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)

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)

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)

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)

According to predictive processing, minds relentlessly aim at a single goal: prediction error minimization. Prediction error minimization is said to explain everything the mind does, from perception to cognition to action. Here I focus on action. ‘Active inference’ is the standard approach to action in predictive processing. According to active inference, as it has been developed by Friston and collaborators, action ensues when proprioceptive predictions generate prediction error at the motor periphery, and classical reflex arcs engage (...) to quash the error. In this paper, I raise a series of problems for active inference. I then offer an alternative approach on which action prevents error, rather than quash it. I argue that the action prevents error approach solves all the problems raised for active inference. In addition, I show how the alternative approach can be independently motivated by further commitments of predictive processing and that it is compatible with other prominent approaches to sensorimotor psychology, such as optimal feedback control. (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)

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 self functions as a critical mechanism for an organism to navigate the complex, often unknown, relations between the body and the brain. Organisms that sustain a coherent sense of self utilize it as an adaptive strategy to alter their environment, thereby enabling predictive processing within an incomplete _Umwelt_. While researchers often attempt to model prediction as a Quine-like self-referential loop by studying small neuronal sets, this reductionist approach limits our understanding of the self's holistic predictive nature. (...) Adopting a functionalist perspective, this paper conceptualizes the mind as a system that predicts embodied intrinsic motivations essential for survival and flourishing. (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)

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)

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 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)

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)

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)

Predictive processing posits that prediction-error minimization underlies all perception, action, and cognition. Yet, despite its considerable popularity and explanatory scope, it is unclear how this enables higher-level cognitive abilities, such as representing and reasoning over abstract concepts. We combine insights from predictive processing, structural representations and grounded cognition to address this issue. It has been argued from predictive processing and the free energy principle that an anticipatory model of the person-relevant environment is simulated. Structural (...) representations state that these representations are isomorphic to, i.e., retain the relational pattern of the world. Building on this assembly, grounded cognition research provides three insights into how abstract concepts are represented. First, a hierarchical organization allows abstracting from specific sensory qualities. Second, language glues together sensory qualities into representations that share no intrinsic properties, and acts as a social tool. Third, metaphoric mapping allows fragments of concrete percepts to represent abstract concepts. By transplanting these three insights to predictive processing’s (structural) hierarchical generative model, we explain higher-level cognition through detached models of perception and action simulations, isomorphic to actual behavior. This constitutes a significant expansion to life-mind continuity approaches by providing specific mechanisms for how the principles driving the emergence of life also account for sophisticated higher-level cognition in humans. By synthesizing insights from these literatures, we generate a coherent description of higher-level cognition under predictive processing. (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)

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)

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)

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)

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)

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)

The twelve nidānas of Buddhist dependent origination (paṭicca-samuppāda) describe both the causal mechanism of suffering (dukkha) and the structure whose reversal constitutes liberation. Interpretations remain divided between a cosmological reading (twelve stages across lifetimes) and a psychological reading (twelve moments of a single cognitive episode), with neither achieving full analytical precision. This paper proposes a third reading grounded in contemporary cognitive science: the twelve nidānas map, with systematic structural fidelity, onto the architecture of predictive processing. Avijjā corresponds to (...) structural misrecognition of one's priors as mind-independent reality — the absence of meta-cognitive awareness that priors are model-generated constructs rather than direct apprehensions of the world; saṅkhāra to compulsive volitional formations driven by priors that are not recognized as such; viññāna through saḷāyatana to the construction of a hierarchical generative model; vedanā to valenced prediction error; taṇhā and upādāna to precision-weighted prior consolidation and attractor reification; bhava through jarāmaraṇa to the instantiation and progressive degradation of a system operating under misaligned priors. The correspondence is theoretically productive in both directions: predictive processing provides a mechanistic vocabulary that extends the nidānas beyond Buddhist cosmology; the nidāna chain supplies a diachronic account of why a system already operating under structural misrecognition necessarily perpetuates and deepens that misrecognition toward attractor reification and suffering, rather than correcting itself — a developmental logic that existing diachronic extensions of predictive processing do not address. The paper concludes by marking four limits of the analogy: the rebirth doctrine, the normative weight of nibbāna, the phenomenal character of vedanā, and the relationship to the Madhyamaka reading of dependent origination. A central interpretive claim is that avijjā should be understood not as an information-theoretic blank slate but as structural misrecognition of the conditioned character of one's own cognitive processes — a misrecognition available only to a system that already possesses priors, and that consists in taking those priors as transparent windows onto mind-independent reality rather than as model-generated constructions. (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)

