8 min read

The Winter of Weird

Or, how I'm trying to wean myself off to-do lists.
The Winter of Weird

Hello, grownups!

I have been having some EXQUISITELY STRANGE dreams recently.

The other night, for instance, I dreamt I was on a road trip and came across a town that had been overrun by the KKK, and all the children in the town had become ghosts because they didn't want to live in a world so ugly, but if you stood in a field at night and read stories to them their little ghost bodies would huddle around you like moths (this was during the week I was super stressed about the U.S. election; read into that WHAT YOU WILL). Last night I dreamt about being in a place that looked like the Drakensberg but was full of these things:

drawing of a birdlike dream creature
they were friendly, but also enormous, the size of mountains

I've always remembered my dreams, but they've been even more vivid and intense (and often weeeeeeird) this year than ever before. And I think it's because of the lockdown. Like there's some kind of direct correlation between the sameyness of my days and the bizarreness of my dreams. Like my brain is starving for novel experiences, so it's just inventing them.

My buddy Sam send me an article from the New Scientist recently that could be a good explanation for this (no, I haven't gone completely mad, this is another Sam, AKA "Sam with a beard"). It describes a theory about why we dream that's called the Overfitted Brain Hypothesis.

Overfitting is an idea from machine learning. When you train a machine learning algorithm, you give it a set of data to learn from, and you try to teach it how to identify a pattern that it can then apply to new data. Overfitting happens when the algorithm gets too fixated on the specific information you gave it to learn from, so it's no good at dealing with new information. It's the machine equivalent of rote-learning something rather than understanding it.

Here's a visual example. You would want a machine learning algorithm to be able to "see"the general pattern (i.e. the black line) so that it could predict whether new dots are blue or red based on where they are. If overfitting happens, the algorithm "sees" the green line instead.

Overfitting is like if you tried to learn spelling by memorizing the spelling of every single word one by one, rather than by understanding the general rules and trying to guess at the spelling of words you've never heard before. Guessing based on rules wouldn't always get it 100% right because English spelling is CHAOS, I TELL YOU (I even wrote an episode of a cartoon show about this), but it would be a heck of a lot faster than learning every word individually!

We haven't really learned something until we can deal with examples we've never seen before. Like here, look at this bizarro animal quick:

This is a lowland-streaked tenrec from Madagascar. What a cute little weirdo! Like a hedgehog going to a costume party dressed like a bee.

Now, you've never seen this adorable idiot before (unless any Malagasy mammalogists are reading this, IDK), but you can probably figure out some stuff about it by looking at it. You can identify its ears, eyes, nose, little hands. You can tell that its long snoot is probably for snarffling around amongst leaves and dirt. And you could probably guess from it's prickles that it's not a good idea to pick it up and give it a cuddle. Congratulations! Your brain has just proved it is a pretty good machine learning algorithm for figuring out how mammals work (it helps that you are one).

In machine learning, one of the ways you can prevent overfitting is by injecting "noise" into the training data. Throwing in some total curveballs. As Robby Berman put it:

To keep machine learning from becoming too fixated on the specific data points in the set being analyzed, programmers may introduce extra, unrelated data as noise or corrupted inputs that are less self-similar than the real data being analyzed.
This noise typically has nothing to do with the project at hand. It's there, metaphorically speaking, to "distract" and even confuse the algorithm, forcing it to step back a bit to a vantage point at which patterns in the data may be more readily perceived and not drawn from the specific details within the data set.

The Overfitted Brain Hypothesis is that this is what dreams are: they're "corrupted inputs" to prevent our brains from becoming too rigid. The things we see and experience in our day-to-day lives are too similar for us to learn from, so the brain invents new data for our mind's "training data set".

If we dreamed only to reinforce memories, it would surely be more useful for our minds to just replay our actual experiences in our dreams, instead of concocting a bizarre narrative about being chased by a chocolate snake around your Primary School or whatever. Instead, the Overfitted Brain Hypothesis suggests that dreams aren't about memory, but about learning. They help us to continue learning things even when we're in a rut.

The whole New Scientist article is here, and it's a hoot, but unfortunately it's behind a paywall.

Having moved countries in the middle of a pandemic means that my world has been very small this year. That's often been a gift and a joy. It's been small enough for me to notice things I would never have noticed. The unfurling of a new leaf on my tomato plant, day by day. How the plumage of the teenage swans in the river near my house are turning from mottled grey to white. The weird yoga positions my cat gets into when he cleans himself. The beautiful way the afternoon sun paints the wall in my bedroom.

