Principal Component Analysis (PCA) is a technique used to find the core components that underlie different variables. It comes in very useful whenever doubts arise about the true origin of three or more variables. There are two main methods for performing a PCA: naive or less naive. In the naive method, you first check some conditions in your data which will determine the essentials of the analysis. In the less-naive method, you set those yourself based on whatever prior information or purposes you had. The ‘naive’ approach is characterized by a first stage that checks whether the PCA should actually be performed with your current variables, or if some should be removed. The variables that are accepted are taken to a second stage which identifies the number of principal components that seem to underlie your set of variables.
We tested whether conceptual processing is modality-specific by tracking the time course of the Conceptual Modality Switch effect. Forty-six participants verified the relation between property words and concept words. The conceptual modality of …
The single dependent variable, RT, was accompanied by other variables which could be analyzed as independent variables. These included Group, Trial Number, and a within-subjects Condition. What had to be done first off, in order to take the usual table? The trials!
An ERP study demonstrating that conceptual modality switch effects emerge within 200ms and increase throughout processing, supporting the role of perceptual simulation in conceptual processing while suggesting that both amodal and modal systems contribute to cognition.