New research shows why you don’t have to be perfect to get the job done

When neuroscientists think about the strategy an animal might use to accomplish a task — such as finding food, hunting prey, or navigating a maze — they often propose a single model that represents the best way to describes the animal to accomplish the task.

But in the real world, animals (and humans) may not use them in the optimal way, which can be resource-intensive. Instead, they use a strategy that’s good enough to get the job done, but requires a lot less brain power.

From new research appearing in Scientific progressJanelia scientists wanted to better understand the possible ways an animal can successfully solve a problem, beyond just the best strategy.

The work shows that there are a large number of ways an animal can accomplish a simple foraging task. It also outlines a theoretical framework for understanding these different strategies, how they relate to each other, and how they solve the same problem in different ways.

Some of these less-than-perfect options for completing a task work almost as well as the optimal strategy, but with much less effort, the researchers found, freeing up animals to use precious resources to complete multiple tasks.

“Once you let go of perfection, you would be surprised how many ways there are to solve a problem,” says Tzuhsuan Ma, a postdoc in the Hermundstad Lab, who led the research.

The new framework could help researchers explore these “good enough” strategies, including why different individuals might adapt different strategies, how these strategies might work together, and how generalizable the strategies are to other tasks. That could help explain how the brain enables behavior in the real world.

“Many of these strategies are strategies that we would never have thought of as possible ways to solve this task, but they do work well, so it’s entirely possible that animals could use them too,” says Janelia Group Leader Ann Hermundstad . “They give us a new vocabulary to understand behavior.”

Looking beyond perfection

The research began three years ago when Ma began wondering what different strategies an animal could possibly use to accomplish a simple but common task: choosing between two options where the probability of reward changes over time .

The researchers were interested in examining a group of strategies that fall between optimal and completely random solutions: “small programs” that have limited resources but still get the job done. Each program specifies a different algorithm for guiding an animal’s actions, based on past observations, allowing it to serve as a model for animal behavior.

It turns out that there are many such programs: about a quarter of a million. To understand these strategies, the researchers first looked at a handful of the best performers. Surprisingly, they found that they essentially did the same thing as the optimal strategy, despite using fewer resources.

“We were a little disappointed,” says Ma. “We’ve been looking all this time for these little programs, and they all follow the same calculation that the field was already able to derive mathematically without all this effort.”

But the researchers were motivated to keep looking: they had a strong intuition that there must exist programs that were good, but different from the optimal strategy. Once they looked beyond the very best programs, they found what they were looking for: about 4,000 programs that fall into this “good enough” category. And more importantly: more than 90% of them did something new.

They could have stopped there, but a question from a fellow Janelian spurred them on: How could they figure out what strategy an animal was using?

The question prompted the team to dive deep into the behavior of individual programs and develop a systematic approach to thinking about the entire collection of strategies. They first developed a mathematical way to describe the relationships of the programs to each other through a network that connected the different programs. They then looked at the behavior described by the strategies and devised an algorithm to reveal how one of these “good enough” programs could evolve from another.

They found that small changes in the optimal program can lead to large changes in behavior while still maintaining performance. If some of these new behaviors are also useful in other tasks, it suggests that the same program could be good enough for solving a range of different problems.

“If you think about the fact that an animal is not a specialist optimized to solve just one problem, but rather a generalist that solves many problems, this is really a new way to study that,” says Ma.

The new work provides researchers with a framework to think beyond single, optimal programs for animal behavior. Now the team is focusing on investigating how generalizable the small programs are to other tasks, and designing new experiments to determine which program an animal might use to perform a task in real time. They are also working with other researchers at Janelia to test their theoretical framework.

“Ultimately, gaining an understanding of an animal’s behavior is an essential prerequisite for understanding how the brain solves different types of problems, including some that our best artificial systems solve only inefficiently or not at all,” says Hermundstad. “The key challenge is that animals can use very different strategies than we might initially think, and this work helps us explore that space of possibilities.”