Wanja Wiese

This paper sketches an account that explains the elusive subjective quality of 'enduring substantiality' of the phenomenal self. It integrates a recent predictive processing account of the self by Chris Letheby and Philip Gerrans with key ideas of Michael Graziano's attention schema theory of consciousness. Similarly to the attention schema theory, the present account posits an internal model of ongoing attentional processing that supports attentional control. In terms of predictive processing, it is a dynamic model of precision estimates that represents the salience of features, objects, and internal and external processes. The apparent substantiality of the self is then explained by a structural feature of this salience model: it binds different dimensions of salience by representations of higher-order dimensions of salience. The result of this salience binding is a phenomenally transparent 'salience object', which is represented as a cause of widespread changes in precision estimates, and results in a form of epistemic self-control. Phenomenologically, this goes along with the experience of the self as an attentional agent, and in this way creates the origin of our consciously experienced first-person perspective: I attend, I am.

This essay addresses Cartesian duality and how its implicit dialectic might be repaired using physics and information theory. Our agenda is to describe a key distinction in the physical sciences that may provide a foundation for the distinction between mind and matter, and between sentient and intentional systems. From this perspective, it becomes tenable to talk about the physics of sentience and ‘forces’ that underwrite our beliefs (in the sense of probability distributions represented by our internal states), which may ground our mental states and consciousness. We will refer to this view as Markovian monism, which entails two claims: (1) fundamentally, there is only one type of thing and only one type of irreducible property (hence monism). (2) All systems possessing a Markov blanket have properties that are relevant for understanding the mind and consciousness: if such systems have mental properties, then they have them partly by virtue of possessing a Markov blanket (hence Markovian). Markovian monism rests upon the information geometry of random dynamic systems. In brief, the information geometry induced in any system—whose internal states can be distinguished from external states—must acquire a dual aspect. This dual aspect concerns the (intrinsic) information geometry of the probabilistic evolution of internal states and a separate (extrinsic) information geometry of probabilistic beliefs about external states that are parameterised by internal states. We call these intrinsic (i.e., mechanical, or state-based) and extrinsic (i.e., Markovian, or belief-based) information geometries, respectively. Although these mathematical notions may sound complicated, they are fairly straightforward to handle, and may offer a means through which to frame the origins of consciousness.

Are there logically possible types of conscious experience that are nomologically impossible, given independently justified assumptions about the neural underpinnings of consciousness in human beings? In one sense, this is trivial: just consider the fact that the types of perceptual experiences we can have are limited by our sensory organs. But there may be non-trivial types of conscious experience that are impossible. For instance, if there is a basic type of self-consciousness, corresponding to a phenomenal property that is nomologically necessary for consciousness, then experiences lacking this phenomenal property will be impossible. More generally, it may be that there are causal dependencies between the neural mechanisms that are required to instantiate distinct phenomenal properties. If this is the case, instantiating one of these phenomenal properties without certain others may be impossible, which means there are non-trivial cases of nomologically impossible types of conscious experience. This paper clarifies this hypothesis, outlines a general methodology for its investigation, and relates it to research on radical disruptions of self-consciousness.

The goal of this short chapter, aimed at philosophers, is to provide an overview and brief explanation of some central concepts involved in predictive processing (PP). Even those who consider themselves experts on the topic may find it helpful to see how the central terms are used in this collection. To keep things simple, we will first informally define a set of features important to predictive processing, supplemented by some short explanations and an alphabetic glossary. The features described here are not shared in all PP accounts. Some may not be necessary for an individual model; others may be contested. Indeed, not even all authors of this collection will accept all of them. To make this transparent, we have encouraged contributors to indicate briefly which of the features are necessary to support the arguments they provide, and which (if any) are incompatible with their account. For the sake of clarity, we provide the complete list here, very roughly ordered by how central we take them to be for “Vanilla PP” (i.e., a formulation of predictive processing that will probably be accepted by most researchers working on this topic). More detailed explanations will be given below. Note that these features do not specify individually necessary and jointly sufficient conditions for the application of the concept of “predictive processing”. All we currently have is a semantic cluster, with perhaps some overlapping sets of jointly sufficient criteria. The framework is still developing, and it is difficult, maybe impossible, to provide theory-neutral explanations of all PP ideas without already introducing strong background assumptions.

