Recognising and representing one’s self as distinct from others is a fundamental component of self-awareness. However, current theories of self-recognition are not embedded within global theories of cortical function and therefore fail to provide a compelling explanation of how the self is processed. We present a theoretical account of the neural and computational basis of self-recognition that is embedded within the free-energy account of cortical function. In this account one’s body is processed in a Bayesian manner as the most likely to be “me”. Such probabilistic representation arises through the integration of information from hierarchically organised unimodal systems in higher-level multimodal areas. This information takes the form of bottom-up “surprise” signals from unimodal sensory systems that are explained away by top-down processes that minimise the level of surprise across the brain. We present evidence that this theoretical perspective may account for the findings of psychological and neuroimaging investigations into self-recognition and particularly evidence that representations of the self are malleable, rather than fixed as previous accounts of self-recognition might suggest.

Key words: self-recognition, self-awareness, voice recognition, face recognition, body ownership, bayesian, free energy, predictive coding, prediction error, rubber hand illusion, enfacement

Brains, it has recently been argued, are essentially prediction machines. They are bundles of cells that support perception and action by constantly attempting to match incoming sensory inputs with top-down expectations or predictions. This is achieved using a hierarchical generative model that aims to minimize prediction error within a bidirectional cascade of cortical processing. Such accounts offer a unifying model of perception and action, illuminate the functional role of attention, and may neatly capture the special contribution of cortical processing to adaptive success. This target article critically examines this “hierarchical prediction machine” approach, concluding that it offers the best clue yet to the shape of a unified science of mind and action. Sections 1 and 2 lay out the key elements and implications of the approach. Section 3 explores a variety of pitfalls and challenges, spanning the evidential, the methodological, and the more properly conceptual. The paper ends (sections 4 and 5) by asking how such approaches might impact our more general vision of mind, experience, and agency.

Key words: action, attention, bayesian brain, expectation, generative model, hierarchy, perception, precision, predictive coding, prediction, prediction error, top-down processing.

Recent work in cognitive and computational neuroscience depicts human brains as devices that minimize prediction error signals: signals that encode the difference between actual and expected sensory stimulations. This raises a series of puzzles whose common theme concerns a potential misfit between this bedrock informationtheoretic vision and familiar facts about the attractions of the unexpected. We humans often seem to actively seek out surprising events, deliberately harvesting novel and exciting streams of sensory stimulation. Conversely, we often experience some wellexpected sensations as unpleasant and to-be-avoided. In this paper, I explore several core and variant forms of this puzzle, using them to display multiple interacting elements that together deliver a satisfying solution. That solution requires us to go beyond the discussion of simple information-theoretic imperatives (such as ‘minimize long-term prediction error’) and to recognize the essential role of species-specific prestructuring, epistemic foraging, and cultural practices in shaping the restless, curious, novelty-seeking human mind.

Key words: prediction, prediction error, surprise, novelty.

Over the past few years, the prediction error minimization (PEM) framework has increasingly been gaining ground throughout the cognitive sciences. A key issue dividing proponents of PEM is how we should conceptualize the relation between brain, body and environment. Clark advocates a version of PEM which retains, at least to a certain extent, his prior commitments to Embodied Cognition and to the Extended Mind Hypothesis. Hohwy, by contrast, presents a sustained argument that PEM actually rules out at least some versions of Embodied and Extended cognition. The aim of this paper is to facilitate a constructive debate between these two competing alternatives by explicating the different theoretical motivations underlying them, and by homing in on the relevant issues that may help to adjudicate between them.

Key words: prediction error minimization, embodied cognition, extended mind hypothesis

In a recent paper, Jakob Hohwy argues that the emerging predictive processing (PP) perspective on cognition requires us to explain cognitive functioning in purely internalistic and neurocentric terms. The purpose of the present paper is to challenge the view that PP entails a wholesale rejection of positions that are interested in the embodied, embedded, extended, or enactive dimensions of cognitive processes. I will argue that Hohwy’s argument from analogy, which forces an evidentiary boundary into the picture, lacks the argumentative resources to make a convincing case for the conceptual necessity to interpret PP in solely internalistic terms. For this reason, I will reconsider the postulation and explanatory role of the evidentiary boundary. I will arrive at an account of prediction error minimization and its foundation on the free energy principle that is fully consistent with approaches to cognition that emphasize the embodied and interactive properties of cognitive processes. This gives rise to the suggestion that explanatory pluralism about the application of PP is to be preferred over Hohwy’s explanatory monism that follows from his internalistic and neurocentric view of predictive cognitive systems.

