Rasmus Bruckner | Adaptive learning under uncertainty: Computational mechanisms and lifespan differences

Learning often takes place in environments, in which it is impossible to exactly know current and future outcomes. To successfully behave in such uncertain environments, humans have to learn appropriate beliefs from past experiences that can be used to predict desirable and undesirable outcomes. Drawing on optimal inference models and behavioural learning tasks, I will illustrate how learning under uncertainty should be regulated from a normative perspective and how learning deficits may emerge from deviations from these computations. I will show how human participants learn in the face of perceptual uncertainty and to which extent the ability to adjust learning in dynamically changing environments differs between age groups across the lifespan. Moreover, I will explore the possibility that the intricate computations to optimally adjust learning may often be simplified by resorting to heuristic strategies that are guided by previous choices. Finally, I will discuss some future directions that follow from these results.