We are constantly bombarded with information in our environment which we must prioritize, process, and make use of in an adaptive manner. Selective attention is the mechanism which facilitates prioritization and processing by enhancing relevant information at the expense of irrelevant information. Although it is often guided by task-relevant goals like searching for food, or captured by intrinsically salient stimuli like a car horn, attention may also be shaped by previous experience (i.e., learning) which can bias the allocation of inherently limited neural resources (Stokes et al., 2012).
This research explores the interaction between visual attention and reinforcement learning, a dominant and ubiquitous form of learning across species. By combining reinforcement learning tasks with those testing visual attention and discrimination, we are studying how learning transfers across tasks and contexts, and the ways in which it biases selective attention. In addition to behavioural and eye-tracking measures, we are using magnetoencephalography (MEG) to investigate the neural basis of this interaction. In particular, we aim to dissociate the roles of posterior visual cortex, the intraparietal sulcus, and the frontal eye fields. This work has implications for the study of drug addiction, in which reward-driven attentional capture plays an important role.
Investigators: Lev Tankelevitch & Mark Stokes
Collaborators: Matthew Rushworth