The idea that predictions shape how we perceive and comprehend the world has become increasingly influential in the field of systems neuroscience. It also forms an important framework for understanding neuropsychiatric disorders, which are proposed to be the result of disturbances in the mechanisms through which prior information influences perception and belief, leading to the production of suboptimal models of the world. There is a widespread tendency to conceptualize the influence of predictions exclusively in terms of ‘top-down’ processes, whereby predictions generated in higher-level areas exert their influence on lower-level areas within an information processing hierarchy. However, this excludes from consideration the predictive information embedded in the ‘bottom-up’ stream of information processing. We describe evidence for the importance of this distinction and argue that it is critical for the development of the predictive processing framework and, ultimately, for an understanding of the perturbations that drive the emergence of neuropsychiatric symptoms and experiences.
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C.T. and P.C.F. are funded by the Wellcome Trust and P.C.F. is funded by the Bernard Wolfe Health Neuroscience Fund.
The authors declare no competing interests.
The authors affirm that human research participants provided informed consent, for publication of the images in Fig. 2.
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In the fields of cybernetics, reinforcement learning and artificial intelligence, an agent is an entity that is capable of acting autonomously to self-regulate in the face of changes in its environment.
- Bayesian decision theory
A theory that describes how decisions are optimized by application of principles from Bayesian probability; that is, by drawing on probability distributions that quantify prior probabilities of events or states. These probabilities are referred to as priors and reflect beliefs about a state before new evidence is taken into account.
- Information theory
The mathematical formulation of how information is coded, transmitted and processed. Informally, information can be thought of as a measure of the reduction of uncertainty. The field of information theory emerged from attempts to solve the problem of how to transfer large datasets within limited-capacity systems and has proven useful in thinking about how neural systems deal with a similar problem.
- Perceptual and cognitive inference
The process by which perceptions and beliefs arise from the combination of sensory evidence and information based on prior experience or knowledge. The process of inference may be optimized by using prior knowledge according to Bayes’ theorem.
Estimates of unobserved or missing information on the basis of a model. Within the predictive processing framework, the model is provided by prior knowledge of the world. Note that predictions can be (but are not necessarily) future-oriented.
- Predictive coding
Within neuroscience, a family of algorithms aiming to capture how the brain performs probabilistic inference using the mismatch between the predicted and the actual magnitude of a signal.
In Bayesian models of perception, action and cognition, the term is used as shorthand for ‘prior probability distributions’, which model the system’s information about a world state before current evidence is assessed. Importantly, priors provide information that is the basis of the formation of predictions. It is important to note that the term is agnostic as to how this prior information is implemented, making combined terms, such as ‘top-down prior’, which implies a specific mechanism, confusing.
In Bayesian decision theory, a function that determines the value of a possible situation or outcome.
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Teufel, C., Fletcher, P.C. Forms of prediction in the nervous system. Nat Rev Neurosci 21, 231–242 (2020). https://doi.org/10.1038/s41583-020-0275-5
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