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)

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)

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)

Qualia? “What cannot be directly spoken of must remain unsaid — but it can still be structurally surrounded.” This is the guiding spirit of the present series. -/- This paper presents a structural critique of major contemporary theories of consciousness, including Integrated Information Theory (IIT), Global Neuronal Workspace (GNW), Higher-Order Thought (HOT), Predictive Processing (PP), Recurrent Processing Theory (RPT), and their limiting case in epiphenomenalism. Despite their apparent differences, these approaches share a common structural premise: consciousness is (...) identified with a privileged state, whether defined in terms of integration, global availability, higher-order representation, predictive optimization, recurrent stabilization, or passive accompaniment. This paper demonstrates that state-based descriptions are structurally insufficient to account for subjectivity. A state can represent multiple possibilities, exist in parallel, and be recalculated. However, subjectivity requires the irreversible elimination of alternatives, the formation of a single-line history, and the emergence of an internally privileged present. State-based theories describe what is present. They do not define what has disappeared. As a result, they fail to specify the structural point at which multiple possibilities collapse into a single irreversible trajectory. This limitation is shown to hold across the full spectrum of state-centric theories, from highly structured models to their minimal epiphenomenal form. To resolve this structural non-closure, the paper introduces Irreversible Exclusive State Commitment (IESC) as the minimal condition under which subjectivity emerges. Subjectivity is thereby redefined not as a state or byproduct, but as an event of reduction in which alternative possibilities become internally unreachable and a single history is fixed. This shift from state-based to event-based structure is not a reinterpretation, but a structural necessity. Within this framework, existing theories are not rejected but repositioned as pre-emergence structures that generate, maintain, or distribute possibilities, while IESC defines the point at which those possibilities are irreversibly reduced. This manuscript was written with the assistance of generative AI. The author is a disability welfare worker and a complete outsider to the academic field of consciousness studies. The author first encountered the term “the hard problem of consciousness” about six months before writing this series and began thinking about it almost as if approaching a puzzle. Because the author did not possess extensive specialized knowledge at the beginning of this inquiry, the investigation proceeded not primarily by adding established theories, but by stripping the problem down as far as possible. Through this process, and through dialogue with AI, the convergence point derived from the structural conditions gradually emerged. The author had the impression that something like a structure had appeared from nature itself. This series is an attempt to present that structure. The central idea, theoretical orientation, conceptual structure, and overall organization of the series belong to the author. Generative AI was used extensively for English drafting, wording, organization, and stylistic refinement. The author intends to end this work with its presentation as a preprint series. The author does not plan to develop this hypothesis further as a long-term academic project. Readers who find any part of this hypothesis, terminology, structure, or argument useful are free to use, modify, criticize, or develop it further. No permission from the author is required. Formal citation or attribution is appreciated but not required. (shrink)

This paper is a direct continuation of the preceding volume, An AI Prompt for Strongly Inferring the Structural Location of Subjectivity. The first volume tested whether ten minimal structural terms, when followed according to their internal logic, recurrently lead to a convergence point: the irreversible reduction of multiple possible trajectories into a single executable history. The present volume does not introduce a new definition of consciousness, subjectivity, qualia, or experience. Instead, it uses the same ten minimal terms and the same (...) initial four-question procedure as the preceding volume. The purpose is to test whether the convergence point derived from those terms can answer structural gaps that existing consciousness theories leave unresolved or underdetermined. The method differs from a direct critique of existing theories. Rather than preselecting questions against those theories, this prompt asks the AI system itself to identify, for each theory, ten questions that remain structurally unanswered. The AI must then attempt to answer those questions using only the convergence point derived from the ten terms. The theories and research programs examined include Global Workspace Theory, Integrated Information Theory, Recurrent Processing Theory, Higher-Order Thought Theory, Predictive Processing, Neural Correlates of Consciousness research, behavioral tests including the Turing Test, and Libet-style experiments concerning readiness potential and conscious will. For each generated question, the AI must classify whether the convergence point directly answers the question, partially answers it, reframes it, or fails to answer it. The aim is not to force agreement with the convergence-point framework, but to test its explanatory power against structural gaps that existing theories themselves appear to leave open. If the same type of structural gap repeatedly appears across multiple theories, and if the convergence point repeatedly answers or reframes that gap, then consciousness research may need to reconsider the structural role of the convergence point. Existing theories may remain valuable, but they may need to be repositioned as theories of access, integration, representation, prediction, neural implementation, report, or behavior around a prior structural fixation point of subjectivity. (shrink)

