Scientists have long studied how people and animals make decisions, often looking at how recent experiences and trial-and-error shape behavior. But traditional models may miss key aspects of how decisions are made, largely because they assume individuals always try to choose the most logical or beneficial option based on past outcomes.
In a new study, researchers took a different approach by using artificial intelligence to explore decision-making in a more realistic way. They created small artificial neural networks to examine what truly influences an individual’s choices, whether those decisions are effective or not.
“Instead of assuming how brains should learn in optimizing our decisions, we developed an alternative approach to discover how individual brains actually learn to make decisions,” explains Marcelo Mattar, an assistant professor in New York University’s Department of Psychology and one of the authors of the paper, which appears in the journal Nature. “This approach functions like a detective, uncovering how decisions are actually made by animals and humans. By using tiny neural networks—small enough to be understood but powerful enough to capture complex behavior—we’ve discovered decision-making strategies that scientists have overlooked for decades.”
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