A Computational Analysis of Abnormal Belief Updating Processes and Their Association With Psychotic Experiences and Childhood Trauma in a UK Birth Cohort.

Background: Psychotic experiences emerge from abnormalities in perception and belief formation and occur more commonly in those experiencing childhood trauma. However, which precise aspects of belief formation are atypical in psychosis is not well understood. We used a computational modeling approach to characterize belief updating in young adults in the general population, examine their relationship with psychotic outcomes and trauma, and determine the extent to which they mediate the trauma-psychosis relationship.

Retrospective Inference as a Form of Bounded Rationality, and Its Beneficial Influence on Learning.

Probabilistic models of cognition typically assume that agents make inferences about current states by combining new sensory information with fixed beliefs about the past, an approach known as Bayesian filtering. This is computationally parsimonious, but, in general, leads to suboptimal beliefs about past states, since it ignores the fact that new observations typically contain information about the past as well as the present. This is disadvantageous both because knowledge of past states may be intrinsically valuable, and because it impairs learning about fixed or slowly changing parameters of the environment.

Modeling subjective belief states in computational psychiatry: interoceptive inference as a candidate framework.

The nascent field computational psychiatry has undergone exponential growth since its inception. To date, much of the published work has focused on choice behaviors, which are primarily modeled within a reinforcement learning framework. While this initial normative effort represents a milestone in psychiatry research, the reality is that many psychiatric disorders are defined by disturbances in subjective states (e.

Older adults fail to form stable task representations during model-based reversal inference.

Older adults struggle in dealing with changeable and uncertain environments across several cognitive domains. This has been attributed to difficulties in forming adequate task representations that help navigate uncertain environments. Here, we investigate how, in older adults, inadequate task representations impact on model-based reversal learning.

Dopaminergic basis for signaling belief updates, but not surprise, and the link to paranoia.

Distinguishing between meaningful and meaningless sensory information is fundamental to forming accurate representations of the world. Dopamine is thought to play a central role in processing the meaningful information content of observations, which motivates an agent to update their beliefs about the environment. However, direct evidence for dopamine's role in human belief updating is lacking.

Sequential inference as a mode of cognition and its correlates in fronto-parietal and hippocampal brain regions.

Normative models of human cognition often appeal to Bayesian filtering, which provides optimal online estimates of unknown or hidden states of the world, based on previous observations. However, in many cases it is necessary to optimise beliefs about sequences of states rather than just the current state. Importantly, Bayesian filtering and sequential inference strategies make different predictions about beliefs and subsequent choices, rendering them behaviourally dissociable.

Dopamine, reward learning, and active inference.

Temporal difference learning models propose phasic dopamine signaling encodes reward prediction errors that drive learning. This is supported by studies where optogenetic stimulation of dopamine neurons can stand in lieu of actual reward. Nevertheless, a large body of data also shows that dopamine is not necessary for learning, and that dopamine depletion primarily affects task performance.

Evidence for surprise minimization over value maximization in choice behavior.

Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents' to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone.

Neural signals encoding shifts in beliefs.

Dopamine is implicated in a diverse range of cognitive functions including cognitive flexibility, task switching, signalling novel or unexpected stimuli as well as advance information. There is also longstanding line of thought that links dopamine with belief formation and, crucially, aberrant belief formation in psychosis. Integrating these strands of evidence would suggest that dopamine plays a central role in belief updating and more specifically in encoding of meaningful information content in observations.

Optimal inference with suboptimal models: addiction and active Bayesian inference.

When casting behaviour as active (Bayesian) inference, optimal inference is defined with respect to an agent's beliefs - based on its generative model of the world. This contrasts with normative accounts of choice behaviour, in which optimal actions are considered in relation to the true structure of the environment - as opposed to the agent's beliefs about worldly states (or the task). This distinction shifts an understanding of suboptimal or pathological behaviour away from aberrant inference as such, to understanding the prior beliefs of a subject that cause them to behave less 'optimally' than our prior beliefs suggest they should behave.

Active inference, evidence accumulation, and the urn task.

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology.

Precision and neuronal dynamics in the human posterior parietal cortex during evidence accumulation.

