We are interested in the brain mechanisms that allow humans to solve complex problems that require closing of the perception-action cycle. We design cognitively challenging tasks and have human participants execute them to investigate their behavioral and/or neural responses using sophisticated analysis techniques developed in the group. These techniques are based on Bayesian, neural network and reinforcement learning approaches.
Next to understanding the empirical basis of complex problem solving, we are interested in the theoretical underpinnings of adaptive behaviour. Specifically, we ask whether computational models that are rooted in AI can provide an account of the learning, inference and control problems that are solved by the human brain. To address this question, we develop new learning algorithms and simulate adaptive behaviour in artificial agents.
Understanding how the human brain solves cognitively challenging tasks is facilitated by the development of computational models that solve these tasks. We train computational models that learn to solve the task at hand and interrogate their internal states to find out how the network accomplishes this. We can then relate these internal states to the behaviour and neural signatures that human participants produce when solving the same task. By combining computational modelling and human behaviour and neural data in this way, we can elucidate the mechanisms that underlie human cognition.