Extinction learning refers to a reduction in fear to a conditioned stimulus (CS) that previously signaled a threat, but now occurs without the expected threat. This mechanism is core to exposure-based treatments for anxiety-related disorders. Enhancing the generalization of extinction learning is crucial for improving treatment outcomes, as it helps reduce fear across a range of generalization stimuli (GSs) resembling the original fear-evoking CS. This narrative review identifies and covers three generalization of extinction learning models: 1) generalization of CS extinction learning, examining how extinction learning to the CS generalizes to novel GSs, 2) generalization of GS extinction learning, assessing how extinction learning to a GS generalizes to other novel GSs or the original CS, and 3) generalization of multiple stimuli extinction learning, where extinction learning involves multiple GSs (and sometimes the CS) and its effect on other novel stimuli. While extinction learning to the CS effectively generalizes to other stimuli, extinction learning to a GS or multiple GSs showed limited generalization to other novel GSs or the original CS. Since real-life exposure-based treatment rarely reproduces the CS, extinction learning involving only the GS(s) may better reflect clinical practice; poor generalization of GS(s) extinction learning may constitute another pathway of return of fear. This review also highlights various factors that influence generalization of extinction learning and call for future research to develop strategies for improving these processes, which can help inform exposure-based treatments.
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