Approximately one third of people with Major Depressive Disorder (MDD) experience a relapse within six months of discontinuing antidepressant medication (ADM), however, reliable predictors of relapse following ADM discontinuation are currently lacking. A putative behavioural predictor is delay discounting, which measures a person’s impatience to receive reward. Previous studies have linked delay discounting to both MDD and reduced serotonergic function, rendering it a plausible candidate predictor. In this multi-site study we measured delay discounting in participants with remitted MDD (N = 97), before and within six months after discontinuation of ADM, and in matched controls without a lifetime history of MDD (N = 54). Using predictive models, we tested whether either baseline discounting, or an early change in discounting following ADM discontinuation, predicted depressive relapse over a six month follow up period. We also tested differences between remitted MDD and control groups in delay discounting at baseline, and associations between discounting and depressive symptoms. We found that the remitted MDD group, compared to the control group, showed significantly higher (p < 0.05; Cohen’s d = 0.34) discounting at baseline. In addition, baseline discounting was positively correlated with depression rating scores (Spearman ρ = 0.24). However, delay discounting did not increase following ADM discontinuation. Neither baseline discounting, nor a change in discounting following ADM discontinuation, predicted subsequent depressive relapse. We conclude that delay discounting is elevated in remitted MDD treated with antidepressant medication. However, delay discounting neither increases following ADM discontinuation, nor does it prospectively predict depressive relapse. These results suggest that delay discounting in Major Depressive Disorder has little relationship with illness trajectory following ADM discontinuation.

Existing models of personality disorder are statistical models: dispersion patterns of personality facets. Although useful in describing personality differences, such models fall short in terms of explaining those differences. Generative models can address these explanatory gaps by explicating the mechanisms that generate descriptive pathologies. In this paper, we aim to move beyond the former descriptive models and toward the latter explanatory ones. To do so, we formalize personality pathology using a generative model that has four properties. First, it is probabilistic: it outlines how humans leverage uncertainty to make sense of their own and others’ ways of being. Second, it is mentalizing: it posits that personality pathology is about poor ways of experiencing and relating to the self and others. Third, it is hierarchical: it accounts for the multiplicity of self- and other- states (in the here-and-now) and traits (in the long-run). Finally, it is dynamic: it outlines how these properties evolve over time, accounting for the development of personality. Simulating data from this model, we demonstrate how it can account for the generation, maintenance, and treatment of various personality problems (from borderline instability to narcissistic grandiosity) by formalizing them as relational problems: problems with navigating relationships. We thus discuss how our model could be used to address recent debates on what is central to personality pathology by clarifying the distinction between description (what personality ‘is’) and explanation (what personality ‘does’). We conclude the paper with a tutorial on our model and suggestions for future research.

Receiving affirmation, whether from ourselves or others, is crucial for interpersonal relationships and goes awry in mental disorders. Meaningful evaluations emerge during interactions, where people can support or let each other down. However, such dynamics are difficult to understand comprehensively without quantitative theory and modeling. Here, we implemented an interactive decision-making game wherein two real-life participants evaluated, i.e. graded their approval, for themselves and their play partner. Young adult participants interacted in a multi-level version of the iterated prisoner’s dilemma. Crucially, each participant did not interact with the other directly, but instructed an avatar to do so on their behalf. This allowed increased experimental control while preserving considerable ecological validity. We tested computational models of participants’ evaluations of self and other, based on their beliefs about the quality of their decisions. However such models were less successful than a novel class of models, where self- and other- evaluations depended directly on the combination of self- and other- outcomes. The winning models suggested that for a given participant, evaluation of the self is proportional to how much one’s partner benefits, and vice versa. We found marked self-positivity bias, especially in dyads where neither partner cooperated. This was consistent with attributional theory, negatively evaluating others rather than the self for adverse outcomes. Between participants, self-positivity bias was explained by a reduced weight of one’s partner’s benefits for self-evaluation, hinting that the negative outcome subject to external attribution was the partner’s, rather than one’s own, poor returns. Preliminary analysis also suggested that a reduced sensitivity to others’ outcomes was associated, in this context when participants may have both cooperative and competitive motives, with reduced earnings for the self. The proposed computational model provides a concise and novel account of self-serving bias in evaluations, clearly observed during interactions.

