The paper is dedicated to particular cases of interaction and mutual impact of philosophy and cognitive science. Thus, philosophical preconditions in the middle of the 20th century shaped the newly born cognitive science as mainly based on conceptual and propositional representations and syntactical inference. Further developments towards neural networks and statistical representations did not change the prejudice much: many still believe that network models must be complemented with some extra tools that would account for proper human cognitive (...) traits. I address some real implemented connectionist models that show how ‘new associationism ’ of the neural network approach may not only surpass Humean limitations, but, as well, realistically explain abstraction, inference and prediction. Then I stay on Predictive Processing theories in a little more detail to demonstrate that sophisticated statistical tools applied to a biologically realist ontology may not only provide solutions to scientific problems or integrate different cognitive paradigms but propose some philosophical insights either. To conclude, I touch on a certain parallelism of Predictive Processing and philosophical inferentialism as presented by Robert Brandom. (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 (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)

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. (shrink)

How to account for the power that art holds over us? Why do artworks touch us deeply, consoling, transforming or invigorating us in the process? In this paper, we argue that an answer to this question might emerge from a fecund framework in cognitive science known as predictive processing (a.k.a. active inference). We unpack how this approach connects sense-making and aesthetic experiences through the idea of an ‘epistemic arc’, consisting of three parts (curiosity, epistemic action and aha (...) experiences), which we cast as aspects of active inference. We then show how epistemic arcs are built and sustained by artworks to provide us with those satisfying experiences that we tend to call ‘aesthetic’. Next, we defuse two key objections to this approach; namely, that it places undue emphasis on the cognitive component of our aesthetic encounters—at the expense of affective aspects—and on closure and uncertainty minimization (order)—at the expense of openness and lingering uncertainty (change). We show that the approach offers crucial resources to account for the open-ended, free and playful behaviour inherent in aesthetic experiences. The upshot is a promising but deflationary approach, both philosophically informed and psychologically sound, that opens new empirical avenues for understanding our aesthetic encounters. -/- This article is part of the theme issue ‘Art, aesthetics and predictive processing: theoretical and empirical perspectives’. (shrink)

This paper considers the question of whether the influential distinction between levels of explanation introduced by David Marr can be used as a general framework for contemplating levels of explanation in cognitive sciences. Marr introduced three levels at which we can explain cognitive processes: the computational, algorithmic, and implementational levels. Some argue that Marr’s levels of explanation can only be applied to modular cognitive systems. However, since many psychological processes are non-modular, it seems that Marr’s levels of (...) explanation cannot explain such psychological processes. To show that the latter claim is not convincing, the paper draws upon an influential paradigm from cognitive sciences that is based on the principle of free energy. Based on this paradigm, the paper argues that even non-modular psychological processes can be computationally analyzed and algorithmically implemented. The conclusion of the paper is that, at least under the assumption that the func- tion of the mind is to minimize free energy, Marr’s levels of explanation can be successfully used as a general framework for understanding psychological processes at different levels of description. (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)

Philosophers interested in the theoretical consequences of predictive processing often assume that predictive processing is an inferentialist and representationalist theory of cognition. More specifically, they assume that predictive processing revolves around approximated Bayesian inferences drawn by inverting a generative model. Generative models, in turn, are said to be structural representations: representational vehicles that represent their targets by being structurally similar to them. Here, I challenge this assumption, claiming that, at present, it lacks an adequate justification. I examine (...) the only argument offered to establish that generative models are structural representations, and argue that it does not substantiate the desired conclusion. Having so done, I consider a number of alternative arguments aimed at showing that the relevant structural similarity obtains, and argue that all these arguments are unconvincing for a variety of reasons. I then conclude the paper by briefly highlighting three themes that might be relevant for further investigation on the matter. (shrink)

