We are very much looking forward to hearing everyone's thoughts and feedback!

Finally, I just want to thank my coauthors Shannon Tubridy and Lila Davachi and my advisor
@todd_gureckis for helping me get this paper to the finish line and putting up with me which I dove into a causal inference rabbit hole! 9/9

We hope that this work contributes to a growing literature, from scholars like
@kordinglab,
@NicoleCRust and many others, on increased clarity and better methods for causal inference in cognitive neuroscience. 8/9

Obtaining these indicators is still a complicated scientific task and we argue in the paper that they are a potentially useful concept for both cognitive theory and translational work. 7/9

We hope that this concept of ICEA will be useful going forward for identifying the neural activity that causes behavior, as similar issues come up even in designs where neural stimulation is involved! 6/9

Unfortunately, we find that many signals proposed in the fMRI literature did not show sufficient statistical evidence that they reflect these indicators of causal neural activity, at least in our study. 5/9

Using causal graphs, we show that while we cannot uniquely identify the specific activity that underlies encoding from standard observational neuroimaging designs, we can identify a slightly larger set that includes “indicators” of that activity. 4/9

Despite there being a large literature on the “subsequent memory effect”, researchers have typically not considered this from a causal inference perspective. 3/9

We demonstrate how thinking through causal inference can really affect our conclusions about a major question in cog neuro: is there activity during encoding of an experience that causes you to remember it better later? 2/9