The Affordance Competition Hypothesis: making decisions with motor structures

Human modern life entails the need to make many abstract, deliberative decisions such as selecting a career or buying a house. This fact prompted the development of theories suggesting that decisions are made within a cognitive system prior to being relayed to the motor system for outputting the desired action: a serial architecture of sensing, thinking, and finally acting. However, the human brain is the product of a long evolution during which animals had to face challenges very different from our modern-life decisions. Most decisions in the animal realm must be made in real-time to cope with urgent priorities in dynamically changing environments: here, there is no time for carefully thinking before acting. A recent theoretical framework has proposed that for effective behavior in such environments, the brain uses a parallel architecture in which multiple potential actions can be specified simultaneously, and one is selected on the basis of current sensory information. This is called the affordance competition hypothesis. According to this theory, decisions emerge from a consensus arising in a distributed brain network, especially involving sensorimotor structures. In this presentation, I will describe the work I have been conducting for the six last years as a postdoctoral researcher at the Cognition and Actions lab of the Institute of Neuroscience in Brussels (UCL). My current projects aim at better understanding how the human brain computes action-based decisions. A first project focused on the contribution of the primary motor cortex to reinforcement learning and decision-making. A second project explored the cortical correlates of spatial attention and action selection during motor decisions. Finally, a third project investigated the motor cortical signatures of urgency during dynamic decision-making. Altogether, they involved a combination of techniques including continuous theta burst stimulation, single-pulse transcranial magnetic stimulation, electroencephalography and various data analysis approaches (e.g., Monte-Carlo permutations on EEG data, and computational modelling of behavioral data) which I will introduce throughout my presentation