has recently been reported by Machtynger and Shallice (2009). Effects of length and a U-shaped normalized serial position curve have been criteria for identifying working memory deficits. We present simulations and analyses to illustrate some of the issues that arise when relating serial position data to specific theories. We show that critical distinctions are often difficult to make based on normalized data. We suggest that curves for different lengths are best presented in their raw form and that binomial regression can be used to answer specific questions about the effects of length, position, and linear or nonlinear shape that are critical to making theoretical distinctions.