Anxiety is characterized by apprehensive expectation regarding the forecasted outcomes of choice. Decision science and in particular reinforcement learning models provide a quantitative framework to explain how the likelihood and value of such outcomes are estimated, thus allowing the measurement of parameters of decision-making that may differ between high- and low- anxiety groups. However, the role of anxiety in choice allocation is not sufficiently understood, particularly regarding the influence of transient threat on current decisions. The presence of threat appears to alter choice behavior and may differentially influence quantitatively derived parameters of learning among anxious individuals. Regarding the neurobiology of reinforcement learning, the dorsolateral prefrontal cortex (dlPFC) has been suggested to play a role in temporally integrating experienced outcomes, as well as in coordinating an overall choice action plan, both of which can be described computationally by learning rate and exploration, respectively. Accordingly, it was hypothesized that high trait anxiety would be associated with a lower reward learning rate, a higher loss learning rate, and diminished exploration of available options, and furthermore that threat would increase the magnitude of these parameters in the high anxiety group. We also hypothesized that the magnitude of neural activation (measured by functional near-infrared spectroscopy; FNIRS) across dissociable regions of the left and right dlPFC would be associated with model parameters, and that threat would further increase the magnitude of activation to model parameters. Finally, it was hypothesized that reward and loss outcomes could be differentiated based on FNIRS channel activation, and that a distinct set of channels would differentiate outcomes in high relative to low anxiety groups. To test these hypotheses, a temporal difference learning model was applied to a decision-making (bandit) task to establish differences in learning parameter magnitudes among individuals high (N=26) and low (N=20) in trait anxiety, as well as the impact of threat on learning parameters.

Results indicated a positive association between anxiety and both the reward and loss learning rate parameters. However, threat was not found to impact model parameters. Imaging results indicated a positive association between exploration and the left dlPFC. Reward and loss outcomes were successfully differentiated in the high, but not low anxiety group.

Results add to a growing literature suggesting anxiety is characterized by differential sensitivity to both losses and rewards in reinforcement learning contexts, and further suggests that the dlPFC plays a role in modulating exploration-based choice strategies.