In this paper I return to Hubert Dreyfus’ old but influential critique of artificial intelligence, redirecting it towards contemporary predictive processing models of the mind (PP). I focus on Dreyfus’ arguments about the “frame problem” for artificial cognitive systems, and his contrasting account of embodied human skills and expertise. The frame problem presents as a prima facie problem for practical work in AI and robotics, but also for computational views of the mind in general, including for PP. Indeed, (...) some of the issues it presents seem more acute for PP, insofar as it seeks to unify all cognition and intelligence, and aims to do so without admitting any cognitive processes or mechanisms outside of the scope of the theory. I contend, however, that there is an unresolved problem for PP concerning whether it can both explain all cognition and intelligent behavior as minimizing prediction error with just the core formal elements of the PP toolbox, and also adequately comprehend (or explain away) some of the apparent cognitive differences between biological and prediction-based artificial intelligence, notably in regard to establishing relevance and flexible context-switching, precisely the features of interest to Dreyfus’ work on embodied indexicality, habits/skills, and abductive inference. I address several influential philosophical versions of PP, including the work of Jakob Hohwy and Andy Clark, as well as more enactive-oriented interpretations of active inference coming from a broadly Fristonian perspective. (shrink)

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

4Es approaches to cognition draw an unconventional picture of cognitive processes and of the mind. Instead of conceiving of cognition as a process that always takes place within the boundaries of the skull and the skin, these approaches hold that cognition is a situated process that often extends beyond human agents’ physical boundaries. In particular, supporters of the extended mind theory and of the enactive approach claim that embodied action in a perceptually complex environment is constitutive of cognitive (...) processes, and thus of the mind. (Chapter 1). Although both draw an extended picture of cognition and of the mind, supporters of the extended mind and supporters of the enactive approach disagree on the value of internal representations in perceptually-guided action. The extended mind theory holds that we need to posit internal action-oriented representations (AORs). AORs are valuable -the argument goes- because they account for action-selection, action-control, and for the prediction of incoming perceptual information. Advocates of the enactive approach to cognition, instead, argue against AORs. Firstly, they argue that AORs do not fulfill the necessary conditions for the identification of representational entities. Therefore, that of AOR is an ill-conceived epistemic construct. Secondly, they argue that AORs are expressive of an internalist prejudice, which makes the extended mind theory weaker than it should be from an explanatory and metaphysical point of view. I endorse both these arguments advanced by supporters of the enactive approach. Moreover, through a semiotic analysis of the construct and of the workings of AORs, I show that AORs are not active at all. Hence, I argue that AORs do not do the job they are supposed to do: extending the mind’s boundaries by explaining our perceptual relation with the world as an active, embodied, and world-involving process. Therefore, I suggest that the construct of AOR ought to be dismissed - at least in current projects in 4Es cognition (Chapter 2). I argue that similar arguments apply to recent attempts to make predictive processing accounts of perceptually-guided action more aligned with 4Es approaches to cognitive science by appealing to predictive internal models of our perceptual world. I thus propose an alternative explanation of the key features of perceptually-guided action that AORs and these internal models are supposed to be grasp. Action-control, action-selection, and the anticipatory aspects of action-perception loops can be explained by appealing to embodied actions in a field of relevant affordances, which are perceived as such based on the agent’s motor skills and motives. Furthermore, I suggest that, by combining the enactive approach to cognition with an analysis of the indexical features of our perceptual environment, we can explain perceptually-guided action in a way that truly accounts for our minds as entities that often extend beyond our skulls and skin. And we can do so without holding onto spooking entities like AORs and internal predictive models. (Chapter 3). (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)

