The role of priors in Bayesian models of perception

In a recent opinion article, Pellicano and Burr (2012) speculate about how a Bayesian architecture might explain many features of autism ranging from stereotypical movement to atypical phenomenological experience. We share the view of other commentators on this paper (Brock, 2012; Friston et al., 2013; Van Boxtel and Lu, 2013) that applying computational methods to psychiatric disorders is valuable (Montague et al., 2012). However, we argue that in this instance there are fundamental technical and conceptual problems which must be addressed if such a perspective is to become useful. Based on the Bayesian observer model (Figure 1), Pellicano and Burr speculate that perceptual abnormalities in autism can be explained by differences in how beliefs about the world are formed, or combined with sensory information, and that sensory processing itself is unaffected (although, confusingly, they also speak of sensory atypicalities in autism). In computational terms, the authors are suggesting that the likelihood function is unaltered in autism and that the posterior is atypical either because of differences in the prior, or because of the way in which prior and likelihood are combined. The latter statement is problematic because within the framework of probability theory, the combination of these two components is fixed as determined by Bayes' theorem: they are multiplied. Put simply, a mathematically consistent Bayesian model cannot accommodate a perceptual abnormality in autism that is due to the way in which belief and sensory information, i.e., prior and likelihood, are combined. Furthermore, if sensory processing is mathematically represented as a likelihood function (as it typically is within Bayesian models), then changes in the prior cannot lead to changes in sensation, as the authors claim (they can only lead to changes in perception)

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Action and behavior: a free-energy formulation

We have previously tried to explain perceptual inference and learning under a free-energy principle that pursues Helmholtz’s agenda to understand the brain in terms of energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-energy formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception

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. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior--in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology

Modularity and the predictive mind

Modular approaches to the architecture of the mind claim that some mental mechanisms, such as sensory input processes, operate in special-purpose subsystems that are functionally independent from the rest of the mind. This assumption of modularity seems to be in tension with recent claims that the mind has a predictive architecture. Predictive approaches propose that both sensory processing and higher-level processing are part of the same Bayesian information-processing hierarchy, with no clear boundary between perception and cognition. Furthermore, it is not clear how any part of the predictive architecture could be functionally independent, given that each level of the hierarchy is influenced by the level above. Both the assumption of continuity across the predictive architecture and the seeming non-isolability of parts of the predictive architecture seem to be at odds with the modular approach. I explore and ultimately reject the predictive approach’s apparent commitments to continuity and non-isolation. I argue that predictive architectures can be modular architectures, and that we should in fact expect predictive architectures to exhibit some form of modularity

A predictive processing theory of sensorimotor contingencies: explaining the puzzle of perceptual presence and its absence in synesthesia

Normal perception involves experiencing objects within perceptual scenes as real, as existing in the world. This property of “perceptual presence” has motivated “sensorimotor theories” which understand perception to involve the mastery of sensorimotor contingencies. However, the mechanistic basis of sensorimotor contingencies and their mastery has remained unclear. Sensorimotor theory also struggles to explain instances of perception, such as synesthesia, that appear to lack perceptual presence and for which relevant sensorimotor contingencies are difficult to identify. On alternative “predictive processing” theories, perceptual content emerges from probabilistic inference on the external causes of sensory signals, however, this view has addressed neither the problem of perceptual presence nor synesthesia. Here, I describe a theory of predictive perception of sensorimotor contingencies which (1) accounts for perceptual presence in normal perception, as well as its absence in synesthesia, and (2) operationalizes the notion of sensorimotor contingencies and their mastery. The core idea is that generative models underlying perception incorporate explicitly counterfactual elements related to how sensory inputs would change on the basis of a broad repertoire of possible actions, even if those actions are not performed. These “counterfactually-rich” generative models encode sensorimotor contingencies related to repertoires of sensorimotor dependencies, with counterfactual richness determining the degree of perceptual presence associated with a stimulus. While the generative models underlying normal perception are typically counterfactually rich (reflecting a large repertoire of possible sensorimotor dependencies), those underlying synesthetic concurrents are hypothesized to be counterfactually poor. In addition to accounting for the phenomenology of synesthesia, the theory naturally accommodates phenomenological differences between a range of experiential states including dreaming, hallucination, and the like. It may also lead to a new view of the (in)determinacy of normal perception

Reinforcement learning or active inference?

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain

The cybernetic Bayesian brain: from interoceptive inference to sensorimotor contingencies

Is there a single principle by which neural operations can account for perception, cognition, action, and even consciousness? A strong candidate is now taking shape in the form of “predictive processing”. On this theory, brains engage in predictive inference on the causes of sensory inputs by continuous minimization of prediction errors or informational “free energy”. Predictive processing can account, supposedly, not only for perception, but also for action and for the essential contribution of the body and environment in structuring sensorimotor interactions. In this paper I draw together some recent developments within predictive processing that involve predictive modelling of internal physiological states (interoceptive inference), and integration with “enactive” and “embodied” approaches to cognitive science (predictive perception of sensorimotor contingencies). The upshot is a development of predictive processing that originates, not in Helmholtzian perception-as-inference, but rather in 20th-century cybernetic principles that emphasized homeostasis and predictive control. This way of thinking leads to (i) a new view of emotion as active interoceptive inference; (ii) a common predictive framework linking experiences of body ownership, emotion, and exteroceptive perception; (iii) distinct interpretations of active inference as involving disruptive and disambiguatory—not just confirmatory—actions to test perceptual hypotheses; (iv) a neurocognitive operationalization of the “mastery of sensorimotor contingencies” (where sensorimotor contingencies reflect the rules governing sensory changes produced by various actions); and (v) an account of the sense of subjective reality of perceptual contents (“perceptual presence”) in terms of the extent to which predictive models encode potential sensorimotor relations (this being “counterfactual richness”). This is rich and varied territory, and surveying its landmarks emphasizes the need for experimental tests of its key contributions

Interoceptive inference, emotion, and the embodied self

The concept of the brain as a prediction machine has enjoyed a resurgence in the context of the Bayesian brain and predictive coding approaches within cognitive science. To date, this perspective has been applied primarily to exteroceptive perception (e.g., vision, audition), and action. Here, I describe a predictive, inferential perspective on interoception: ‘interoceptive inference’ conceives of subjective feeling states (emotions) as arising from actively-inferred generative (predictive) models of the causes of interoceptive afferents. The model generalizes ‘appraisal’ theories that view emotions as emerging from cognitive evaluations of physiological changes, and it sheds new light on the neurocognitive mechanisms that underlie the experience of body ownership and conscious selfhood in health and in neuropsychiatric illness