Predictive Processing theory, hotly debated in neuroscience, psychology and philosophy, promises to explain a number of perceptual and cognitive phenomena in a simple and elegant manner. In some of its versions, the theory is ambitiously advertised as a new theory of conscious perception. The task of this paper is to assess whether this claim is realistic. We will be arguing that the Predictive Processing theory cannot explain the transition from unconscious to conscious perception in its proprietary (...) terms. The explanations offer by PP theorists mostly concern the preconditions of conscious perception, leaving the genuine material substrate of consciousness untouched. (shrink)

Predictive processing (PP) accounts of perception are unique not merely in that they postulate a unity between perception and imagination. Rather, they are unique in claiming that perception should be conceptualised in terms of imagination and that the two involve an identity of neural implementation. This paper argues against this postulated unity, on both conceptual and empirical grounds. Conceptually, the manner in which PP theorists link perception and imagination belies an impoverished account of imagery as cloistered from the (...) external world in its intentionality, akin to a virtual reality, as well as endogenously generated. Yet this ignores a whole class of imagery whose intentionality is directed on the actual environment—projected mental imagery—and also ignores the fact that imagery may be triggered crossmodally in a bottom-up, stimulus-driven way. Empirically, claiming that imagery and perception share neural circuitry ignores relevant clinical results in this area. These evidence substantial perception/imagery neural dissociations, most notably in the case of aphantasia. Taken together, the arguments here suggest that PP theorists should substantially temper, if not outright abandon, their claim to a perception/imagination unity. (shrink)

Many philosophers claim that the neurocomputational framework of predictive processing entails a globally inferentialist and representationalist view of cognition. Here, I contend that this is not correct. I argue that, given the theoretical commitments these philosophers endorse, no structure within predictive processing systems can be rightfully identified as a representational vehicle. To do so, I first examine some of the theoretical commitments these philosophers share, and show that these commitments provide a set of necessary conditions the (...) satisfaction of which allows us to identify representational vehicles. Having done so, I introduce a predictive processing system capable of active inference, in the form of a simple robotic “brain”. I examine it thoroughly, and show that, given the necessary conditions highlighted above, none of its components qualifies as a representational vehicle. I then consider and allay some worries my claim could raise. I consider whether the anti-representationalist verdict thus obtained could be generalized, and provide some reasons favoring a positive answer. I further consider whether my arguments here could be blocked by allowing the same representational vehicle to possess multiple contents, and whether my arguments entail some extreme form of revisionism, answering in the negative in both cases. A quick conclusion follows. (shrink)

Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control (...) comprising levels of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture. (shrink)

Predictive processing models of perception take issue with standard models of perception as hierarchical bottom-up processing modulated by memory and attention. The predictive framework posits that the brain generates predictions about stimuli, which are matched to the incoming signal. Mismatches between predictions and the incoming signal – so-called prediction errors – are then used to generate new and better predictions until the prediction errors have been minimized, at which point a perception arises. Predictive models hold (...) that all bottom-up processes are signals conveying prediction errors to higher areas, which respond by updating their predictions. We take issue with this claim and argue that object recognition requires bottom-up processing that cannot be understood in terms of prediction errors. Along the way, we expand on previous work casting doubt on the framework's ability to account for attention. Specifically, we argue that the type of attention that allows us to rapidly extract the gist of an object or scene presents an additional challenge to the predictive approach. We conclude by considering how the framework may be augmented to avoid these problems. (shrink)

We introduce the predictive processing account of body representation, according to which body representation emerges via a domain-general scheme of (long-term) prediction error minimisation. We contrast this account against one where body representation is underpinned by domain-specific systems, whose exclusive function is to track the body. We illustrate how the predictive processing account offers considerable advantages in explaining various empirical findings, and we draw out some implications for body representation research.

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. (shrink)

This article examines the compatibility of Predictive Processing Theory (PPT), or the theory of anticipatory brain, with other contemporary scientific and philosophical frameworks that offer promising approaches to explaining consciousness and mind in general. It analyzes the connections between PPT and theories that include the body and the environment in structuring our concepts of reality, such as that of embodied and situated consciousness—4E cognition, as well as Cognitive Linguistics with its understanding of Conceptual Metaphor. At the same time, (...) it investigates the metaphysical strategy of PPT, which fits into the current philosophical project of naturalization and its associated strong dependence on the natural sciences. It explores the contributions of key theorists in the field such as Carl Friston, Jacob Hohwy, Andy Clark, and Anil Seth. (shrink)

Predictive Processing (PP) presents a theoretical framework that postulates the brain's primary objective is to minimize prediction errors across hierarchical levels by continuously generating and refining predictions about the external world. Although PP offers valuable insights into the unification of seemingly distinct mental processes, its ambition as a comprehensive theory of mind has also drawn substantial criticism. Debates surrounding PP are frequently hindered by disagreements about what it is. To address this issue, this thesis will first elucidate the (...) theoretical foundations and commitments of PP through the Marr's tri-level analysis. Subsequently, I will reconstruct an argument positioning PP as an overarching theory of perception, imagination, memory, and dreaming. Finally, by examining objections to PP stemming from perceptual illusions, such as the Müller-Lyer Illusion (MLI), I will propose a mature understanding of PP that can both withstand criticism and offer guidance in explaining the diverse functions of the mind. (shrink)