Predictive processing theorists have claimed PTSD and depression are maladaptive and epistemically distorting because they entail undesirably wide gaps between top-down models and bottom-up information inflows. Without denying this is sometimes so, the “maladaptive” label carries questionable normative assumptions. For instance, trauma survivors facing significant risk of subsequent attacks may overestimate threats to circumvent further trauma, “bringing forth” concretely safer personal spaces, to use enactive terminology, ensuring the desired gap between predicted worries and outcomes. The violation of predictive (...) processing can go in the opposite direction too, as when depression coincides with energy-depletion, and hence objectively strenuous situations in which things look farther away because they are (accurately anticipated to be) harder to reach. These examples partly encapsulate what predictive processing theorists call “active inference,” yet with differences. In the first case, actions fruitfully obviate predictions rather than fulfilling them. In the second, mental models do not dysregulate bodily processes, making coping harder. Instead, problems (e.g., personal obstacles, gastric illness) deplete energy, eliciting a depressive and adaptive slow-down. Some predictive proponents apply correspondence criteria when alleging mismatches between internal models and the world, while incongruously asserting that the brain did not evolve to see things veridically, but to execute actions. An alternative is to adopt pluralistic, pragmatic epistemologies suited to the complexity of mind. The upshot is that mental outlooks can depart from the norm without epistemically being distorted and that mismatches between anticipatory worries and outcomes, when they actually exist, can be a measure of adaptive and epistemic success. (shrink)

I argue that Sellars’s distinction between signifying and picturing should be taken seriously by philosophers of mind, language, and cognition. I begin with interpretations of key Sellarsian texts in order to show that picturing is best understood as a theory of non-linguistic cognitive representations through which animals navigate their environments. This is distinct from the kind of discursive cognition that Sellars called ‘signifying’ and which is best understood in terms of socio-linguistic inferences. I argue that picturing is required because (...) reflection on signifying cannot adequately explain our need for cognitive friction. I then show how the idea of picturing is further developed by Paul Churchland, Ruth Garrett Millikan, and Huw Price. I finally turn to predictive processing as a theory of cognitive representation, and in particular Andy Clark’s ‘radical predictive processing’, to further characterize picturing. However, doing so has the cost of pushing picturing and signifying further apart than Sellars intended. (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)

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 (...) class='Hi'>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)

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)

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)

In his paper, Jakob Hohwy outlines a theory of the brain as an organ for prediction-error minimization, which he claims has the potential to profoundly alter our understanding of mind and cognition. One manner in which our understanding of the mind is altered, according to PEM, stems from the neurocentric conception of the mind that falls out of the framework, which portrays the mind as “inferentially-secluded” from its environment. This in turn leads Hohwy to reject certain theses of embodied cognition. (...) Focusing on this aspect of Hohwy’s argument, we first outline the key components of the PEM framework such as the “evidentiary boundary,” before looking at why this leads Hohwy to reject certain theses of embodied cognition. We will argue that although Hohwy may be correct to reject specific theses of embodied cognition, others are in fact implied by the PEM framework and may contribute to its development. We present the metaphor of the “body as a laboratory” in order to highlight wha... (shrink)

O presente artigo objetiva fornecer uma apresentação crítica, compreensiva e inédita na língua portuguesa do Processamento Preditivo (PP) – um esquema teórico para a compreensão da cognição que propõe uma inversão de nosso entendimento padrão da ação, percepção, sensação e sua relação. Aqui, nosso objetivo primário será introduzir os principais conceitos e ideias do PP, tratando-o como um modelo moderadamente corporificado de cognição e analisando suas credenciais como uma proposta teórica unificadora. Para tanto, partiremos de uma contextualização histórica de algumas (...) correntes de pensamento que teriam fomentado seu desenvolvimento inicial, passando por uma descrição não-matemática do Princípio da Energia Livre, que fundamenta e subentende a atuação de suas especificidades para, então, esclarecermos o papel que, segundo o PP, a inferência bayesiana, a minimização dos erros de predição e a chamada Inferência Ativa possuiriam na manutenção homeostática de nossos cérebros e corpos preditivos. Por fim, forneceremos uma síntese de algumas consequências daquilo que o PP poderia trazer à compreensão contemporânea do cérebro e comportamento humanos, concluindo que, embora sua descrição da cognição como um processo preditivo único e contínuo prometa eventualmente unificar paradigmas explicativos e níveis de análise distintos, por ora, talvez seja melhor concebê-lo de forma mais modesta, como uma ferramenta ou heurística para nos auxiliar a repensar vários dos tópicos centrais ao estudo científico e filosófico da mente. (shrink)

