A deep dive into the world of computational modeling and its applications
For decades, scientists have sought to understand how humans make decisions — whether we’re choosing what to eat for lunch or navigating high-stakes clinical treatments. Traditional computational models of decision-making often rest on fixed assumptions about how people learn from rewards and punishments. Yet these assumptions can struggle to reflect the rich, adaptive ways in which humans actually behave.
In an effort to tackle this complexity, Dezfouli and colleagues introduced a novel framework based on recurrent neural networks (RNNs) in their paper: Models that learn how humans learn: The case of decision-making and its disorders.
Their approach aims to capture the nuanced processes behind human learning by training an RNN to imitate the next action a participant would take in a decision-making task. Critically, the researchers tested this model on both healthy individuals and those living with unipolar or bipolar depression.
By comparing these groups, the study not only revealed the RNN’s capacity to model complex behaviors more accurately than traditional reinforcement-learning methods, but also opened…