Attention
We are conducting a series of studies examining how the medial-frontal reinforcement learning system biases the human attention system over the time course of learning.
Computational Modelling
We are currently developing theoretical and computational models to improve the explanatory power of our behavioral and neural data. These models encompass decision making, learning, movement planning, and movement execution.
Decision Making
We are currently interested in the neural responses associated with the decision to either explore or exploit during Markovian decision making.
Game Theory
Reinforcement learning rules can be used to learn simple games such as Rock Paper Scissors and Tic Tac Toe. We are currently comparing the output of computational models that learn to play these simple games with the neural responses of humans in similar game situations.
Heuristics
Reinforcement learning algorithms tend to learn much more slowly than humans. We are currently investigating how heuristics such as the scarcity heuristic can impact the output of prediction error signals generated within medial-frontal cortex.
Motor Skill Acquisition
Seminal work in our laboratory examined the role of medial-frontal cortex in motor skill acquisition. We have continuing studies ongoing in this area.
Ownership
Recent work suggests that cognitive processing is biases by perceived object ownership. We are interested in how perceived object ownership biases the output of the medial-frontal reinforcement learning system.
Perceptual Expertise
We continue to be interested in the relationship between changes in visual processing associated with the acquisition of perceptual expertise and the role of medial-frontal cortex in these changes.
We are conducting a series of studies examining how the medial-frontal reinforcement learning system biases the human attention system over the time course of learning.
Computational Modelling
We are currently developing theoretical and computational models to improve the explanatory power of our behavioral and neural data. These models encompass decision making, learning, movement planning, and movement execution.
Decision Making
We are currently interested in the neural responses associated with the decision to either explore or exploit during Markovian decision making.
Game Theory
Reinforcement learning rules can be used to learn simple games such as Rock Paper Scissors and Tic Tac Toe. We are currently comparing the output of computational models that learn to play these simple games with the neural responses of humans in similar game situations.
Heuristics
Reinforcement learning algorithms tend to learn much more slowly than humans. We are currently investigating how heuristics such as the scarcity heuristic can impact the output of prediction error signals generated within medial-frontal cortex.
Motor Skill Acquisition
Seminal work in our laboratory examined the role of medial-frontal cortex in motor skill acquisition. We have continuing studies ongoing in this area.
Ownership
Recent work suggests that cognitive processing is biases by perceived object ownership. We are interested in how perceived object ownership biases the output of the medial-frontal reinforcement learning system.
Perceptual Expertise
We continue to be interested in the relationship between changes in visual processing associated with the acquisition of perceptual expertise and the role of medial-frontal cortex in these changes.