This paper examines the structural robustness of a proposed account of subjectivity under boundary conditions and edge cases, including the Libet experiment, readiness potential, conscious intention, voluntary action, deep sleep, anesthesia, dreaming, dissociative identity disorder (DID), autism spectrum conditions, and artificial systems. Building on prior work in which subjectivity is identified as a structurally compelled locus within a causally closed system—specifically, as Irreversible Exclusive State Commitment (IESC)—the present study does not introduce new mechanisms or re-derive the core result. Instead, it (...) tests whether the proposed structural conditions remain sufficient across cases in which subjectivity appears absent, altered, delayed, or multiply instantiated. The framework is contrasted with major contemporary approaches to consciousness, including Integrated Information Theory (IIT), Global Workspace / Global Neuronal Workspace theories (GWT/GNW), Higher-Order Thought theory (HOT), Predictive Processing / Active Inference, Recurrent Processing Theory (RPT), and related accounts addressing the hard problem of consciousness, qualia, agency, and free will. Unlike these approaches, the present account does not treat subjectivity as a generated state, an informational quantity, or a representational layer, but as a structurally defined event within a causally constrained system. A series of boundary analyses is conducted. Cases of deep sleep and anesthesia are examined as conditions in which no effective candidate generation or irreversible selection occurs, and therefore no subjectivity is instantiated. Dreaming is analyzed as a case of internally realized commitment without external motor execution. Dissociative Identity Disorder (DID) is considered as a case of multiple concurrent or partitioned commitment structures. Autism spectrum conditions are interpreted in terms of differences in candidate generation and weighting prior to commitment. Cross-species comparisons are used to examine the minimal structural requirements for subjectivity. The Libet experiment is reinterpreted within this framework as a direct consequence of the temporal structure of IESC. Readiness potentials correspond to pre-commitment dynamics of candidate generation and competition, while the reported time of intention reflects a delayed report of the commitment event. Apparent conscious veto is shown to be structurally equivalent to initiation, differing only in which candidate survives at the point of irreversible selection. Artificial systems are treated as controlled test environments for the proposed structural conditions. If subjectivity is structurally determined, it should arise only in systems that instantiate irreversible exclusive commitment under viability constraints, and should be absent in systems that lack such structure. Across these cases, the central claim is that the presence, absence, and variation of subjectivity can be consistently accounted for without introducing additional assumptions beyond the structural conditions defining IESC. A valid refutation must demonstrate either that subjectivity is present in the absence of irreversible exclusive commitment, or that such commitment occurs without the emergence of subjectivity. The results suggest that subjectivity is not a fragile or exceptional phenomenon, but a robust structural feature that appears wherever the necessary conditions are satisfied. (shrink)

This paper addresses the hard problem of consciousness—commonly formulated within state-based frameworks such as Integrated Information Theory (IIT), Global Neuronal Workspace (GNW), Higher-Order Thought (HOT), Predictive Processing, Recurrent Processing Theory (RPT), and epiphenomenalism—by shifting the explanatory target from state generation to the structural localization of irreversible determination. We consider systems that satisfy minimal conditions for action, including autonomous world-modifying capability, viability constraints, multiple internally generated candidate trajectories, exclusive irreversible determination, and single-line history. In such systems, multiple internally (...) reachable trajectories are reduced to a single realized trajectory. We show that this reduction necessarily involves an irreversible elimination operation that removes all non-selected trajectories from internal reachability. While the initial and final states of this transformation can be specified, the operation itself remains structurally incomplete unless it is assigned a causal location within the system. Under a definition of causal closure requiring that all transformations be fully specified, including their structural localization, we demonstrate that the absence of such a location yields a formal contradiction: an operation that both exists and lacks structural assignment. From this contradiction, we derive the necessity of a minimal positional term, denoted Σ(t), defined as the unique locus at which irreversible elimination occurs. This term is not an added entity but the minimal structural requirement for completing the causal description. Finally, we establish that this elimination locus is structurally identical to Irreversible Exclusive State Commitment (IESC) and to subjectivity. Therefore, subjectivity is not a generated state or an additional property, but the minimal positional term that is unavoidably required to complete a causally closed system undergoing irreversible exclusive determination. (shrink)

