The Computational Anatomy of Psychosis

By Rick A Adams, Klaas E. Stephan, Harriet R. Brown, ChrisD . Frith, Karl J. Friston. Frontiers in Psychiatry, 2013

789 citations recorded by the source corpus.

This paper considers psychotic symptoms in terms of false inferences or beliefs. It is based on the notion that the brain is an inference machine that actively constructs hypotheses to explain or predict its sensations. This perspective provides a normative (Bayes-optimal) account of action and perception that emphasizes probabilistic representations; in particular, the confidence or precision of beliefs about the world. We will consider hallucinosis, abnormal eye movements, sensory attenuation deficits, catatonia, and delusions as various expressions of the same core pathology: namely, an aberrant encoding of precision. From a cognitive perspective, this represents a pernicious failure of metacognition (beliefs about beliefs) that can confound perceptual inference. In the embodied setting of active (Bayesian) inference, it can lead to behaviors that are paradoxically more accurate than Bayes-optimal behavior. Crucially, this normative account is accompanied by a neuronally plausible process theory based upon hierarchical predictive coding. In predictive coding, precision is thought to be encoded by the post-synaptic gain of neurons reporting prediction error. This suggests that both pervasive trait abnormalities and florid failures of inference in the psychotic state can be linked to factors controlling post-synaptic gain – such as NMDA receptor function and (dopaminergic) neuromodulation. We illustrate these points using biologically plausible simulations of perceptual synthesis, smooth pursuit eye movements and attribution of agency – that all use the same predictive coding scheme and pathology: namely, a reduction in the precision of prior beliefs, relative to sensory evidence.

Subfields: Cognitive Neuroscience, Psychiatry and Mental health, Philosophy

View Paper: https://www.semanticscholar.org/paper/d8978c97484146a81da63ada8f6229789925e6da

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Citations in the corpus

LFP and oscillations—what do they tell us? (2015, background): “This attenuation has been linked to sensory attenuation during self-made acts [49], suggesting a failure of sensory attenuation in Parkinson’s disease that rests on dopaminergic modulation of beta activity [5,50].”
Uncertainty in perception and the Hierarchical Gaussian Filter (2014, positive): “This accords well with recent accounts of psychopathological symptoms as a failure of sensory attenuation (Adams et al., 2013).”
Precise minds in uncertain worlds: predictive coding in autism. (2014, background): “…that may be important to consider are the specific level of origin of the prediction errors (conceptual or action vs perceptual prediction errors, Adams et al., 2013; Fletcher & Frith, 2009) and the subjective confidence level (precision) that top-down beliefs can take on (to explain their…”
Neural Elements for Predictive Coding (2016, background): “Accordingly, precision is associated with the top– down deployment of attention in the sensory domain and with action selection in the motor domain (Friston et al., 2011; Adams et al., 2013a).”
Generative Predictive Codes by Multiplexed Hippocampal Neuronal Tuplets. (2018, neutral): “Intrinsic-Unlikely Signals Selectively Update the Generative Internal Model During spatial exploration, incorporation of novel information from the external world would result in perturbation and editing of intrinsically generated neuronal sequences and encoding of the difference from the internal m”
An aberrant precision account of autism (2014, positive): “…these particular illusions in autism, the link between the precision of sensory evidence and the propensity to perceive illusory precepts offers an intriguing explanation for reports of reduced susceptibility to certain visual illusions in autism (and schizophrenia: e.g., Adams et al., 2013b).”
Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation (2017, background): “…between the subject’s beliefs (hyperparameters) about different sources of uncertainty and the true parameters can lead to systematic over-and underconfidence (Adams et al., 2013; Dru-gowitsch et al., 2014), and thus potentially account for variability across individuals in metacognitive bias.”
Active interoceptive inference and the emotional brain (2016, background): “Crucially, the delicate balance of precision over hierarchical levels can have a profound effect on inference – and may underlie false beliefs in psychopathology (53).”
The Predictive Coding Account of Psychosis (2018, background): “The resulting aberrant encoding of precision could lead to an abnormally strong weighting of PEs, which in turn leads to aberrant learning and the formation of delusional beliefs (53,58,59,64).”