Grant: Discovery Launch Supplement

The long-term goal of my research program is to move towards a naturalistic or ecologically valid understanding of human cognition, with a particular focus on learning and decision-making. These topics are often studied using simple experiments in a highly controlled laboratory setting. However, it has recently been argued that our theories of learning and decision-making are incomplete due to a focus on overly simplistic tasks. My research program will address this long-term goal by working towards the short-term objectives of measuring and interpreting reward-related brain activity in naturalistic tasks. This includes brain activity recorded while: (1) exploring complex environments, (2) making self-paced or rapid decisions, and (3) learning from continuous stimuli. These studies will all measure scalp-recorded potentials (electroencephalography or EEG) and use recently developed techniques for unmixing rapidly occurring or continuous brain activity. The novelty of this research program lies in acknowledging that cognitive neuroscience has been understudied in naturalistic environments due to methodological challenges, but that these challenges can be overcome using new approaches. This is worth doing to test whether cognitive processes (e.g., learning and decision-making processes) scale to naturalistic tasks. Another novel aspect of this work is that it will also consider the scalability of computational models related to learning and decision-making, such as reinforcement learning. Besides answering theoretical questions about cognitive and computational scalability, this work will result in new analysis approaches, novel and interesting neuroimaging data, and important training opportunities for undergraduate scholars preparing to enter either graduate school or Canada's growing neurotechnology industry.