Studies of visual event individuation often consider people's representations of activities involving agents performing complex tasks. Concomitantly, theories of event individuation emphasize predictions about agents' intentions. Studies that have examined simple, non-agential occurrences leave open the possiblity that principles of visual object individuation play a role in visual event individuation. Unearthing principles that may be sufficient for event individuation which are distinct both from predictions about agents' intentions and from visual object individuation, we draw on and extend studies that reveal (...) object and event representation to be deeply analogous in our cognitive economy. We provide evidence that ‘temporal shaping' is a sufficient low-level perceptual criterion for the visual individuation of events. In our study, temporal shaping is effected by the introduction of pauses into an otherwise continuous process. Future studies should address other visual mechanisms for introducing temporal shaping (e.g., color changes). (shrink)

Within contemporary philosophy of mind, it is taken for granted that externalist accounts of meaning and mental content are, in principle, orthogonal to the matter of whether cognition itself is bound within the biological brain or whether it can constitutively include parts of the world. Accordingly, Clark and Chalmers (1998) distinguish these varieties of externalism as ‘passive’ and ‘active’ respectively. The aim here is to suggest that we should resist the received way of thinking about these dividing lines. With (...) reference to Brandom’s (1994; 2000; 2008) broad semantic inferentialism, we show that a theory of meaning can be at the same time a variety of active externalism. While we grant that supporters of other varieties of content externalism (e.g., Putnam 1975 and Burge 1986) can deny active externalism, this is not an option for semantic inferentialists: On this latter view, the role of the environment (both in its social and natural form) is not ‘passive’ in the sense assumed by the alternative approaches to content externalism. (shrink)

