The goal of our research is to understand the learning and decision processes which allow humans to function effectively in an uncertain and dynamic world. By developing computational models and assessing how they compare to human behaviour in experimental tasks, we hope to gain deeper insight into how information is processed and represented during learning and decision making.
How do people learn to make better decisions from experience?
How do people integrate and use sensory information to decide which actions to take?
How do people learn to behave pro-environmentally by observing traces of other people’s actions?
Optimize experiments whilst you run them