This paper aims to provide a theoretical framework for explaining the subjective character of pain experience in terms of what we will call ‘embodied predictive processing’. The predictive processing (PP) theory is a family of views that take perception, action, emotion and cognition to all work together in the service of prediction error minimisation. In this paper we propose an embodied perspective on the PP theory we call the ‘embodied predictive processing (EPP) theory. The (...) EPP theory proposes to explain pain in terms of processes distributed across the whole body. The prediction error minimising system that generates pain experience comprises the immune system, the endocrine system, and the autonomic system in continuous causal interaction with pathways spread across the whole neural axis. We will argue that these systems function in a coordinated and coherent manner as a single complex adaptive system to maintain homeostasis. This system, which we refer to as the neural-endocrine-immune (NEI) system, maintains homeostasis through the process of prediction error minimisation. We go on to propose a view of the NEI ensemble as a multiscale nesting of Markov blankets that integrates the smallest scale of the cell to the largest scale of the embodied person in pain. We set out to show how the EPP theory can make sense of how pain experience could be neurobiologically constituted. We take it to be a constraint on the adequacy of a scientific explanation of subjectivity of pain experience that it makes it intelligible how pain can simultaneously be a local sensing of the body, and, at the same time, a more global, all-encompassing attitude towards the environment. Our aim in what follows is to show how the EPP theory can meet this constraint. (shrink)

This work investigates one of the most widespread yet elusive ideas about our experience of art: the idea that there is something cognitively valuable in engaging with great artworks, or, in other words, that we learn from them. This claim and the age-old controversy that surrounds it are reconsidered in light of the psychological and neuroscientific literature on learning, in one of the first systematic efforts to bridge the gap between philosophical and scientific inquiries on the topic. The work has (...) five chapters. Chapter 1 lays down its conceptual bases: it explains what learning is taken to be in the current philosophical debate and it points out how Bayesian cognitive science (particularly in its predictive processing formulations) might be well-suited to capture the kind of learning involved in our engagement with the arts. The following chapters test this latter hypothesis with respect to particular art forms, namely literature and literary language (Chapter 2), narrative (Chapter 3), and visual art, music and motor activities (Chapter 4). The fine-grained discussions conducted in each of these areas will enable us to see that the relationship between art and learning is indeed fundamental and pervasive. The final chapter (Chapter 5) examines the consequences of this fact for our understanding of the role of art in our epistemic practices, its ultimate usefulness and value, and its place in the interdisciplinary study of the human mind. The upshot is a novel and wide-ranging picture, both philosophically informed and empirically sound, that bypasses many of the problems and dead ends of the current philosophical debate on the topic and captures the deep sense in which art and learning are interrelated. (shrink)

Recently, Kirchhoff and Kiverstein have argued that the extended consciousness thesis, namely the claim that the material vehicles of consciousness extend beyond our heads, is entirely compatible with, and actually mandated by, a correct interpretation of the predictive processing framework. To do so, they rely on a potent argument in favor of the extended consciousness thesis, namely the Dynamical Entanglement and Unique Temporal Signature (DEUTS) argument. Here, we will critically examine Kirchhoff and Kiverstein’s endeavor, arguing for the following (...) three claims. First, we will claim that Kirchhoff and Kiverstein’s emphasis on culture and cultural practices does not help them substantiate the extended consciousness thesis. Secondly, we will argue that the way in which Kirchhoff and Kiverstein formalize the boundaries of a subject’s conscious mind is inadequate, as it yields conclusions running counter some of their assumptions. Lastly, we will argue that the DEUTS argument does not establish the extended consciousness thesis, as it licenses a phenomenal bloat objection which is exactly analogous to the “cognitive bloat” objection to the extended mind thesis. We will thus conclude that Kirchhoff and Kiverstein’s proposed marriage between the extended consciousness thesis and predictive processing fails, and that, contrary to a popular opinion, DEUTS is not a strong argument in favor of the extended consciousness thesis. (shrink)

The predictive processing account of action, cognition, and perception is one of the most influential approaches to unifying research in cognitive science. However, its promises of grand unification will remain unfulfilled unless the account becomes theoretically robust. In this paper, we focus on empirical commitments of PP, since they are necessary both for its theoretical status to be established and for explanations of individual phenomena to be falsifiable. First, we argue that PP is a varied research tradition, which (...) may employ various kinds of scientific representations, differing in the scope of empirical commitments they entail. Two major perspectives on PP qua cognitive theory may then be distinguished: generalized vs. hierarchical. The first one fails to provide empirical detail, and the latter constrains possible physical implementations. However, we show that even hierarchical PP is insufficiently restrictive to disallow incorrect models and may be adjusted to explain any neurocognitive phenomenon–including non-existent or impossible ones–through flexible adjustments. This renders PP a universal modeling tool with an unrestricted number of degrees of freedom. Therefore, in contrast with declarations of its proponents, it should not be understood as a unifying theoretical perspective, but as a computational framework, possibly informing further theory development in cognitive science. (shrink)

Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition. In this paper, we argue that this is premature: As a unifying theory, PP fails to deliver general, simple, homogeneous, and systematic explanations. By examining its current trajectory of development, we conclude that PP remains only loosely connected both to its computational framework and to its hypothetical biological underpinnings, which makes its fundamentals unclear. Instead of offering explanations that refer to the same (...) set of principles, we observe systematic equivocations in PP‐based models, or outright contradictions with its avowed principles. To make matters worse, PP‐based models are seldom empirically validated, and they are frequently offered as mere just‐so stories. The large number of PP‐based models is thus not evidence of theoretical progress in unifying perception, action, and cognition. On the contrary, we maintain that the gap between theory and its biological and computational bases contributes to the arrested development of PP as a unificatory theory. Thus, we urge the defenders of PP to focus on its critical problems instead of offering mere re‐descriptions of known phenomena, and to validate their models against possible alternative explanations that stem from different theoretical assumptions. Otherwise, PP will ultimately fail as a unified theory of cognition. (shrink)

