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 complete a task—such as finding food, hunting prey, or navigating a maze—they often propose a single model that describes the best way the animal can accomplish that task.

Yet in the real world, animals – and humans – may not take the optimal path, which can be resource-intensive. Instead, they use a strategy that is good enough to get the job done but requires much less brain power.

In a new study published in Scientific advancesJanelia scientists wanted to better understand the possible ways an animal can successfully solve a problem, rather than just choosing the best strategy.

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

The researchers found that some of these imperfect ways of accomplishing a task work almost as well as the optimal strategy, but require significantly less effort, giving the animals more time to devote valuable resources to completing multiple tasks.

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

The new concept could help researchers study these “good enough” strategies, including why different people use different strategies, how these strategies work together, and how generalizable the strategies are to other tasks. This could help explain how the brain enables behavior in the real world.

“We could never have imagined many of these strategies as possible solutions to this problem, but they work well, so it’s quite possible that animals use them too,” says Janelia group leader Ann Hermundstad. “They give us a new vocabulary for understanding behavior.”

Looking beyond perfection

The research began three years ago when Ma started thinking about the different strategies an animal might use to accomplish a simple but mundane task: choosing between two options where the chance of receiving a reward changes over time.

The researchers wanted to study a group of strategies that lie between optimal and completely random solutions: “small programs” that have limited resources but still get the job done. Each program specifies a different algorithm to guide an animal’s actions based on previous observations, so it can serve as a model of animal behavior.

As it turns out, 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 performing ones. Surprisingly, they found that they essentially did the same thing as the optimal strategy, although they used fewer resources.

“We were a little disappointed,” says Ma. “We spent so much time looking for these little programs, and they all follow the same calculation that the field could already derive mathematically without all this effort.”

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

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

This question led the team to look closely at the behavior of individual programs and develop a systematic approach to thinking about the entire collection of strategies. First, they developed a mathematical method to describe the relationships of the programs to each other through a network that connected the different programs. Next, they examined the behavior described by the strategies and developed an algorithm to show how one of these “good enough” programs could evolve from another.

They found that small changes to the optimal program can lead to large changes in behavior without affecting performance. If some of these new behaviors are also useful in other tasks, it suggests that the same program might be good enough to solve a range of different problems.

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

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

“Ultimately, a good 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 inefficiently or not at all,” says Hermundstad. “The biggest challenge is that animals may use very different strategies than we initially assume, and this work helps us uncover this space of possibilities.”