Why elephants never forget, but fleas have the same attention span as fleas

Every day we wake up in a new world and adapt to it. Businesses face new challenges and competitors and adapt or fail. In biology, it is a matter of survival: every organism, from bacteria to blue whales, faces the challenge of adapting to an environment that is constantly changing. Animals must learn where to look for nutritious food, even though these food sources change with the seasons. However, learning requires time and energy: an organism that learns too slowly will lag behind environmental changes, and one that learns too quickly will waste effort trying to keep track of meaningless fluctuations.

A new mathematical model provides a quantitative answer to the question: what is the optimal rate of learning for an organism in a changing world? “The key idea is that the ideal rate of learning increases at the same rate regardless of the rate of environmental change, whether the organism changes its environment or changes its interactions with it. This suggests a generalizable phenomenon that may underlie learning across different ecosystems. ” states CSH PostDoc Eddie Lee.

The researchers’ model imagines an environment that alternates between different states, such as wet and dry seasons, at a characteristic pace. The body senses this state of the environment and records memories of past states. However, over time, the importance of old memories declines at a rate that determines the body’s learning time frame.

LEARNING WITH THE SQUARE ROOT OF CHANGE

What is the optimal training period for maximum adaptation to the environment? The model predicts a universal law: training time should scale as the square root of environmental time.

For example, if the environment fluctuates at half the rate, the body’s learning rate should decrease by a factor of 1.4 (the square root of 2). This square root scaling represents the optimal compromise between learning too fast and learning too slowly. It is important to note that the square root ratio indicates diminishing returns from longer memory.

“The model also simulates organisms that do not just passively learn, but can actively change their environment, an ability called niche creation,” says Li, an ESPRIT fellow at the Austrian Science Foundation (FWF) at CSH. If an organism has “stabilizing” abilities to make its environment more constant, it gains an evolutionary advantage. However, this advantage only arises if the organism can monopolize the benefits of a stable environment. If freeloading competitors also exploit a stabilized niche, the niche strategy falls apart. Example: Beavers actively shape their environment by building dams on rivers and creating sustainable ponds that provide habitat for themselves and other species. This design gives them a significant evolutionary advantage by providing them with a constant supply of food and protection from predators. However, this advantage may decrease if other organisms, such as muskrats or fish, exploit the resources of the created habitat.

METABOLIC FINDINGS FOR LARGE ANIMALS

Finally, the researchers evaluate how learning ability interacts with the metabolic cost of living, that is, the body’s energy needs. They predict that for small, short-lived creatures such as insects, the costs of learning and memory are of paramount importance. In contrast, for larger, longer-lived animals such as mammals, the learning costs are negligible due to metabolic costs.

This predicts that small, short-lived organisms have a highly tuned memory of their environment. “In contrast, larger organisms such as elephants have longer memories, but how long they retain information may depend more on non-learning costs or on other types of environments, such as social groups, that impose additional cognitive demands,” says Lee. Thus, it might not be entirely appropriate to ridicule the well-tuned “flea memory.”

The new model offers a quantitative framework for understanding how organisms balance the competing demands of learning and other survival imperatives in an ever-changing world. The results suggest an optimal rate of adaptation tailored to the rate of environmental change and organismal lifespan throughout the living world, from microbes to humans.