Primate studies show slow ramping activity in posterior parietal cortex (PPC) neurons during perceptual decision-making. These findings have inspired a rich theoretical literature to account for this activity. These accounts are largely unrelated to Bayesian theories of perception and predictive coding, a related formulation of perceptual inference in the cortical hierarchy.

Revealing a brain network endophenotype in families with idiopathic generalised epilepsy.

Idiopathic generalised epilepsy (IGE) has a genetic basis. The mechanism of seizure expression is not fully known, but is assumed to involve large-scale brain networks. We hypothesised that abnormal brain network properties would be detected using EEG in patients with IGE, and would be manifest as a familial endophenotype in their unaffected first-degree relatives.

Cross-modal effects of value on perceptual acuity and stimulus encoding.

Cross-modal interactions are very common in perception. An important feature of many perceptual stimuli is their reward-predicting properties, the utilization of which is essential for adaptive behavior. What is unknown is whether reward associations in one sensory modality influence perception of stimuli in another modality.

The Dopaminergic Midbrain Encodes the Expected Certainty about Desired Outcomes.

Dopamine plays a key role in learning; however, its exact function in decision making and choice remains unclear. Recently, we proposed a generic model based on active (Bayesian) inference wherein dopamine encodes the precision of beliefs about optimal policies. Put simply, dopamine discharges reflect the confidence that a chosen policy will lead to desired outcomes.

Model averaging, optimal inference, and habit formation.

Postulating that the brain performs approximate Bayesian inference generates principled and empirically testable models of neuronal function-the subject of much current interest in neuroscience and related disciplines. Current formulations address inference and learning under some assumed and particular model. In reality, organisms are often faced with an additional challenge-that of determining which model or models of their environment are the best for guiding behavior.

Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging.

A pressing need exists to disentangle age-related changes from pathologic neurodegeneration. This study aims to characterize the spatial pattern and age-related differences of biologically relevant measures in vivo over the course of normal aging. Quantitative multiparameter maps that provide neuroimaging biomarkers for myelination and iron levels, parameters sensitive to aging, were acquired from 138 healthy volunteers (age range: 19-75 years).

Transcranial direct current stimulation of right dorsolateral prefrontal cortex does not affect model-based or model-free reinforcement learning in humans.

There is broad consensus that the prefrontal cortex supports goal-directed, model-based decision-making. Consistent with this, we have recently shown that model-based control can be impaired through transcranial magnetic stimulation of right dorsolateral prefrontal cortex in humans. We hypothesized that an enhancement of model-based control might be achieved by anodal transcranial direct current stimulation of the same region.

Reward-related activity in ventral striatum is action contingent and modulated by behavioral relevance.

Multiple features of the environment are often imbued with motivational significance, and the relative importance of these can change across contexts. The ability to flexibly adjust evaluative processes so that currently important features of the environment alone drive behavior is critical to adaptive routines. We know relatively little about the neural mechanisms involved, including whether motivationally significant features are obligatorily evaluated or whether current relevance gates access to value-sensitive regions.

Disruption of dorsolateral prefrontal cortex decreases model-based in favor of model-free control in humans.

Human choice behavior often reflects a competition between inflexible computationally efficient control on the one hand and a slower more flexible system of control on the other. This distinction is well captured by model-free and model-based reinforcement learning algorithms. Here, studying human subjects, we show it is possible to shift the balance of control between these systems by disruption of right dorsolateral prefrontal cortex, such that participants manifest a dominance of the less optimal model-free control.

Working memory and anticipatory set modulate midbrain and putamen activity.

To behave adaptively, an organism must balance the accurate maintenance of information stored in working memory with the ability to update that information when the context changes. This trade-off between fidelity and flexibility may depend upon the anticipated likelihood that updating will be necessary. To address the neurobiological basis of anticipatory optimization, we acquired functional magnetic resonance imaging data, while healthy human subjects performed a modified delayed-response task.

Characterizing aging in the human brainstem using quantitative multimodal MRI analysis.

Aging is ubiquitous to the human condition. The MRI correlates of healthy aging have been extensively investigated using a range of modalities, including volumetric MRI, quantitative MRI (qMRI), and diffusion tensor imaging. Despite this, the reported brainstem related changes remain sparse.