ON SOME PROBLEMS OF THE STATIC AND DYNAMIC ACCURACY OF LOGARITHMIC MULTIPLIERS

Apathy and anhedonia are common syndromes of motivation that are associated with a wide range of brain disorders and have no established therapies. Research using animal models suggests that a useful framework for understanding motivated behaviour lies in effort-based decision making for reward. The neurobiological mechanisms underpinning such decisions have now begun to be determined in individuals with apathy or anhedonia, providing an important foundation for developing new treatments. The findings suggest that there might be some shared mechanisms between both syndromes. A transdiagnostic approach that cuts across traditional disease boundaries provides a potentially useful means for understanding these conditions

Measuring cognitive effort without difficulty

An important finding in the cognitive effort literature has been that sensitivity to the costs of effort varies between individuals, suggesting that some people find effort more aversive than others. It has been suggested this may explain individual differences in other aspects of cognition; in particular that greater effort sensitivity may underlie some of the symptoms of conditions such as depression and schizophrenia. In this paper, we highlight a major problem with existing measures of cognitive effort that hampers this line of research, specifically the confounding of effort and difficulty. This means that behaviour thought to reveal effort costs could equally be explained by cognitive capacity, which influences the frequency of success and thereby the chance of obtaining reward. To address this shortcoming, we introduce a new test, the Number Switching Task (NST), specially designed such that difficulty will be unaffected by the effort manipulation and can easily be standardised across participants. In a large, online sample, we show that these criteria are met successfully and reproduce classic effort discounting results with the NST. We also demonstrate the use of Bayesian modelling with this task, producing behavioural parameters which can be associated with other measures, and report a preliminary association with the Need for Cognition scale

Power-up: a reanalysis of 'power failure' in neuroscience using mixture modelling

Evidence for endemically low statistical power has recently cast neuroscience findings into doubt. If low statistical power plagues neuroscience, this reduces confidence in reported effects. However, if statistical power is not uniformly low, such blanket mistrust might not be warranted. Here, we provide a different perspective on this issue, analysing data from an influential paper reporting a median power of 21% across 49 meta-analyses (Button et al., 2013). We demonstrate, using Gaussian mixture modelling, that the sample of 730 studies included in that analysis comprises several subcomponents; therefore the use of a single summary statistic is insufficient to characterise the nature of the distribution. We find that statistical power is extremely low for studies included in meta-analyses that reported a null result; and that it varies substantially across subfields of neuroscience, with particularly low power in candidate gene association studies. Thus, while power in neuroscience remains a critical issue, the notion that studies are systematically underpowered is not the full story: low power is far from a universal problem. SIGNIFICANCE STATEMENT: Recently, researchers across the biomedical and psychological sciences have become concerned with the reliability of results. One marker for reliability is statistical power: the probability of finding a statistically significant result, given that the effect exists. Previous evidence suggests that statistical power is low across the field of neuroscience. Our results present a more comprehensive picture of statistical power in neuroscience: on average, studies are indeed underpowered-some very seriously so-but many studies show acceptable or even exemplary statistical power. We show that this heterogeneity in statistical power is common across most subfields in neuroscience (psychology, neuroimaging, etc.). This new, more nuanced picture of statistical power in neuroscience could affect not only scientific understanding, but potentially policy and funding decisions for neuroscience research

Adaptation of social and non-social cues to direction in adults with autism spectrum disorder and neurotypical adults with autistic traits

Perceptual constancy strongly relies on adaptive gain control mechanisms, which shift perception as a function of recent sensory history. Here we examined the extent to which individual differences in magnitude of adaptation aftereffects for social and non-social directional cues are related to autistic traits and sensory sensitivity in healthy participants (Experiment 1); and also whether adaptation for social and non-social directional cues is differentially impacted in adults with Autism Spectrum Disorder (ASD) relative to neurotypical (NT) controls (Experiment 2). In Experiment 1, individuals with lower susceptibility to adaptation aftereffects, i.e. more 'veridical' perception, showed higher levels of autistic traits across social and non-social stimuli. Furthermore, adaptation aftereffects were predictive of sensory sensitivity. In Experiment 2, only adaptation to eye-gaze was diminished in adults with ASD, and this was related to difficulties categorizing eye-gaze direction at baseline. Autism Diagnostic Observation Schedule (ADOS) scores negatively predicted lower adaptation for social (head and eye-gaze direction) but not non-social (chair) stimuli. These results suggest that the relationship between adaptation and the broad socio-cognitive processing style captured by 'autistic traits' may be relatively domain-general, but in adults with ASD diminished adaptation is only apparent where processing is most severely impacted, such as the perception of social attention cues

Enhanced Risk Aversion, But Not Loss Aversion, in Unmedicated Pathological Anxiety.