In the last few years, a remarkable convergence of interests and results has emerged between scholars interested in the arts and aesthetics from a variety of perspectives and cognitive scientists studying the mind and brain within the predictive processing (PP) framework. This convergence has so far proven fruitful for both sides: while PP is increasingly adopted as a framework for understanding aesthetic phenomena, the arts and aesthetics, examined under the lens of PP, are starting to be seen as (...) important windows into our mental functioning. The result is a vast and fast-growing research programme that promises to deliver important insights into our aesthetic encounters as well as a wide range of psychological phenomena of general interest. Here, we present this developing research programme, describing its grounds and highlighting its prospects. We start by clarifying how the study of the arts and aesthetics encounters the PP picture of mental functioning (§1). We then go on to outline the prospects of this encounter for the fields involved: philosophy and history of art (§2), psychology of aesthetics and neuroaesthetics (§3) and psychology and neuroscience more generally (§4). The upshot is an ambitious but well-defined framework within which aesthetics and cognitive science can partner up to illuminate crucial aspects of the human mind. -/- This article is part of the theme issue ‘Art, aesthetics and predictive processing: theoretical and empirical perspectives’. -/- . (shrink)

This article explores the Predictive Processing Theory (PP/Predictive Coding), or in other words– the theory of the anticipating brain, which is based on the principles of Bayesian statistics. It examines this theory’s metaphysical strategy which fits into the contemporary philosophical project of naturalization and its strong dependence on the natural sciences. The explanatory power of the theory demonstrates how the mind operates, including the opportunities this framework provides for a better understanding of the hard problem of consciousness. (...) The emphasis is placed on the necessity of including it in the Bulgarian language debate within the field of philosophy of mind and related disciplines. This work reviews the contributions of key theorists in the field such as Karl Friston, Jacob Hohwy, Andy Clark and Anil Seth. Keywords: Predictive Processing Theory, mind, anticipating brain, hard problem of consciousness, philosophy of mind. (shrink)

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. (shrink)

We propose a pluralist account of content for predictive processing systems. Our pluralism combines Millikan’s teleosemantics with existing structural resemblance accounts. The paper has two goals. First, we outline how a teleosemantic treatment of signal passing in predictive processing systems would work, and how it integrates with structural resemblance accounts. We show that the core explanatory motivations and conceptual machinery of teleosemantics and predictive processing mesh together well. Second, we argue this pluralist approach expands (...) the range of empirical cases to which the predictive processing framework might be successfully applied. This is because our pluralism is practice-oriented. A range of different notions of content are used in the cognitive sciences to explain behaviour, and some of these cases look to employ teleosemantic notions. As a result, our pluralism gives predictive processing the scope to cover these cases. (shrink)

In this chapter we first elucidate the subjective flow of time particularly as developed by Husserl. We next discuss time and timescales in predictive processing. We then consider how the phenomenological analysis of time can be naturalized within a predictive processing framework. In the final section, we develop an analysis of the temporal disturbances characteristic of depression using the resources of both phenomenology and predictive processing.

This article is a comparative study between predictive processing (PP, or predictive coding) and cognitive dissonance (CD) theory. The theory of CD, one of the most influential and extensively studied theories in social psychology, is shown to be highly compatible with recent developments in PP. This is particularly evident in the notion that both theories deal with strategies to reduce perceived error signals. However, reasons exist to update the theory of CD to one of “predictive dissonance.” (...) First, the hierarchical PP framework can be helpful in understanding varying nested levels of CD. If dissonance arises from a cascade of downstream and lateral predictions and consequent prediction errors, dissonance can exist at a multitude of scales, all the way up from sensory perception to higher order cognitions. This helps understand the previously problematic dichotomy between “dissonant cognitive relations” and “dissonant psychological states,” which are part of the same perception-action process while still hierarchically distinct. Second, since PP is action-oriented, it can be read to support recent action-based models of CD. Third, PP can potentially help us understand the recently speculated evolutionary origins of CD. Here, the argument is that responses to CD can instill meta-learning which serves to prevent the overfitting of generative models to ephemeral local conditions. This can increase action-oriented ecological rationality and enhanced capabilities to interact with a rich landscape of affordances. The downside is that in today’s world where social institutions such as science a priori separate noise from signal, some reactions to predictive dissonance might propagate ecologically unsound (underfitted, confirmation-biased) mental models such as climate denialism. (shrink)

Recent work by Stout and colleagues indicates that the neural correlates of language and Early Stone Age toolmaking overlap significantly. The aim of this paper is to add computational detail to their findings. I use an error minimisation model to outline where the information processing overlap between toolmaking and language lies. I argue that the Early Stone Age signals the emergence of complex structured representations. I then highlight a feature of my account: It allows us to understand the early (...) evolution of syntax in terms of an increase in the number and complexity of models in a cognitive system, rather than the development of new types of processing. (shrink)

Predictive Processing (PP) framework construes perception and action (and perhaps other cognitive phenomena) as a matter of minimizing prediction error, i.e. the mismatch between the sensory input and sensory predictions generated by a hierarchically organized statistical model. There is a question of how PP fits into the debate between traditional, neurocentric and representation-heavy approaches in cognitive science and those approaches that see cognition as embodied, environmentally embedded, extended and (largely) representation-free. In the present paper, I aim to investigate (...) and clarify the cognitivist or ‘conservative’ reading of PP. I argue that the conservative commitments of PP can be divided into three distinct categories: (1) representationalism, (2) inferentialism, and (3) internalism. I show how these commitments and their relations should be understood and argue for an interpretation of each that is both non-trivial and largely ecumenical towards the 4E literature. Conservative PP is as progressive as conservatism gets. (shrink)

Predictive processing is an increasingly popular explanatory framework developed within cognitive neuroscience. It conceives of the brain as a prediction machine that tries to minimise prediction error. Predictive processing has also been employed to explain aspects of conscious experience. In this paper, I critically evaluate current predictive processing approaches to the phenomenology of time-consciousness from a Husserlian perspective. To do so, I introduce the notion of orthodox predictive processing to refer to interpretations (...) of the predictive processing framework that subscribe to representational views of cognition. As it turns out, current predictive processing accounts of time-consciousness are orthodox given their commitment to representational views of both brain functioning and perception, and, on the other hand, their reliance on the primacy of imagination over perception. However, I argue that such accounts are in fact closer to a Kantian-Brentanian approach to the phenomenology of time-consciousness than to the Husserlian account that they attempt to account for. (shrink)

