Linking individual differences in the electroencephalogram (EEG) to variation in personality can provide important insights into the biological basis of human individuality and constitutes a prominent approach within the young and burgeoning field of Personality Neuroscience. Frequently investigated EEG measures include frontal EEG asymmetry, error and feedback-related negativities/frontal-midline theta, and the late positivities. Despite decades of research, the links between these measures and individual differences in personality remain poorly understood due to concerns of low replicability that currently thwart many areas of Psychology and Neuroscience. It has now become clear that low statistical power and undisclosed flexibility in data analysis are at the core of the current replicability crisis.
The present project aims to alleviate this unsatisfactory status quo and provide a firm basis for future EEG personality neuroscience work through initial application of a novel approach for empirical research termed “collaborative forking path analysis” (cFPA). Briefly, cFPA is based on close cooperation among teams of researchers sharing the load of data collection for an a priori agreed-upon, highly standardized experimental setup in order to achieve high statistical power and conducting a systematic scripted analysis (and comparison) of all defensible data analysis paths identified in joint expert meetings.
The present application of the cFPA approach entails the collection of data from several paradigms allowing quantification of each of the EEG measures noted above from N = 720 participants distributed across nine laboratories. This unprecedented collaborative effort within the format of a regular individual grant will allow us to:
Probe the replicability of prominent EEG-personality associations
Provide a systematic investigation of the influence of analysis choices on each of the effects of interest
Provide initial direct tests of potential moderators for several such associations
Validate several novel methods for quantifying individual differences in EEG activity
Derive initial estimates for the influence of inter-laboratory variability on each of the EEG measures and their associations with other variables
Provide an easily accessible high-quality multivariate data set for further EEG personality neuroscience work by researchers around the globe.
Most importantly, if demonstrated to be feasible in this initial application to a methodologically relatively demanding field, the cFPA approach may serve as a blueprint for significantly changing the practice of empirical research in Personality Neuroscience and various other areas towards increased statistical power and transparency.