For many years, scientists have sought to know how people make choices — whether or not we’re selecting what to eat for lunch or navigating high-stakes medical therapies. Conventional computational fashions of decision-making usually relaxation on fastened assumptions about how individuals study from rewards and punishments. But these assumptions can battle to mirror the wealthy, adaptive methods during 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 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 activity. Critically, the researchers examined this mannequin on each wholesome people and people residing with unipolar or bipolar melancholy.
By evaluating these teams, the research not solely revealed the RNN’s capability to mannequin complicated behaviors extra precisely than conventional reinforcement-learning strategies, but in addition opened…