Your Brain Forgets on Purpose (It's Basically Garbage Collection)

Your brain is built to forget. On purpose. That sounds like an excuse for not remembering how git rebase --onto works. It's actually the argument of a 2017 review in Neuron by two University of Toronto researchers, Blake Richards and Paul Frankland. Their claim, in one line from the paper's abstract: "the goal of memory is not the transmission of information through time, per se. Rather, the goal of memory is to optimize decision-making." Read that again as a developer. Memory isn't a database. It's closer to a cache with an opinionated eviction policy. The old view: forgetting is a bug For most of the last century, neuroscience mostly studied remembering. How a synapse strengthens, how a memory gets stored, how it gets pulled back up. Forgetting was treated as the failure case. The storage leaked, or the retrieval broke. Richards and Frankland pulled together a newer body of work that looks at forgetting as its own process, with its own machinery. In CIFAR's write-up of the paper, Frankland put it plainly: "We find plenty of evidence from recent research that there are mechanisms that promote memory loss, and that these are distinct from those involved in storing information." So the brain doesn't just fail to keep things. It spends energy getting rid of them. Two ways the brain deletes The first mechanism is the one you'd guess. Connections between neurons that hold a memory get weakened or removed. Use it less, and the wiring thins out. The second is stranger. Your hippocampus, the region that handles new memories, keeps growing new neurons through life. Frankland's lab tested what that does to old memories in a 2014 study in Science led by Katherine Akers (abstract here). They trained mice to fear a particular box, then boosted the growth of new neurons afterwards. The adult mice forgot the fear. The new cells wire themselves into existing circuits, and in doing so they overwrite some of what was stored there. They also looked at babies. Infant mice, which grow new hippocampal neurons at a much higher rate, forgot the box within days. When the researchers slowed that neuron growth, the infant mice held on longer. Guinea pigs and degus, which make most of those neurons before birth, didn't show the infant forgetting at all, until the team boosted neurogenesis and the forgetting showed up. That's one proposed explanation for why you can't remember being two. Your brain was rebuilding too fast to keep the files. New neurons (green) wiring into older hippocampal circuits can make old memories harder to reach. Why would a brain do this? This part will feel familiar to anyone who's trained a model. Richards works on applying machine learning ideas to the brain, and the paper leans on two of them. The first is flexibility. Outdated information is actively harmful. If your brain kept every old address, every old password and every old version of an API with equal strength, it would keep serving you conflicting answers. As Richards said in the CIFAR piece, "If you're trying to navigate the world and your brain is constantly bringing up multiple conflicting memories, that makes it harder for you to make an informed decision." Anyone who has typed a deprecated method name from muscle memory knows exactly what he means. The second is generalization. In machine learning, a model that memorizes every detail of its training data overfits. It does great on what it has seen and badly on anything new. Regularization fixes that by pushing the model toward simpler patterns. Richards and Frankland argue that forgetting details does the same job for the brain. You keep the gist of how authentication flows work and lose the exact config from that one project in 2021. The gist is what transfers to the next project. The paper doesn't say forgetting is always good. It says the two forces, keeping and dropping, have to work together, and the balance depends on how stable your world is. Your environment sets the eviction policy This is the part I find most useful. The CIFAR piece gives an example: a cashier who meets many new people a day will remember their names briefly, while someone who sees the same clients every week holds on to them. "One of the things that distinguishes an environment where you're going to want to remember stuff versus an environment where you want to forget stuff is this question of how consistent the environment is and how likely things are to come back into your life," Richards said. Your brain is constantly estimating whether something will come back. If it doesn't come back, the brain bets that it won't matter. Which explains a lot about developer life. You read the Kubernetes docs once, during an incident, at 2am. Nothing about that reading told your brain "this will come up again." So it didn't keep it. Forgotten usually means hard to reach There's a related idea from the psychologists Robert and Elizabeth Bjork at UCLA, called the new theory of disuse (1992). They split memory into two strengths. Storage strength is how well something is learned. Retrieval strength is how easy it is to pull up right now. Their example is a phone number you used for five years, twenty years ago. It's still stored. You just can't get to it. Your hotel room number on day three of a trip is the opposite: easy to recall, barely stored, gone a week later. The useful bit: in their theory, retrieving something when it has become harder to reach builds more storage strength than retrieving it when it's fresh. A bit of forgetting before you review makes the review count for more. Most of what you've learned is still on the shelf. Retrieval strength decides what's within reach. What to do with this If forgetting is your brain's bet that something won't come back, the fix is simple. Make it come back. Try this with one thing you learned this week. A new library, a tricky bug, a concept from a talk. Close the tab. Tomorrow, without looking, write down three things you remember about it. Then check. Do it again in a few days, and again in a week or two, leaving longer gaps each time. Each return is a signal to your brain: this keeps showing up, keep it. And the gaps matter. A little struggle to recall is the point, according to the Bjorks. And for everything else, the stuff that never comes back? Let it go without guilt. That's the brain doing its job. I built Recl to handle the "make it come back" part for the things I save. You drop in a video, article or post, it writes a short summary, and it brings the save back later as a quick question, with the gaps getting longer when you remember it. Some example summaries live in the public library. In Recl, a save comes back as a quick question, with longer gaps each time you get it right. Your brain is going to forget most of what you read today. It probably should. The trick is choosing what you give it a reason to keep. I'm Asutosh. I write about how memory and learning work, and I build Recl, a tool for remembering what you watch and read.