BACKGROUND: Anxiety disorders are associated with disruptions in both emotional processing and decision making. As a result, anxious individuals often make decisions that favor harm avoidance. However, this bias could be driven by enhanced aversion to uncertainty about the decision outcome (e.g., risk) or aversion to negative outcomes (e.g., loss). Distinguishing between these possibilities may provide a better cognitive understanding of anxiety disorders and hence inform treatment strategies. METHODS: To address this question, unmedicated individuals with pathological anxiety (n = 25) and matched healthy control subjects (n = 23) completed a gambling task featuring a decision between a gamble and a safe (certain) option on every trial. Choices on one type of gamble-involving weighing a potential win against a potential loss (mixed)-could be driven by both loss and risk aversion, whereas choices on the other type-featuring only wins (gain only)-were exclusively driven by risk aversion. By fitting a computational prospect theory model to participants' choices, we were able to reliably estimate risk and loss aversion and their respective contribution to gambling decisions. RESULTS: Relative to healthy control subjects, pathologically anxious participants exhibited enhanced risk aversion but equivalent levels of loss aversion. CONCLUSIONS: Individuals with pathological anxiety demonstrate clear avoidance biases in their decision making. These findings suggest that this may be driven by a reduced propensity to take risks rather than a stronger aversion to losses. This important clarification suggests that psychological interventions for anxiety should focus on reducing risk sensitivity rather than reducing sensitivity to negative outcomes per se

Approach-avoidance reinforcement learning as a translational and computational model of anxiety-related avoidance

Although avoidance is a prevalent feature of anxiety-related psychopathology, differences in the measurement of avoidance between humans and non-human animals hinder our progress in its theoretical understanding and treatment. To address this, we developed a novel translational measure of anxiety-related avoidance in the form of an approach-avoidance reinforcement learning task, by adapting a paradigm from the non-human animal literature to study the same cognitive processes in human participants. We used computational modelling to probe the putative cognitive mechanisms underlying approach-avoidance behaviour in this task and investigated how they relate to subjective task-induced anxiety. In a large online study (n = 372), participants who experienced greater task-induced anxiety avoided choices associated with punishment, even when this resulted in lower overall reward. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards. We replicated these findings in an independent sample (n = 627) and we also found fair-to-excellent reliability of measures of task performance in a sub-sample retested 1 week later (n = 57). Our findings demonstrate the potential of approach-avoidance reinforcement learning tasks as translational and computational models of anxiety-related avoidance. Future studies should assess the predictive validity of this approach in clinical samples and experimental manipulations of anxiety

Models of affective decision-making: how do feelings predict choice?

Intuitively, how we feel about potential outcomes will determine our decisions. Indeed, one of the most influential theories in psychology, Prospect Theory, implicitly assumes that feelings govern choice. Surprisingly, however, we know very little about the rules by which feelings are transformed into decisions. Here, we characterize a computational model that uses feelings to predict choice. We reveal that this model predicts choice better than existing value-based models, showing a unique contribution of feelings to decisions, over and above value. Similar to Prospect Theory value function, feelings showed diminished sensitivity to outcomes as value increased. However, loss aversion in choice was explained by an asymmetry in how feelings about losses and gains were weighed when making a decision, not by an asymmetry in the feelings themselves. The results provide new insights into how feelings are utilized to reach a decision

Modeling Avoidance in Mood and Anxiety Disorders Using Reinforcement Learning.

BACKGROUND: Serious and debilitating symptoms of anxiety are the most common mental health problem worldwide, accounting for around 5% of all adult years lived with disability in the developed world. Avoidance behavior-avoiding social situations for fear of embarrassment, for instance-is a core feature of such anxiety. However, as for many other psychiatric symptoms the biological mechanisms underlying avoidance remain unclear. METHODS: Reinforcement learning models provide formal and testable characterizations of the mechanisms of decision making; here, we examine avoidance in these terms. A total of 101 healthy participants and individuals with mood and anxiety disorders completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. RESULTS: We show an increased reliance in the mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepotent (Pavlovian) bias to withhold responding in the face of negative outcomes. This was particularly the case when the mood and anxiety group was under stress. CONCLUSIONS: This formal description of avoidance within the reinforcement learning framework provides a new means of linking clinical symptoms with biophysically plausible models of neural circuitry and, as such, takes us closer to a mechanistic understanding of mood and anxiety disorders

Investigating associations between empathy, morality and psychopathic personality traits in the general population

Although atypical moral and empathy processing are considered core features of psychopathic personality, little is known about how these constructs are associated with psychopathic traits in the general population. One-hundred and twenty-four adult males from the community were administered the Self-Report Psychopathy Scale 4 Short Form, as well as a wide battery of affect, empathy and morality tasks and questionnaires. Our findings indicate that both core affective-interpersonal, as well as lifestyle-antisocial features of psychopathy are associated with weaker empathic responses to fearful faces. However, only the unique variance of the affective-interpersonal features is associated with weaker empathic response to happy stories, lower propensity to feel empathic concern and less difficulty in making decisions on moral dilemmas. In contrast, the unique variance of the lifestyle-antisocial features is associated with greater propensity to feel empathic concern. These preliminary findings extend previous research and suggest that, while the joint variance between affective-interpersonal and lifestyle-antisocial features might drive some ‘deficits’ associated with psychopathy, there also appears also to be unique ‘deficits’ associated with the core affective-interpersonal features, particularly in relation to affective aspects of moral processing