We often think of learning through the lens of preparing for an exam or teaching a dog to sit, but humans and other mammals are not the only entities capable of adapting to their environment – schools of fish, robots and even our genes can learn new behaviors, explain Jan De Houwer and Sean Hughes (University of Ghent) in a new Perspectives on Psychological Science article. Adopting a broader definition of learning that includes any behavioral adaptation developed in response to regular features of an environment could help researchers collaborate in the fields of psychology, computer science, sociology and genetics, explained De Houwer in an interview.
“Most people think of learning as some kind of mechanism for storing new information, but this makes it very difficult to compare learning in different systems because different systems probably use different mechanisms for storing information,” De Houwer said. “We define learning as changes in the way a system responds to its environment, that is, as learned behavior. »
Much like Darwin’s theory of evolution, De Houwer and Hughes’ functional definition of learning focuses on how systems adapt to their environment, regardless of the mechanisms by which these adaptations may occur. . The “system” in question could be an individual organism, a part of an organism such as a gene or the spinal cord, or a community of organisms. In fact, De Houwer added, evolution itself could be thought of as a form of learning in which an animal species is viewed as a system that adapts to its environment.
“Because our definition of learning is ‘mechanismless’, it allows for interactions between scientists studying learning in different systems,” said De Houwer. “It breaks down the barriers between the different sciences and allows an exchange of ideas which can only promote the study of learning in general. »
In addition to supporting comparisons between learning in different types of systems, this definition can also help researchers examine more closely how these systems may influence each other, write De Houwer and Hughes. A corn plant can learn to become more drought resistant, for example, because its genes have an epigenetic response to dehydration that prompts its cells to retain more water, ultimately influencing the learned behavior of the entire plant.
Learning can also occur at the group level, such as in a school of fish, due to some members of that group learning, but not all, De Houwer added. A fish at the head of a shoal can learn to avoid a shipwreck after repeatedly finding sharks there, for example, while fish at the back of the shoal can adopt a similar behavior by simply continuing to follow the fish. before them without knowing more about the Shipwreck.
This analysis can also be applied to the study of robots and artificial intelligence. While each can be studied separately, a robot’s ability to learn to navigate obstacles also depends on how its algorithm responds to the environment, the researchers explain.
It is important to note, however, that a system cannot be described as learning simply because its behavior has changed in response to the environment. A system can only be said to have learned something if it changes the way it responds to a stimulus due to regularities in its environment, such as repeated exposure to a stimulus or the co-occurrence of stimuli, De Houwer said. . Learning researchers examine the conditions under which regularities in the environment alter behavior, he continued.
Developing a precise definition of learning can help scientists communicate existing findings and promote new interdisciplinary research, conclude De Houwer and Hughes.
“Definitions are tools for better science,” they write. “Our definition allows scientists to share their knowledge and thus explore new ways of studying learning in different systems. »
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A broader definition of learning could help spur cross-disciplinary research – News Artificial intelligence and robotics
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