During high-stake interactions, people not only evaluate policies or outcomes, but also themselves and others. Such evaluations may be crucial for long-term outcomes, such as harmonious marriage, confident leadership and indeed mental health. Powerful evaluations occur during interactions, where people can support or let each other down. Thus, we implemented an interactive decision-making game, wherein two real-life participants explicitly evaluated themselves and their play-partner while playing an ecologically framed, probabilistic, iterated prisoner’s dilemma. To separate preferences from abilities, participants did not interact with the other directly, but instructed a computer avatar on how to play on their behalf. We tested a range of computational models of participants’ person-evaluations. In some, self-evaluation relied on regret or satisfaction regarding one’s decisions. However, the winning models relied directly on observed gains and losses. Here, evaluation of the self was proportional to how much one’s partner benefited, and vice versa. We found a marked self-positivity bias, which was most prominent in dyads where both partners often defected. Between participants, a self-positivity bias was explained by a reduced weight of one’s partner’s benefits onto self-evaluation. This suggests that the negative outcomes claimed to attract defensive, external attribution by attribution theorists are one’s partner’s poor outcomes. Further analysis suggested that a reduced sensitivity to others’ outcomes was associated with reduced earnings for the self, hinting at a functional role for person-evaluations in decision-making. Thus, we introduce a novel computational model that provides a concise account of self-serving bias in evaluations, as observed during risky dyadic interactions.

In decision-making, the likelihood of outcomes is often partly unknown, a form of uncertainty known as ambiguity. Previous studies report that people tend to be averse to ambiguity. However, existing models of decision making under uncertainty fail to explain why people will sometimes show an actual preference for ambiguity, particularly in contexts where reward appears unlikely. Likewise, models of ambiguity do not provide predictions regarding decisions under risk, wherein reward probabilities are explicit. Here we apply a model wherein ambiguity attitudes hinge on a Bayesian average over prior beliefs about reward probabilities, where priors correspond to alternative latent causes governing the distribution of reward. By postulating that all gambles inherently embody a degree of ambiguity, our approach can seamlessly integrate decisions made under both risk and ambiguity. We provide empirical support for predictions of this model in two behavioural experiments. Firstly, as predicted by our model, we show that ambiguity attitude seamlessly transitions from ambiguity seeking at low reward probabilities to ambiguity aversion at higher reward probabilities. Secondly, the model accounts for an empirical observation of non-linear probability weighting for both risky and ambiguous choices. Our approach highlights a continuum between risk and ambiguity, providing an integrated framework for interpreting decision-making under uncertainty.

Humans discount delayed relative to more immediate reward. A plausible explanation is that impatience arises partly from uncertainty, or risk, implicit in delayed reward. Existing theories of discounting-as-risk focus on a probability that delayed reward will not materialize. By contrast, we examine how uncertainty in the magnitude of delayed reward contributes to delay discounting. We propose a model wherein reward is discounted proportional to the rate of random change in its magnitude across time, termed volatility. We find evidence to support this model across three experiments (total N = 158). First, using a task where participants chose when to sell products, whose price dynamics they previously learned, we show discounting increases in line with price volatility. Second, we show that this effect pertains over naturalistic delays of up to 4 months. Using functional magnetic resonance imaging, we observe a volatility-dependent decrease in functional hippocampal-prefrontal coupling during intertemporal choice. Third, we replicate these effects in a larger online sample, finding that volatility discounting within each task correlates with baseline discounting outside of the task. We conclude that delay discounting partly reflects time-dependent uncertainty about reward magnitude, that is volatility. Our model captures how discounting adapts to volatility, thereby partly accounting for individual differences in impatience. Our imaging findings suggest a putative mechanism whereby uncertainty reduces prospective simulation of future outcomes.

