Selective maintenance of aversive memories as a mechanism of spontaneous recovery of fear
Abstract
Return of fear after exposure poses a significant challenge for treatment of anxiety dis- orders. In this study, we used computational modeling to test competing mechanisms underlying spontaneous recovery of fear over time. We fit computational models of a novel theory of spontaneous recovery—selective maintenance of aversive memories— to behavior from a fear conditioning and extinction task (N=316), and showed that they uniquely captured spontaneous recovery and quantitatively outperformed alternative models embodying theories from the literature. The results were supported across mul- tiple datasets, including a preregistered replication (N=355) and a sample with mental health symptoms (N=520). The selective maintenance modeling framework addition- ally offers mechanistic insights into overgeneralization and the development of anxi- ety. Indeed, in the symptomatic sample we found that symptoms of generalized anxiety disorder correlated with estimates of overgeneralization in the model. Through simu- lations, we further demonstrated that insights from our model can explain how targeted interventions such as retrieval cues and cognitive interventions can prevent the return of fear. These results highlight selective maintenance of aversive events in memory as a critical and testable target for improving anxiety treatments and preventing relapse.
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BibTeX
@misc{berwian2026selective,
title = {{Selective maintenance of aversive memories as a mechanism of spontaneous recovery of fear}},
author = {Isabel Berwian and Yongjing Ren and Sashank Pisupati and Jialing Ding and Seohyun Moon and Jamie C. Chiu and Deepta Chandrasekhar and Yael Niv},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/2kdtf_v3},
url = {https://osf.io/preprints/psyarxiv/2kdtf_v3/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Selective maintenance of aversive memories as a mechanism of spontaneous recovery of fear
AU - Isabel Berwian
AU - Yongjing Ren
AU - Sashank Pisupati
AU - Jialing Ding
AU - Seohyun Moon
AU - Jamie C. Chiu
AU - Deepta Chandrasekhar
AU - Yael Niv
PY - 2026
DA - 2026/10/10
PB - PsyArXiv
DO - 10.31234/osf.io/2kdtf_v3
UR - https://osf.io/preprints/psyarxiv/2kdtf_v3/
AB - Return of fear after exposure poses a significant challenge for treatment of anxiety dis- orders. In this study, we used computational modeling to test competing mechanisms underlying spontaneous recovery of fear over time. We fit computational models of a novel theory of spontaneous recovery—selective maintenance of aversive memories— to behavior from a fear conditioning and extinction task (N=316), and showed that they uniquely captured spontaneous recovery and quantitatively outperformed alternative models embodying theories from the literature. The results were supported across mul- tiple datasets, including a preregistered replication (N=355) and a sample with mental health symptoms (N=520). The selective maintenance modeling framework addition- ally offers mechanistic insights into overgeneralization and the development of anxi- ety. Indeed, in the symptomatic sample we found that symptoms of generalized anxiety disorder correlated with estimates of overgeneralization in the model. Through simu- lations, we further demonstrated that insights from our model can explain how targeted interventions such as retrieval cues and cognitive interventions can prevent the return of fear. These results highlight selective maintenance of aversive events in memory as a critical and testable target for improving anxiety treatments and preventing relapse.
ER -