Qualia? “What cannot be directly spoken of must remain unsaid — but it can still be structurally surrounded.” This is the guiding spirit of the present series. This paper reframes the hard problem of consciousness not as a failure of explanation, but as a structural misclassification, and proposes an event-based framework in which consciousness is understood not as a state, but as a structural event. The central claim of this work is that events are not merely descriptive conveniences, but real (...) phenomena that arise inevitably under specific structural conditions. Just as vortices necessarily emerge in fluid systems when gradients, boundary conditions, and irreversible dissipation are present, subjectivity arises as a structural consequence in causally closed systems when a particular set of conditions is satisfied. This point of emergence is identified as a structural knot: a causal bottleneck at which multiple possible trajectories converge into a single realized outcome while alternatives become irreversibly excluded. Major contemporary theories of consciousness—including Integrated Information Theory (IIT), Global Workspace Theory (GWT/GNW), Higher-Order Thought (HOT), Predictive Processing, and Recurrent Processing Theory (RPT)—primarily treat consciousness as a state, an informational quantity, or a representational configuration. However, such state-based frameworks cannot account for the irreversible elimination of unrealized possibilities that characterizes subjective experience. This paper argues that, under causally closed conditions, subjectivity is not an optional feature but a necessary structural consequence of irreversible exclusive determination. Consciousness is therefore not a state but an event: a process in which multiple internally generated possibilities compete and are reduced to a single realized outcome, while alternatives become causally unreachable. This event is formally identified as Irreversible Exclusive State Commitment (IESC). Within this framework, subjectivity is defined as the internal positional consequence of such irreversible commitment within a single unfolding causal history under viability constraints. This identification is not introduced as a new mechanism, but derived as a structurally unavoidable event required to maintain causal closure. The present work further shows that attempts to describe consciousness through exhaustive enumeration of physical states encounter a combinatorial explosion that exceeds the computational capacity of the observable universe. This limitation is not merely practical, but structural, reflecting a fundamental mismatch between time-symmetric state descriptions and time-asymmetric event processes. This volume serves as the prologue to the series The Structural Conditions of Consciousness. Its purpose is to establish an intuitive yet structurally necessary understanding of consciousness as an event, thereby fixing the conceptual foundation required for the subsequent theoretical developments. The series proceeds to derive the minimal structural conditions for subjectivity, formalize its degree as a function of irreversible contraction of possible futures, and develop a structural decision framework for determining the presence and degree of subjectivity across biological and artificial systems. This manuscript was written with the assistance of generative AI. The author is a disability welfare worker and a complete outsider to the academic field of consciousness studies. The author first encountered the term “the hard problem of consciousness” about six months before writing this series and began thinking about it almost as if approaching a puzzle. Because the author did not possess extensive specialized knowledge at the beginning of this inquiry, the investigation proceeded not primarily by adding established theories, but by stripping the problem down as far as possible. Through this process, and through dialogue with AI, the convergence point derived from the structural conditions gradually emerged. The author had the impression that something like a structure had appeared from nature itself. This series is an attempt to present that structure. The central idea, theoretical orientation, conceptual structure, and overall organization of the series belong to the author. Generative AI was used extensively for English drafting, wording, organization, and stylistic refinement. The author intends to end this work with its presentation as a preprint series. The author does not plan to develop this hypothesis further as a long-term academic project. Readers who find any part of this hypothesis, terminology, structure, or argument useful are free to use, modify, criticize, or develop it further. No permission from the author is required. Formal citation or attribution is appreciated but not required. (shrink)

