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Studies on the theory of mind (TOM) and alexithymia in borderline personality disorder (BPD) have yielded inconsistent results. Also, the relationship between TOM abilities and alexithymia facets as two domains of social cognition has not been studied in BPD. This study aimed to fill this gap. Participants were 50 outpatients with BPD and 50 age and gender-matched healthy controls. Assessments performed using Reading the Mind in Eyes Task (RMET), Toronto Alexithymia Scale (TAS-20), Faux Pas Task (FPT), and Digit Span subtest of Wechsler Adult Intelligence Scale. Results showed that BPD patients scored lower on overall FPT (p .001) and its cognitive (p .001) and affective TOM (p .001) subtests but were comparable with healthy controls in emotion recognition ability assessed by RMET (p = .241). The BPD group also scored significantly lower in overall alexithymia (p .001) and subscales of difficulty identifying feelings (DIF; p .001) and difficulty describing emotions (DDF; p = .001). However, they performed similarly to the healthy control group in externally oriented thinking (EOT; p = .164). Correlation analysis revealed a significant negative correlation between EOT and RMET in the BPD group (r = -0.33, p .05). No association, however, was found between FPT and RMET. This study suggests that BPD patients are impaired in the complex TOM abilities and have lower self-awareness of emotions, but their recognition of others' emotions is intact. Also, the results demonstrate that a heightened level of EOT is associated with difficulties in facial emotion recognition in BPD patients.

This paper proposes an approach for finite-horizon control of partially-observed Boolean dynamical systems (POBDS) with uncertain continuous control input and infinite observation space. To cope with the partial observability of states, the proposed method first maps the POBDS to an unnormalized belief space. The nonlinear dynamics in this continuous belief space are linearized over a nominal trajectory. Then, the optimal feedback controller is derived, based on the well-known linear quadratic regulator (LQR), to push the system to follow the nominal trajectory. This nominal trajectory is computed in a planning stage before starting execution, and updated efficiently during execution, whenever the system is found to deviate from the nominal trajectory. We prove that, under mild regularization conditions, the proposed controller approaches the cost of the nominal trajectory as the linearization error approaches zero. The performance of the proposed controller is demonstrated by numerical experiments with a Melanoma gene regulatory network observed through noisy gene expression measurements.

Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear state-space models that provide a rich framework for modeling many complex dynamical systems. The model consists of a hidden Boolean state process, observed through an arbitrary noisy mapping to a measurement space. The optimal minimum mean-square error (MMSE) POBDS state estimators are the Boolean Kalman Filter and Smoother. However, in many practical problems, the system parameters are not fully known and must be estimated. In this paper, for POBDS under model uncertainty, we derive an optimal Bayesian estimator for state and parameter estimation. The exact algorithms are derived for the case of discrete and finite parameter space, and for general parameter spaces, an approximate Markov-Chain Monte-Carlo (MCMC) implementation is introduced. We demonstrate the performance of the proposed methodology by means of numerical experiments with POBDS models of gene regulatory networks observed through noisy measurements.