The notions of information, representation and enaction entertain historical and complex relations with cognition. Historical relations because representational structures belong to the central hypothesis of cognitive sciences. Complex relations because cognitive sciences apply the notion of representation to animals, humans and robots, and also because the enactive approach tends to disregard the GOFAI type of representations. In this wide horizon of relations, we propose to look at a systemic approach that could bring up a common denominator for information (...) and representations in the build up of cognition, and also keep a link with the enactive approach. Our purpose is to show that systems submitted to constraints can generate meaningful information to maintain their natures, and consequently build up meaningful representations that have some compatibility with the enactive approach. Such a systemic approach to the notion of meaningful information could then make available a link between enaction and meaningful representations. The first part of the presentation is about reminding that cognition does not exist per se, but is related to the system that builds it. We look at cognition as constituted by dynamic meaningful representations built up by systems that have constraints to satisfy in their environments. Cognition is considered here at the level of the system that builds it and uses it in order to maintain its nature in its environment. Such a systemic approach fits with evolution. Organisms build representations to cope with survival constraints (frogs build representations of moving black dots in order to satisfy food constraints). Humans build representations and cognition to satisfy constraints that are conscious and unconscious. Artificial systems can use representations and cognition to run activities related to constraints implemented by the designers or coming from the environment (a goal to reach being considered as a constraint to satisfy). In the second part of the presentation we define what are a meaningful information and a representation for a system submitted to a constraint in its environment, and we link these to the enactive approach. We define a meaningful information (a meaning) as an information generated by a system submitted to a constraint when it receives an external information that has a connection with the constraint. The meaning is precisely that connection. The meaning belongs to the interactions that link the system to its environment. The function of the meaning is to participate to the determination of an action that will be implemented in order to satisfy the constraint. (Menant, 2003). The satisfaction of the constraint goes with maintaining the nature of the system in its environment. A Meaning Generator System (MGS) is defined correspondingly. It is a building block for higher level systems. We present some characteristics of the MGS (groundings of a meaning, domain of efficiency and transfer of meanings, networking of meanings, evolutionary usage). The MGS approach is close to a simplified version of the Peircean triadic theory of signs (Menant, 2003, 2005 ). We define the representation of an item for a system as being the dynamic set of meaningful information corresponding to the item for the system in its environments (an elementary representation being made of a single meaningful information). These representations link the system to its environment by their meaningful components related to the nature of the system. These representations are different from the GOFAI ones. The possibilities for linking these notions of meaning and representation with the enactive approach come from the structure of the MGS: the need for an action is the cause of the meaning generation by and for the system. The action on the environment is for the system to maintain its nature (its identity). The MGS links together the generation of meaningful representations, the nature of the system, and the interactions with the environment. This can be considered as close to enacting a world by meaning generation (Di Paolo and all 2007), and to the enactive concept of sense making (De Jaegher, Di Paolo 2007). We propose that basing the definition of a representation on the notion of meaningful information generated by a system submitted to a constraint can open a way for making the notion of representation compatible with the enactive approach. In the third part of the presentation, we consider some cases of meaningful information and representations for organisms and for robots. Regarding organisms, the MGS can be used in an evolutionary context by looking at the evolution of the systems and of the constraints. Purpose is to modelize the generation of meanings and of representations in order to make available a tool usable for different levels of evolution, as evolution has a place in cognitive sciences (Proust, 2007). Constraints for basic life are survival constraints (individual and species). Group life constraints are also to be considered. Reaching the level of humans in evolution brings in new constraints that cannot be clearly identified as they have to take into account human consciousness which is today a mystery (the “hard problem”). On an evolutionary standpoint, human constraints come in addition to the ones existing for non human organisms. We can make some hypothesis on the nature of human constraints (Maslow pyramid based constraints, anxiety limitation…). For robots, the MGS is initially based on the design of the robot. The meaning generated within a robot is initially derived from the constraints implemented by the designer and from the environment. But some non calculable or non predictable evolutions of the robot can introduce meanings that look proper to the robot. This last point can be linked to the notion of autonomy in robots. In such examples, the dynamic management of meanings thru the MGSs in their environments keeps the link with the enactive approach. We finish the presentation by summarising the points addressed and by proposing several continuations. (shrink)

This monograph is an in-depth and engaging discourse on the deeply cognitive roots of human scientific quest. The process of making scientific inferences is continuous with the day-to-day inferential activity of individuals, and is predominantly inductive in nature. Inductive inference, which is fallible, exploratory, and open-ended, is of essential relevance in our incessant efforts at making sense of a complex and uncertain world around us, and covers a vast range of cognitive activities, among which scientific exploration constitutes the (...) pinnacle. Inductive inference has a personal aspect to it, being rooted in the cognitive unconscious of individuals, which has recently been found to be of paramount importance in a wide range of complex cognitive processes. One other major aspect of the process of inference making, including the making of scientific inferences, is the role of a vast web of beliefs lodged in the human mind, as also of a huge repertoire of heuristics, that constitute an important component of ‘unconscious intelligence’. Finally, human cognitive activity is dependent in a large measure on emotions and affects, that operate mostly at an unconscious level. Of special importance in scientific inferential activity is the process of hypothesis making, which is examined in this book, along with the above aspects of inductive inference, at considerable depth. The book focuses on the inadequacy of the viewpoint of naive realism in understanding the contextual nature of scientific theories, where a cumulative progress towards an ultimate truth about Nature appears to be too simplistic a generalization. It poses a critique to the commonly perceived image of science which is seen as the last word in logic and objectivity, the latter in the double sense of being independent of individual psychological propensities and, at the same time, approaching a correct understanding of the workings of a mind-independent nature. Adopting the naturalist point of view, it examines the essential tension between the cognitive endeavours of individuals and scientific communities, immersed in belief systems and cultures, on the one hand, and the engagement with a mind-independent reality on the other. In the end, science emerges as an interpretation of nature, which is perceived by us only contextually, as successively emerging cross-sections of limited scope and extent. Successive waves of theory building in science appear as episodic and kaleidoscopic changes in perspective as certain in-built borders are crossed, rather than as a cumulative progress towards some ultimate truth. While written in an informal and conversational style, the book raises a number of deep and intriguing questions located at the interface of cognitive psychology and philosophy of science, meant for both the general lay reader and the specialist. Of particular interest is the way it explores the role of belief (aided by emotions and affects) in making the process of inductive inference possible since belief is a subtle though all-pervasive cognitive factor not adequately investigated in the current literature. (shrink)