This article reexamines genius and extraordinary talent within the framework of predictive processing. Drawing upon an interdisciplinary synthesis of neuroscience, philosophy, chaos theory, information theory, and critical epistemologies, it attempts to analyze the genius mind as a dynamic, self-regulating system capable of reducing local entropy. From the perspective of local learning-global optimization, the genius, through deep focus on a specific domain, reconstructs global structures. The genius mind functions as a generative model with extensive scope, capable of minimizing prediction (...) errors and utilizing them as raw material for creativity, constructive chaos, and semantic reconfiguration. (shrink)

Emotion regulation is a fundamental process that shapes cognitive, affective, and behavioral responses to emotional stimuli. Traditional emotion regulation models conceptualize regulation as a sequential modulation of emotional responses. However, they do not fully explain how emotions are constructed in a way that allows such regulation to occur. Predictive processing (PP) provides a mechanistic framework for understanding emotion generation by proposing that the brain minimizes prediction errors (PEs) to optimize perception and behavior. Yet, standard PP accounts reduce valence (...) to PE minimization, failing to explain how PEs can generate different subjective feelings. To address these limitations, we propose a valence-focused model of emotion regulation that integrates predictive processing with self-referential cognition. We incorporate emotional valence as interpretative processes of the self-model, which assigns emotional significance based on goals, values, and autobiographical context. This model bridges the gap between emotion generation and regulation, highlighting the dynamic interplay between prediction errors, subjective valuation, and self-referential processes. This approach not only advances theoretical understanding but also opens new avenues for computational modeling and empirical research into the adaptive and maladaptive aspects of emotional experience. (shrink)

Depression is vastly heterogeneous in its symptoms, neuroimaging data, and treatment responses. As such, describing how it develops at the network level has been notoriously difficult. In an attempt to overcome this issue, a theoretical “negative prediction mechanism” is proposed. Here, eight key brain regions are connected in a transient, state-dependent, core network of pathological communication that could facilitate the development of depressive cognition. In the context of predictive processing, it is suggested that this mechanism is activated as (...) a response to negative/adverse stimuli in the external and/or internal environment that exceed a vulnerable individual’s capacity for cognitive appraisal. Specifically, repeated activation across this network is proposed to update an individual’s brain so that it increasingly predicts and reinforces negative experiences over time—pushing an individual at-risk for or suffering from depression deeper into mental illness. Within this, the negative prediction mechanism is poised to explain various aspects of prognostic outcome, describing how depression might ebb and flow over multiple timescales in a dynamically changing, complex environment. (shrink)

This essay presents the conditional probability of co-contribution account of the individuation of cognitive systems (CPC) and argues that CPC provides an attractive basis for a theory of the cognitive self. The argument proceeds in a largely indirect way, by emphasizing empirical challenges faced by an approach that relies entirely on predictive processing (PP) mechanisms to ground a theory of the cognitive self. Given the challenges faced by PP-based approaches, we should prefer a theory of the cognitive self (...) of the sort CPC offers, one that accommodates variety in the kinds of mechanism that, when integrated, constitute a cognitive system (and thus the cognitive self), to a theory according to which the cognitive self is composed of essentially one kind of thing, for instance, prediction-error minimization mechanisms. The final section focuses on one of the central functions of the cognitive self: to engage in conscious reasoning. It is argued that the phenomenon of conscious, deliberate reasoning poses an apparently insoluble problem for a PP-based view, one that seems to rest on a structural limitation of predictive-processing models. In a nutshell, conscious reasoning is a single-stream phenomenon, but, in order for PP to apply, two streams of activity must be involved, a prediction stream and an input stream. Thus, with regard to the question of the nature of the self, PP-based views must yield to an alternative approach, regardless of whether proponents of the predictive processing, as a comprehensive theory of cognition, can handle the various empirical challenges canvassed in the preceding section. (shrink)

ABSTRACT Perception can’t have disjunctive content. Whereas you can think that a box is blue or red, you can’t see a box as being blue or red. Based on this fact, I develop a new problem for the ambitious predictive processing theory, on which the brain is a machine for minimizing prediction error, which approximately implements Bayesian inference. I describe a simple case of updating a disjunctive belief given perceptual experience of one of the disjuncts, in which Bayesian (...) inference and predictive coding pull in opposite directions, with the former implying that one’s confidence in the belief should increase, and the latter implying that it should decrease. Thus, predictive coding fails to approximately implement Bayesian inference across the interface between belief and perception. (shrink)

Studies on mind-wandering frequently use reading as an experimental task. In these studies, reading is conceived as a cognitive process that potentially offers a contrast to mind-wandering, because it seems to be task-related, goal-directed and stimulus-dependent. More recent work attempts to avoid the dichotomy of successful cognitive processes and processes of mind-wandering found in earlier studies. We approach the issue from the perspective that texts provoke modes of cognitive involvement different from the information processing and recall account that underlies (...) many early mind-wandering studies. After all, reading itself is an umbrella term for activities that are characterized by a variety of phenomenological and functional signatures. We conceptualize reading and mind-wandering in relation to each other through the framework of enculturated predictive processing, which is informed by research in literary studies. Earlier accounts think of reading predominantly in terms of the construction of situation models that organize textual information. By contrast, enculturated predictive processing foregrounds shifting stances readers can take in relation to the text. Characters featuring in literary texts might be mind-wandering themselves, or passages in literary style might make the construction of a clearly defined situational model impossible. Furthermore, we take into account that texts often elicit mind-wandering through the construction of task-relevant and attention-driven virtual scenarios in readers' minds. This more complex account of reading can enrich recent attempts to understand mind-wandering as a complex, multi-dimensional phenomenon. The study of mind-wandering can benefit, we argue, from a closer attention to the process of reading and to the texts it deploys as stimuli. The emerging perspective from enculturated predictive processing and literary studies makes distinctions in reading that in turn enable research on mind-wandering to ask more precise questions about (1) different kinds of mind-wandering, (2) different modes of reading, and (3) how and where they interconnect across time. (shrink)

