The Psychological and Neural Basis of Loss Aversion

Loss aversion is a central element of prospect theory, the dominant theory of decision making under uncertainty for the past four decades, and refers to the overweighting of potential losses relative to equivalent gains, a critical determinant of risky decision making. Recent advances in affective and decision neuroscience have shed new light on the psychological and neurobiological mechanisms underlying loss aversion. Here, integrating disparate literatures from the level of neurotransmitters to subjective reports of emotion, we propose a novel neural and computational framework that links norepinephrine to loss aversion and identifies a distinct role for dopamine in risk taking for rewards. We also propose that loss aversion specifically relates to anticipated emotions and aspects of the immediate experience of realized gains and losses but not their long-term emotional consequences, highlighting an underappreciated temporal structure. Finally, we discuss challenges to loss aversion and the relevance of loss aversion to understanding psychiatric disorders. Refining models of loss aversion will have broad consequences for the science of decision making and for how we understand individual variation in economic preferences and psychological well-being across both healthy and psychiatric populations

Neurocomputational mechanisms underpinning aberrant social learning in young adults with low self-esteem

Funder: UCLH NIHR BRCAbstract: Low self-esteem is a risk factor for a range of psychiatric disorders. From a cognitive perspective a negative self-image can be maintained through aberrant learning about self-worth derived from social feedback. We previously showed that neural teaching signals that represent the difference between expected and actual social feedback (i.e., social prediction errors) drive fluctuations in self-worth. Here, we used model-based functional magnetic resonance imaging (fMRI) to characterize learning from social prediction errors in 61 participants drawn from a population-based sample (n = 2402) who were recruited on the basis of being in the bottom or top 10% of self-esteem scores. Participants performed a social evaluation task during fMRI scanning, which entailed predicting whether other people liked them as well as the repeated provision of reported feelings of self-worth. Computational modeling results showed that low self-esteem participants had persistent expectations that others would dislike them, and a reduced propensity to update these expectations in response to social prediction errors. Low self-esteem subjects also displayed an enhanced volatility in reported feelings of self-worth, and this was linked to an increased tendency for social prediction errors to determine momentary self-worth. Canonical correlation analysis revealed that individual differences in self-esteem related to several interconnected psychiatric symptoms organized around a single dimension of interpersonal vulnerability. Such interpersonal vulnerability was associated with an attenuated social value signal in ventromedial prefrontal cortex when making predictions about being liked, and enhanced dorsal prefrontal cortex activity upon receipt of social feedback. We suggest these computational signatures of low self-esteem and their associated neural underpinnings might represent vulnerability for development of psychiatric disorder

Neurocomputational mechanisms underpinning aberrant social learning in young adults with low self-esteem.

Funder: UCLH NIHR BRCLow self-esteem is a risk factor for a range of psychiatric disorders. From a cognitive perspective a negative self-image can be maintained through aberrant learning about self-worth derived from social feedback. We previously showed that neural teaching signals that represent the difference between expected and actual social feedback (i.e., social prediction errors) drive fluctuations in self-worth. Here, we used model-based functional magnetic resonance imaging (fMRI) to characterize learning from social prediction errors in 61 participants drawn from a population-based sample (n = 2402) who were recruited on the basis of being in the bottom or top 10% of self-esteem scores. Participants performed a social evaluation task during fMRI scanning, which entailed predicting whether other people liked them as well as the repeated provision of reported feelings of self-worth. Computational modeling results showed that low self-esteem participants had persistent expectations that others would dislike them, and a reduced propensity to update these expectations in response to social prediction errors. Low self-esteem subjects also displayed an enhanced volatility in reported feelings of self-worth, and this was linked to an increased tendency for social prediction errors to determine momentary self-worth. Canonical correlation analysis revealed that individual differences in self-esteem related to several interconnected psychiatric symptoms organized around a single dimension of interpersonal vulnerability. Such interpersonal vulnerability was associated with an attenuated social value signal in ventromedial prefrontal cortex when making predictions about being liked, and enhanced dorsal prefrontal cortex activity upon receipt of social feedback. We suggest these computational signatures of low self-esteem and their associated neural underpinnings might represent vulnerability for development of psychiatric disorder

Dopamine Increases a Value-Independent Gambling Propensity

Although the impact of dopamine on reward learning is well documented, its influence on other aspects of behavior remains the subject of much ongoing work. Dopaminergic drugs are known to increase risk-taking behavior, but the underlying mechanisms for this effect are not clear. We probed dopamine’s role by examining the effect of its precursor L-DOPA on the choices of healthy human participants in an experimental paradigm that allowed particular components of risk to be distinguished. We show that choice behavior depended on a baseline (ie, value-independent) gambling propensity, a gambling preference scaling with the amount/variance, and a value normalization factor. Boosting dopamine levels specifically increased just the value-independent baseline gambling propensity, leaving the other components unaffected. Our results indicate that the influence of dopamine on choice behavior involves a specific modulation of the attractiveness of risky options—a finding with implications for understanding a range of reward-related psychopathologies including addiction

Measuring self-regulation in everyday life: reliability and validity of smartphone-based experiments in alcohol use disorder

Self-regulation, the ability to guide behavior according to one’s goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test–retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures’ construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks

Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder

Self-regulation, the ability to guide behavior according to one's goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test-retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures' construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks

Treating Pediatric Neuromuscular Disorders: The future is now

Pediatric neuromuscular diseases encompass all disorders with onset in childhood and where the primary area of pathology is in the peripheral nervous system. These conditions are largely genetic in etiology, and only those with a genetic underpinning will be presented in this review. This includes disorders of the anterior horn cell (e.g., spinal muscular atrophy), peripheral nerve (e.g., Charcot-Marie-Tooth disease), the neuromuscular junction (e.g., congenital myasthenic syndrome), and the muscle (myopathies and muscular dystrophies). Historically, pediatric neuromuscular disorders have uniformly been considered to be without treatment possibilities and to have dire prognoses. This perception has gradually changed, starting in part with the discovery and widespread application of corticosteroids for Duchenne muscular dystrophy. At present, several exciting therapeutic avenues are under investigation for a range of conditions, offering the potential for significant improvements in patient morbidities and mortality and, in some cases, curative intervention. In this review, we will present the current state of treatment for the most common pediatric neuromuscular conditions, and detail the treatment strategies with the greatest potential for helping with these devastating diseases

Mood dynamics are associated with learning and not choice

Updating predictions about which stimuli are associated with reward is an important aspect of adaptive behaviour believed to relate to prediction errors, the difference between experienced and predicted outcomes. Behavioural sensitivity to prediction errors flexibly adapts to environmental statistics. Prediction errors also influence affective states during risky choice tasks that do not require learning, but the relationship between emotions and adaptive behaviour is unknown. Here, using computational modelling we found that mood dynamics, like behaviour, are sensitive to learning-relevant model variables (i.e., probability prediction error). Unlike behaviour, mood dynamics are not sensitive to model variables that influence choice (i.e., expected value), and increasing volatility does not reduce how many trials influence affective state. Finally, depressive symptoms reduce overall mood more in volatile than stable environments. Our findings suggest that mood dynamics are selective for variables relevant to adaptive behaviour and suggest a greater role for mood in learning than choice