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Wemake many decisions every day, based on factors such as our goals at the moment and the value we perceive in certain actions or choices (i.e., “good for me” or “bad for me”). With many of these decisions, we have the opportunity to experience their outcome. At times, that outcome is expected, while at others it can be surprising. By comparing what we had expected would happen towhat really did, we learn how tomakebetter choices next time.This process is so prevalent, and so often automatic, thatmany individualsmay not realize how frequently it happens. Yet, a great many neural resources are dedicated to continually making predictions, experiencing the consequences of choices, and adjusting behavior to optimize outcomes. Our brains have been described as Bayesianmachines thatoptimizebehaviorbasedonpredictions andoutcomes (1). It is thennot surprising that this adaptiveprocess is perturbed in various ways across psychopathology. Patients with generalized anxiety disorder, the focus of a study byWhite et al. (2) reported in this issue of the Journal, seem at least phenomenologically to be impaired in this process of prediction, feedback, and learning (3). The degree to which these patients may seemmaladaptively “stuck in their ways” can frustrate even the most experienced clinician. Despite what might appear as persistent negative feedback for this style of decision making, often little seems to change for these patients. How do we go about understanding the processes that have gone awry in their brains? Andwhat does investigating biological perturbations in decision making teach us about the pathophysiology and treatment of generalized anxiety disorder? Such answers may be found by applying computational models to an experimental probe of decision making: operant reinforcement learning. A method for understanding a process like learning and decision making can be considered computational when it utilizes complex mathematics to model mechanistic components describing how specificmental functions interact to drive behavior. Such models can be very powerful in that they can predict future behavior and explain which components of information processing may be altered under certain conditions or in certain populations. The study of operant learning via reinforcing decisions through rewards or punishments has developed iterations of these models over the past decade and found that they do a fairly accurate job of tracking and predicting choice behavior (4). Central to these models is calculation of the expected value of a stimulus or action, encoding of the reward or punishment that was received after a choice was made, and the calculation of the discrepancy between what was received and what was expected (called a prediction error). A positive prediction error indicates a better outcome than expected, while a negative prediction error indicates a worse outcome. The signaling of a prediction error, which a large body ofwork in experimental animals and humans has attributed to dopaminergic neurons, results in updating of the expected value signal (moderated by a learning rate, which dictates the degree to which information from the previous trial is carried into the next one). If this process works well, the individual quickly minimizes prediction errors by adjusting the expected value of a choice, and hence its selection, to its most optimal level. Brain imaging studies have picked up on this modeling work over the past decade (4) and found that trial-to-trial variations in signals such as value and prediction error can be observed in trial-to-trial variations in brain activity. Often value signaling has been linked to activity in the ventromedial prefrontal cortex and ventral striatum (5, 6), while prediction error signaling has been linked to activity in the ventral tegmental area, the striatum, the dorsal cingulate andmedial prefrontal cortices, the anterior insula, and other locations (7). While the terminology associated with computational neuroscience (or in this context often called “computational psychiatry”) can seem foreign to the general clinical reader, it is meant to describe a learning process that is inherently intuitive to all. In their experiment, White et al. gave patients with generalized anxiety disorder andhealthy comparison participants a decision-making task in which responses were associated with different probabilities of reward and punishment, thus presenting an opportunity to learn from both reward and punishment. Patients failed to learn from both rewards and punishments as well as healthy individuals did, as reflected in a persistently high error rate among patients, while healthy subjects progressively decreased their errors. Turning to the brain, the authors’ primary finding was a profound and widespread reduction in prediction error signaling in patients. That is, activity in regions like the cingulate and medial prefrontal The authors’ primary finding was a profound and widespread reduction in prediction error signaling in patients.

Learning in Generalized Anxiety Disorder Benefits From Neither the Carrot Nor the Stick