Predictive processing approaches to the mind are increasingly popular in the cognitive sciences. This surge of interest is accompanied by a proliferation of philosophical arguments, which seek to either extend or oppose various aspects of the emerging framework. In particular, the question of how to position predictive processing with respect to enactive and embodied cognition has become a topic of intense debate. While these arguments are certainly of valuable scientific and philosophical merit, they risk underestimating the variety of approaches gathered (...) under the predictive label. Here, we first present a basic review of neuroscientific, cognitive, and philosophical approaches to PP, to illustrate how these range from solidly cognitivist applications—with a firm commitment to modular, internalistic mental representation—to more moderate views emphasizing the importance of ‘body-representations’, and finally to those which fit comfortably with radically enactive, embodied, and dynamic theories of mind. Any nascent predictive processing theory must take into account this continuum of views, and associated theoretical commitments. As a final point, we illustrate how the Free Energy Principle attempts to dissolve tension between internalist and externalist accounts of cognition, by providing a formal synthetic account of how internal ‘representations’ arise from autopoietic self-organization. The FEP thus furnishes empirically productive process theories by which to guide discovery through the formal modelling of the embodied mind. (shrink)
We review some of the main implications of the free-energy principle (FEP) for the study of the self-organization of living systems – and how the FEP can help us to understand (and model) biotic self-organization across the many temporal and spatial scales over which life exists. In order to maintain its integrity as a bounded system, any biological system - from single cells to complex organisms and societies - has to limit the disorder or dispersion (i.e., the long-run entropy) of (...) its constituent states. We review how this can be achieved by living systems that minimize their variational free energy. Variational free energy is an information theoretic construct, originally introduced into theoretical neuroscience and biology to explain perception, action, and learning. It has since been extended to explain the evolution, development, form, and function of entire organisms, providing a principled model of biotic self-organization and autopoiesis. It has provided insights into biological systems across spatiotemporal scales, ranging from microscales (e.g., sub- and multicellular dynamics), to intermediate scales (e.g., groups of interacting animals and culture), through to macroscale phenomena (the evolution of entire species). A crucial corollary of the FEP is that an organism just is (i.e., embodies or entails) an implicit model of its environment. As such, organisms come to embody causal relationships of their ecological niche, which, in turn, is influenced by their resulting behaviors. Crucially, free-energy minimization can be shown to be equivalent to the maximization of Bayesian model evidence. This allows us to cast natural selection in terms of Bayesian model selection, providing a robust theoretical account of how organisms come to match or accommodate the spatiotemporal complexity of their surrounding niche. In line with the theme of this volume; namely, biological complexity and self-organization, this chapter will examine a variational approach to self-organization across multiple dynamical scales. (shrink)
If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on (...) Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure. (shrink)
Why do brains have so many connections? The principles exposed by Andy Clark provide answers to questions like this by appealing to the notion that brains distil causal regularities in the sensorium and embody them in models of their world. For example, connections embody the fact that causes have particular consequences. This commentary considers the imperatives for this form of embodiment.
Viewing the brain as an organ of approximate Bayesian inference can help us understand how it represents the self. We suggest that inferred representations of the self have a normative function: to predict and optimise the likely outcomes of social interactions. Technically, we cast this predict-and-optimise as maximising the chance of favourable outcomes through active inference. Here the utility of outcomes can be conceptualised as prior beliefs about final states. Actions based on interpersonal representations can therefore be understood as minimising (...) surprise – under the prior belief that one will end up in states with high utility. Interpersonal representations thus serve to render interactions more predictable, while the affective valence of interpersonal inference renders self-perception evaluative. Distortions of self-representation contribute to major psychiatric disorders such as depression, personality disorder and paranoia. The approach we review may therefore operationalise the study of interpersonal representations in pathological states. (shrink)
This paper considers the Cartesian theatre as a metaphor for the virtual reality models that the brain uses to make inferences about the world. This treatment derives from our attempts to understand dreaming and waking consciousness in terms of free energy minimization. The idea here is that the Cartesian theatre is not observed by an internal audience but furnishes a theatre in which fictive narratives and fantasies can be rehearsed and tested against sensory evidence. We suppose the brain is driven (...) by the imperative to infer the causes of its sensory samples; in much the same way as scientists are compelled to test hypotheses about experimental data. This recapitulates Helmholtz's notion of unconscious inference and Gregory's treatment of perception as hypothesis testing. However, we take this further and consider the active sampling of the world as the gathering of confirmatory evidence for hypotheses based on our virtual reality. The ensuing picture of consciousness resolves a number of seemingly hard problems in consciousness research and is internally consistent with current thinking in systems neuroscience and theoretical neurobiology. In this formalism, there is a dualism that distinguishes between the process of inference and the process that entails inference. This separation is reflected by the distinction between beliefs and the physical brain states that encode them. This formal approach allows us to appeal to simple but fundamental theorems in information theory and statistical thermodynamics that dissolve some of the mysterious aspects of consciousness. (shrink)