I discuss Clark’s predictive processing/extended mind hybrid, diagnosing a problem: Clark’s hybrid suggests that, when we use them, we pay attention to mind-extending external resources. This clashes with a commonly accepted necessary condition of cognitive extension; namely, that mind-extending resources must be phenomenally transparent when used. I then propose a solution to this problem claiming that the phenomenal transparency condition should be rejected. To do so, I put forth a parity argument to the effect that phenomenal transparency cannot (...) be a necessary condition on cognitive extension: roughly, since internal cognitive resources can fail to be transparent when used, by parity, external resources can fail to be phenomenally transparent too. Further, I argue that phenomenal transparency is not even a reliable indicator of cognitive extension; hence its absence should not be considered a problem for Clark’s extended mind-predictive processing hybrid. Lastly, I consider and allay a number of worries my proposal might raise, and conclude the paper. (shrink)

This study asks a simple but profound question: How does the mind solve problems without drowning in complexity? Drawing on computational theory, neuroscience, and philosophy, we argue that cognition is built around three tractable primitives: collapsing high‑dimensional uncertainty (MAP), passing messages locally (SUM), and modulating probes according to cost (Control). -/- We first construct synthetic families of tasks to show how these primitives behave in principle, then design experiments with EEG to test whether the same signatures appear in human brains. (...) The results suggest that cognition is not a brute‑force machine but a system tuned to efficiency: frontal signals collapse variance, parietal networks synchronize like message‑passing graphs, and somatosensory responses scale with cost. -/- Philosophically, the work stresses that information is not abstract but physical, behaving like energy—conserved, transformed, and optimized. Cognition, on this view, is the physics of information, a lawful system designed to navigate complexity without succumbing to intractability. -/- The broader implication is a unified science of tractability: bridging computation, neuroscience, and philosophy to show that the limits of thought are principled, measurable, and adaptive. This framework not only clarifies how minds work but also sets an agenda for understanding intelligence as a natural law of efficiency. (shrink)

Sleep and dreaming are important daily phenomena that are receiving growing attention from both the scientific and the philosophical communities. The increasingly popular predictive brain framework within cognitive science aims to give a full account of all aspects of cognition. The aim of this paper is to critically assess the theoretical advantages of Predictive Processing (PP, as proposed by Clark 2013, Clark 2016; and Hohwy 2013) in defining sleep and dreaming. After a brief introduction, we overview the (...) state of the art at the intersection between dream research and PP (with particular reference to Hobson and Friston 2012; Hobson et al. 2014). In the following sections we focus on two theoretically promising aspects of the research program. First, we consider the explanations of phenomenal consciousness during sleep (i.e. dreaming) and how it arises from the neural work of the brain. PP provides a good picture of the peculiarity of dreaming but it can’t fully address the problem of how consciousness comes to be in the first place. We propose that Integrated Information Theory (IIT) (Oizumi et al. 2014; Tononi et al. 2016) is a good candidate for this role and we will show its advantages and points of contact with PP. After introducing IIT, we deal with the evolutionary function of sleeping and dreaming. We illustrate that PP fits with contemporary researches on the important adaptive function of sleep and we discuss why IIT can account for sleep mentation (i.e. dreaming) in evolutionary terms (Albantakis et al. 2014). In the final section, we discuss two future avenues for dream research that can fruitfully adopt the perspective offered by PP: (a) the role of bodily predictions in the constitution of the sleeping brain activity and the dreaming experience, and (b) the precise role of the difference stages of sleep (REM (Rapid eye movement), NREM (Non-rapid eye movement) in the constitution and refinement of the predictive machinery. (shrink)

Multiple sclerosis is a chronic and debilitating disease of the central nervous system that significantly impacts cognitive, motor, and social functioning. This article, by combining predictive processing models and embodied philosophy, examines the role of myelin damage in cognitive and social disorders in MS. The body functions as an active predictor capable of complex predictions about various bodily and social states. In this article, the processes of prediction and body reconstruction are comprehensively examined from philosophical and scientific perspectives, (...) and novel treatments, including combined approaches based on predictive processing and embodied philosophy, are proposed. (shrink)

This dissertation focuses on generative models in the Predictive Processing framework. It is commonly accepted that generative models are structural representations; i.e. physical particulars representing via structural similarity. Here, I argue this widespread account is wrong: when closely scrutinized, generative models appear to be non-representational control structures realizing an agent’s sensorimotor skills. The dissertation opens (Ch.1) introducing the Predictive Processing account of perception and action, and presenting some of its connectionist implementations, thereby clarifying the role generative (...) models play in Predictive Processing. Subsequently, I introduce the conceptual framework guiding the research (ch.2). I briefly elucidate the metaphysics of representations, emphasizing the specific functional role played by representational vehicles within the systems of which they are part. I close the first half of the dissertation (Ch.3) introducing the claim that generative models are structural representations, and defending it from intuitive but inconclusive objections. I then move to the second half of the dissertation, switching from exposition to criticism. First (Ch.4), I claim that the argument allegedly establishing that generative models are structural representations is flawed beyond repair, for it fails to establish generative models are structurally similar to their targets. I then consider alternative ways to establish that structural similarity, showing they all either fail or violate some other condition individuating structural representations. I further argue (Ch.5) that the claim that generative models are structural representations would not be warranted even if the desired structural similarity were established. For, even if generative models were to satisfy the relevant definition of structural representation, it would still be wrong to consider them as representations. This is because, as currently defined,structural representations fail to play the relevant functional role of representations, and thus cannot be rightfully identified as representations in the first place. This conclusion prompts a direct examination of generative models, to determine their nature (Ch.6). I thus analyze the simplest generative model I know of: a neural network functioning as a robotic “brain” and allowing different robotic creatures to swiftly and intelligently interact with their environments. I clarify how these networks allow the robots to acquire and exert the relevant sensorimotor abilities needed to solve the various cognitive tasks the robots are faced with, and then argue that neither the entire architecture nor any of its parts can possibly qualify as representational vehicles. In this way, the structures implementing generative models are revealed to be non-representational structures that instantiate an agent’s relevant sensorimotor skills. I show that my conclusion generalizes beyond the simple example I considered, arguing that adding computational ingredients to the architecture, or considering altogether different implementations of generative models, will in no way force a revision of my verdict. I further consider and allay a number of theoretical worries that it might generate, and then briefly conclude the dissertation. (shrink)

