MJD 59,436

A week ago, for the first time in decades, I spent several days in a row doing many hours of hands-on engineering work in a CAD tool (for my rover project), and noticed that I had particularly vivid dreams on those nights. My sleep data from my Whoop strap confirmed that I’d had higher REM sleep on those nights.

These were dreams that lacked narrative and emotional texture, but involved a lot of logistics. For example, one dream I took note of involved coordinating a trip with multiple people, with flights randomly canceled. When I shared this on Twitter, several people replied to say that they too had similar dreams after days of intense but relatively routine information work. A couple of people mentioned dreaming of Tetris after playing a lot, something I too have experienced. High-REM dreaming sleep seems to be involved in integrating cognitive skill memories. This paper by Erik Hoel, The Overfitted Brain: Dreams evolved to assist generalization, pointed out by @nosilver, argues that dreaming is about mitigating overfitting of learning experiences, a problem also encountered in deep learning. This tracks for me. It sounds reasonable that my “logistics” dreams were attempts to generalize some CAD skills to other problems with similar reasoning needs. REM sleep is like the model training phase of deep learning.

This got me interested in the idea of tapping into the unconscious towards pragmatic ends. For example, using the type of dreams you have to do feedback regulation of the work you do during the day. I made up a half-assed hypothesis: the type of dream you have at night depends on 2 variables relating to the corresponding daytime activity — the level of conflict in the activity (low/high) and whether or not the learning loop length is longer or shorter than 24 hours. If it is shorter, you can complete multiple loops in a day and the night-time dream will need to generalize from multiple examples. If it’s less than a complete loop, there is no immediate resolution, so instead you get more dreams that integrate experiences in an open-loop way, using narrative-based explanations. If there’s no conflict and no closed loops, you get low dreaming. There’s nothing to integrate, and you didn’t do much thinking that day (which for me tends to translate to poor sleep). I made up the 2×2 above for this idea.

I have no idea whether this particular 2×2 makes any sense, but it is interesting that such phenomenology lends itself to apprehension in such frameworks at all. I rarely remember dreams, but I think even I could maintain a dream journal based on this scheme, and try to modulate my days based on my nights.

This also helps explain why people in similar situations might have similar dreams (such as all the “corona dreams” that many of us were having during early lockdown months). It also lends substance to the narrative conceits of stories like H. P. Lovecraft’s Call of Cthulhu, which begins with people having widespread similar dreams (where it turns into science fiction is in the specificity of the shared motifs and symbols that appear).

You don’t need to buy into dubious Jungian or Freudian theories of individual and collective dreaming to think about this stuff in robust ways. The development of deep learning, in particular, offers us a much more robust handle on this phenomenology. Dreams are perhaps our journeys into our private latent spaces, undertaken for entirely practical purposes like cleaning up our messy daytime learning (there’s other theories of dreaming too of course, like David Eagleman’s theory that we dream to prevent the visual centers from getting colonized by other parts of the brain at night, but we’re only hypothesizing contributing causes, not determinative theories).