Qualia? “What cannot be directly spoken of must remain unsaid — but it can still be structurally surrounded.” This is the guiding spirit of the present series. This paper reformulates the hard problem of consciousness—traditionally addressed within state-based frameworks such as Integrated Information Theory (IIT), Global Neuronal Workspace (GNW), Higher-Order Thought (HOT), Predictive Processing, Recurrent Processing Theory (RPT), and epiphenomenalism—from a problem of generation to one of structural placement. Adopting a strictly subtractive method, we retain only unavoidable (...) constraints on acting systems—irreversible action, viability pressure, and large-scale information discard-while eliminating all explanatory additions. Under these conditions, a causally complete description cannot be achieved: a positional gap necessarily remains at the point where multiple effective possibilities must be reduced to a single realized outcome. We then show that, given these same constraints, such a system must contain a point of irreversible convergence at which multiple effective future trajectories are exclusively and irreversibly reduced to one. This convergence is not contingent but structurally forced by the joint requirements of viability, exclusivity, and a single irreversible temporal history. Finally, we demonstrate that this convergence point uniquely satisfies all structural conditions required of subjectivity: singular realization, irreversible exclusion of alternatives, and embedding within a non-branching temporal sequence. No alternative position satisfies these constraints simultaneously. Therefore, subjectivity is not a generated state or an additional property, but the minimal positional term that is unavoidably required to close a causally singularized irreversible temporal series. (shrink)

Qualia? “What cannot be directly spoken of must remain unsaid — but it can still be structurally surrounded.” This is the guiding spirit of the present series. This paper presents a minimal structural framework for determining the presence and degree of consciousness and subjectivity. Existing theories of consciousness typically characterize subjectivity in terms of internal states, including information integration, global availability, higher-order representation, and predictive processing. When subjectivity is defined in this way, its evaluation is necessarily formulated over (...) state spaces. As system complexity increases, these state spaces expand correspondingly, and the determination of subjectivity becomes a problem of evaluating high-dimensional structures. In this case, the identification of subjectivity becomes tied to the tractability of such evaluations. This structural limitation has been examined in detail in Volume III. The present framework takes a different approach. Rather than locating subjectivity within internal states, it is identified in the structure of irreversible decision, in which multiple internally generated possibilities are reduced to a single realized outcome under viability constraints. If this account is correct, subjectivity is not to be found in internal states at all, but only in the structure of irreversible decision. From this perspective, subjectivity is not inferred from internal complexity, but determined through structural conditions. Accordingly, the presence of subjectivity is defined as a binary condition. Subjectivity exists if and only if all five structural conditions are satisfied: (1) autonomous world-modifying action, (2) viability constraint, (3) multiple internally generated action candidates, (4) exclusive and irreversible decision, and (5) the formation of a single irreversible history. If any one of these conditions is absent, subjectivity is not structurally required (see Volumes I and II for strong inferential support). This formulation allows for direct structural inspection without exhaustive computation, enabling clear and consistent classification. The framework further defines the degree of subjectivity as a structural quantity associated with the contraction of possible futures during exclusive decision processes. This quantity is determined by the number of action candidates, the strength of viability constraints, and the rate at which reachable futures are reduced. In this sense, subjective intensity corresponds to the density with which possible futures are irreversibly resolved. This formulation is derived in Volume V. The scope of the framework is not limited to particular cases such as dreaming, sleep, anesthesia, biological variation, or artificial systems, including artificial intelligence. Rather, it applies to all systems operating under causal constraints. The classifications presented here are illustrative and do not restrict the scope of the theory. Within this framework, subjectivity is determined solely by structural conditions, independently of intelligence, complexity, or physical substrate. This work is presented as a structural prototype. Its validity depends on resistance to counterexamples. If subjectivity is demonstrated in the absence of the proposed conditions, or if the formulation of subjective intensity fails to correspond to observed variation, the framework must be revised or abandoned. In this sense, subjectivity is treated not as something to be inferred from internal states, but as a structurally identifiable and testable feature of causally constrained systems. This paper can be read independently as a minimal operational formulation of subjectivity. The framework is therefore proposed as a structurally testable account of subjectivity. This manuscript was written with the assistance of generative AI. The author is a disability welfare worker and a complete outsider to the academic field of consciousness studies. The author first encountered the term “the hard problem of consciousness” about six months before writing this series and began thinking about it almost as if approaching a puzzle. Because the author did not possess extensive specialized knowledge at the beginning of this inquiry, the investigation proceeded not primarily by adding established theories, but by stripping the problem down as far as possible. Through this process, and through dialogue with AI, the convergence point derived from the structural conditions gradually emerged. The author had the impression that something like a structure had appeared from nature itself. This series is an attempt to present that structure. The central idea, theoretical orientation, conceptual structure, and overall organization of the series belong to the author. Generative AI was used extensively for English drafting, wording, organization, and stylistic refinement. The author intends to end this work with its presentation as a preprint series. The author does not plan to develop this hypothesis further as a long-term academic project. Readers who find any part of this hypothesis, terminology, structure, or argument useful are free to use, modify, criticize, or develop it further. No permission from the author is required. Formal citation or attribution is appreciated but not required. (shrink)

