Paweł Gładziejewski

Dennett famously argued that constituents of the manifest (commonsense) image of the world are real patterns, where patternhood is grounded in data compressibility. This paper builds upon Dennett’s original formulation by connecting it with recent work in computational cognitive (neuro)science. The aim is to use the notion of real patterns to shed light on the genealogy of the ontological commitments of the common sense, arguing that the processes by which humans learn and update internal models of the environment can be understood as extracting real patterns from sensory data. In particular, I trace a conceptual and mathematical progression linking Kolmogorov-Chaitin complexity and minimum description length to predictive coding, Bayesian inference, and predictive processing accounts of cognition. Then, I argue that this cognitive interpretation of Dennett’s core idea suggests a structuralist (and Kantian) perspective on the relationship between mind and world, whereby the manifest image represents a structure present in sensory data. The paper concludes by sketching how this cognitive form of real-pattern view connects with (and possibly illuminates) metaphysical debates regarding the reality of two types of commonsense entities: selves and ordinary physical objects.

According to the predictive coding theory of cognition (PCT), brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system.

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.

No more than a few years ago could open an article concerning neurophenomenology with a statement describing recent rediscovery of the problem of consciousness by the cognitive sciences and pointing to the fact that right now, explaining conscious experience in neuroscientific or computational terms poses the greatest challenge for those sciences. Today however, constatations of this sort start to sound like trivial descriptions of a universally recognized state of affairs. The question of “how the water of the physical brain is turned into a wine of consciousness” is now among the mainstream problems of cognitive science.

No more than a few years ago could open an article concerning neurophenomenology with a statement describing recent rediscovery of the problem of consciousness by the cognitive sciences and pointing to the fact that right now, explaining conscious experience in neuroscientific or computational terms poses the greatest challenge for those sciences. Today however, constatations of this sort start to sound like trivial descriptions of a universally recognized state of affairs. The question of “how the water of the physical brain is turned into a wine of consciousness” is now among the mainstream problems of cognitive science.

There is a growing consensus that explanation in cognitive science is a form of mechanistic explanation. According to this view, explaining a cognitive capacity of a system consists in describing a mechanism responsible for it, where a mechanism is understood as a collection of appropriately organized, functionally specified, interacting components. The article employs a mechanistic view on cognitive scientific explanation in order to discuss the problem of the status of mental representations as explanatory posits invoked by cognitive scientists. The article argues that mechanistic outlook on the problem of mental representations enables one to formulate explicit conditions that need to be fulfilled in order for a given explanation to be legitimately representational. Furthermore, the article develops the thesis that there is (at least) one notion of mental representation that meets these conditions. This notion is based on understanding mental representations as internal models or simulations, where representing is based on structural resemblance relation holding between the vehicle of representation and what is represented.

Paweł Gładziejewski, Is empathy is mental simulation? Remarks on the representative approach based on the concept of mirror neurons., Diametros 27 This paper draws on the theoretical achievements of analytic philosophy of mind and the empirical results of psychology and cognitive neuroscience in order to understand the nature of empathy and the sub-personal mechanisms upon which it is based. The paper distinguishes two types of empathy, which are often not sufficiently clearly distinguished in the literature, empathy as a kind of social perception and empathy as compassion. The argument attempts show to that neither of these can be identified with a mental simulation process understood as a sub-personal process. Nevertheless mental simulation may play a limited explanatory role with respect to these two types of empathy.

The main aim of this article is to present and defend a thesis according to which conceptual representations of some types of mental states are encoded in the same neural structures that underlie the first-personal experience of those states. To support this proposal here, I will put forth a novel account of the cognitive function played by ‘shared representations’ of emotions and bodily sensations, i.e. neural structures that are active when one experiences a mental state of a certain type as well as when one observes someone else experiencing a state of the same type. I will argue that shared representations in fact constitute vehicles of certain mental state concepts (more precisely, concepts of specific types of emotions and somatosensory states). The main line of arguing for this will consist in showing that shared representations exhibit specific, ‘conceptual’ functional properties:(1) causal effect on forming metacognitive judgments, (2) cognitive penetrability, (3) diversity of input types.

