Neural Correlates of Approach and Avoidance Learning in Behavioral Inhibition

Behavioral inhibition is a temperamental trait characterized in infancy and early childhood by a tendency to withdraw from novel or familiar stimuli. Recent neuroimaging research indicates that BI individuals have atypical neural responses to information regarding reward and punishment in the striatum and amygdala--regions of the brain that receive information about salient stimuli and use it to guide motivated behavior. Activation to rewarding and punishing stimuli in these regions follows a "prediction error" pattern. My research examines whether behaviorally inhibited young adults display atypical prediction error responses, and whether these responses are specific to rewarding or aversive events. Prediction error signals are theorized to be critical for approach and avoidance learning, and a second study examined probabilistic approach and avoidance learning in the same sample, examining differences in approach and avoidance learning between behaviorally inhibited and non-inhibited individuals, and the relation between learning and neural prediction error signals to reward and punishment

Predicting risky choices from brain activity patterns.

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

Predicting risky choices from brain activity patterns.

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