How can you tell if a decision is impulsive or risky? We use the Cambridge Gambling Task to see how brain areas, drugs, and dopamine affect our choices.
The Cambridge Gambling Task (CGT) measures decision-making under risk. Here, the outcome is uncertain, but we know the chances of each result (Camerer & Weber, 1992). This idea is like how betting platforms like 22Bet work. Players make smart choices based on the odds and probabilities they see.
In the CGT, the ratio of red to blue boxes makes the odds of winning or losing explicit. Participants choose if a token is behind a red or blue box. Then, they bet a part of their points on that choice. This is like how 22Bet users balance potential gains with known risks when they place a bet.
The CGT is different from traditional gambling tasks. It has the key ability to separate risk-taking from impulsivity. This is due to the different bet conditions of the task. In the ascending condition, the bet value goes up on screen. This means the participant must wait and not act impulsively to place a high-stakes bet.
The task demands little from learning and working memory. Information from one trial doesn’t help with the next. The CGT clearly shows how we make decisions under risk. It’s a great way to study the neuroscience of these choices. Here we will discuss some key research into this area that has used CGT.
The Role of Different Brain Regions in Decision-making
Insights from Lesion Studies
Lesion studies can be very informative in differentiating the roles of various brain regions in decision-making.
Clark et al. studied how the ventromedial prefrontal cortex (vmPFC) affects decision-making under risk. They also looked at the insular cortex’s role in this process. (2008). The study showed that participants with vmPFC lesions made larger bets. They did this no matter the odds of winning. Participants with insular cortex lesions took riskier bets when the odds were low. This choice led to more ‘bankruptcies.’
These findings suggest that the vmPFC and insular cortex play crucial roles in decision-making under risk.
The Effect of Drugs on Decision-making
An example from frontotemporal dementia
The frontal variant of frontotemporal dementia (fvFTD) disrupts the prefrontal cortex. As a result, it can hinder decision-making and various other functions.
A study conducted by Rahman et al. in 2006 examined how a single 40 mg dose of methylphenidate, known as ‘Ritalin,’ might reduce risky decisions in FvFTD patients. The study showed that patients made less risky choices on the CGT task after taking the drug. They placed lower value bets while on the drug compared to when they were off it (Rahman et al., 2006).
The authors indicated that this ‘normalized’ decision-making might be due to drug effects on the orbitofrontal cortex. This is like the effects seen in the vmPFC (Rahman et al., 2006). This region is often affected in fvFTD (Salmon et al., 2003).
The Link Between Dopamine and Decision-making
As exemplified by Parkinson’s Disease
From the age of about 20 onwards, dopamine function is lost at a rate of about 10% per decade (Mukherjee et al., 2002). Losing dopamine may partly explain why we become less sensitive to rewards. This change often leads to a lower appetite for risk as we age. A more extreme loss of dopaminergic function results in Parkinson’s disease.
L-DOPA is a common treatment for Parkinson’s disease. It helps restore dopamine in damaged brain areas, like the dorsal striatum. In healthy areas like the ventral striatum, this medication can cause too much dopamine. Researchers refer to this as an “overdose effect.”
In one study, patients took L-DOPA on one day and skipped it on another. When on medication, they made logical choices but placed more impulsive bets. Scientists say this occurs when high levels of dopamine trigger brain areas related to reward and impulsivity.
Conclusion
In summary, the CGT is a useful and well-validated measure of decision-making. The CGT looks at decision-making beyond a learning context. It helps separate impulsive choices from risky ones by focusing on how tasks are carried out.
CGT is an effective tool for identifying neural networks that make poor decisions. It also reveals how drug treatments can alter these networks.