Modelling cognitive flexibility with deep neural networks

Neural networks trained with deep reinforcement learning can perform many complex tasks at similar levels to humans. However, unlike people, neural networks converge to a fixed solution during optimisation, limiting their ability to adapt to new challenges. In this opinion, we highlight three key new methods that allow neural networks to be posed as models of human cognitive flexibility. In the first, neural networks are trained in ways that allow them to learn complementary 'habit' and 'goal'based policies. In another, flexibility is 'meta-learned' during pre-training from large and diverse data, allowing the network to adapt 'in context' to novel inputs. Finally, we discuss work in which deep networks are meta-trained to adapt their behaviour to the level of control they have over the environment. We conclude by discussing new insights about cognitive flexibility obtained from the training of large generative models with reinforcement learning from human feedback.