While anger may help us to detect and respond to threat, individuals high in trait anger and aggression often perceive danger where little exists. Drawing on predictive processing—a framework describing how the brain combines past experiences with current sensory input—we propose that anger-prone individuals overweight prior threat beliefs while underweighting current input. This imbalance may create a persistent state of alertness and readiness for anger and aggression. We identify disruptions in three core error-correction processes— selective attention , belief updating , and active inference —as potential mechanisms underlying this threat overestimation. Finally, we outline clinical implications, showing how predictive processing can help explain why existing anger treatments are effective, where they may be limited, and how they might be optimized.
In the past decade, spontaneous and dynamic facial expression databases (FEDBs) have expanded rapidly, advancing emotion research beyond static, posed expressions. Yet existing reviews remain fragmented due to limited search strategies and a lack of systematic methods. This article synthesizes 128 spontaneous and dynamic FEDBs, examining their conceptual, technical, and practical dimensions. It summarizes how emotions are represented, measured, and validated across diverse populations; how databases vary in composition, recording methods, and modalities; and how accessibility, usability, and ethics shape their use. By integrating these perspectives, the review consolidates current knowledge, identifies methodological gaps, and outlines directions for developing more diverse and ecologically valid FEDBs that better capture the complexity of human emotion.
Social sharing of emotion (SSE) is widespread, yet digital contexts may selectively foreground certain traditional motives and alter how they are expressed. This paper proposes an integrative model explaining how traditional SSE motives are enacted, prioritized, and organized on social media. Drawing on SSE theory and perspectives on self-presentation, hyperpersonal communication, social presence, and media richness, the model distinguishes antecedent factors that activate sharing from structural conditions that shape how sharing unfolds and is interpreted. Heightened visibility, indeterminate audiences, and anonymity selectively foreground five digitally salient SSE motives, taking the form of identity signaling, risk-managed disclosure, networked problem-solving, validation seeking, and visibility mobilization. These forms are conceptualized as context-specific expressions of traditional SSE motives rather than novel psychological needs, preserving theoretical continuity while capturing how these motives are differentially prioritized and expressed on social media.
Agnes Moors challenges William James’s theory of emotions on multiple grounds. She argues that: James wrongly commits to the view that emotions must have perceptual causes; he wrongly insists that all emotions are embodied, ignoring emotions such as hope and regret; he errs in treating bodily feeling as sufficient for emotions; he cannot account for the “heat” (valence and intensity) of emotions; and he fails to account for emotions’ intentionality. This commentary addresses each of these objections, while also agreeing with Moors that James’s theory needs to be updated. An imperatival theory of emotions is proposed and compared to Moors’ response evaluation theory.
