How the Brain Simplifies Goals to Learn Faster
Humans can assign value to new, abstract outcomes, but this process is hard on the brain. A new study shows that people learn better when they simplify these goals into basic rules over time.
In short: Humans can assign value to new, abstract outcomes, but this process is hard on the brain. A new study shows that people learn better when they simplify these goals into basic rules over time.
Have you ever noticed that tasks get easier the more you practice them? Researchers have new insights into how the brain handles this by shrinking complicated choices into simple rules.
What happened, in plain words
Researchers studied how people learn by assigning value to new and abstract outcomes. They found that learning at first uses up a lot of mental space in your working memory. As people gain consistent experience, they create a compressed reward function, which is a simplified rule that moves into long-term memory. Across six experiments, the scientists showed that learning slows down when there are too many goals to juggle, but gets better when those goals can be compressed. Through computer models, they also found that people who process rewards faster tend to make more accurate choices.
Key points
- Working memory handles early learning: When you first try to learn something new with specific goals, your brain relies on a capacity-limited working memory, which makes the process mentally costly and less efficient.
- Brains create simplified rules: With consistent experience, learners form a compressed reward function, turning complex goals into a simplified rule that shifts over to long-term memory.
- Automatic evaluation frees mental space: Moving these rules to long-term memory allows for automatic evaluation when you receive feedback, freeing up your working memory resources and boosting your learning efficiency.
- Experiment results: Across six experiments, researchers demonstrated that learning suffers when the goal space is large, but improves when that space allows for compression.
- Computer models reveal differences: Computational modeling showed that individual differences in how efficiently someone compresses information—measured by their reward processing speed—led to higher choice accuracy.
Terms explained
- working memory — The part of your brain that holds and works with small amounts of information for a short time. Example: Trying to hold a phone number in your head just long enough to type it into a phone.
- compressed reward function — A simplified rule your brain makes from experience to figure out what results are good or bad. Example: Instead of thinking through every single rule of a game, you just remember a simple shortcut like 'stay away from red obstacles'.
- reinforcement learning — A type of learning where you figure out how to act or make decisions based on rewards and feedback. Example: Training a pet by giving them a treat every time they sit on command.
Why it matters
Understanding how the human brain simplifies complex choices to learn more efficiently gives us a better picture of how we adapt to new tasks. While the exact algorithmic details still need to be established, these findings help explain why practice and repetition eventually make hard tasks feel automatic and easier to handle.
What we still don't know
The exact algorithmic details of how this process works remain to be established. The findings come from six experiments and computer models, so more research is needed to fully understand the mechanics behind how individuals process rewards and compress goals.
Source: Nature Communications. The original is licensed CC BY. This text is an AI-assisted adaptation (summarized, simplified and translated) and may differ from the original.