The effects and neural correlates of combined conscious fear control and extinction learning on fear reduction and relapse

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Project Summary/Abstract The best available treatments for fear-based disorders, exposure-based therapies, are vulnerable to relapse through the return of fear. This application will test an intervention that may prevent the return of fear. The Rescorla-Wagner model of associative learning posits that repeated experiences of prediction error (i.e., mismatch between expectancy of US and its non-occurrence) extinguishes conditional fear responses 4. In contrast, the ‘valuation-based’ model of emotion regulation emphasizes cognitive change that modulates the evaluation of a fearful stimulus from “bad for me” to “not bad for me”. While interventions from both models are often used together, there has been theoretical debate as to whether such combinations are advantageous or disadvantageous 6. According to the Rescorla-Wagner model, the more intense the US, the more likely its absence during extinction trials will elicit prediction error, which in turn enhances the formation of a CS-noUS memory important for extinction and preventing the return of fear. Measures taken to reduce US intensity prior to extinction training, such as using cognitive reappraisal strategies, limits prediction error. In contrast, valuation-based models of emotion regulation posit that “not bad for me” cognitive appraisals more strongly reduce emotional responding relative to experience-based learning alone. Neurobiological data suggest that both reappraisal and extinction training increases activation in vmPFC-vACC, indicating that the two processes may complement each other. An extension of the Rescorla-Wagner model, called the computational implementation model, explains how this complementation may come about. It posits that emotional responding is determined by prediction error and the cognitive costs required for regulation. Thus for reappraisals to complement extinction, they should be designed such that the cognitive costs are minimized, and prediction error be preserved. The current application tests this perspective in a series of four studies. Study 1 will examine the neural overlap between reappraisal and extinction learning. Participants will undergo extinction training while in the fMRI scanner and then complete a test of trait reappraisal use. Their performance in the reappraisal task will then be correlated with vmPFC-vACC activation. Studies 2-4 will examine the impact of combining reappraisal with extinction training experimentally to prevent the return of fear. Experiments combine an extinction learning paradigm with either an instructed reappraisal, instructed suppression, or an instruction to the participant to react naturally. Effects will be evaluated in terms of fearful responding after a period of one week under four return of fear conditions: spontaneous recovery (Studies 2-4), rapid reacquisition (Study 2), reinstatement (Study 3), and context renewal (Study 4). These studies will be the first to characterize and test a theoretically potent combination of reappraisal and extinction training. The outcome of this research will provide insight into improving clinical decision-making and maximizing the effectiveness of treatments such as exposure and cognitive restructuring for fear-based disorders.