Dr. Emily Cook

So the things that we do and the things that we worry about during the day is the raw material that is used to build the often very strange stories in our dream content.

But one of the big challenges that we've always faced in this field is at the level of a single dream, we often can't pinpoint exactly what's going on.

So I know there's all sorts of dream dictionaries people often try and sell that really don't do the job.

They're not very good interpretive sources.

But what Kyle and I work on is dreams at scale.

So these patterns, these stories, and these clues in dreams are very, very quiet.

But when we amplify that signal over hundreds, usually thousands of dreams, it comes through a lot more clearly.

So these very small patterns, we can see them in our big datasets.

Well, yes, but there's a little bit more to it.

So one way of thinking about the continued hypothesis is very literally.

So if you play soccer during the day, you'll have dreams about soccer during the nighttime.

But it's not always a direct reenactment.

So as you probably can guess from how bizarre your dreams are, often things happen in your dreams that never would happen in real life.

For example, maybe you're playing soccer with Donald Trump.

Sometimes the way that the dreams reenact our experiences during the day, whether or not that's an actual activity you participated in, a thought that you had, a memory that came back to you, they will reenact them during the night, sort of like living metaphors.

So it might not be a direct mapping.

It all gets jumbled up.

And like I say, that's why we need the big data sets to really piece apart what kind of continuity is happening in the dream narrative.

Well, for this study, we found two big differences.

The first is perhaps not surprising.