Predictive processing theories are increasingly popular in philosophy of mind; such process theories often gain support from the Free Energy Principle (FEP)—a nor- mative principle for adaptive self-organized systems. Yet there is a current and much discussed debate about conflicting philosophical interpretations of FEP, e.g., repre- sentational versus non-representational. Here we argue that these different interpre- tations depend on implicit assumptions about what qualifies (or fails to qualify) as representational. We deploy the Free Energy Principle (FEP) instrumentally to dis- (...) tinguish four main notions of representation, which focus on organizational, struc- tural, content-related and functional aspects, respectively. The various ways that these different aspects matter in arriving at representational or non-representational interpretations of the Free Energy Principle are discussed. We also discuss how the Free Energy Principle may be seen as a unified view where terms that tradition- ally belong to different ontologies—e.g., notions of model and expectation versus notions of autopoiesis and synchronization—can be harmonized. However, rather than attempting to settle the representationalist versus non-representationalist debate and reveal something about what representations are simpliciter, this paper demon- strates how the Free Energy Principle may be used to reveal something about those partaking in the debate; namely, what our hidden assumptions about what represen- tations are—assumptions that act as sometimes antithetical starting points in this persistent philosophical debate. (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)

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)

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). (...) This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog). (shrink)

Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity—the anhedonia, incentive salience, and reward prediction error hypotheses—we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. More (...) generally, we suggest that the form of scientific progress in the cognitive sciences is unlikely to be a single overarching grand unifying theory. (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)

In this thesis, I investigated the self in autism using tools from philosophy and experimental cognitive science. Our self-representation shapes how we act in the world, and the feedback we receive in turn shapes how we represent ourselves. In the predictive processing framework I use, autism is characterised by differences in modelling or predicting the world under uncertainty which impacts both perception and action. Findings from the thesis show that individuals with more autistic traits are more prone to (...) act early in the face of rising uncertainty. The thesis also raises questions about the appropriate core features of autism. (shrink)

A cognitivist account of decision-making views choice behaviour as a serial process of deliberation and commitment, which is separate from perception and action. By contrast, recent work in embodied decision-making has argued that this account is incompatible with emerging neurophysiological data. We argue that this account has significant overlap with an embodied account of predictive processing, and that both can offer mutual development for the other. However, more importantly, by demonstrating this close connection we uncover an alternative perspective on (...) the nature of decision-making, and the mechanisms that underlie our choice behaviour. This alternative perspective allows us to respond to a challenge for predictive processing, which claims that the satisfaction of distal goal-states is underspecified. Answering this challenge requires the adoption of an embodied perspective. (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a (...) popular analysis technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities (...) on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes. (shrink)