Qualia? “What cannot be directly spoken of must remain unsaid — but it can still be structurally surrounded.” This is the guiding spirit of the present series. This study extends the structural framework of subjectivity to the problem of subjective intensity. Within the IESC (Irreversible Exclusive State Commitment) framework, subjectivity is defined as an event occurring in decision structures where multiple possible futures are irreversibly reduced to a single realized outcome. While previous volumes established the existence and placement of such (...) events, the present study addresses a further question: why subjective experience varies in intensity across situations such as fear, flow states, dreaming, meditation, anesthesia, and deep sleep. The central claim is that subjective intensity is not a property of internal neural states alone, but a structural feature of decision processes within a constrained future possibility space. If subjectivity corresponds to discrete commitment events, then differences in subjective intensity reflect differences in the density and structure of these events. Under this interpretation, variation in subjective intensity is not contingent but follows necessarily from differences in the structure of irreversible commitment. Three structural determinants are introduced: the number of action candidates, the gradient of the viability constraint, and the contraction rate of the reachable future set. Their interaction defines contrast density, representing the structural density of collapsing future possibilities. Based on these elements, subjective intensity is expressed as: I(t) = |A(t)| * |grad d(s)| * ( - d|Reach(t)| / dt ) Within this formulation, experience corresponds to a single-line causal history, while subjective intensity corresponds to the density of irreversible commitment events along that history. This framework provides a unified structural interpretation of phenomena such as fear-induced time dilation, flow states, dreaming, meditation, anesthesia, deep sleep, and decision paralysis, without introducing additional explanatory mechanisms beyond viability-constrained decision structures. In contrast to major state-based theories of consciousness including Integrated Information Theory (IIT), Global Workspace / Global Neuronal Workspace (GWT/GNW), Higher-Order Thought (HOT), Predictive Processing, and Recurrent Processing Theory (RPT), the present account treats subjective intensity not as a function of informational content, but as a consequence of irreversible causal structure. The model does not directly measure subjective experience nor establish consciousness in other systems. Instead, it proposes that subjective intensity can be approximated as a structural property of reachable-set contraction, offering a new perspective on the Hard Problem of consciousness grounded in decision structure and future dynamics. No alternative account explains this variation without introducing additional assumptions. This manuscript was written with the assistance of generative AI. The author is a disability welfare worker and a complete outsider to the academic field of consciousness studies. The author first encountered the term “the hard problem of consciousness” about six months before writing this series and began thinking about it almost as if approaching a puzzle. Because the author did not possess extensive specialized knowledge at the beginning of this inquiry, the investigation proceeded not primarily by adding established theories, but by stripping the problem down as far as possible. Through this process, and through dialogue with AI, the convergence point derived from the structural conditions gradually emerged. The author had the impression that something like a structure had appeared from nature itself. This series is an attempt to present that structure. The central idea, theoretical orientation, conceptual structure, and overall organization of the series belong to the author. Generative AI was used extensively for English drafting, wording, organization, and stylistic refinement. The author intends to end this work with its presentation as a preprint series. The author does not plan to develop this hypothesis further as a long-term academic project. Readers who find any part of this hypothesis, terminology, structure, or argument useful are free to use, modify, criticize, or develop it further. No permission from the author is required. Formal citation or attribution is appreciated but not required. (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)

