Michał Piekarski

The book series “Polish Christian Philosophy in the 20th Century” comprises monographs on the thought of prominent representatives of this philosophical tradition, active primarily in the 20th century and at the beginning of the 21st century. A separate volume is devoted to each philosopher, organized as follows: a biography and bibliography of the author, sources of philosophical inspiration (the influence of their own philosophical school, borrowings from other systems), the author’s preferred philosophical concept, the problems addressed and proposed solutions, a glossary of basic terms, and the polemics and discussions the author engaged in both domestically and internationally. Additionally, each volume is supplemented by an anthology of philosophical texts representative of the author. This volume is dedicated to Jan Krokos—a philosopher of knowledge, phenomenologist and epistemologist.

Predictive Processing (PP) is commonly described as a mechanism sketch—an incomplete, primarily mechanistic representation of cognitive processes. While this characterization rightly emphasizes PP's structural and causal explanatory aspects, I argue that it tends to overlook important functional and normative dimensions that are essential for a comprehensive account of cognitive processes. In response, I claim that PP, while providing mechanism sketches in the standard sense, simultaneously performs a second, normative-functional epistemic role. This expanded view on PP preserves the value of its mechanistic explanation while clarifying the essential role of functional constraints in shaping predictive models. Specifically, I show how PP models postulate explanatory constraints—formal, structural, and functional—that are inherently normative, as they specify the conditions viable mechanisms must satisfy to be biologically plausible and adaptive. Drawing on Levenstein et al. (2023), I situate this account within a pragmatic framework that recognizes the legitimacy of mechanistic, normative, and descriptive theories without reducing one to another. This pluralistic approach enables genuine integration across explanatory levels and points toward more adequate future models in cognitive neuroscience.

This paper proposes a reinterpretation of predictive coding (PC) in terms of heterarchical networks of control mechanisms. Standard accounts of PC typically assume a rigid hierarchical organization, in which information flows in fixed directions across levels of abstraction. Such a framework has, how-ever, faced growing theoretical criticism and appears increasingly difficult to reconcile with recent re-search on predictive routing, which suggests that predictive systems exhibit greater flexibility than hi-erarchical models allow. In response to these challenges, we propose integrating work on PC with both complex network theory and William Bechtel’s conception of control mechanisms. Our central hypothesis is that predictive systems can be understood as emergent patterns arising from the interac-tion between hierarchical production mechanisms and heterarchical networks of control mechanisms. On this view, local computational hierarchies are dynamically modulated by flexible contextual con-straints, thereby preserving the advantages of hierarchical models while avoiding their rigidity. The paper is theoretical in nature and highlights the need for empirical validation of the proposed frame-work, as well as for a systematic integration of network methodologies with research on predictive processing.

This paper examines the recently proposed phenomenological taxonomy of altered states of consciousness (Cardeña et al., 2025). While this taxonomy provides valuable phenomenological organization, I argue that it lacks the explanatory depth necessary to address fundamental questions: why such states occur, how different inductions lead to similar phenomenology, and what adaptive functions they may serve (though evolutionary evidence remains limited). In response, I outline an integration of phenomenological taxonomy with the REBUS (Relaxed Beliefs Under Psychedelics) model - a mechanistic framework that explains psychedelic effects through reduced precision of high-level beliefs in hierarchical predictive processing, combined with entropic brain theory. I contend that such integration offers both mechanistic explanations (through precision-weighted modulation in hierarchical predictive processing) and normative explanations (adaptive belief revision in therapeutic contexts), thereby enhancing the taxonomy's explanatory power. While I focus on REBUS as an illustrative framework, the proposed integration approach applies to other mechanistic models as well. Crucially, however, this integration requires mutual constraints: detailed phenomenological descriptions should test mechanistic and functional explanations, while the latter should inform phenomenological classification, enabling comprehensive explanation that extends beyond mere description.

This paper examines the role of predictive mechanisms in explaining normativity by engaging critically with Mark Bickhard’s argument that mechanisms alone are insufficient for naturalizing normative functions. While Bickhard emphasizes the necessity of asymmetries between system and environment—particularly those sustaining far-from-equilibrium conditions—I argue that the free energy principle provides conceptual and formal tools to model a mechanistic layer of normativity embedded within such asymmetries. By focusing on systems that minimize variational free energy, I show how predictive mechanisms can be understood as normatively organized insofar as they contribute to the system’s adaptive functioning and stability. Rather than opposing Bickhard’s view, the paper proposes a complementary account: normativity arises both from the organism’s relation to its environment and from the internal causal architecture that supports anticipatory, representational, and action-guiding capacities. On this view, mechanisms are not sufficient in an ontological sense, but they can be explanatorily sufficient for modeling how normativity operates within living systems.