Acquiring competence in psychotherapy is a mandatory part of psychiatric training in the UK. Within their first 3 years of 'Core Psychiatry' training, doctors are expected to deliver both short-term and long-term psychotherapy treatments, supervised by the local Medical Psychotherapy tutor. During the Covid-19 pandemic, these treatments and their supervisions were carried out remotely. This pan-London qualitative research study, commissioned by the Health Education England London School of Psychiatry, aimed to explore trainees' and trainers' experiences of the psychotherapy curriculum within Core Psychiatric training, as well as their experiences of remote work during the pandemic. Seventeen participants were interviewed (out of 19 who came forward), including both trainees and trainers working within the London region. Thematic analysis of the transcripts of the semi-structured interviews identified five main themes with associated sub-themes. The results suggest that trainees found their psychotherapy experience to be enriching. However, there is work to be done around barriers and anxieties faced by trainees, for instance concerning the impact of patient drop out on training progression. Remote work posed additional issues for trainers and trainees in addressing psychotherapy competencies, with feelings of disconnection and loss being prominent.

Interpersonal dynamics have long been acknowledged as instrumental to the generation and alleviation of psychopathology problems. Recent computational approaches have been argued to be uniquely suited for investigating such dynamics either with exploratory models (machine learning) or confirmatory models (generative modeling). However, the utility of such models to the study of interpersonal problems has not yet been scrutinized. We thus conducted a systematic review to assess the validity, reliability, and openness of computational models to the study of interpersonal psychopathology problems. Candidate studies (n = 2,944), including peer-reviewed conference manuscripts, were derived from five databases (MEDLINE, Embase, PsycINFO, Web of Science, Google Scholar) up to September 2024. A total of 55 studies met inclusion criteria and were assessed for their computational approach, validity, reliability, and openness. Results indicated that Bayesian modeling was the most commonly used approach (k=19), followed by machine learning (k=16), dynamical systems modeling (k=10), and reinforcement learning (k=10). Quality assessments revealed considerable heterogeneity in the validity of these approaches, with some scoring high primarily on empirical validity (reinforcement learning), others on theoretical validity (dynamical systems), and yet others on generative validity (Bayesian models). Finally, and most strikingly, few studies reported comprehensive performance metrics and even fewer studies adopted open science practices (specifically, 2 pre-registered their hypotheses and 8 shared their data and code online). We discuss these matters and conclude with more optimistic messages regarding how, when rigorously and openly conducted, computational approaches have the potential to advance the field of interpersonal psychopathology by enabling us to formalize and examine historically elusive social concepts (like mental representations of the self and others) and their role in psychopathology problems.

A tendency to merge mental representations of self and other is thought to underpin the intense and unstable relationships that feature in Borderline Personality Disorder (BPD). However, clinical theories of BPD do not specify, in computational terms, how the perspectives of self and other might become confused. To address this question, we used a probabilistic false belief task (p-FBT) to examine how individuals with BPD (N=38) and matched controls from the general population (N=74) selectively assigned beliefs to self or other. The p-FBT requires participants to track a gradually changing quantity, whilst also predicting another person’s belief about that quantity. We found that BPD participants showed less selectivity in belief assignment compared with controls (Cohen’s d = 0.64). Behaviourally, participants with BPD tended to predict that others’ beliefs resembled their own. Modelling analysis revealed that BPD participants were prone to generalise their own learning signals to others. Furthermore, this generalising tendency correlated with BPD symptomatology across participants, even when controlling for demographic factors and affective psychopathology. Our results support a computational account of self-other mergence, based on a generalisation of learning across agents. Self-other generalisation in learning purports to explain key clinical features of BPD, and suggests a potential transdiagnostic marker of mentalising capability.

