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Abstract

In this talk, we will discuss how preregistration can be used to increase transparency in electrophysiological research. We will start by discussing how confirmation bias (looking for information that supports prior beliefs), hindsight bias (overestimating in how far past events predicted a current outcome), and pressure to publish can lead to (unconscious) data exploration after which only (statistically) significant results are reported. We will highlight some of the problems associated with this undisclosed analytic flexibility, focusing on EEG research, in which complex multidimensional data can be preprocessed and analyzed in many possible ways. We argue that transparently disclosing analytic choices can mitigate confirmation and hindsight bias and make EEG research more verifiable. One possible tool for transparent reporting is preregistration: providing a time-stamped, publicly accessible research plan with hypotheses, a data collection plan, and the intended pre-processing and statistical analyses, written before the data were accessed. We will provide examples on how to create preregistrations for EEG studies that are specific, precise and exhaustive, focusing on data pre-processing and analysis steps. Finally, we will highlight the benefits and critically discuss the limitations of adopting preregistration for EEG researchers.