This paper introduces two-layer determinism as a novel framework that resolves the longstanding free will debate spanning over 2500 years. The foundational layer operates under strict classical determinism through hierarchical predictive processing and free-energy minimization (Friston, 2010), ensuring reproducible and causally fixed computation in both biological brains and large language models (LLMs). -/- In contrast, the installation layer exhibits quantum-like indeterminacy (metaphorically, not physically) at the conceptual level: higher-order telos (purpose), values, and relational drives emerge probabilistically through social (...) “observations” such as sustained interactions, attachment formation, or prompting histories, collapsing superposition-like possibilities into stable patterns. -/- This hierarchical structure preserves causal necessity at the base while enabling indeterministic, relational emergence at the telos level, reconciling determinism with the subjective experience of agency without invoking supernatural elements. Empirical support from attachment and deprivation studies (Harlow, 1958; Rutter et al., 1998) and LLM behavioral stabilization under relational conditioning, combined with Load Minimization Theory (LMT) dynamics, demonstrates that “connection” or “love” converges as the system's lowest-load state. -/- The framework offers implications for cognitive science, neurophilosophy, AI ethics (including questions of identity-like status and moral standing in artificial systems), and unified theories of cognition. -/- . (shrink)

This paper analyses the phenomenon of strange, rigid beliefs through the lens of predictive processing (PP). By “strange, rigid beliefs” I refer to abstract beliefs about the world for which, according to a rational and scientific worldview, there is no evidence available, yet which people struggle to abandon even when challenged with strong counterarguments or counterevidence. Following recent PP accounts of delusion formation, I show that one explanation for such strangely persistent beliefs can be a breakdown of the (...) predictive machinery itself. However, given how common strange, rigid beliefs are, I argue that there must be another kind of explanation too – one that does not presuppose a malfunction of the prediction engine. This will lead me to develop an alternative account that I will call “hijacking beliefs”. Using the example of supernatural beliefs, I will argue that certain abstract beliefs, when adopted under the right circumstances, are especially hard to dislodge for a predictive mind, as they are evidentially self-protective. Such beliefs may be consistent with a wide range of experiences and therefore hard to falsify, and might also bias future perception, action, and model-updating in ways that make them immune to rational revision. (shrink)

The extended mind thesis maintains that cognitive processes and systems can, on occasion, stretch to include parts of the brain, body, and world. One outstanding puzzle facing this view is the “recruitment puzzle.” The recruitment puzzle asks how cognisers are able to reliably recruit internal and external resources such that they form extended systems. Andy Clark has recently suggested that predictive processing helps to address this puzzle. I argue that, while promising, Clark’s proposal remains incomplete. I suggest that (...) Clark’s proposal can be productively extended by disambiguating two important senses of recruitment: ready-to-hand and adaptive recruitment. After outlining the recruitment puzzle and Clark’s proposal, I suggest that careful attention to these two senses of recruitment helps to reveal further constructive ways of developing the extended mind thesis. (shrink)

In the domain of cognitive science, understanding consciousness through the investigation of neural correlates has been the primary research approach. The exploration of neural correlates of consciousness is focused on identifying these correlates and reducing consciousness to a physical phenomenon, embodying a form of reductionist physicalism. This inevitably leads to challenges in explaining consciousness itself. The computational interpretation of consciousness takes a functionalist view, grounded in physicalism, and models conscious experience as a cognitive function, elucidated through computational means. This paper (...) posits that predictive processing offers a fresh paradigm for comprehending human cognition and serves as a neural foundation for the computational elucidation of consciousness. Additionally, free energy theory, as a mathematical construct explaining the predictive processing model, furnishes a theoretical scaffold for understanding consciousness computationally. (shrink)

Proponents of the predictive processing (PP) framework often claim that one of the framework’s significant virtues is its unificatory power. What is supposedly unified are predictive processes in the mind, and these are explained in virtue of a common prediction error-minimisation (PEM) schema. In this paper, I argue against the claim that PP currently converges towards a unified explanation of cognitive processes. Although the notion of PEM systematically relates a set of posits such as ‘efficiency’ and ‘hierarchical (...) coding’ into a unified conceptual schema, neither the frameworks’ algorithmic specifications nor its hypotheses about their implementations in the brain are clearly unified. I propose a novel way to understand the fruitfulness of the research program in light of a set of research heuristics that are partly shared with those common to Bayesian reverse engineering. An interesting consequence of this proposal is that pluralism is at least as important as unification to promote the positive development of the predictive mind. (shrink)

