Monday, February 24, 2014

Predicting risky choices from brain activity patterns

Helfinstein et al. find some predictive neural correlates of avoiding versus taking risks. Even course global patterns of brain activity reflect enhanced activity when preparing to avoid a risk, suggesting that risk taking may reflect a failure of control systems necessary to initiate a safe choice. It is changes in regions related to risk aversion that most reliably predict whether a subject will make a risky or safe choice. They
...used the Balloon Analog Risk Task (BART), in which subjects receive points as they pump up balloons but risk losing those points should the balloon explode before they choose to stop pumping and “cash out.” Each pump opportunity is a risky decision, where subjects must choose whether to pump again to gain more points or to cash out to secure those points already accrued. The structure of the task, where subjects make sequential risky choices with feedback, is common to many real-world risk-taking situations and matches both the economic and lay definition of risk, in that each successive pump opportunity for a given balloon has both greater variance in possible outcomes and increased exposure to loss. Performance on this task has also been shown, in numerous behavioral studies, to relate to self-reported sensation seeking and to naturalistic risk-taking behaviors, such as smoking, drug use, sexual risk-taking, and unsafe driving behaviors. Because performance on this task consistently correlates with naturalistic risk-taking behaviors, the cognitive processes at work during the task are likely to be comparable to those used during real-world risky decision making.
Here is their abstract:
Previous research has implicated a large network of brain regions in the processing of risk during decision making. However, it has not yet been determined if activity in these regions is predictive of choices on future risky decisions. Here, we examined functional MRI data from a large sample of healthy subjects performing a naturalistic risk-taking task and used a classification analysis approach to predict whether individuals would choose risky or safe options on upcoming trials. We were able to predict choice category successfully in 71.8% of cases. Searchlight analysis revealed a network of brain regions where activity patterns were reliably predictive of subsequent risk-taking behavior, including a number of regions known to play a role in control processes. Searchlights with significant predictive accuracy were primarily located in regions more active when preparing to avoid a risk than when preparing to engage in one, suggesting that risk taking may be due, in part, to a failure of the control systems necessary to initiate a safe choice. Additional analyses revealed that subject choice can be successfully predicted with minimal decrements in accuracy using highly condensed data, suggesting that information relevant for risky choice behavior is encoded in coarse global patterns of activation as well as within highly local activation within searchlights.

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