This paper examines the role of predictive mechanisms in explaining normativity by engaging critically with Mark Bickhard’s argument that mechanisms alone are insufficient for naturalizing normative functions. While Bickhard emphasizes the necessity of asymmetries between system and environment—particularly those sustaining far-from-equilibrium conditions—I argue that the free energy principle provides conceptual and formal tools to model a mechanistic layer of normativity embedded within such asymmetries. By focusing on systems that minimize variational free energy, I show how predictive mechanisms can be understood as normatively organized insofar as they contribute to the system’s adaptive functioning and stability. Rather than opposing Bickhard’s view, the paper proposes a complementary account: normativity arises both from the organism’s relation to its environment and from the internal causal architecture that supports anticipatory, representational, and action-guiding capacities. On this view, mechanisms are not sufficient in an ontological sense, but they can be explanatorily sufficient for modeling how normativity operates within living systems.

The purpose of this article is to present Andrzej Lewicki’s account of cognition as orientation in the environment, comparing it with James J. Gibson’s ecological psychology. To do so, we conduct a comparative analysis of the former’s theory of indicators and the latter’s theory of affordance. The theoretical frame for our study is cognitive ecology, a research tradition characteristic of various studies of cognition, including contemporary ones. This allows us to show that, despite differences in the backgrounds and methodologies of these researchers, Lewicki can be considered one of the pioneers of contemporary ecological trends in cognitive science, although his influence has not been as widespread as that of Gibson. Our analysis proceeds in several steps. We begin with an overview of the biographies, backgrounds, and interests of the two researchers, as well as a brief introduction to cognitive ecology and related terms. Next, we discuss action/value indicators theory and affordances theory. We then compare Lewicki’s and Gibson’s approaches in more detail in terms of their use of similar research heuristics. The article ends with conclusions that go beyond historical issues.

Niniejszy zbiór tekstów poświęconych zagadnieniu poznania rozproszonego składa się z siedmiu artykułów napisanych przez naukowców z różnych środowisk akademickich w Polsce i różnych dyscyplin. Jego celem jest wywołanie interdyscyplinarnej dyskusji na temat kontrowersji związanych z filozoficznie rozwiniętą koncepcją poznania rozproszonego Witolda Wachowskiego, pojęciem afordancji oraz tzw. heurystyką ekologiczną. Książka Wachowskiego, Poznanie rozproszone. Od heurystyk do mechanizmów (2022), stanowi naturalny punkt wyjścia ze względu na wysiłek teoretyczny podjęty przez jej autora.

The paper is a discussion of the book by Witold Wachowski entitled Poznanie rozproszone: Od heurystyk do mechanizmów (Distributed Cognition: From Heuristics to Mechanisms). At the beginning, I introduce the theoretical context of the reviewed publication. Then, I discuss the content of the publication and the research thesis. In the further part, I draw attention to certain difficulties related to the application of ecological heuristics and the possibility of its connection with the research tradition developed on the basis of the so-called free energy principle. In the last part, I indicate the importance of the discussed publication for the contemporary debateconducted on the basis of the philosophy of cognitive science.

The issue of the relationship between predictive processing (PP) and the free energy principle (FEP) remains a subject of debate and controversy within the research community. Many researchers have expressed doubts regarding the actual integration of PP with the FEP, questioning whether the FEP can truly contribute significantly to the mechanistic understanding of PP or even undermine such integration altogether. In this paper, I present an alternative perspective. I argue that, from the viewpoint of the constraint-based mechanisms approach, the FEP imposes an important constraint, namely variational free energy, on the mechanistic architecture proposed by PP. According to the constraint-based mechanisms approach, high-level cognitive mechanisms are integral parts of extensive heterarchical networks that govern the physiology and behavior of agents. Consequently, mechanistic explanations of cognitive phenomena should incorporate constraints and flows of free energy as relevant components, given that the implemented constraints operate as long as free energy is available. Within this framework, I contend that the FEP provides a relevant constraint for explaining at least some biological cognitive mechanisms described in terms of Bayesian generative models that minimize prediction errors.

