Psychosis as a disorder of muscarinic signalling: psychopathology and pharmacology.

Dopaminergic receptor antagonism is a crucial component of all licensed treatments for psychosis, and dopamine dysfunction has been central to pathophysiological models of psychotic symptoms. Some clinical trials, however, indicate that drugs that act through muscarinic receptor agonism can also be effective in treating psychosis, potentially implicating muscarinic abnormalities in the pathophysiology of psychosis. Here, we discuss understanding of the central muscarinic system, and we examine preclinical, behavioural, post-mortem, and neuroimaging evidence for its involvement in psychosis.

A Developmental Framework for Embodiment Research: The Next Step Toward Integrating Concepts and Methods.

Embodiment research is at a turning point. There is an increasing amount of data and studies investigating embodiment phenomena and their role in mental processing and functions from across a wide range of disciplines and theoretical schools within the life sciences. However, the integration of behavioral data with data from different biological levels is challenging for the involved research fields such as movement psychology, social and developmental neuroscience, computational psychosomatics, social and behavioral epigenetics, human-centered robotics, and many more.

Computational Psychosomatics and Computational Psychiatry: Toward a Joint Framework for Differential Diagnosis.

This article outlines how a core concept from theories of homeostasis and cybernetics, the inference-control loop, may be used to guide differential diagnosis in computational psychiatry and computational psychosomatics. In particular, we discuss 1) how conceptualizing perception and action as inference-control loops yields a joint computational perspective on brain-world and brain-body interactions and 2) how the concrete formulation of this loop as a hierarchical Bayesian model points to key computational quantities that inform a taxonomy of potential disease mechanisms. We consider the utility of this perspective for differential diagnosis in concrete clinical applications.

Hierarchical prediction errors in midbrain and septum during social learning.

Social learning is fundamental to human interactions, yet its computational and physiological mechanisms are not well understood. One prominent open question concerns the role of neuromodulatory transmitters. We combined fMRI, computational modelling and genetics to address this question in two separate samples (N = 35, N = 47).

Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression.

This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy).

Neural Signatures of Controlled and Automatic Retrieval Processes in Memory-based Decision-making.

Decision-making often requires retrieval from memory. Drawing on the neural ACT-R theory [Anderson, J. R.

Inferring on the intentions of others by hierarchical Bayesian learning.

Inferring on others' (potentially time-varying) intentions is a fundamental problem during many social transactions. To investigate the underlying mechanisms, we applied computational modeling to behavioral data from an economic game in which 16 pairs of volunteers (randomly assigned to "player" or "adviser" roles) interacted. The player performed a probabilistic reinforcement learning task, receiving information about a binary lottery from a visual pie chart.