Comparing reappraisal and acceptance strategies to understand the neural architecture of emotion regulation: a meta-analytic approach

Front Psychol. 2023 Jul 21:14:1187092. doi: 10.3389/fpsyg.2023.1187092. eCollection 2023.

Abstract

Introduction: In the emotion regulation literature, the amount of neuroimaging studies on cognitive reappraisal led the impression that the same top-down, control-related neural mechanisms characterize all emotion regulation strategies. However, top-down processes may coexist with more bottom-up and emotion-focused processes that partially bypass the recruitment of executive functions. A case in point is acceptance-based strategies.

Method: To better understand neural commonalities and differences behind different emotion regulation processes, in the present study, we applied the Activation Likelihood Estimation (ALE) method to perform a meta-analysis on fMRI studies investigating task-related activity of reappraisal and acceptance. Both increased and decreased brain activity was taken into account in the contrast and conjunction analysis between the two strategies.

Results: Results showed increased activity in left-inferior frontal gyrus and insula for both strategies, and decreased activity in the basal ganglia for reappraisal, and decreased activity in limbic regions for acceptance.

Discussion: These findings are discussed in the context of a model of common and specific neural mechanisms of emotion regulation that support and expand the previous dual-routes models. We suggest that emotion regulation may rely on a core inhibitory circuit, and on strategy-specific top-down and bottom-up processes distinct for different strategies.

Keywords: acceptance; activation likelihood estimation method; emotion regulation processes; meta-analysis; reappraisal.

Publication types

  • Systematic Review

Grants and funding

BM stipend was supported by a grant from the Italian Ministry of University and Research (Excellence Department Grant awarded to the Department of Psychology and Cognitive Science, University of Trento, Italy).