Marco Facchin

Narrative practices appear to matter to psychological well-being, yet dominant frameworks generally sideline narrative practices in mental health contexts, treating them, at best, as marginal. We argue that if we are to give narrative practices their due in mental health contexts, we must set aside certain misleading imaginative frames that dominate our thinking in this domain. To illustrate this, we focus on Weinrabe and Murphy’s ( 2026 ) scaffolding–plumbing (S–P) metaphor which was newly introduced to defend the medical model by treating mental dysfunction as a harmful failure in neurocognitive ‘plumbing’, while allowing external ’scaffolding’ a role in installing and shaping that plumbing. The S–P metaphor was introduced to remind us that when focusing on the importance of external factors which shape cognition, we ought not thereby underplay the importance of what goes on within cognizers. While acknowledging this, we nonetheless give reasons for thinking the S-P metaphor itself is philosophically problematic and unhelpful for understanding how narrative practices make the sort of difference they apparently do make to psychological well-being in the here-and-now. Drawing on a Radically Enactive/Embodied account of Cognition (REC) and the Narrative Practice Hypothesis (NPH), we offer an alternative way of thinking about these matters. Within that alternative framework, narrative practices are not merely developmental supports but ongoing world-involving activities that can shape agency, possibilities, and the state of people’s mental health in real time. We illustrate how this clarification can liberate our thinking about what matters to our mental health. Focusing on the case of addiction we show, for example, how narrative practices can sustain or disrupt addictive cycles even in the presence of significant neuroadaptation. Far from ignoring the brain, our non-reductive alternative reconceives the brain as a participant in dynamic organism–environment interactions rather than the sole or main locus of psychopathology.

Availability of the notion that the brain or mind represents the world by instantiating structures similar to relations amongst external items is crucial to the idea than an AI could represent the world in the same way that a human being does. This paper looks at the historical emergence of this notion within the structuralist movement in science, mathematics and philosophy seen in the late nineteenth and early twentieth century. Cassirer’s philosophy is a crystallisation of some these tendencies, and Heidegger’s disagreement with Cassirer over the role of sensory intuition in Kant’s epistemology forms the basis for a Heideggerian critique of AI different from the generalised anti-representationalism of Dreyfus and other proponents of embodied cognition.

There are two forms of argument for eliminativism which ought to be distinguished, but which generally are not. One of these, heterogeneity eliminativism, starts from the claim that the extension of a given term is heterogeneous, that is, does not form a natural kind. The other, pluralistic eliminativism, starts from the claim that a term is ‘pluralistic’, demanding different precise definitions, measures, and generalizations in different specialist contexts of use. These two claims are related in various interesting ways, but it is nevertheless worth viewing these as two distinct forms of argument for eliminativism. They make different claims about the relevant terms, face different objections, and rely on different views about the requirements of good taxonomy. My aim in distinguishing between heterogeneity eliminativism and pluralistic eliminativism is to contribute to a much broader literature on kinds of and reasons for eliminativism, one which outstrips the concerns of philosophy of mind and philosophy of science alone. However, the taxonomies on which I am most immediately building are most closely tied to philosophy of mind and philosophy of science.

Whether neurons represent or play a mere causal role is a foundational issue in philosophy of neuroscience. Evidence that neurons perform a representational role is weakened by the possibility of explaining experimental results by appeal to brute causal processes alone. Despite this, neuroscientists ascribe representational content to patterns of neural activity to explain experimental results. An important problem with this practice is determining which content to ascribe to the neural representation. One view is that researchers are only warranted in ascribing the content determined by particular experimental results. An alternative view is that researchers are warranted in appealing to the broader research domain to determine the content of a putative neural representation. In this paper, I argue that both are warranted; either alone is insufficient. Using optogenetics research on memory engrams as a case study, I show how researchers ascribe content to neural representations and justify their approach. Whether a particular content ascription is warranted depends on particular experimental results, the broader research domain that is appealed to, and how results from various animal models, probes, and experimental paradigms are generalized.

Representational mechanisms are responsible for processing information to modify readiness for action. While structural similarity has been proposed as foundational to neural representation, how these mechanisms systematically harness correspondence-based information remains undertheorized. This paper introduces a Correspondence Network Framework that elucidates how representational systems exploit multiple channels of structural similarity concurrently. We demonstrate that structural similarity engenders networks of infocorrespondences between informational structures, allowing representational mechanisms to evaluate information across channels, detect inconsistencies, and establish satisfaction conditions for representational content. Our framework extends beyond single-channel accounts by explaining how multiple classification systems can operate simultaneously over the same neural vehicles, enabling content-sensitive processing. To illustrate the framework's utility, we analyze representational similarity analysis (RSA) in cognitive neuroscience as detecting correspondence networks. This analysis reveals why taxonomic organization consistently emerges in neural representational spaces and provides principled responses to anti-representationalist critiques. While RSA alone cannot fully characterize representational mechanisms, the Correspondence Network Framework delineates its interpretive constraints and provides valuable heuristics for further empirical inquiry.