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Early theories of schizophrenia considered the illness as a fragmentation of mental content in response to psychological trauma. Here we present a case of very late onset schizophrenia in a previously high-functioning man in his mid-60s, precipitated by having lost his family in a terrorist attack, while he was living in Africa. He presented with symptoms consistent with post-traumatic stress disorder, however also exhibited visual and auditory hallucinations and marked deterioration in daily functioning. He showed mild impairment on cognitive testing, however brain imaging and screening for reversible causes of cognitive impairment were normal. The case highlights the need for a formulation-based approach to understanding and managing responses to severe trauma, from resolution through to psychotic disintegration.

People often form polarized beliefs about others. In a clinical setting this is referred to as a dichotomous or ‘split’ representation of others, whereby others are not imbued with possessing mixtures of opposing properties. Here, we formalise these accounts as an oversimplified categorical model of others’ internal, intentional, states. We show how a resulting idealization and devaluation of others can be stabilized by attributing unexpected behaviour to fictive external factors. For example, under idealization, less-than-perfect behaviour is attributed to unfavourable external conditions, thereby maintaining belief in the other’s goodness. This feature of the model accounts for how extreme beliefs are buffered against counter-evidence, while at the same time being prone to precipitous changes of polarity. Equivalent inference applied to the self creates an oscillation between self-aggrandizement and self-deprecation, capturing oscillatory relational and affective dynamics. Notably, such oscillatory dynamics arise out of the Bayesian nature of the model, wherein a subject arrives at the most plausible explanation for their observations, given their current expectations. Thus, the model we present accounts for aspects of splitting that appear ‘defensive’, without the need to postulate a specific defensive intention. By contrast, we associate psychological health with a fine-grained representation of internal states, constrained by an integrated prior, corresponding to notions of ‘character’. Finally, the model predicts that extreme appraisals of self or other are associated with causal attribution errors.

A dislike of waiting for pain, aptly termed ‘dread’, is so great that people will increase pain to avoid delaying it. However, despite many accounts of altruistic responses to pain in others, no previous studies have tested whether people take delay into account when attempting to ameliorate others' pain. We examined the impact of delay in 2 experiments where participants (total N = 130) specified the intensity and delay of pain either for themselves or another person. Participants were willing to increase the experimental pain of another participant to avoid delaying it, indicative of dread, though did so to a lesser extent than was the case for their own pain. We observed a similar attenuation in dread when participants chose the timing of a hypothetical painful medical treatment for a close friend or relative, but no such attenuation when participants chose for a more distant acquaintance. A model in which altruism is biased to privilege pain intensity over the dread of pain parsimoniously accounts for these findings. We refer to this underestimation of others' dread as a ‘Dread Empathy Gap’.

Impatience can be formalized as a delay discount rate, describing how the subjective value of reward decreases as it is delayed. By analogy, selfishness can be formalized as a social discount rate, representing how the subjective value of rewarding another person decreases with increasing social distance. Delay and social discount rates for reward are correlated across individuals. However no previous work has examined whether this relationship also holds for aversive outcomes. Neither has previous work described a functional form for social discounting of pain in humans. This is a pertinent question, since preferences over aversive outcomes formally diverge from those for reward. We addressed this issue in an experiment in which healthy adult participants ( N = 67) chose the timing and intensity of hypothetical pain for themselves and others. In keeping with previous studies, participants showed a strong preference for immediate over delayed pain. Participants showed greater concern for pain in close others than for their own pain, though this hyperaltruism was steeply discounted with increasing social distance. Impatience for pain and social discounting of pain were weakly correlated across individuals. Our results extend a link between impatience and selfishness to the aversive domain.

