This chapter comes in two related but distinct parts. The first presents general trends in the neurosciences and considers how these impact upon psychiatry as a clinical science. The second picks up a recent and important development in neuroscience which seeks to explain mental functions such as perception and has been profitably extended into explanations of psychopathology. The second part can be viewed as a working example of the first’s overarching themes.

Mirror neurons fire while both performing and observing an action and enable us to understand and predict what others are doing. This function arises because a) the visual-motor matching of mirror neurons are a consequence of stimulus-response mapping mechanisms that transform sensory input of observing someone else’s action into a matching motor response, or b) we understand what we have done ourselves and what others are doing simply because action and action observation are coded in the same representational format, and mirror neurons are an instantiation of such common coding.

Prediction in the motor domain, but perhaps also in the cognitive domain, is a universal function of the human cerebellum. The cerebellum contains and maintains two internal models of the world to coordinate and control behavior: an inverse model to generate motor commands and a forward prediction model; as well as an error detection mechanism and a learning process that corrects the prediction errors.

Connectionist networks consisting of large numbers of simple connected processing units implicitly or explicitly model aspects of human predictive behavior. Prediction in connectionist models can occur in different ways and with quite different connectionist architectures. Connectionist neural networks offer a useful playground and ‘hands-on way’ to explore prediction and to figure out what may be special about how the human mind predicts.

The brain does not passively register sensory input but actively predicts it. The activity of the sensory input is ‘explained away’ and only activity that was not predicted remains. This remaining activity is treated as an error signal that is used to update the predictive coding system. Learning is predictive coding.

Prediction science is likely to push on toward distinct reconceptualizations or the dismantling of the cornerstones of traditional cognitive science, away from rule-based symbol manipulation and toward a comprehensive systems prediction science, toward theoretical unification and simplicity, toward figuring out the pros and cons of the representation-light and representation-heavy, toward incorporating analog representations and common codes, toward proactive, probabilistic, mechanistic, and formalized theories, and computationally specified models of the predictive mind. The paradigm shift of the predictive revolution is no longer only emerging: it is continuing at an ever-increasing pace.

Perception and action are continuously running cycles of sensing and perceiving, predicting, acting, and adjusting. Sensation and perception are assumed to be intrinsically functional and forward-looking in the service of action. This is because relevant information from the environment is needed to guide our actions.

Information processing is a process of uncertainty resolution. Information-theoretic constructs such as surprisal and entropy reflect the fine-grained probabilistic knowledge which people have accumulated over time. The information-theoretic constructs explain the extent of processing difficulty that people encounter, for example when comprehending language. Processing difficulty and cognitive effort in turn are a direct reflection of predictability.

People use fast and simple mental shortcuts for their predictions rather than making weighed assessments and rational decisions based on huge amounts of data. Heuristics are a cognitively cheap and efficient way to solve complex or novel problems because large amounts of the information available in the environment can be ignored.

Mental imagery can be used to simulate imminent, distant possible, or even impossible futures. Such mental simulation enables people to explore the consequences of different actions they want to perform or the consequences of being in different kinds of situations. Predictive simulation retrieves embodied knowledge but also creates new knowledge because people can compare different simulated scenarios and draw conclusions from that.

Prediction is the preactivation of likely upcoming stored mental representations. Pattern completion is the related theory that prediction involves the multiscale completion of patterns of the future. Prediction in pattern completion ignores the presence of gaps and incomplete structures by ‘jumping to conclusions’ about the complete pattern. Preactivation and the completion or continuation of encountered patterns in a forward direction are explicit descriptions of how predictive processing arises in the mind.

Bayesian inference is one way prediction can be formalized. It combines an estimation of the prior probability that an event will take place and an assessment of the likelihood of new data to give a new updated estimate of the posterior probability of the event. Important concepts in Bayesian inference are rational analysis, the notions of optimal inference or an ideal observer, and that processing can be corrupted in a noisy channel.

The myriad of ways prediction has been considered in the scientific literature in the past highlights the need for a clear and inclusive definition. It is essential to be conceptually clear about what predictions are, one simple reason being that this is needed to establish what experimental evidence can be considered relevant and what not. First, prediction must be defined in such a way that what is called prediction is clearly about the future; environmental input which is likely to be upcoming or encountered soon.

This means that phenomena and processes that are retrospective or retrodictive, that is, utilize information to explain the past, should not more or less arbitrarily be called prediction. Second, prediction must be defined in an inclusive way such that phenomena and approaches that are clearly about the future are not more or less arbitrarily excluded from the discussion. In consideration of these requirements, prediction in the present book is defined as the conscious or subconscious use of information from previous experiences for the conscious or subconscious processing of information about future states of the body and environment.

Quantum theory provides another way to formalize uncertainty. Quantum probability theory can be used to model phenomena such as order effects which cannot be straightforwardly modeled within classical probability theory. Key concepts of quantum theory including superposition states, noncommutative operations, and entanglement provide new angles and explanations for some predictive phenomena.

Neurobiological theories draw on neurobiological evidence from fMRI but also plenty of other neuroscientific methods for theory development: On a fundamental level, neurobiological theories are neurobiological explanations about the nature of the brain-behavior link.

The predictive mind makes use of two broadly distinct collections of cognitive systems, types of processing, or families of cognitive operations: an implicit (priming) system and an explicit and active ‘smart route’ to prediction. Though theorists differ greatly in what they mean by two systems and dual processing accounts, there is considerable agreement among most behavioral scientists about the general distinctions. System 1 is assumed to be unconscious, fast, implicit, automatic, and reflexive whereas System 2 is conscious, slow, explicit, controlled, and reflective.

The idea that thinking, and thinking about the future, is based on logical content and the computer-like processing of variables has been a cornerstone of the cognitive revolution. The fact that so few theories of prediction and so few prediction scientists explicitly incorporate any form of symbolic rule-based inferencing into their theorizing is another indication of a true paradigm shift in the mind and brain sciences toward a new probabilistic, and at least partly associationist, nonsymbolic and non-rule-based science of the predictive mind.