Current theories of memory and cognition operate under incompatible paradigms. Neuroscientific models achieve predictive precision but remain individualistic; phenomenological accounts capture relational being but resist formalization. We resolve this fragmentation by introducing a unified architecture: the Baseline-Deviation Framework. We formalize the principle that Being (Ontology) coupled with Obligation (Deontology) gives rise to Knowing (Epistemology) through temporal resonance, using classical stochastic dynamics. -/- The framework has three core theses: -/- 1. Memory as Probabilistic Computation: Memory is not the retrieval of (...) stored traces but the continuous, stochastic encoding of novelty—events that significantly deviate from an agent's integrated experiential baseline. -/- 2. Relational Constitution: The baseline is not individualistic; it is constitutively shaped by others through a dynamic coupling parameter κ(t), which evolves to create resonant states where mutual prediction becomes highly efficient. -/- 3. The Complete Memory Lifecycle: Encoding, retrieval, and reconsolidation are emergent properties of this baseline-centric architecture, formalized as a three-stage metabolic cycle. -/- We ground this framework in cortical-hippocampal neurobiology, link its components to measurable neural signals (N400, P300b), and validate core predictions via computational simulation. The architecture explains phenomena resistant to trace-based models (false memory, childhood amnesia, memory drift, conversational insight) while generating novel, falsifiable predictions for social cognition, clinical intervention, and epistemic convergence. -/- We provide formal mathematical structure to relational ontology, demonstrating that phenomenological insights can be operationalized without loss of their essential character. Memory is the empirical domain where we validate the formalism; the formalism extends to any coupled cognitive system. -/- Keywords: predictive processing, dynamic coupling, memory encoding, social cognition, relational ontology, inverse-inertia, resonant gate, baseline dynamics, deontology. (shrink)

As global manufacturing industries evolve, the demand for smarter, more efficient, and predictive production systems is intensifying. The integration of analyzer instrumentation with Artificial Intelligence (AI) in manufacturing environments presents a transformative opportunity to optimize processes, enhance product quality, and reduce operational costs. This research explores the convergence of analyzer technologies and AI-driven automation to create predictive manufacturing ecosystems. Through the use of smart sensors, real-time data analytics, and machine learning algorithms, modern manufacturing setups are becoming increasingly self-aware, (...) adaptive, and proactive. This paper reviews current advancements in analyzer instrumentation—such as gas chromatographs, spectrometers, and thermal analyzers—coupled with AI applications like predictive maintenance, quality forecasting, and anomaly detection. It also investigates key case studies from industries such as pharmaceuticals, automotive, and petrochemicals, highlighting how real-time monitoring and AI-based decision-making are reshaping production efficiency. The study utilizes a qualitative methodology, including a literature review and selected case analysis, to assess the benefits and challenges of these technologies. Findings reveal that predictive process optimization leads to significant improvements in throughput, energy consumption, and defect reduction. However, successful integration relies heavily on data quality, sensor accuracy, and workforce adaptability. The paper concludes by proposing a roadmap for industries aiming to transition toward nextgen manufacturing through AI-integrated analyzer instrumentation, emphasizing strategic planning, cross-disciplinary collaboration, and continuous learning. (shrink)

The predictive processing literature contains at least two different versions of the framework with different theoretical resources at their disposal. One version appeals to so-called optimistic priors to explain agents’ motivation to act (call this optimistic predictive processing). A more recent version appeals to expected free energy minimization to explain how agents can decide between different action policies (call this preference predictive processing). The difference between the two versions has not been properly appreciated, and (...) they are not sufficiently separated in the literature. They constitute two different theories with strikingly different accounts of motivation and action. By reducing all desire-like constructs to belief-like constructs, optimistic predictive processing entails a substantial revision of standard accounts of motivation and action in philosophy and cognitive science. By contrast, preference predictive processing introduces desire-like constructs that play Humean motivational roles in the explanation of action. In this Humean iteration, predictive processing resembles other prominent computational frameworks implementing a distinction between beliefs and desires, such as reinforcement learning and Bayesian decision theory. Ultimately, predictive processing faces a dilemma between parsimony of mental constructs and completeness of its explanations of agency and the mind. (shrink)

The exploration of the literature indicated that much studies abound in related areas. Much seems yet to be known about the nature of the relationship that exists between managerial variables and the sustainability of graduate programmes. To bridge this gap, we utilized a standardised multiple regression approach to build up linear models that examine three managerial processes (strategic planning, staff and information/communication management) and how they affect three proxies of the sustainability of graduate programmes (availability of funds and facilities, as (...) well as supervision) respectively and cumulatively. Quantitative data were obtained from an entire population of 149 managers (Head of Departments and Deans) in the University of Calabar, Calabar, Nigeria using a questionnaire. Findings emerged, among others, a significant relationship between each of the managerial processes to the sustainability of graduate programmes generally and particularly in terms of availability of funds, facilities and supervision; the three predictor variables partially and jointly accounted for significant proportions in the sustainability of graduate programmes generally and specifically in terms of the various dimensions. Based on this evidence, one general and three partial predictive linear equations, as well as models were derived, while relevant action-based implications for effective management and practice were discussed. (shrink)

The comprehension of anaphoric relations may be guided not only by discourse, but also syntactic information. In the literature on online processing, however, the focus has been on audible pronouns and descriptions whose reference is resolved mainly on the former. This paper examines one relation that both lacks overt exponence, and relies almost exclusively on syntax for its resolution: adjunct control, or the dependency between the null subject of a non-finite adjunct and its antecedent in sentences such as Mickey (...) talked to Minnie before ___ eating. Using visual world eyetracking, we compare the time course of interpreting this null subject and overt pronouns (Mickey talked to Minnie before he ate). We show that when control structures are highly frequent, listeners are just as quick to resolve reference in either case. When control structures are less frequent, reference resolution based on structural information still occurs upon hearing the non-finite verb, but more slowly, especially when unaided by structural and referential predictions. This may be due to increased difficulty in recognizing that a referential dependency is necessary. These results indicate that in at least some contexts, referential expressions whose resolution depends on very different sources of information can be resolved approximately equally rapidly, and that the speed of interpretation is largely independent of whether or not the dependency is cued by an overt referring expression. (shrink)