In this paper, I attempt to address some of the themes of Ludwig Wittgenstein’s Tractatus logico-philosophicus with the aim of their deconstructionist interpretation. My analysis is based on David Gunkel’s book Deconstruction (MIT Press 2021). Based on some of its findings, I show how the Tractatus allows deconstruction and its practice to be thought. I show that the graphic structure of signs is crucial for the young Wittgenstein’s analysis and that it justifies the metaphysical findings in favor of which he argues. ------------------------- Received:11/09/2022. Reviewed: 17/11/2022. Accepted: 1/12/2022.

Celem niniejszych rozważań jest zarysowanie nowego podejścia w analizowaniu, czym jest normatywność. Po pierwsze wskazuję na miejsce problemu normatywności we współczesnej filozofii, koncentrując się sporze pomiędzy naturalizmem i antynaturalizmem. Następnie omawiam tzw. odgórną drogę w badaniu normatywności, którą przeciwstawiam inspirowanej przez współczesną filozofię nauki, drodze oddolnej. W integracji podejścia odgórnego i oddolnego upatruję możliwość takiego badania zjawisk normatywnych, które nie będzie ich redukowało do jednego z rodzajów normatywności.

The aim of the present investigation is to sketch a new approach to analysing normativity. First I locate the problem of normativity in the landscape of contemporary philosophy and focus on the dispute between naturalism and antinaturalism. Then I discuss the so-called top-down approach to studying normativity, to which I oppose the bottom-up approach inspired by contemporary philosophy of science. I see the integration of these approaches as enabling investigations of normative phenomena that do not reduce them to just one type of normativity.

The article is an attempt at explaining the category of logical form used by Ludwig Wittgenstein in his Tractatus logico-philosophicus by using concepts from Gottfried Wilhelm Leibniz’s The Monadology. There are many similarities and analogies between those works, and the key concept for them is the category of the inner and acknowledged importance of consideration based on basic categories of thinking about the world. The Leibnizian prospect allows for a broader look at Wittgenstein’s analysis of the relation between propositions and facts, between language and the world. Using the Hanoverian philosopher’s terminology allows for the demonstration of the ambivalence of the concept of logical form in the philosophy of Wittgenstein and also the metaphysical nature of his first book.

