A deep dive into the world of computational modeling and its functions
For many years, scientists have sought to grasp how people make selections — whether or not we’re selecting what to eat for lunch or navigating high-stakes medical therapies. Conventional computational fashions of decision-making typically relaxation on mounted assumptions about how folks be taught from rewards and punishments. But these assumptions can battle to mirror the wealthy, adaptive methods by which people truly behave.
In an effort to deal with this complexity, Dezfouli and colleagues launched a novel framework primarily based on recurrent neural networks (RNNs) of their paper: Fashions that learn the way people be taught: The case of decision-making and its issues.
Their strategy goals to seize the nuanced processes behind human studying by coaching an RNN to mimic the subsequent motion a participant would absorb a decision-making job. Critically, the researchers examined this mannequin on each wholesome people and people dwelling with unipolar or bipolar despair.
By evaluating these teams, the examine not solely revealed the RNN’s capability to mannequin complicated behaviors extra precisely than conventional reinforcement-learning strategies, but in addition opened…