For many years, scientists have sought to know 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 fastened assumptions about how folks be taught from rewards and punishments. But these assumptions can wrestle to mirror the wealthy, adaptive methods by which people truly behave.
In an effort to sort out this complexity, Dezfouli and colleagues launched a novel framework based mostly on recurrent neural networks (RNNs) of their paper: Models that learn how humans learn: The case of decision-making and its disorders.
Their method goals to seize the nuanced processes behind human studying by coaching an RNN to mimic the following motion a participant would absorb a decision-making activity. Critically, the researchers examined this mannequin on each wholesome people and people residing with unipolar or bipolar despair.
By evaluating these teams, the research not solely revealed the RNN’s capability to mannequin advanced behaviors extra precisely than conventional reinforcement-learning strategies, but in addition opened…
