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

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 come up with a single model that presents the best way for the animal to do so. accomplish this task.

But in the real world, animals – and humans – may not use the optimal method, which can be resource-intensive. Instead, they use a strategy that’s effective enough to get the job done, but requires much less brain power.

In new research published in Scientists progressJanelia scientists sought to better understand the ways in which an animal might successfully solve a problem, beyond simple strategy.

The work shows that there are a very large number of ways for an animal to accomplish a simple task of foraging. It also establishes 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 imperfect options for accomplishing a task work almost as well as the optimal strategy but with much less effort, the researchers found, freeing up animals to use valuable resources to handle multiple tasks.

“As soon as you free yourself from perfection, you would be surprised how many possible ways to solve a problem,” says Tzuhsuan Ma, a postdoctoral fellow at Hermundstad Lab, who led the research.

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

“Many of these strategies are ways we would never have imagined to solve this problem, but they work well, so it’s entirely possible that animals are using them too,” says Ann Hermundstad, head of the Janelia group. “They give us a new vocabulary to understand behavior.”

Look beyond perfection

The research began three years ago when Ma began wondering about the different strategies an animal could possibly use to accomplish a simple but common task: choosing between two options whose chances of being rewarded change with the time.

The researchers wanted to examine a group of strategies that fall somewhere 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 past observations, allowing it to serve as a model of animal behavior.

It turns out that there are many such programs: about a quarter of a million. To make sense of these strategies, the researchers first looked at some of the most successful ones. Surprisingly, they found that they did essentially the same thing as the optimal strategy, even though they used fewer resources.

“We were a little disappointed,” Ma says. “We spent all this time researching these little programs, and they all follow the same calculation that the field already knew to derive mathematically without all that 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. Once they looked beyond the top programs, they found what they were looking for: about 4,000 programs falling into that “pretty good” category. And more importantly, over 90% of them did something new.

They could have stopped there, but a question from another Janelian got them thinking: How could they tell what strategy an animal was using?

The question prompted the team to dig deeper into the behavior of individual programs and develop a systematic approach to thinking about overall strategies. They first developed a mathematical way of describing the relationships between programs through a network connecting the different programs. Next, they examined the behavior described by the strategies, designing an algorithm to reveal 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 while preserving performance. If some of these new behaviors are also useful in other tasks, it suggests that the same program could be enough to solve a whole range of different problems.

“If you think about an animal not being a specialist optimized to solve a single problem, but rather a generalist that solves many problems, it’s really a new way to study that,” Ma says.

This new work provides a framework for researchers to start thinking beyond single, optimal programs for animal behavior. The team is now focused on examining how well the small programs generalize 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 Janelia researchers to test their theoretical framework.

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