Associate Professor, Department of Exercise Science, Physical & Health Education
Research Area: neuroeconomics, learning, and decision-making
Much like his 6-year-old son, Dr. Olav Krigolson is driven by an endless source of natural curiosity that leaves him constantly asking, “Why, why, why?” Particularly when it comes to understanding why we humans make the bizarre decisions we often do.
Krigolson’s educational background is, in his own words, a “weird one”, majoring in both physics and physical education during his undergraduate days. The history of his budding interest in neuroeconomics even more peculiar — a desire to apply its philosophies around learning and decision-making to the basketball team he was coaching at the time.
Now a professor in UVic’s Department of Exercise Science, Physical & Health Education, Krigolson is applying his expertise in cognitive neuroscience to the recently emerging field of neuroeconomics — one that combines principles from psychology, economics, and neuroscience.
Krigolson uses computational modeling and neuroimaging (both EEGs and functional MRIs) to study the role our brains play in learning and decision-making. He creates and tests theoretical predictions, particularly those involving classical learning rules and ethical dilemmas, on participants in his research laboratory.
“People learn when outcomes deviate from expectations,” says Krigolson.
This is the premise behind the Rescorla-Wagner Rule. For instance, if you were expecting to receive a test score of 80% on an exam, but you only got 55%, the negative prediction error — the difference between what you predicted and the actual outcome — would, hopefully, motivate a change in your study behaviors.
Indeed, Krigolson’s research group’s formative work demonstrates that the computational predications behind the Rescorla-Wagner theory actually hold true in the neural learning responses observed with EEG. These findings were recently published in the Journal of Cognitive Neuroscience in an article entitled, How We Learn to Make Decisions: Rapid Propagation of Reinforcement Learning Prediction Errors in Humans.
Clearly, if our behaviors were purely dictated by the logical principles of economic theory, we would consistently make rational choices. Yet our behavior does not reflect this. This is where psychology comes in.
Krigolson’s research demonstrates, through brain imaging, that there are actually multiple neural systems working in competition during decision-making.
This becomes most evident when individuals, who are undergoing neural monitoring, are presented with scenarios that involve exceptionally difficult decisions, such as, “Would you push an old man in front of an oncoming train if his death prevented the deaths of a family of 5 walking on the track farther down?”
“When people make emotional decisions, you get a lot of activity in emotional parts of the brain and that correlates with irrational decisions, while a more active prefrontal cortex is associated with more logical, rational choices,” says Krigolson. “You can actually use this in a predictive fashion.” Indeed this is exactly what he and others observe in fMRI images and EEG waveforms.
Krigolson has also used neuroimaging to study brain activity during gambling, particularly in relationship to perceived ownership. His research has demonstrated that the reward-processing centre in the medial frontal cortex of the brain is far less sensitive to the outcomes of gambles made for ‘others’ versus gambles made for ‘self’. This means that you might want to think twice about letting someone else manage your stock portfolio.
Krigolson’s current research projects include:
Clinical decision-making. Specifically, Krigolson is studying why physicians make the choices they do, and how these choices change as a function of expertise and fatigue.
Learning-induced changes and EEG signals. In particular, Krigolson is looking at prediction errors, monitoring associated learning signals, and studying whether the changes observed in brain signaling follow computational principles.
Applied learning. “Neuroeducation is a big push for us,” says Krigolson, who plans to use portable EEG units to monitor and measure how people are learning in a real classroom environment. By looking at neural signals in students who are learning effectively, he hopes to create a blueprint for learning success and facilitate earlier intervention for struggling students.
Ultimately, Krigolson hopes to create a monitoring/diagnostic system in order to chart learning behavior, associate specific neural signals with specific learning behaviors, and improve learning environments.
So just how close is Krigolson to making neuroeducation a reality?
“We have the scientific knowledge to start this and the technology is advancing rapidly,” says Krigolson. “The grants are being written. We are a lot further along than most people know.”