The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by a specific theoretical goal associated with the need to indicate those conditions that should be met by the non-trivial theory of normative mechanisms and the specific models proposed by PP supporters. In this work, I use classical philosophical methods, such as conceptual analysis and critical reflection. I also analyze selected studies in the field of cognitive science, cognitive psychology, neurology, information theory and biology in terms of the methodology, argumentation and language used, in accordance with their theoretical importance for the issues discussed in this study. In this sense, the research presented here is interdisciplinary. My research framework is informed by the mechanistic model of explanation, which defines the necessary and sufficient conditions for explaining a given phenomenon. The research methods I chose are therefore related to the problems that I intend to solve. In the introductory chapter, “The concept of predictive processing”, I discuss the nature of PP as well as its main assumptions and theses. I also highlight the key concepts and distinctions for this research framework. Many authors argue that PP is a contemporary version of Kantianism and is exposed to objections similar to those made against the approach of Immanuel Kant. I discuss this thesis and show that it is only in a very general sense that the PP framework is neo-Kantian. Here we are not dealing with transcendental deduction nor with the application of transcendental arguments. I argue that PP is based on reverse engineering and abduction inferences. In the second part of this chapter, I respond to the objection formulated by Dan Zahavi, who directly accuses this research framework of anti-realistic consequences. I demonstrate that the position of internalism, present in the so-called conservative PP, does not imply anti-realism, and that, due to the explanatory role played in it by structural representations directed at real patterns, it is justified to claim that PP is realistic. In this way, I show that PP is a non-trivial research framework, having its subject, specific methods and its own epistemic status. Finally, I discuss positions classified as the socalled radical PP. In the chapter “Predictive processing as a Bayesian explanatory model” I justify the thesis according to which PP offers Bayesian modeling. Many researchers claim that the brain is an implemented statistical probabilistic network that is an approximation of the Bayesian 7 rule. In practice, this means that all cognitive processes are to apply Bayes' rule and can be described in terms of probability distributions. Such a solution arouses objections among many researchers and is the subject of wide criticism. The purpose of this chapter is to justify the thesis that Bayesian PP is a non-trivial research framework. For this purpose, I argue that it explains certain phenomena not only at the computational level described by David Marr, but also at the level of algorithms and implementation. Later in this chapter I demonstrate that PP is normative modeling. Proponents of the use of Bayesian models in psychology or decision theory argue that they are normative because they allow the formulation of formal rules of action that show what needs to be done to make a given action optimal. Critics of this approach emphasize that such thinking about the normativity of Bayesian modeling is unjustified and that science should shift from prescriptive to descriptive positions. In a polemic with Shira Elqayam and Jonathan Evans (2011), I show that the division they propose into prescriptivism and Bayesian descriptivism is apparent, because, as I argue, there are two forms of prescriptivism, i.e. the weak and the strong. I argue that the weak version is epistemic and can lead to anti-realism, while the strong version is ontic and allows one to justify realism in relation to Bayesian models. I argue that a weak version of prescriptivism is valid for PP. It allows us to adopt anti-realism in relation to PP. In practice, this means that you can explain phenomena using Bayes' rule. This does not, however, imply that they are Bayesian in nature. However, the full justification of realism in relation to the Bayesian PP presupposes the adoption of strong prescriptivism. This position assumes that phenomena are explained by Bayesian rule because they are Bayesian as such. If they are Bayesian in nature, then they should be explained using Bayesian modeling. This thesis will be substantiated in the chapters “Normative functions and mechanisms in the context of predictive processing” and “Normative mechanisms and actions in predictive processing”. In the chapter “The Free Energy Principle in predictive processing”, I discuss the Free Energy Principle (hereinafter FEP) formulated by Karl Friston and some of its implications. According to this principle, all biological systems (defined in terms of Markov blankets) minimize the free energy of their internal states in order to maintain homeostasis. Some researchers believe that PP is a special case of applying this principle to cognition, and that predictive mechanisms are homeostatic mechanisms that minimize free energy. The discussion of FEP is important due to the fact that some authors consider it to be important for explanatory purposes and normative. If this is the case, then FEP turns out to be crucial in explaining normative predictive mechanisms and, in general, any normative biological mechanisms. To define the explanatory possibilities of this principle, I refer to the discussion of its supporters on the issue they define as the problem of continuity and discontinuity between life and mind. A critical analysis of this discussion and the additional arguments I have formulated have allowed me to revise the explanatory ambitions of FEP. I also reject the belief that this principle is necessary to explain the nature of predictive mechanisms. I argue that the principle formulated and defended by Friston is an important research heuristic for PP analysis. 8 In the chapter “Normative functions and mechanisms in predictive processing”, I start my analyzes by formulating an answer to the question about the normative nature of homeostatic mechanisms. I demonstrate that predictive mechanisms are not homeostatic. I defend the view that a full explanation of normative mechanisms presupposes an explanation of normative functions. I discuss the most important proposals for understanding the normativity of a function, both from a systemic and teleosemantic perspective. I conclude that the non-trivial concept of a function must meet two requirements which I define as explanatory and normative. I show that none of the theories I have invoked satisfactorily meets both of these requirements. Instead, I propose a model of normativity based on Bickhard's account, but supplemented by a mechanistic perspective. I argue that a function is normative when: (1) it allows one to explain the dysfunction of a given mechanism; (2) it contributes to the maintenance of the organism's stability by shaping and limiting possible relations, processes and behaviors of a given system; and when (3) (according to the representational and predictive functions) it enables explaining the attribution of logical values of certain representations / predictions. In such an approach, a mechanism is normative when it performs certain normative functions and when it is constitutive for a specific action or behavior, despite the fact that for some reason it cannot realize it either currently or in the long-term. Such an understanding of the normativity of mechanisms presupposes the acceptance of the epistemic hypothesis. I argue that this hypothesis is not cognitively satisfactory, and therefore the ontic hypothesis should be justified, which is directly related to adopting the position of ontic prescriptivism. For this reason, referring to the mechanistic theory of scientific explanations, I formulate an ontical interpretation of the concept of a normative mechanism. According to this approach, a mechanism or a function is normative when they perform such and such causal roles in explaining certain actions and behaviors. With regard to the normative properties of predictive mechanisms and functions, this means that they are the causes of specific actions an organism carries out in the environment. In this way, I justify the necessity of accepting the ontic hypothesis and rejecting the epistemic hypothesis. The fifth chapter, “Normative mechanisms and actions in predictive processing”, is devoted to the dark room problem and the related exploration-exploitation trade-off. A dark room is the state that an agent could be in if it minimized the sum of all potential prediction errors. I demonstrate that, in accordance with the basic assumption of PP about the need for continuous and long-term minimization of prediction errors, such a state should be desirable for the agent. Is it really so? Many authors believe it is not. I argue that the test of the value of PP is the possibility of a non-trivial solution of this problem, which can be reduced to the choice between active and uncertainty-increasing exploration and safe and easily predictable exploitation. I show that the solution proposed by PP supporters present in the literature does not enable a fully satisfactory explanation of this dilemma. Then I defend the position according to which the full explanation of the normative mechanisms, and, subsequently, the solution to the dilemma of exploration and exploitation, involves reference to the existence of constraints present in the environment. The constraints 9 include elements of the environment that make a given mechanism not only causal but also normative. They are therefore key to explaining the predictive mechanisms. They do not only play the role of the context in which the mechanism is implemented, but, above all, are its constitutive component. I argue that the full explanation of the role of constraints in normative predictive mechanisms presupposes the integration of individual models of specific cognitive phenomena, because only the mechanistic integration of PP with other models allows for a non-trivial explanation of the nature of normative predictive mechanisms that would have a strong explanatory value. The explanatory monism present in many approaches to PP makes it impossible to solve the problem of the dark room. Later in this chapter, I argue that the Bayesian PP is normative not because it enables the formulation of such and such rules of action, but because the predictive mechanisms themselves are normative. They are normative because they condition the choice of such and such actions by agents. In this way, I justify the hypothesis that normative mechanisms make it possible to explain the phenomenon of agent motivation, which is crucial for solving the dark room problem. In the last part of the chapter, I formulate the hypothesis of distributed normativity, which assumes that the normative nature of certain mechanisms, functions or objects is determined by the relations into which these mechanisms, functions or objects enter. This means that what is normative (in the primary sense) is the relational structure that constitutes the normativity of specific items included in it. I suggest that this hypothesis opens up many areas of research and makes it possible to rethink many problems. In the “Conclusion”, I summarize the results of my research and indicate further research perspectives.

