Hierarchical Interoceptive–Social Inference Dysregulation: A Computational Psychiatry Framework for Borderline Personality Disorder with Level-1 Active Inference Simulation and Implications for Cluster B Pathology

Borderline Personality Disorder (BPD), the prototypic Cluster B condition, is characterised by profound instability in affect, self-image, and interpersonal relationships, including rapid oscillation between idealisation and devaluation. Despite decades of neurobiological and psychotherapeutic research, no unified mechanistic theory accounts for the simultaneous presence of interoceptive dysregulation, interpersonal hypersensitivity, unstable identity, and splitting. Here we (i) review the hot research topics in Cluster B personality disorders in the recent literature, including triple-network neuroimaging dysconnectivity, allostatic interoceptive overload, and computational psychiatry approaches; (ii) identify the critical gap — the absence of a formal mechanistic account coupling interoceptive and social predictive hierarchies; (iii) propose the Hierarchical Interoceptive–Social Inference Dysregulation (HISID) framework, positing that BPD arises from a precision dissociation in which interoceptive precision is chronically suppressed while social-threat precision is pathologically elevated, leaving the self-model anchored exclusively to volatile social-affective inputs; and (iv) present a Level-1 computational simulation implementing two active inference agents — a healthy control (HC) and a BPD agent characterised by suppressed interoceptive precision, asymmetric social-threat amplification, a bimodal (splitting) social prior, and a hysteresis commitment mechanism — embedded in a 300-trial social-threat paradigm. While the HISID framework is introduced within the broader context of Cluster B personality disorders, the formal theory, parameterisation, simulations, and empirical predictions presented in this paper are specific to BPD as the prototypic Cluster B condition. Extension of the precision-dissociation formalism to narcissistic, antisocial, and histrionic personality disorders — each of which is predicted to occupy a distinct parametric region within the same generative architecture — is deferred to subsequent work. Across $$n=40$$ agents per group, the BPD agent produces a 4.22 × increase in self-model variance, a 10.33 × increase in per-agent median threat-recovery time, and a 4.45 × increase in unweighted cumulative prediction-error accumulation (alongside a 2.61 × increase in precision-weighted free energy; the unweighted measure is reported as a control on precision-induced mechanical scaling). A factorial control study crossing precision profile with generative architecture decomposes the BPD phenotype into distinct contributions: identity-instability variance is driven primarily by the bimodal-prior architecture, whereas threat-recovery slowing is driven primarily by the asymmetric threat-conditional precision, with a super-additive interaction in the full agent. These differences are large and internally consistent within the simulation and constitute demonstrations of model sufficiency, not empirical theory validation. A sensitivity analysis across a 3× 3 precision grid confirms qualitative robustness. A key architectural finding is that the bimodal prior requires Bayesian mixture belief updating with hysteresis commitment — not gradient descent — providing a candidate formalisation of splitting as discrete pole-occupancy inference. The HISID framework, grounded in the Free Energy Principle, generates five operationalised empirical predictions testable with existing neuroimaging and computational phenotyping methods.