Key words: dynamical systems theory, embodied cognition, explanatory pluralism, free energy principle, prediction error minimization.

Many of our cognitive capacities are the result of enculturation. Enculturation is the temporally extended transformative acquisition of cognitive practices in the cognitive niche. Cognitive practices are embodied and normatively constrained ways to interact with epistemic resources in the cognitive niche in order to complete a cognitive task. The emerging predictive processing perspective offers new functional principles and conceptual tools to account for the cerebral and extra-cerebral bodily components that give rise to cognitive practices. According to this emerging perspective, many cases of perception, action, and cognition are realized by the on-going minimization of prediction error. Predictive processing provides us with a mechanistic perspective that helps investigate the functional details of the acquisition of cognitive practices. The argument of this paper is that research on enculturation and recent work on predictive processing are complementary. The main reason is that predictive processing operates at a sub-personal level and on a physiological time scale of explanation only. A complete account of enculturated cognition needs to take additional levels and temporal scales of explanation into account. This complementarity assumption leads to a new framework – enculturated predictive processing – that operates on multiple levels and temporal scales for the explanation of the enculturated predictive acquisition of cognitive practices. Enculturated predictive processing is committed to explanatory pluralism. That is, it subscribes to the idea that we need multiple perspectives and explanatory strategies to account for the complexity of enculturation. The upshot is that predictive processing needs to be complemented by additional considerations and conceptual tools to realize its full explanatory potential.

Key words: predictive processing, enculturation, neural plasticity, cognitive practice, embodiment, scaffolded learning

A free-energy principle has been proposed recently that accounts for action, perception and learning. This review looks at some key brain theories in the biological (for example, neural Darwinism) and physical (for example, information theory and optimal control theory) sciences from the free-energy perspective. Crucially, one key theme runs through each of these theories – optimization. Furthermore, if we look closely at what is optimized, the same quantity keeps emerging, namely value (expected reward, expected utility) or its complement, surprise (prediction error, expected cost). This is the quantity that is optimized under the free-energy principle, which suggests that several global brain theories might be unified within a free-energy framework.

We distinguish between three philosophical views on the neuroscience of predictive models: predictive coding (associated with internal Bayesian models and prediction error minimization), predictive processing (associated with radical connectionism and ‘simple’ embodiment) and predictive engagement (associated with enactivist approaches to cognition). We examine the concept of active inference under each model and then ask how this concept informs discussions of social cognition. In this context we consider Frith and Friston’s proposal for a neural hermeneutics, and we explore the alternative model of enactivist hermeneutics.

Key words: Predictive coding, free energy principle, active inference, social cognition, enactivism, hermeneutics.

According to the predictive coding theory of cognition (PCT), brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status – that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures – namely action-guiding, detachable, structural models that afford representational error detection – that play genuinely representational functions within the cognitive system.

Key words: cartographic maps, generative models, job description challenge, mental representation, predictive coding, structural representation.

Binocular rivalry occurs when the eyes are presented with different stimuli and subjective perception alternates between them. Though recent years have seen a number of models of this phenomenon, the mechanisms behind binocular rivalry are still debated and we still lack a principled understanding of why a cognitive system such as the brain should exhibit this striking kind of behaviour. Furthermore, psychophysical and neurophysiological (single cell and imaging) studies of rivalry are not unequivocal and have proven difficult to reconcile within one framework. This review takes an epistemological approach to rivalry that considers the brain as engaged in probabilistic unconscious perceptual inference about the causes of its sensory input. We describe a simple empirical Bayesian framework, implemented with predictive coding, which seems capable of explaining binocular rivalry and reconciling many findings. The core of the explanation is that selection of one stimulus, and subsequent alternation between stimuli in rivalry occur when: (i) there is no single model or hypothesis about the causes in the environment that enjoys both high likelihood and high prior probability and (ii) when one stimulus dominates, the bottom–up, driving signal for that stimulus is explained away while, crucially, the bottom–up signal for the suppressed stimulus is not, and remains as an unexplained but explainable prediction error signal. This induces instability in perceptual dynamics that can give rise to perceptual transitions or alternations during rivalry.