The aim of this paper is to present the ontic approach to the normativity of cognitive functions and mechanisms, which is directly related to the understanding of biological normativity in terms of normative mechanisms. This approach assumes the hypothesis that cognitive processes contain a certain normative component independent of external attributions and researchers’ beliefs. This component consists of specific cognitive mechanisms, which I call normative. I argue that a mechanism is normative when it constitutes given actions or behaviors of a system. More precisely, it means that, on the one hand, it is their constitutive cause, and on the other hand, it determines a certain field of possibilities from which the system, guided by its own goals, preferences, environmental constraints, etc., chooses the appropriate action or behavior according to a given situation. The background for the analyses presented here is the predictive processing framework, in which it can be shown that at least some of the predictive mechanisms are in fact normative mechanisms. I refer here to the existence of a motivational relation which determines the normative dependence of the agent’s actions due to specific predictions and environmental constraints.

In this article I discuss the thesis put forward by David Gunkel and Mark Coeckelbergh in their essay Facing Animals:A Relational, Other-Oriented Approach to Moral Standing. The authors believe that the question about the status of animals needs to be reconsidered. In their opinion, traditional attempts to justify the practice of ascribing rights to animals have been based on the search for what is common to animals and people. This popular conviction rests on the intuition according to which we tend to treat better those beings that are closer to us and resemble man in one way or the other. The attempts to ascribe a special status to animals are therefore based on the question “What properties does the animal have?”. However, the question is not well formulated because it leads to a number of ontological and epistemological problems. The question should rather be “What are the conditions under which an entity becomes a moral subject?”. Whilst fully subscribing to the suggestion, I cannot agree to the way the question is understood by both authors. I will demonstrate that the question opens up a transcendental dimension of reflections and may provide a clear justification of the need to engage in animal ethics. To do so, I will separate the easy and hard problems of animal ethics and use a different approach from the one suggested by Gunkel and Coeckelbergh to demonstrate how the need to pursue animal ethics may be justified.