The use of principal component and factor analysis to measure fundamental cognitive processes in neuropsychological data

For years, dissociation studies on neurological single cases with brain lesions were the dominant method to infer fundamental cognitive functions in neuropsychology. In contrast, the association between deficits was considered to be of less epistemological value and even misleading. Still, principal component analysis (PCA) - an associational method for dimensionality reduction - recently became popular for the identification of fundamental functions. The current study evaluated the ability of PCA and factor analysis (FA) to overcome the association problem in behavioural data of neurological patients and to identify the fundamental variables underlying a battery of measures. Synthetic data were simulated to resemble neuropsychological data with typical dissociation patterns and, thereby, typical patterns of dependence between variables. In most experiments, PCA and FA succeeded to measure the underlying target variables with high up to almost perfect precision. However, this success was fragile and relied on the success of factor rotation, which failed its intended purpose when no test scores existed that primarily measured each underlying target variable. Further, commonly used strategies to estimate the number of meaningful factors appear to underfactor neuropsychological data, thereby consistently underestimating the dimensionality of the data. Finally, simulations suggested a high potential of PCA to denoise data, with factor rotation providing an additional filter function. This can be invaluable in neuropsychology, where measures are often inherently noisy, and PCA can be superior to common compound measures, such as the arithmetic mean, in the measurement of variables with high reliability. In summary, PCA and FA appear to be powerful tools in neuropsychology that are well capable to infer fundamental cognitive functions with high precision, but the typical structure of neuropsychological data and limited informative value of associations in neuropsychology place clear limitations and a risk of a complete failure on the methods.