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

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

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

In new research that appears in Advances in scienceJanelia scientists tried to better understand the possible ways an animal could successfully solve a problem, beyond the best strategy.

The work shows that there are a large number of ways 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 in different ways.

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

“Once you break free from being perfect, you’ll be surprised how many ways there are to solve a problem,” says Tzuhsuan Ma, a postdoctoral fellow at the Hermundstad Laboratory who led the research.

The new framework can help researchers begin to examine these “good enough” strategies, including why different individuals may adapt different strategies, how these strategies may work together, and how generalizable the strategies are across tasks. others. 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 of solving this task, but they work so well, so it’s entirely possible that animals use them too,” says the Leader of Janelia Group, 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 accomplish a simple but common task: choosing between two options where the chance of being rewarded changes over time.

The researchers were interested in examining a set of strategies that fall between optimal and completely random solutions: “small programs” that are limited by 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 of animal behavior.

As it turns out, there are a lot of such programs – about a quarter of a million. To understand these strategies, the researchers first looked at a small subset of the best performers. Surprisingly, they found that they were essentially doing the same as the optimal strategy, despite using 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 best programs, they found what they were looking for: about 4,000 programs that fall into this “good enough” category. And most 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 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 relationship of programs to each other through a network that connected different programs. They then looked at the behavior described by the strategies, creating an algorithm to discover 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 still maintaining performance. If some of these new behaviors are also useful in other tasks, this suggests that the same program may be good enough for solving a variety of different problems.

“If you’re thinking about an animal not being a specialist that’s optimized to solve just one problem, but a generalist that solves many problems, this is really a new way to study it,” Ma says.

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

“After all, gaining a solid understanding of animal behavior is an essential prerequisite for understanding how the brain solves different kinds of problems, including some that our best artificial systems solve only inefficiently, if at all,” says Hermundstad. “The main challenge is that animals can use very different strategies than we might have originally assumed, and this work is helping us uncover that space of possibilities.”

More information:
Tzuhsuan Ma et al, A wide space of compact strategies for effective decisions, Advances in science (2024). DOI: 10.1126/sciadv.adj4064

Provided by Howard Hughes Medical Institute

citation: New research shows why you don’t need to be perfect to get the job (2024, June 24) retrieved June 24, 2024 from https://phys.org/news/2024-06-dont-job.html

This document is subject to copyright. Except for any fair agreement for study or private research purposes, no part may be reproduced without written permission. The content is provided for informational purposes only.

7StarHD.Com