Biology subjects

Roiser, J. P.

Publications and source records attributed to Roiser, J. P..

6 recordsLinked to original sources

bioRxiv2026

Electrophysiological Correlates of Reinforcement Learning in the Human Ventral Tegmental Area

The ventral tegmental area (VTA) is the principal source of dopaminergic input to the human prefrontal cortex and is hypothesised to play a central role in reward-based learning. However, direct electrophysiological evidence in humans has been lacking. Here, using intracranial recordings obtained during an instrumental learning task in patients undergoing deep brain stimulation surgery for chronic cluster headache (n=13), we show that VTA local field potentials selectively encode rewarding outcomes compared with losses or neutral outcomes independently of whether behavioural learning occurred. In a subset of participants (n= 8) who learned to prefer a high-reward option, VTA responses to unexpected rewards were larger than to expected rewards. In two participants who sufficiently explored both options, VTA activity reflected the expected value of the chosen option during decision-making. These findings provide direct electrophysiological evidence that the human VTA encodes reward-related signals consistent with reinforcement learning mechanisms described in animal models.

neuroscience↗

bioRxiv2024

A computational approach to understanding effort-based decision-making in depression

BackgroundMotivational dysfunction is a core feature of depression, and can have debilitating effects on everyday function. However, it is unclear which disrupted cognitive processes underlie impaired motivation, and whether impairments persist following remission. Decision-making concerning exerting effort to obtain rewards offers a promising framework for understanding motivation, especially when examined with computational tools which can offer precise quantification of latent processes. MethodsEffort-based decision-making was assessed using the Apple Gathering Task, where participants decide whether to exert effort via a grip-force device to obtain varying levels of reward; effort levels were individually calibrated and varied parametrically. We present a comprehensive computational analysis of decision-making, initially validating our model in healthy volunteers (N=67), before applying it in a case-control study including current (N=41) and remitted (N=46) unmedicated depressed individuals, and healthy volunteers with (N=36) and without (N=57) a family history of depression. ResultsFour fundamental computational mechanisms that drive patterns of effort-based decisions, which replicated across samples, were identified: overall bias to accept effort challenges; reward sensitivity; and linear and quadratic effort sensitivity. Traditional model-agnostic analyses showed that both depressed groups showed lower willingness to exert effort. In contrast with previous findings, computational analysis revealed that this difference was primarily driven by lower effort acceptance bias, but not altered effort or reward sensitivity. ConclusionsThis work provides insight into the computational mechanisms underlying motivational dysfunction in depression. Lower willingness to exert effort could represent a trait-like factor contributing to symptoms, and might represent a fruitful target for treatment and prevention.

neuroscience↗

bioRxiv2022

Individual Variation in Risky Decisions Is Related to Age and Gender but not to Mental Health Symptoms

Risky decisions involve choosing between options where the outcomes are uncertain. Cognitive tasks such as the CANTAB Cambridge Gamble Task (CGT) have revealed that patients with depression make more conservative decisions, but the mechanisms of choice evaluation underlying such decisions, and how they lead to the observed differences in depression, remain unknown. To test this, we used a computational modelling approach in a broad general-population sample (N = 753) who performed the CANTAB CGT and completed questionnaires assessing symptoms of mental illness, including depression. We fit five different computational models to the data, including two novel ones, and found that a novel model that uses an inverse power function in the loss domain (contrary to standard Prospect Theory accounts), and is influenced by the probabilities but not the magnitudes of different outcomes, captures the characteristics of our dataset very well. Surprisingly, model parameters were not significantly associated with any mental health questionnaire scores, including depression scales; but they were related to demographic variables, particularly age, with stronger associations than typical model-agnostic task measures. This study showcases a new methodology to analyse data from CANTAB CGT, describes a noteworthy null finding with respect to mental health symptoms, and demonstrates the added precision that a computational approach can offer.

neuroscience↗

bioRxiv2021

Reliability of decision-making and reinforcement learning computational parameters

Computational models can offer mechanistic insight into cognition and therefore have the potential to transform our understanding of psychiatric disorders and their treatment. For translational efforts to be successful, it is imperative that computational measures capture individual characteristics reliably. Here we examine the reliability of reinforcement learning and economic models derived from two commonly used tasks. Healthy individuals (N=50) completed a restless four-armed bandit and a calibrated gambling task twice, two weeks apart. Reward and punishment learning rates from the reinforcement learning model showed good reliability and reward and punishment sensitivity from the same model had fair reliability; while risk aversion and loss aversion parameters from a prospect theory model exhibited good and excellent reliability, respectively. Both models were further able to predict future behaviour above chance within individuals. This prediction was better when based on participants own model parameters than other participants parameter estimates. These results suggest that reinforcement learning, and particularly prospect theory parameters, as derived from a restless four-armed bandit and a calibrated gambling task, can be measured reliably to assess learning and decision-making mechanisms. Overall, these findings indicate the translational potential of clinically-relevant computational parameters for precision psychiatry.

neuroscience↗

bioRxiv2020

The acute effects of cannabidiol on emotional processing and anxiety: a neurocognitive imaging study

BackgroundThere is growing interest in the therapeutic potential of cannabidiol (CBD) across a range of psychiatric disorders. CBD has been found to reduce anxiety during experimentally-induced stress in anxious individuals and healthy controls. However, the mechanisms underlying the putative anxiolytic effects of CBD are unknown. We therefore sought to investigate the behavioural and neural effects of a single dose of CBD vs. placebo on a range of emotion-related measures to test cognitive-mechanistic models of its effects on anxiety. MethodsWe conducted a randomised, double-blind, placebo-controlled, crossover, acute oral challenge of 600 mg of CBD in 24 healthy participants on emotional processing, with neuroimaging (viewing emotional faces during fMRI) and cognitive (emotional appraisal) measures as well as subjective response to experimentally-induced anxiety. ResultsCBD did not produce effects on BOLD responses to emotional faces, cognitive measures of emotional processing, or modulate experimentally-induced anxiety, relative to placebo. ConclusionsGiven the rising popularity of CBD for its putative medical benefits, further research is warranted to investigate the clinical potential of CBD for the treatment of anxiety disorders.

neuroscience↗

bioRxiv2016

Modelling avoidance in pathologically anxious humans using reinforcement-learning

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 behaviour -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. Reinforcement-learning models provide formal and testable characterizations of the mechanisms of decision-making; here, we examine avoidance in these terms. One hundred and one healthy and pathologically anxious individuals completed an approach-avoidance go/no-go task under stress induced by threat of unpredictable shock. We show an increased reliance in the anxious 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 anxious individuals were under stress. 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 pathological anxiety.

animal behavior and cognition↗