Yuyue Jiang

Second-person neuroscience holds social cognition as embodied meaning co-regulation through reciprocal interaction, modeled here as coupled active inference with affect emerging as inference over identity-relevant surprise. Each agent maintains a self-model that tracks violations in its predictive coherence while recursively modeling the other. Valence is computed from self-model prediction error, weighted by self-relevance, and modulated by prior affective states and by what we term temporal aiming, which captures affective appraisal over time. This accommodates shifts in the self-other boundary, allowing affect to emerge at individual and dyadic levels. We propose a novel method termed geometric hyperscanning, based on the Forman-Ricci curvature, to empirically operationalize these processes: it tracks topological reconfigurations in inter-brain networks, with its entro-py serving as a proxy for affective phase transitions such as rupture, co-regulation, and re-attunement.

Emotion regulation is a fundamental process that shapes cognitive, affective, and behavioral responses to emotional stimuli. Traditional emotion regulation models conceptualize regulation as a sequential modulation of emotional responses. However, they do not fully explain how emotions are constructed in a way that allows such regulation to occur. Predictive processing (PP) provides a mechanistic framework for understanding emotion generation by proposing that the brain minimizes prediction errors (PEs) to optimize perception and behavior. Yet, standard PP accounts reduce valence to PE minimization, failing to explain how PEs can generate different subjective feelings. To address these limitations, we propose a valence-focused model of emotion regulation that integrates predictive processing with self-referential cognition. We incorporate emotional valence as interpretative processes of the self-model, which assigns emotional significance based on goals, values, and autobiographical context. This model bridges the gap between emotion generation and regulation, highlighting the dynamic interplay between prediction errors, subjective valuation, and self-referential processes. This approach not only advances theoretical understanding but also opens new avenues for computational modeling and empirical research into the adaptive and maladaptive aspects of emotional experience.

Background: The p53 tumor suppressor and its related protein, p73, share a homologous DNA binding domain, and mouse genetics studies have suggested that they have overlapping as well as distinct biological functions. Both p53 and p73 are activated by genotoxic stress to regulate an array of cellular responses. Previous studies have suggested that p53 and p73 independently activate the cellular apoptotic program in response to cytotoxic drugs. The goal of this study was to compare the promoter-binding activity of p53 and p73 at steady state and after genotoxic stress induced by hydroxyurea. Results: We employed chromatin immunoprecipitation, the NimbleGen promoter arrays and a model-based algorithm for promoter arrays to identify promoter sequences enriched in anti-p53 or anti-p73 immunoprecipitates, either before or after treatment with hydroxyurea, which increased the expression of both p53 and p73 in the human colon cancer cell line HCT116-3. We calculated a model-based algorithm for promoter array score for each promoter and found a significant correlation between the promoter occupancy profiles of p53 and p73. We also found that after hydroxyurea treatment, the p53-bound promoters were still bound by p73, but p73 became associated with additional promoters that that did not bind p53. In particular, we showed that hydroxyurea induces the binding of p73 but not p53 to the promoter of MLH3, which encodes a mismatch repair protein, and causes an up-regulation of the MLH3 mRNA. Conclusion: These results suggest that hydroxyurea exerts differential effects on the promoter-binding functions of p53 and p73 and illustrate the power of model-based algorithm for promoter array in the analyses of promoter occupancy profiles of highly homologous transcription factors. © 2009 Huang et al; licensee BioMed Central Ltd.

Self models contribute to key functional domains of human intelligence that are not yet presented in today’s artificial intelligence. One important aspect of human problem-solving involves the use of conceptual self-knowledge to detect self-relevant information presented in the environment, which guides the subsequent retrieval of autobiographical memories that are relevant to the task at hand. This process enables each human to behave self-consistently in our own way across complex situations, manifested as self-interest and trait-like characteristics. In this paper, we outline a computational framework that implements the conceptual aspect of human self models through a modified version of the joint-embedding predictive architecture. We propose that through the incorporation of human-like autobiographical memory retrieval and self-importance evaluation, the modified architecture could support machine agents with significantly enhanced self-consistency, which could be applied to deliver more believable simulations of human behaviors.