But clearly my brain's been telling me that it is BORED with these peaceful observations. It needs some spontaneity, some chaos!

So, I'm declaring the next few months my Winter of Weird.

I started setting themes for my seasons at the start of this year, after watching this magnificent video by CGP Grey about why he stopped making New Year's Resolutions, and started giving each year a theme instead. It's so great. Give it a watch.

I've tried a lot of different productivity systems over the years. I am a goddam connoisseur of productivity systems. Setting up new productivity systems is my favourite way to procrastinate, in fact.

Some of them stick - I've been following some version of the Getting Things Done  (GTD) approach for over a decade now. GTD is basically about never keeping your to-do list in your brain: building a system that lets you collect anything you want to do in an inbox (anything from vague "maybe I should take up pottery..." to a "crap, that article was due yesterday"), and regularly processing that inbox, turning those things into clear, prioritised, actionable to-do lists.

GTD is the perfect system for anal type-As who love lists, i.e. it's Sam Crack, but it has it's limitations. It's very good at helping you make progress towards actionable productive-y goals, but useless at more holistic health/attitude/mindset-type intentions. Things that aren't about achieving specific, pre-defined things, but are rather about habits, the small decisions we make day-to-day that end up defining who we are. The virtues we cultivate.

As Grey points out, the other problem with setting goals is that we usually fail to reach them, and that makes us feel shitty and de-motivated.

Themes, unlike goals, are gentler. They're broad, so that they can be re-defined as your situation changes. They help you change in a direction, but they don't specify an outcome. Over the years, people have tried themes like The Year of Poise, The Year of Home, Learning, Attention, Joy, Structure, Reading, Gratitude, Music, Growth, Health, and Simplicity.

I've been trying them all year, and loving them. I've been doing "Seasons of..." rather than whole years, because that just felt right for me. I started with the Winter of Small Joys, which was all about making time for tiny things that make me happy like candles and walks and long baths. I did a Spring of Germination, which was a bit obvious, fine, but was about gently nurturing new ideas and new projects and not giving up on them too soon, and was also a time I was growing a LOT of vegetables. I did a Summer of Excavation, where I challenged myself to just write a draft of my novel even though I didn't know what "shape" it had yet. And now I'm finishing my Autumn of Movement, which has been all about being in my body, walking and running and cycling and stretching and dancing, and also about moving on emotionally from some stuff I needed to let go of.

In my Winter of Weird, I am challenging myself to inject some STRANGE back into my life. I think this will mean a lot of creative challenges (like StoryCubes and Oblique Strategies), learning stuff outside of my usual areas of interest (signing up for the oddest UCT Summer School courses I can find, playing WikiRoulette), maybe even trying out some induced hallucination techniques like Ganzfeld goggles. Who knows! But it's going to get odd up in here. I'm PSYCHED.

William Burroughs and Brion Gyson
Brion Gyson and William Burroughs used to induce sober hallucinations through flickering lights created by this gizmo called the Dreammachine.

These themes have given so much shape and purpose to this year, for me. I highly recommend giving it a try! There's just one month left until 2021: why not test-drive a "micro-theme" between now and then, and see if you like it?

Wishing you pleasant dreams,


Updates from Sam-Land

  • I've spent the week in workshops talking about climate change with scientists and sci-fi writers for the choose-your-own adventure story I'm making with SESYNC. I am too brain-fried today to tell you all the bizarro stuff I learned but it was fascinating. My biggest takeaway was that we need to stop talking about how "we're all going to die" because of climate change. There's almost no chance that all humans will die over the coming century, but a HIGH chance that many millions will die. Somehow, that's much worse. It's also something we have to face. Climate nihilism ("ah, what's the point, we're all just going to die lol") is as unhelpful as climate denial, just another way to avoid the urgency and importance of what we have to do. I will write more about this soon, once I have digested it all!
  • I'm about halfway through writing you all postcards - they'll be in the mail on their way to you soon :)
  • There are a whole bunch new goodies up in the Store, including sticker packs and book-merch combos. We've got kid-friendly and non-kid friendly versions!

And finally, let me leave you with an obligatory Digby photo:

Look at his little toofie! I DIE.