In evolutionary biology, niche construction is sometimes described as a genuine evolutionary process whereby organisms, through their activities and regulatory mechanisms, modify their environment such as to steer their own evolutionary trajectory, and that of other species. There is ongoing debate, however, on the extent to which niche construction ought to be considered a bona fide evolutionary force, on a par with natural selection. Recent formulations of the variational free-energy principle as applied to the life sciences describe the properties of (...) living systems, and their selection in evolution, in terms of variational inference. We argue that niche construction can be described using a variational approach. We propose new arguments to support the niche construction perspective, and to extend the variational approach to niche construction to current perspectives in various scientific fields. (shrink)
We present a multiscale integrationist interpretation of the boundaries of cognitive systems, using the Markov blanket formalism of the variational free energy principle. This interpretation is intended as a corrective for the philosophical debate over internalist and externalist interpretations of cognitive boundaries; we stake out a compromise position. We first survey key principles of new radical views of cognition. We then describe an internalist interpretation premised on the Markov blanket formalism. Having reviewed these accounts, we develop our positive multiscale account. (...) We argue that the statistical seclusion of internal from external states of the system—entailed by the existence of a Markov boundary—can coexist happily with the multiscale integration of the system through its dynamics. Our approach does not privilege any given boundary, nor does it argue that all boundaries are equally prescient. We argue that the relevant boundaries of cognition depend on the level being characterised and the explanatory interests that guide investigation. We approach the issue of how and where to draw the boundaries of cognitive systems through a multiscale ontology of cognitive systems, which offers a multidisciplinary research heuristic for cognitive science. (shrink)
The effects of striatal dopamine (DA) on behavior have been widely investigated over the past decades, with “phasic” burst firings considered as the key expression of a reward prediction error responsible for reinforcement learning. Less well studied is “tonic” DA, where putative functions include the idea that it is a regulator of vigor, incentive salience, disposition to exert an effort and a modulator of approach strategies. We present a model combining tonic and phasic DA to show how different outflows triggered (...) by either intrinsically or extrinsically motivating stimuli dynamically affect the basal ganglia by impacting on a selection process this system performs on its cortical input. The model, which has been tested on the simulated humanoid robot iCub interacting with a mechatronic board, shows the putative functions ascribed to DA emerging from the combination of a standard computational mechanism coupled to a differential sensitivity to the presence of DA across the striatum. (shrink)
This commentary takes a closer look at how “constructive models of subjective perception,” referred to by Collerton et al. (sect. 2), might contribute to the Perception and Attention Deficit (PAD) model. It focuses on the neuronal mechanisms that could mediate hallucinations, or false inference – in particular, the role of cholinergic systems in encoding uncertainty in the context of hierarchical Bayesian models of perceptual inference (Friston 2002b; Yu & Dayan 2002).
This paper considers psychotic symptoms in terms of false inferences or beliefs. It is based on the notion that the brain is an organ of inference that actively constructs hypotheses to explain or predict its sensations. This perspective provides a normative account of action and perception that emphasises probabilistic representations; in particular, the confidence or precision of beliefs about the world. We consider sensory attenuation deficits, catatonia and delusions as various expressions of the same core pathology: namely, an aberrant encoding (...) of precision in a predictive coding hierarchy. In predictive coding, precision is thought to be encoded by the postsynaptic 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 postsynaptic gain—such as NMDA receptor function and neuromodulation. We illustrate these points using a biologically plausible simulation of attribution of agency—showing how a reduction in the precision of prior beliefs, relative to sensory evidence, can lead to false inference. (shrink)
Tsuda examines the potential contribution of nonlinear dynamical systems, with many degrees of freedom, to understanding brain function. We offer suggestions concerning symmetry and transients to strengthen the physiological motivation and theoretical consistency of this novel research direction: Symmetry plays a fundamental role, theoretically and in relation to real brains. We also highlight a distinction between chaotic “transience” and “itineracy.”.
This commentary considers how far one can go in making inferences about functional modularity or segregation, based on the sorts of analyses used by Caplan & Waters in relation to the underlying neuronal infrastructure. Specifically, an attempt is made to relate the “functionalist” approach adopted in the target article to “neuroreductionist” perspectives on the same issue.