Se Yong Bae

Shame is often portrayed as an evolutionarily ancient social emotion that manages reputation through appeasement and withdrawal. Yet it also seems capable of moral autonomy. It can arise in private, persist without witnesses, and motivate self-criticism and repair even when social approval is absent or explicitly rejected. This dual profile generates the tension between heteronomous shame, which is responsive to social evaluation, and autonomous shame, which is responsive to self-endorsed moral standards. Building on Kurth’s identity-based account, I develop a constructionist predictive processing reconstruction that makes the underlying architecture explicit. On my view, shame is constructed when temporally deep self-models and audience models jointly generate an identity-level disturbance. Whether an episode takes a heteronomous or autonomous shape depends on how a predictive control system allocates precision across competing expectations, such as audience-facing cues and moral identity priors that structure self-conception over time. This framework models graded, mixed, and shifting shame profiles within a single control architecture, rather than requiring distinct kinds of shame. Finally, I argue that the evolutionary evidence often cited in support of a dedicated shame module can be accommodated by selection for domain-general predictive and social-regulatory capacities, with shame emerging as one way those capacities are recruited in norm-governed social niches.

Prinz argues that emotions represent relational properties that are matters of concern in the organism-environmental relationship, such as danger or loss. While I agree that emotions represent relational properties, I argue that his account fails to provide an adequate mechanism for how this representation occurs. Drawing on constructionism about emotions and the predictive processing framework, I propose an alternative account in which emotions are constructed through the brain’s domain-general predictive system. On this view, emotion concepts operate as structured, hierarchical priors that integrate interoceptive and exteroceptive signals, playing a constitutive role in determining emotional content. By embedding emotion construction within the brain’s predictive architecture, my account offers a novel neurocomputational framework for understanding how emotions represent evaluative significance in a context-sensitive and dynamically embodied way.

In this paper, I develop a constructionist account of anxiety by integrating constructionism about emotion with the predictive processing framework. On this view, anxiety is not a biologically hardwired state but a high-level inferential state that arises when the brain uses learned emotion concepts to interpret and regulate unresolved threats. Emotion concepts are conceived as structured probabilistic models that guide perception, bodily regulation, and action through hierarchical prediction, shaped by developmental and social contexts. This account explains the forward-looking, epistemically charged character of anxiety as a form of stalled inference, while also accounting for its adaptive and maladaptive variants within a unified predictive architecture. By addressing Charlie Kurth’s objections to constructionism concerning unconscious emotions, the distinction between moods and emotions, and the role of concepts in behavior, the constructionist predictive processing account avoids reliance on separate fixed subsystems characteristic of Kurth’s biocognitive account. It offers a dynamic and neurocomputational framework that bridges philosophical and scientific approaches to emotion and clarifies the role of anxiety in human cognition and behavior.