Bayesian cognitive science is a research programme that relies on modelling resources from Bayesian statistics for studying and understanding mind, brain, and behaviour. Conceiving of mental capacities as computing solutions to inductive problems, Bayesian cognitive scientists develop probabilistic models of mental capacities and evaluate their adequacy based on behavioural and neural data generated by humans (or other cognitive agents) performing a pertinent task. The overarching goal is to identify the mathematical principles, algorithmic procedures, and causal mechanisms that (...) enable cognitive agents to take uncertainty into account and weigh it appropriately in producing adaptive behaviour. -/- The appeal of Bayesian cognitive science derives from its transparency, normativity, and unifying power. Resources from Bayesian statistics allow cognitive scientists to characterise transparently, in terms of random variables and probabilistic dependencies between them, the inductive problems that many mental capacities are presumed to solve. Given this characterisation, researchers can define an upper bound on one’s performance on that problem and use it as a benchmark for interpreting experimental results, formulating hypotheses about why and when deviations from optimal performance should be expected, and seeking explanations of mental capacities within one encompassing theoretical framework. -/- Although critics question the testability, explanatory power, and plausibility of Bayesian models of mental capacities, Bayesian cognitive science has proved itself to be an incredibly fruitful research programme. Fuelled by empirical and theoretical results from psychology, neuroscience, philosophy, computer science, artificial intelligence, and evolution, Bayesian cognitive science has: advanced our understanding of why cognitive systems should keep track of uncertainty and use it to facilitate adaptive behaviour; clarified how the brain might encode information about the uncertainty of its stimuli and perform computations with probability distributions; and crystallised the insight that cognitive agents’ management of uncertainty might be grounded in predictive processes aimed at avoiding surprising exchanges with the environment. (shrink)

An adequate semantics for generic sentences must stake out positions across a range of contested territory in philosophy and linguistics. For this reason the study of generic sentences is a venue for investigating different frameworks for understanding human rationality as manifested in linguistic phenomena such as quantification, classification of individuals under kinds, defeasible reasoning, and intensionality. Despite the wide variety of semantic theories developed for generic sentences, to date these theories have been almost universally model-theoretic and representational. This essay outlines (...) a range of proof-theoretic analyses for characterizing generics. Particular attention is given to an expressivist proof-theory that can be traced to 1) work on logical syntax that Carnap undertook prior to his turn toward truth-conditional model theory in the late 1930s, and 2) research on sequent calculi and natural deduction systems that originate in work from Gentzen and Prawitz.11 This essay offers a more precise exposition of some of the material discussed in the first two chapters of my dissertation (Stovall Citation2015a). These ideas developed in the context of a working group on non-monotonic logic run by Robert Brandom, and they were subsequently clarified through the work of Nissim Francez (particularly Francez Citation2015). I am indebted to many people for feedback and criticism on this essay, though I am sure none among them would agree with everything I say and do here. I would particularly like to thank Brandom, Francez, Ulf Hlobil, Daniel Kaplan, Jared Millson, Bernhard Nickel, Jaroslav Peregrin, Jeffry Pelletier, Thomas Ricketts, Mark Risjord, Shawn Standefer, and two reviewers for this journal. Work on this essay was supported by the joint Lead-Agency research grant between the Austrian Science Foundation (FWF) and the Czech Science Foundation (GAČR), Inferentialism and Collective Intentionality, GF17-33808L. It was substantially revised prior to its final submission. (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 (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)

Is action-guiding vision cognitively penetrable? More specifically, is the visual processing that guides our goal-directed actions sensitive to semantic information from cognitive states? This paper critically examines a recent family of arguments whose aim is to challenge a widespread and influential view in philosophy and cognitive science: the view that action-guiding vision is cognitively impenetrable. I argue, in response, that while there may very well be top–down causal influences on action-guiding vision, they should not be taken to be (...) an instance of cognitive penetration. Assuming otherwise is to assign a computational role to the influencing states that they cannot perform. Although questions about cognitive penetrability are ultimately empirical, the issues addressed in this paper are largely philosophical. The discussion here highlights an important set of considerations that help better understand the relations between cognition, vision, and action. (shrink)

Enactivist (Embodied, Embedded, etc.) approaches in cognitive science and philosophy of mind are sometimes, though not always, conjoined with an anti-representational commitment. A weaker anti-representational claim is that ascribing representational content to internal/sub-personal processes is not compulsory when giving psychological explanations. A stronger anti-representational claim is that the very idea of ascribing representational content to internal/sub-personal processes is a theoretical confusion. This paper criticises some of the arguments made by Hutto & Myin (2013, 2017) for the stronger anti-representational claim (...) and suggests that the default Enactivist view should be the weaker non-representational position. (shrink)