This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be represented as states of a hierarchy of dynamical systems. Phenomenologically, they suggest a more fine-grained analysis of the perceptual contents of the specious present, in terms of a hierarchy of temporal wholes. Visual perception of static scenes not only contains perceived objects and regions but also spatial gist; similarly, auditory perception of temporal sequences, such as melodies, involves not only perceiving individual notes but also slightly more abstract features (temporal gist), which have longer temporal durations (e.g., emotional character or rhythm). Further investigations into these elusive contents of conscious perception may be facilitated by findings regarding its neural underpinnings. Predictive processing models suggest that sensorimotor areas may influence these contents.

Paweł Gładziejewski has recently argued that the framework of predictive processing postulates genuine representations. His focus is on establishing that certain structures posited by PP actually play a representational role. The goal of this paper is to promote this discussion by exploring the contents of representations posited by PP. Gładziejewski already points out that structural theories of representational content can successfully be applied to PP. Here, I propose to make the treatment slightly more rigorous by invoking Francis Egan’s distinction between mathematical and cognitive contents. Applying this distinction to representational contents in PP, I first show that cognitive contents in PP are determined by mathematical contents, at least in the sense that computational descriptions in PP put constraints on ascriptions of cognitive contents. After that, I explore to what extent these constraints are specific. I argue that the general mathematical contents posited by PP do not constrain ascriptions of cognitive content in a specific way. However, there are at least three aspects of PP that constrain ascriptions of cognitive contents in more specific ways: formal PP models posit specific mathematical contents that define more specific constraints; PP entails claims about how computational mechanisms underpin cognitive phenomena ; the processing hierarchy posited by PP goes along with more specific constraints.

The problem of phenomenal unity consists in providing a phenomenological characterization of the difference between phenomenally unified and disunified conscious experiences. Potential solutions to PPU are faced with an important challenge. I show that this challenge can be conceived as a phenomenological dual to what is known as Bradley’s regress. This perspective facilitates progress on PPU by finding duals to possible solutions to Bradley’s regress and makes it intelligible why many characterize phenomenal unity in terms of the existence of a single global conscious state. I call this latter view the “single state conception”. SSC is superficially attractive, because it seems to provide a solution to the phenomenological dual to Bradley’s regress, but should still be rejected, because it does not solve PPU; instead, it creates more problems; these problems can be avoided by alternative conceptions of phenomenal unity.

The Predictive Mind by Jakob Hohwy is the first monograph to address the philosophical significance of what Hohwy calls the prediction error minimization framework. The central claim of the book is that, on a conceptual level, perception, action, and cognition can be understood by reference to a single principle: prediction error minimization. The corresponding empirical hypothesis is that the brain implements a hierarchical generative model that generates predictions about sensory inputs and their hidden causes. When sensory signals arrive, only their divergence from the predictions has to be further processed. The general strategy of using predictions derived from generative models to compress and transmit information is also known as predictive coding.Perception is thus not construed as a purely bottom–up process, but.

According to active inference, action is enabled by a top-down modulation of sensory signals. Computational models of this mechanism complement ideomotor theories of action representation. Such theories postulate common neural representations for action and perception, without specifying how action is enabled by such representations. In active inference, motor commands are replaced by proprioceptive predictions. In order to initiate action through such predictions, sensory prediction errors have to be attenuated. This paper argues that such top-down modulation involves systematic misrepresentations. More specifically, the paper first argues for the following conditional claim. If active inference provides an accurate computational description of how action is enabled in the brain, then action is enabled by systematic misrepresentations. Furthermore, it is argued that an inference to the best explanation provides reason for believing the antecedent is true: Firstly, active inference provides a crucial extension to ideomotor theories. Secondly, active inference explains otherwise puzzling phenomena related to sensory attenuation, e.g. in force-matching or self-tickling paradigms. Taken together, these reasons support the claim that action is indeed enabled by systematic misrepresentations. The claim casts doubt on the assumption that representations are systematically beneficial to the extent that they are true: if the argument in this paper is sound, systematically beneficial misrepresentations may lie at the heart of our neural architecture.