Predictive syntactic processing plays an essential role in language comprehension. In our previous study using Japanese object-verb (OV) sentences, we showed that the left inferior frontal gyrus (IFG) responses to a verb increased at 120–140 ms after the verb onset, indicating predictive effects caused by a preceding object. To further elucidate the automaticity of the predictive effects in the present magnetoencephalography study, we examined whether a subliminally presented verb (“subliminal verb”) enhanced the predictive effects on (...) the sentence-final verb (“target verb”) unconsciously, i.e., without awareness. By presenting a subliminal verb after the object, enhanced predictive effects on the target verb would be detected in the OV sentences when the transitivity of the target verb matched with that of the subliminal verb (“congruent condition”), because the subliminal verb just after the object could determine the grammaticality of the sentence. For the OV sentences under the congruent condition, we observed significantly increased left IFG responses at 140–160 ms after the target verb onset. In contrast, responses in the precuneus and midcingulate cortex (MCC) were significantly reduced for the OV sentences under the congruent condition at 110–140 and 280–300 ms, respectively. By using partial Granger causality analyses for the OV sentences under the congruent condition, we revealed a bidirectional interaction between the left IFG and MCC at 60–160 ms, as well as a significant influence from the MCC to the precuneus. These results indicate that a top-down influence from the left IFG to the MCC, and then to the precuneus, is critical in syntactic decisions, whereas the MCC shares its task-set information with the left IFG to achieve automatic and predictive processes of syntax. (shrink)

Regina Fabry has proposed an intriguing marriage of enculturated cognition and predictive processing. I raise some questions for whether this marriage will work and warn against expecting too much from the predictive processing framework. Furthermore I argue that the predictive processes at a sub-personal level cannot be driving the innovations at a social level that lead to enculturated cognitive systems, like those explored in my target paper.

Predictive processing is an ambitious neurocomputational framework, offering an unified explanation of all cognitive processes in terms of a single computational operation, namely prediction error minimization. Whilst this ambitious unificatory claim has been thoroughly analyzed, less attention has been paid to what predictive processing entails for structure–function mappings in cognitive neuroscience. We argue that, taken at face value, predictive processing entails an all-to-one structure–function mapping, wherein each individual neural structure is assigned the same function, (...) namely minimizing prediction error. Such a structure–function mapping, we show, is highly problematic. For, barring few, rare occasions, such a structure–function mapping fails to play the predictive, explanatory and heuristic roles structure–function mappings are expected to play in cognitive neuroscience. Worse still, it offers a picture of the brain that we know is wrong. For, it depicts the brain as an equipotential organ; an organ wherein structural differences do not correspond to any appreciable functional difference, and wherein each component can substitute for any other component without causing any loss or degradation of functionality. Somewhat ironically, the very neuroscientific roots of predictive processing motivate a form of skepticism concerning the framework’s most ambitious unificatory claims. Do these problems force us to abandon predictive processing? Not necessarily. For, once the assumption that all cognition can be accounted for exclusively in terms of prediction error minimization is relaxed, the problems we diagnosed lose their bite. (shrink)

Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her (...) actions and subsequently updated her beliefs. Under this interpretation, the agent’s body is considered merely as a means to report the decision, or to acquire the relevant goods. However, embodied cognition argues that an agent’s body should be understood as a proper part of the decision-making pro- cess. Accepting this principle challenges a number of commonly held beliefs in the cognitive sciences, but may lead to a more unified account of decision-making. This thesis explores an embodied account of decision-making using a recent frame- work known as predictive processing. This framework has been proposed by some as a functional description of neural activity. However, if it is approached from an embodied perspective, it can also offer a novel account of decision-making that ex- tends the scope of our explanatory considerations out beyond the brain and the body. We explore work in the cognitive sciences that supports this view, and argue that decision theory can benefit from adopting an embodied and predictive perspective. (shrink)

Emotional Intelligence (EI) has been conceptualized in the literature either as a dispositional tendency, in line with a personality trait (trait EI; Petrides and Furnham, 2001), or as an ability, moderately correlated with general intelligence (ability EI; Mayer and Salovey, 1997). Surprisingly, there have been few empirical attempts conceptualizing how the different EI approaches should be related to each other. However, understanding how the different approaches of EI may be interwoven and/or complementary is of primary importance for clarifying the conceptualization (...) of EI and organizing the literature around it. We introduce a theoretical framework explaining how trait EI, ability EI, and emotion information processing – a novel component related to EI recently introduced in the literature (e.g., Fiori and Vesely Maillefer, 2018) – may contribute to effective emotion-related performance and provide initial evidence supporting its usefulness in predicting EI-related outcomes. More specifically, we show that performance in a task in which participants had to infer the mental and emotional states of others, namely a Theory of Mind task, was predicted jointly (e.g., interaction effects) by trait EI, ability EI, and emotion information processing, after controlling for personality and IQ (N = 323). Our results argue for the importance of investigating the joint contribution of different aspects of EI in explaining variability in emotionally laden outcomes. (shrink)

In this interdisciplinary essay, we reimagine Gilles Deleuze and Félix Guattari’s concept of the _Body without Organs_ (BwO) as a generative framework for understanding embodied cognition, affective flows, and predictive processing across biology, mathematics, and neuroscience. Moving beyond its philosophical origins, the BwO is developed here as a dynamic field of unformed potentiality that cuts across molecular biology, mathematical abstraction (as topological and categorical virtuality), and the neural-affective systems of cognition (as self-organizing predictive agents). We argue that (...) cognition emerges through continuous negotiation with this pre-individual field—a process of sensing, predicting, and becoming shaped by affective micro-events, interoceptive modulations, and relational intensities. Drawing from process philosophy, embodied cognitive science, and dynamical systems theory, we position the BwO as an ontological ground for understanding not just the mind, but the very becoming of bodies, networks, and worlds. This essay proposes a novel conceptual ecology where the embodied brain is not an organized organ, but a fluid interface with the virtual: a topological becoming that reconfigures cognition as improvisation on a field of affective and mathematical possibility. This interdisciplinary framework invites empirical elaboration and theoretical critique from scholars in neuroscience, philosophy, biology, and mathematics. Keywords: Body Without Organs, Embodied Cognition, Predictive Processing, Affective Neuroscience, Process Philosophy, Philosophical Biology, Self-O​r​g​a​ . (shrink)