In the last decade, a phenomenon in artificial intelligence known as “double descent” has surprised researchers who study learning. The expectation was that the best AI models were neither too simple nor too complex. But certain models (like systems that classify images) actually learn best when the model grows extremely complex, far more than the data.
Over a century of psychological research assumed this was impossible: no system, animal or artificial, should be able to learn through such a method. Given constrained resources for storing information, systems should forget most of what they experience and only hold onto key themes.
Now that there’s proof of AI models successfully learning not through simplifying and forgetting, but through supercharged complexity, researchers are investigating whether humans can do something similar.
A recent paper in Brain and Behavioral Sciences aims to spark such investigations by proposing a new framework for human learning. Each year, the journal accepts a mere dozen or so manuscripts. Then it solicits up to 40 commentaries from outside experts, and publishes the robust scientific debate.
“We are challenging a long-standing assumption that says humans can’t learn complex representations and successfully generalize their knowledge,” says co-author and SFI Complexity Postdoctoral Fellow Marina Dubova, a cognitive scientist. “And we are proposing a framework that can be used to test if a certain human behavior actually demonstrates excess-capacity learning.”
The paper defines “capacity” as how many resources a learning system, like a human or AI model, devotes to representing its experiences. There are three kinds of capacity learning: constrained (where you don’t remember every experience), sufficient (where you remember every experience), and excess (where you remember every experience and supposedly irrelevant details about these experiences).
Imagine you want to learn what kind of food you like. In the traditional psychological “constrained” view, you forget the details of each meal you try, only remembering that you like burgers.
But one night, you have a bad burger. Excess-capacity learning means you memorize the exact details of the bad meal, and use these details to make predictions about future meals. Your predictions might depend on relevant details (the restaurant where you ate the bad burger) and details that may at first appear irrelevant (the day of the week).
Surprisingly, this excess complexity can help you make a better prediction about your next meal — while the day of the week alone may not directly affect burger quality, it could indicate which chef is working that day.
“Excess-capacity learning means that once you have memorized the exact details, you don’t need to discard the specifics to generalize. Instead, by retaining and even expanding on the details, you can arrive at a representation that generalizes well, sometimes even better than simplifying would. Cognitive science hasn’t fully explored this mode of learning,” says Dubova, who co-authored the paper with Sabina Sloman of the University of Manchester.
Psychological evidence already shows excess-capacity learning may exist in humans. Humans come up with more complex interpretations for phenomena than needed, and they often memorize redundant information.
Dubova and Sloman hope researchers who investigate intelligence and learning in the broadest sense — for example, biologists, philosophers, linguists, computer scientists, and cognitive scientists — will submit a commentary. Experts should register their interest by May 15, 2026.
Read the paper "Excess Capacity Learning" in Behavioral and Brain Sciences, April 17, 2026. DOI: 10.1017/S0140525X2610510X