How Do We Reach Decisions? Researchers Pioneer AI Method to Uncover Cognitive Strategies

The study’s authors note that small neural networks — simplified versions of the neural networks typically used in commercial AI applications — can predict the choices of animals much better than classical cognitive models, which assume optimal behavior, because of their ability to illuminate suboptimal behavioral patterns. In laboratory tasks, these predictions are also as good as those made by larger neural networks, such as those powering commercial AI applications. 

“An advantage of using very small networks is that they enable us to deploy mathematical tools to easily interpret the reasons, or mechanisms, behind an individual’s choices, which would be more difficult if we had used large neural networks such as the ones used in most AI applications,” adds author Ji-An Li, a doctoral student in the Neurosciences Graduate Program at UC San Diego.

“Large neural networks used in AI are very good at predicting things,” says author Marcus Benna, an assistant professor of neurobiology at UC San Diego’s School of Biological Sciences. “For example, they can predict which movie you would like to watch next. However, it is very challenging to describe succinctly what strategies these complex machine learning models employ to make their predictions — such as why they think you will like one movie more than another one. By training the simplest versions of these AI models to predict animals’ choices and analyzing their dynamics using methods from physics, we can shed light on their inner workings in more easily understandable terms.”

Understanding how animals and humans learn from experience to make decisions is not only a primary goal in the sciences, but, more broadly, useful in the realms of business, government and technology. However, existing models of this process, because they are aimed at depicting optimal decision-making, often fail to capture realistic behavior.

Overall, the model described in the new Nature study matched the decision-making processes of humans, non-human primates and laboratory rats. Notably, the model predicted decisions that were suboptimal, thereby better reflecting the “real-world” nature of decision-making — and in contrast to assumptions of traditional models, which are focused on explaining optimal decision-making. Moreover, the NYU and UC San Diego scientists’ model was able to predict decision-making at the individual level, revealing how each participant deploys different strategies in reaching their decisions.

“Just as studying individual differences in physical characteristics has revolutionized medicine, understanding individual differences in decision-making strategies could transform our approach to mental health and cognitive function,” concludes Mattar.

The research was supported by grants from the National Science Foundation (CNS-1730158, ACI-1540112, ACI-1541349, OAC-1826967, OAC-2112167, CNS-2100237, CNS-2120019), the Kavli Institute for Brain and Mind, the University of California Office of the President and UCSD’s California Institute for Telecommunications and Information Technology/Qualcomm Institute.

— Adapted from a New York University news release

Learn more about research and education at UC San Diego in: Artificial Intelligence