Breaking the circularity in circular analyses: Simulations and formal treatment of the flattened average approach.

There has been considerable debate and concern as to whether there is a replication crisis in the scientific literature. A likely cause of poor replication is the multiple comparisons problem. An important way in which this problem can manifest in the M/EEG context is through post hoc tailoring of analysis windows (a.k.a. regions-of-interest, ROIs) to landmarks in the collected data. Post hoc tailoring of ROIs is used because it allows researchers to adapt to inter-experiment variability and discover novel differences that fall outside of windows defined by prior precedent, thereby reducing Type II errors. However, this approach can dramatically inflate Type I error rates. One way to avoid this problem is to tailor windows according to a contrast that is orthogonal (strictly parametrically orthogonal) to the contrast being tested. A key approach of this kind is to identify windows on a fully flattened average. On the basis of simulations, this approach has been argued to be safe for post hoc tailoring of analysis windows under many conditions. Here, we present further simulations and mathematical proofs to show exactly why the Fully Flattened Average approach is unbiased, providing a formal grounding to the approach, clarifying the limits of its applicability and resolving published misconceptions about the method. We also provide a statistical power analysis, which shows that, in specific contexts, the fully flattened average approach provides higher statistical power than Fieldtrip cluster inference. This suggests that the Fully Flattened Average approach will enable researchers to identify more effects from their data without incurring an inflation of the false positive rate.

Breakthrough percepts of famous faces.

Recently, we showed that presenting salient names (i.e., a participant's first name) on the fringe of awareness (in rapid serial visual presentation, RSVP) breaks through into awareness, resulting in the generation of a P3, which (if concealed information is presented) could be used to differentiate between deceivers and nondeceivers. The aim of the present study was to explore whether face stimuli can be used in an ERP-based RSVP paradigm to infer recognition of broadly familiar faces. To do this, we explored whether famous faces differentially break into awareness when presented in RSVP and, importantly, whether ERPs can be used to detect these breakthrough events on an individual basis. Our findings provide evidence that famous faces are differentially perceived and processed by participants' brains as compared to novel (or unfamiliar) faces. EEG data revealed large differences in brain responses between these conditions.