UI, Caltech study reveals timing of brain activity involved in risk and reward decisions
Human brains process reward information followed by risk assessment in decision-making
The specialized neurosurgical expertise of University of Iowa physician-scientists and the generous contributions of UI Health Care neurosurgery patients who volunteer to participate in research were instrumental to new findings that reveal the sequence of brain activity involved when people evaluate risk and reward during decision-making.
The study, conducted by researchers at Caltech in collaboration with the UI neurosurgery Human Brain Research Laboratory team members, reveals how neural activity within the brain enables humans to first estimate reward probabilities and then use this information to make a risk versus reward calculation when making an economic decision.
“These findings significantly advance our understanding of the locations, timing, and sequence of neural activity in the human brain that is responsible for how reward and risk computations unfold,” says Matthew Howard, III, MD, UI professor and chair of neurosurgery. “That in turn helps us understand how healthy brains learn and make decisions, as well as how those processes might be disrupted in certain neuropsychiatric diseases.”
Real-time measurement of brain activity during decision-making
Making everyday economic decisions, like whether to buy stocks, involves balancing the expected reward against the potential risk of a bad outcome.
The new research aimed to understand how the brain implements these kinds of decisions by testing a computational model that proposes how representations of reward and risk are built from experience.
The neural processes that underlie this type of decision-making happen on a microsecond time scale, too fast to be measured by the brain imaging technique known as functional magnetic resonance imaging (fMRI). However, the UI team are experts at measuring neural activity using electrodes implanted deep inside patients’ brains. This technique known as intracranial electroencephalography (iEEG), can measure electrical brain activity in real time, and allowed the research team to track the sequence of brain activity underlying reward and risk-related computations.
Previous work from the Caltech team identified a deep brain region called the anterior insula as a key area that is activated when people assess risk and process uncertainty.
In the new study, the UI researchers use iEEG to measure electrical activity directly from the anterior insula and other brain regions in real time as patients played a card game where they made decisions based on their estimates of reward and risk.
The patients who volunteered to participate in this study were being evaluated for epilepsy and had the electrodes implanted to monitor their seizure activity.
As expected from the computational model, the so-called reward prediction error (the difference between the expected value and the observed value) appears first and is followed by the risk prediction error (the difference between the expected uncertainty and the actual uncertainty), which relied on the same neural processes as the reward prediction error. Both signals were found in the anterior insula. These findings suggest that the reward prediction error is used to calculate the risk prediction error, which can then be used to learn to assess riskiness, which is a necessary guide to decision-making.
The findings were published recently in the journal Nature Communications.
In addition to Howard, the UI researchers involved in the study were Phillip Gander, Masahiro Sawada, Christopher Kovach, Hiroto Kawasaki, and Hiroyuki Oya. The Caltech research team included Vincent Man, John O'Doherty, and Jeffrey Cockburn; and Oliver Flouty of the University of South Florida was also part of the research team.