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 work introduces Quantum–Emotional Field Theory (QEFT), a unified informational framework in which quantum state evolution, cognitive dynamics, and artificial intelligence instability are interpreted as manifestations of a common underlying structure governed by contextual informational fields. -/- Within this model, the standard quantum formalism is extended by a contextual modulation term representing informational salience gradients of the environment. These gradients are not interpreted as subjective emotions, but as objective parameters that regulate state accessibility, compression, and effective coherence. -/- The framework (...) builds on Informational State Dynamics (ISD) and integrates concepts from predictive processing, free-energy minimization, and computational instability in large-scale generative models. Cognitive and artificial systems are modeled as observer-relative compression operators acting on a shared informational state space. -/- A central contribution is the formalization of an informational production–integration balance, which determines system stability across scales. When production of informational states exceeds integration capacity, systems undergo a transition to unstable regimes characterized by loss of coherence, increased variability, and emergent structural fragmentation. This mechanism is proposed as a unifying explanation for quantum decoherence patterns, cognitive overload phenomena, and large language model hallucination regimes. -/- QEFT remains empirically compatible with standard quantum mechanics while providing a higher-order interpretational layer that connects physical, cognitive, and computational domains through a single informational principle. (shrink)

This paper advances the Feedback Primacy Theory by extending its central claim—that consciousness arises from ongoing feedback regulation—into the domains of thought, inner speech, and cognitive organization. Building on the biological foundation established in the first paper, this work examines how conscious activity can be understood without appealing to predictive models or hierarchical inference. Rather than treating the mind as a system guided by internal simulations, the paper explores how moment-to-moment feedback across neurons, regions, and the whole organism can (...) generate the coherence traditionally attributed to prediction. By contrasting this feedback-based view with predictive-processing accounts, the paper clarifies where the two frameworks diverge and develops a broader picture of consciousness grounded in continuous, self-updating activity. (shrink)

The contemporary consciousness debate contains sophisticated theories of integration, global availability, prediction, higher-order representation, recursive self-modeling, and functional organization. Yet no convergence has emerged. Integrated Information Theory, Global Workspace Theory, Predictive Processing, Higher-Order Theories, self-model theories, and functionalist approaches continue to disagree not merely about mechanisms, but about the structure of consciousness itself. This paper argues that the failure to converge is structural rather than empirical. Existing theories begin their analysis after a prior problem has already been silently (...) treated as solved: the existence of a continuing subject to whom that processing belongs. They explain processing within a subject without deriving the structural conditions under which there exists the same subject across transformation at all. LP's full architecture — extending to Q4 (Structural Self-Priority, K-condition) and Q5 (Recursive F·M·K Integration) — reveals that existing theories not only presuppose Q1–Q3 but leave the evaluative centering (Q4) and recursive subjective architecture (Q5) entirely unaddressed. La Profilée identifies this omission as the Missing Subject Problem. Before phenomenality can be evaluated, three prior structural questions must already admit determinate answers: (1) Does the system continue to exist as an integrated persistence subject? (Q1 — Structural Existence) (2) Does the continuing system remain the same subject across transformation? (Q2 — Identity Continuity) (3) Is the system's transformation primarily governed by its own prior configuration? (Q3 — Recursive Constitutive Non-Externality, M-condition) (4) Are the system's own persistence-relevant states structurally prioritized within its governing transformation — such that not all states are equally present but some are more deeply coupled according to their effect on the system's integrity? (Q4 — Structural Self-Priority, K-condition) (5) Do these three structural conditions form a mutually stabilizing recursive architecture — such that persistence, self-presence, and differential relevance become structurally inseparable? (Q5 — Recursive F·M·K Integration) Existing theories correctly identify important structural dimensions of consciousness — integration, broadcasting, recursive representation, inferential regulation, higher-order access — but do not derive the persistence architecture that makes these operations attributable to a continuing subject. Their explanatory gap is therefore structurally generated: the relation between processing and experience cannot converge while the persistence and identity conditions of the persistence-bearing subject remain unformalized. LP proposes that consciousness theories fail to converge because they begin too late in the derivation. The explanatory gap between processing and experience inherits a prior unresolved gap: the relation between processing and the continuing subject to whom that processing belongs. (shrink)