Sarah K. Fineberg

The doxastic shear pin: delusions as errors of learning and memory

ABSTRACT We reconsider delusions in terms of a “doxastic shear pin”, a mechanism that errs so as to prevent the destruction of the machine (brain) and permit continued function (in an attenuated capacity). Delusions may disable flexible (but energetically expensive) inference. With each recall, delusions may be reinforced further and rendered resistant to contradiction. We aim to respond to deficit accounts of delusions – that delusions are only a problem without any benefit – by considering delusion formation and maintenance in terms of predictive coding. We posit that brains conform to a simple computational principle: to minimize prediction error (the mismatch between prior top-down expectation and current bottom-up input) across hierarchies of brain regions and psychological representation. Recent data suggest that delusions may form in the absence of constraining top-down expectations. Then, once formed, they become new priors that motivate other beliefs, perceptions, and actions by providing strong (sometimes overriding) top-down expectation. We argue that delusions form when the shear-pin breaks, permitting continued engagement with an overwhelming world, and ongoing function in the face of paralyzing difficulty. This crucial role should not be ignored when we treat delusions: we need to consider how a person will function in the world without them..

A Computational Account of Borderline Personality Disorder: Impaired Predictive Learning about Self and Others Through Bodily Simulation.

Social dysfunction is a prominent and disabling aspect of borderline personality disorder. We reconsider traditional explanations for this problem, especially early disruption in the way an infant feels physical care from its mother, in terms of recent developments in computational psychiatry. In particular, social learning may depend on reinforcement learning though embodied simulations. Such modeling involves calculations based on structures outside the brain such as face and hands, calculations on one’s own body that are used to make inferences about others. We discuss ways to test the role of embodied simulation in BPD and potential implications for treatment.

Computational Psychiatry in Borderline Personality Disorder

Purpose of ReviewWe review the literature on the use and potential use of computational psychiatry methods in borderline personality disorder.Recent FindingsComputational approaches have been used in psychiatry to increase our understanding of the molecular, circuit, and behavioral basis of mental illness. This is of particular interest in BPD, where the collection of ecologically valid data, especially in interpersonal settings, is becoming more common and more often subject to quantification. Methods that test learning and memory in social contexts, collect data from real-world settings, and relate behavior to molecular and circuit networks are yielding data of particular interest.SummaryResearch in BPD should focus on collaborative efforts to design and interpret experiments with direct relevance to core BPD symptoms and potential for translation to the clinic.

Utility of pretransplant psychological measures to predict posttransplant outcomes in liver transplant patients: a systematic review.

OBJECTIVE Evaluation of liver transplant (LT) candidacy involves psychosocial evaluation to ensure appropriate organ allocation. However, the utility of pre-LT psychiatric and neuropsychological factors in predicting posttransplant outcomes remains uncertain. We reviewed current evidence on the prognostic value of pre-LT psychological factors for outcomes after LT. METHOD We conducted a systematic review of studies with adult LT recipients that investigate the relationship between pre-LT psychiatric and neuropsychological variables and posttransplant outcomes. We searched Ovid, MEDLINE, PsycINFO, EMBASE/Scopus, Cochrane Controlled trials register and Web of Science (January 1975 to May 2015) for longitudinal, peer-reviewed studies of at least 20 subjects and written in English. RESULTS The 19 studies included in this review are heterogeneous in population, prognosis and duration of follow-up (from 20days to more than 3 years). Findings on the prognostic value of pre-LT depression or anxiety on post-LT outcomes are mixed, though depression appears to predict lower quality of life (QOL). Pre-LT suicidal thoughts in particular are associated with post-LT depression. High submissiveness may predict rejection within 20days of LT, and low conscientiousness is associated with greater nonadherence. Whereas pre-LT cognitive performance has not been shown to predict survival, poorer performance may predict poorer QOL after LT. CONCLUSION Further studies are needed to examine this important element of LT candidacy evaluation. Studies should evaluate psychiatric factors in large samples, include systematic evaluations by mental health clinicians and explore broader neuropsychological domains in predicting posttransplant outcomes.

Idiom use in a young man with schizophrenia and prominent sexual delusions.

History of Present Illness Mr. L first came to psychiatric attention at age 19 when he was hospitalized for disorganized and threatening behavior toward his family, including destruction of property at home. He was hospitalized twice that month, and symptoms improved with risperidone (oral, then long-acting injectable), valproate (500 mg daily), and lorazepam (1 mg twice daily). He returned home and was well enough to continue career training. He had a second episode of acute psychosis two years later in the context of a change back to oral medication (and likely decreased adherence to medication regimen). Symptoms included religious and persecutory delusions, disorganized behavior, and auditory hallucinations. He reported ideas of reference, such as God playing songs for him on the radio. He was quite agitated, threatening his mother and breaking a glass door. He was hospitalized twice in six months. A four-month trial of 25 mg fluphenazine

Amazon Mechanical Turk as a platform for borderline personality disorder research

Researchers investigating the psychological processes underlying specific mental health problems often have difficulties achieving large enough samples for adequately powered studies. This can be particularly problematic when studying psychopathology with low base rates in typical samples (i.e., undergraduate and community). A relatively new approach to recruitment and testing employs online crowdsourcing to rapidly measure the characteristics and behavior of large numbers of people. We tested the feasibility of researching borderline personality disorder (BPD) in this manner using one large crowdsourcing site, Amazon Mechanical Turk (MTurk). Specifically, we examined prevalence rates of psychopathology in a large MTurk sample, as well as the demographic, psychosocial, and psychiatric characteristics of individuals who met criteria for BPD. These characteristics were compared across three groups: those who met criteria for BPD currently, those who met criteria for remitted BPD, and those who had never met criteria for BPD. The results suggest that MTurk may be ideally suited for studying individuals with a wide range of pathology, from healthy to intensely symptomatic to remitted.

Differential Valuation and Learning From Social and Nonsocial Cues in Borderline Personality Disorder

Background Volatile interpersonal relationships are a core feature of borderline personality disorder (BPD) and lead to devastating disruption of patients’ personal and professional lives. Quantitative models of social decision making and learning hold promise for defining the underlying mechanisms of this problem. In this study, we tested BPD and control subject weighting of social versus nonsocial information and their learning about choices under stable and volatile conditions. We compared behavior using quantitative models. Methods Subjects (n = 20 BPD, n = 23 control subjects) played an extended reward learning task with a partner (confederate) that requires learning about nonsocial and social cue reward probability (the social valuation task). Task experience was measured using language metrics: explicit emotions/beliefs, talk about the confederate, and implicit distress (using the previously established marker self-referentiality). Subjects’ weighting of social and nonsocial cues was tested in mixed-effect regression models. Subjects’ learning rates under stable and volatile conditions were modeled (Rescorla–Wagner approach) and group × condition interactions tested. Results Compared to control subjects, BPD subject debriefings included more mentions of the confederate and less distress language. BPD subjects also weighted social cues more heavily but had blunted learning responses to (nonsocial and social) volatility. Conclusions This is the first report of patient behavior in the social valuation task. The results suggest that BPD subjects expect higher volatility than control subjects. These findings lay the groundwork for a neurocomputational dissection of social and nonsocial belief updating in BPD, which holds promise for the development of novel clinical interventions that more directly target pathophysiology.