According to the predictive coding theory of cognition , brains are predictive machines that use perception and action to minimize prediction error, i.e. the discrepancy between bottom–up, externally-generated sensory signals and top–down, internally-generated sensory predictions. Many consider PCT to have an explanatory scope that is unparalleled in contemporary cognitive science and see in it a framework that could potentially provide us with a unified account of cognition. It is also commonly assumed that PCT is a representational theory of sorts, in the sense that it postulates that our cognitive contact with the world is mediated by internal representations. However, the exact sense in which PCT is representational remains unclear; neither is it clear that it deserves such status—that is, whether it really invokes structures that are truly and nontrivially representational in nature. In the present article, I argue that the representational pretensions of PCT are completely justified. This is because the theory postulates cognitive structures—namely action-guiding, detachable, structural models that afford representational error detection—that play genuinely representational functions within the cognitive system.

In his book Representation Reconsidered, William Ramsey argues against the view that the concept of mental representation employed by cognitive scientists should be based on the folk-psychological concept of mental representation as propositional attitudes. The author of the present article will attempt to show that, contrary to what Ramsey himself claims, the fact that the folk concept of mental representation will most likely not be a part of the conceptual repository of cognitive science does not imply that beliefs, desires or the rationality of human cognition should be eliminated from the naturalistic outlook on the nature of mind and cognitive processes. The key to see that this is the case lies in understanding cognitive systems as multi-level, hierarchically organized mechanisms.

This paper centers around the notion that internal, mental representations are grounded in structural similarity, i.e., that they are so-called S-representations. We show how S-representations may be causally relevant and argue that they are distinct from mere detectors. First, using the neomechanist theory of explanation and the interventionist account of causal relevance, we provide a precise interpretation of the claim that in S-representations, structural similarity serves as a “fuel of success”, i.e., a relation that is exploitable for the representation using system. Then, we discuss crucial differences between S-representations and indicators or detectors, showing that—contrary to claims made in the literature—there is an important theoretical distinction to be drawn between the two.

The aim of this article is to critically examine what I call Action-Centric Theories of Representation (ACToRs). I include in this category theories of representation that (1) reject construing representation in terms of a relation that holds between representation itself (the representational vehicle) and what is represented, and instead (2) try to bring the function that representations play for cognitive systems to the center stage. Roughly speaking, according to proponents of ACToRs, what makes a representation (that is, what is constitutive of it being a representation) is its being functionally involved in preselecting or guiding the actions of cognitive systems. I intend to argue that while definitely valuable, ACToRs are underconstrained and thus not entirely satisfying, since there exist structures that would count as representations according to ACToRs, but which do not play functional roles that could be nontrivially or in an explanatorily valuable way classified as representing something for a cognitive system. I outline a remedy for this theoretical situation by postulating that a fully satisfying theory of representation in cognitive science should have two factors; i.e., it should combine the pragmatic, action-oriented aspect present in ACToRs with an element that emphasizes the importance of the relation holding between a representational vehicle and what is represented.

Despite the fact that the notion of internal representation has - at least according to some - a fundamental role to play in the sciences of the mind, not only has its explanatory utility been under attack for a while now, but it also remains unclear what criteria should an explanation of a given cognitive phenomenon meet to count as a representational explanation in the first place. The aim of this article is to propose a solution to this latter problem. I will assume that representational explanations should be construed as a form of mechanistic explanations and proceed by proposing a general sketch of a functional architecture of a representational cognitive mechanism. According to the view on offer here, representational mechanisms are mechanisms that meet four conditions: the structural resemblance condition, the action-guidance condition, the decouplability condition, and the error-detection condition.