Selectively attributing beliefs to specific agents is core to reasoning about other people and imagining oneself in different states. Evidence suggests humans might achieve this by simulating each other’s computations in agent-specific neural circuits, but it is not known how circuits become agent-specific. Here we investigate whether agent-specificity adapts to social context. We train subjects on social learning tasks, manipulating the frequency with which self and other see the same information. Training alters the agent-specificity of prediction error (PE) circuits for at least 24 h, modulating the extent to which another agent’s PE is experienced as one’s own and influencing perspective-taking in an independent task. Ventromedial prefrontal myelin density, indexed by magnetisation transfer, correlates with the strength of this adaptation. We describe a frontotemporal learning network, which exploits relationships between different agents’ computations. Our findings suggest that Self-Other boundaries are learnable variables, shaped by the statistical structure of social experience.

Summary The dystopian scenario of an ‘artificial intelligence takeover’ imagines artificial intelligence (AI) becoming the dominant form of intelligence on Earth, rendering humans redundant. As a society we have become increasingly familiar with AI and robots replacing humans in many tasks, certain jobs and even some areas of medicine, but surely this is not the fate of psychiatry? Here a computational neuroscientist (Janaina Mourão-Miranda) and psychiatrist (Justin Taylor Baker) suggest that psychiatry as a profession is relatively safe, whereas psychiatrists Christian Brown and Giles William Story predict that robots will be taking over the asylum.

When people anticipate uncertain future outcomes, they often prefer to know their fate in advance. Inspired by an idea in behavioral economics that the anticipation of rewards is itself attractive, we hypothesized that this preference of advance information arises because reward prediction errors carried by such information can boost the level of anticipation. We designed new empirical behavioral studies to test this proposal, and confirmed that subjects preferred advance reward information more strongly when they had to wait for rewards for a longer time. We formulated our proposal in a reinforcement-learning model, and we showed that our model could account for a wide range of existing neuronal and behavioral data, without appealing to ambiguous notions such as an explicit value for information. We suggest that such boosted anticipation significantly drives risk-seeking behaviors, most pertinently in gambling.

BACKGROUND:Second-generation antipsychotics (SGAs) are often prescribed in the treatment of Behavioral and Psychological Symptoms of Dementia (BPSD), however, their use has been discouraged in light of clinical trials suggesting that they cause an increased risk of cerebrovascular accidents (CVAs).OBJECTIVE:Aim of the study was to assess relative risk of CVA in dementia patients prescribed SGA rather than first-generation antipsychotics (FGAs), through meta-analysis of population-based studies.METHODS:A literature search was conducted using several relevant databases. Five studies were included in the review and data were pooled to conduct meta-analysis using the inverse variance method.RESULTS:A total of 79,910 patients were treated with SGAs and 1287 cases of CVA were reported. Of 48,135 patients treated with FGAs, a total of 511 cases of CVA were reported. The relative risk of CVA was 1.02 (95% CI 0.56-1.84) for the SGA group. There was no significant difference in the risk of stroke (p = 0.96) between groups, but significant heterogeneity was found among the results of included studies (p < 0.001).CONCLUSION:Meta-analysis of population-based data suggested that the use of SGAs as opposed to FGAs to control BPSD is not associated with significantly increased risk of CVA. Copyright © 2016 John Wiley & Sons, Ltd.

Impatience for reward is a facet of many psychiatric disorders. We draw attention to a growing literature finding greater discounting of delayed reward, an important aspect of impatience, across a range of psychiatric disorders. We propose these findings are best understood by considering the goals and motivation for discounting future reward. We characterize these as arising from either the opportunity costs of waiting or the uncertainty associated with delayed reward. We link specific instances of higher discounting in psychiatric disorder to heightened subjective estimates of either of these factors. We propose these costs are learned and represented based either on a flexible cognitive model of the world, an accumulation of previous experience, or through evolutionary specification. Any of these can be considered suboptimal for the individual if the resulting behavior results in impairments in personal and social functioning and/or in distress. By considering the neurochemical and neuroanatomical implementation of these processes, we illustrate how this approach can in principle unite social, psychological and biological conceptions of impulsive choice.