This paper deals with the question of agency and intentionality in the context of the free-energy principle. The free-energy principle is a system-theoretic framework for understanding living self-organizing systems and how they relate to their environments. I will first sketch the main philosophical positions in the literature: a rationalist Helmholtzian interpretation (Hohwy 2013; Clark 2013), a cybernetic interpretation (Seth 2015b) and the enactive affordance-based interpretation (Bruineberg and Rietveld 2014; Bruineberg et al. 2016) and will then show how agency and intentionality (...) are construed differently on these different philosophical interpretations. I will then argue that a purely Helmholtzian is limited, in that it can account only account for agency in the context of perceptual inference. The cybernetic account cannot give a full account of action, since purposiveness is accounted for only to the extent that it pertains to the control of homeostatic essential variables. I will then argue that the enactive affordance-based account attempts to provide broader account of purposive action without presupposing goals and intentions coming from outside of the theory. In the second part of the paper, I will discuss how each of these three interpretations conceives of the sense agency and intentionality in different ways. (shrink)

According to Brandom’s conceptual role semantics, to grasp a concept involves a commitment to drawing certain inferences. This is a consequence of the inferentialist thesis that the meaning of a term is given by its justification through assertibility conditions. Inferential commitments come out from a material notion of inference which underwrites human rational discourse and activity. In this paper I discuss a problem of Brandom’s semantics allegedly exposed in an argument by Paul Boghossian against Dummett’s and Brandom’s (...) substantive conception of meaning. I contend that Boghossian’s analysis is dubious because it overlooks an important difference between Dummett’s and Brandom’s positions related respectively to a monotonic and a non-monotonic view of the norm underwriting meaning. (shrink)

The papers in this special issue make important contributions to a longstanding debate about how we should conceive of and explain mental phenomena. In other words, they make a case about the best philosophical paradigm for cognitive science. The two main competing approaches, hotly debated for several decades, are representationalism and enactivism. However, recent developments in disciplines such as machine learning and computational neuroscience have fostered a proliferation of intermediate approaches, leading to the emergence of completely new positions, in (...) particular the Predictive Processing approach. Here, we will consider the different approaches discussed in this volume. (shrink)

The third-generation cognitive science adopts the method of imaging and simulating the human brain, whereby it uses high-tech brain imaging technology and computer neural simulation technology to explain the complex relationship between human cognitive activity, language ability and brain nerves, and reveal the secrets of the advanced functions of the human brain. Based on the third- generation cognitive science, this paper introduces the neural mechanisms of language production and language processing and reveals the interrelationship between language research (...) and neural simulation under the third-generation cognitive science. (shrink)

The traditional approach in cognitive sciences holds that cognition is a matter of manipulating abstract symbols followingcertain rules. According to this view, the body is merely an input/output device, which allows the computationalsystem—the brain—to acquire new input data by means of the senses and to act in the environment following its com-mands. In opposition to this classical view, defenders of embodied cognition (EC) stress the relevance of the body inwhich the cognitive agent is embedded in their explanation of (...) cognitive processes. From a representationalist frameworkregarding our conscious experience, in this article, I will offer a novel argument in favor of EC and show that cognitionconstitutively—and no merely causally—depends upon body activity beyond that in the brain. In particular, I will arguethat in order to solve the problem derived from the empirical evidence in favor of the possibility of shifted spectrum,representationalist should endorse the view that experiences concern its subject: the content of experience isde se.Ishow that this claim perfectly matches the phenomenological observation and helps explaining the subjective characterof the experience. Furthermore, I argue that entertaining this kind of representation constitutively depends on bodilyactivity. Consequently, insofar as cognition depends on consciousness, it is embodied. (shrink)

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal (...) data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data. (shrink)

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) (...) inferential promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (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)