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.

Keywords:

• Serial position
• Short-term memory
• Graphemic buffer
• Dysgraphia
• Spelling

Notes

¹ This is a concrete example of a situation long recognized in the computer science literature devoted to matching text patterns (see algorithms for Levenshtein or edit distance, e.g., Gusfield, 1997). Reconstruction of the changes that produce a response from a target cannot be done with certainty. Since an infinite number of transformations are possible, any one can only be assigned a value that indicates its likelihood, and scoring errors is an optimization problem that involves picking the changes that are most likely to have occurred given the target and response.