Blog

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 model that outlines the best way for the animal to accomplish the task.

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

In new research appearing in Scientific progressJanelia scientists seek to better understand the possible ways in which an animal can successfully solve a problem, in addition to the best strategy.

The work shows that there are a huge number of ways in which an animal can perform 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 differently.

Some of these less-than-perfect variants of a task perform almost as well as the optimal strategy, but with much less effort, the researchers found, freeing the animals to use valuable resources to tackle multiple tasks.

“Once you let go of being perfect, you’ll be surprised how many ways there are to solve a problem,” says Zuxuan Ma, a postdoctoral fellow in the Hermundstad lab who led the research.

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

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

Looking beyond perfection

The research began three years ago when Ma began to wonder about the different strategies an animal might use to complete a simple but common task: choosing between two options in which the chance of being rewarded changes over time.

The researchers were interested in exploring a group of strategies that fall between optimal and completely random solutions: “little programs” that are resource-constrained but still get the job done. Each program defines a different algorithm to guide the animal’s actions based on past observations, allowing it to serve as a model of animal behavior.

As it turns out, there are a lot of such programs — about a quarter of a million. To make sense of these strategies, the researchers first looked at a handful of the most effective. Surprisingly, they found that they essentially did the same as the optimal strategy, although they used fewer resources.

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

But the researchers were motivated to keep looking—they had a strong intuition that there must be programs that were good but different from the optimal strategy. After looking beyond the top 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 prompted them: How could they tell which strategy an animal was using?

The question led the team to take a deep dive 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 programs to each other through a network that connects the different programs. They then looked at the behavior described by the strategies, devising an algorithm to reveal how one of these “good enough” programs might evolve from another.

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

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

The new work provides a framework for researchers to begin thinking beyond single optimal programs for animal behavior. The team is now focusing on investigating how well the small programs can generalize to other tasks and designing new experiments to determine which program the animal can use to complete a task in real time. They are also working with other researchers at Janelia to test their theoretical framework.

“Ultimately, understanding animal behavior well is an essential prerequisite for understanding how the brain solves different kinds of problems, including some that our best artificial systems solve inefficiently, if at all,” says Hermundstad. “The main challenge is that animals may use very different strategies than we first assume, and this work helps us open up this space of possibilities.”