Despite explicit warnings from Shannon to tread carefully when applying Information Theory to fields for which it was not designed, contemporary neuroscientists adopting the framework of Information Theory have fallen right into the traps Shannon and others cautioned against. What makes the neuroscientist more than anyone prone to fall prey to confusion is that neuroscience looks back on a tradition—which long predates Shannon—in which information and related semantic communication notions (message, codes) are invoked as central explanatory posits. In the first part of this paper, I shall give a selective historical overview of this tradition, which runs via Aristotle over Descartes to the groundbreaking work of the early nerve physiologists. At the same time, I will show how the history of explaining brain activity in semantic communication terms is characterised by a process of de-metaphorisation. Next, I will show how semantic notions, including semantic information, are still routinely relied upon within contemporary information theoretic approaches to the brain, despite the fact that Shannon deemed meaning irrelevant for his theory. There are, then, two very different notions of information at work within one and the same scientific discipline. I will demonstrate how these two notions get as a rule tangled up here. Next, I will show how invoking semantic-intentional information at the level of the brain raises two specific problems: first, notions that aim to explain by invoking semantic content or meaning, like the pre-theoretical notion of information, in principle resist integration within causal-mechanistic explanations; second, such notions are part of a mode of explanation which logically presupposes a cognitive agent. As I will also show, however, we indeed find that the neuroscientist has embraced the habit of treating neurons as cognitive agents, attributing to them capacities we would normally only attribute to persons.

Toon (2023) argues that “the mind is a useful fiction.” The mind, for Toon, is essentially an “inner world” or “inner grotto,” which is “private” and which “houses our mental states – our beliefs, desires, hopes, fears, and the rest.” His position, according to which the metaphor of mind is a “story that we cannot avoid telling” risks elevating the concept of mind to that of a “sacrosanct given.” Work in philosophy, history, and anthropology shows that talk of the states we count as “mental” is far older and far more widespread than the concept of mind or the category mental – the metaphor of mind therefore does not appear to be inevitable. A strand of anti-Cartesian philosophy argues that not only is the concept of mind avoidable, but also that it should be avoided – that it encourages a vision of human beings which is deeply impoverished and confused. This paper offers a genealogy of the concept of mind and category mental, building on work by Rorty and Wilkes, buttressed by recent work in history and anthropology. It then reconstructs some of the arguments that the concept is harmful, rather than useful, for our understanding of ourselves and the world.

This paper argues that the so-called scaling-up problem (representation-hunger problem) can be resolved within the mechanistic framework of explanation. Emphasising the problem’s character as an empirical challenge for non-representationalists to provide explanations of cognitive phenomena involving sensitivity to the abstract and absent, the paper surveys and rejects prominent non-representationalist answers. An important epistemic aspect of the problem is identified: the need for general heuristics for formulating non-representational explanations of representation-hungry phenomena. In response, a strategy based on the idea of mechanistic compositionality is introduced. Mechanistic compositionality means that the phenomenon exhibited by a mechanism depends solely on the component entities, activities, and the way they are organised. This strategy is shown to provide an important heuristic for formulating candidate mechanistic explanations for representation-hungry phenomena, and thus to resolve the epistemic aspect of the scaling-up problem. Parallels are drawn to the way in which representationalist theories of cognition resolve their version of the scaling-up problem by invoking semantic compositionality. Nevertheless, it is argued that mechanistic compositionality differs from semantic compositionality and relying on mechanistic compositionality to resolve the scaling-up problem does not imply acceptance of representationalism.

Intentional actions exhibit control in a way that mere lucky successes do not. A longstanding tradition in action theory characterizes actional control in terms of the _knowledge_ with which one acts when acting intentionally. Given that action theorists, no less than epistemologists, typically take for granted the orthodox thesis that knowledge is in the head (viz., realized exclusively by brainbound cognition), the idea that intentional action is controlled in virtue of knowledge is tantamount to the idea that the knowledge by which intentional actions exhibit control supervenes intracranially. We raise some challenges for this idea, and in doing show, we show how epistemic theories of actional control are naturally aligned moreso than has been appreciated with cognitive extension in the theory of mind.

Emoticons and facial emojis are ubiquitous in contemporary digital communication, where it has been proposed that they make up for the lack of social information from real faces. In this paper, I construe them as cultural artifacts that exploit the neurocognitive mechanisms for face perception. Building on a step-by-step comparison of psychological evidence on the perception of faces vis-à-vis the perception of emoticons/emojis, I assess to what extent they do effectively vicariate real faces with respect to the following four domains: (1) the expression of emotions, (2) the cultural norms for expressing emotions, (3) conveying non-affective social information, and (4) attention prioritization.

The mechanism underlying action potentials is routinely used to explicate the mechanistic model of explanation in the philosophy of science. However, characterisations of action potentials often fixate on neurons, mentioning plant cells in passing or ignoring them entirely. The plant sciences are also prone to neglecting non-neuronal action potentials and their role in plant biology. This oversight is significant because plant action potentials bear instructive similarities to those generated by neurons. This paper helps correct the imbalance in representations of action potentials by offering an overview of the mechanism for plant action potentials and highlighting their similarity to those in neurons. Furthermore, it affirms the role of plant action potentials in discovering the evolution and function of mechanisms of action potentials more broadly. We stress the potential of plants for producing generalisations about action potentials and the possible role of plants as experimental organisms.

Predictive coding – sometimes also known as ‘predictive processing’, ‘free energy minimisation’, or ‘prediction error minimisation’ – claims to offer a complete, unified theory of cognition that stretches all the way from cellular biology to phenomenology. However, the exact content of the view, and how it might achieve its ambitions, is not clear. This series of articles examines predictive coding and attempts to identify its key commitments and justification. The present article begins by focusing on possible confounds with predictive coding: claims that are often identified with predictive coding, but which are not predictive coding. These include the idea that the brain employs an efficient scheme for encoding its incoming sensory signals; that perceptual experience is shaped by prior beliefs; that cognition involves minimisation of prediction error; that the brain is a probabilistic inference engine; and that the brain learns and employs a generative model of the world. These ideas have garnered widespread support in modern cognitive neuroscience, but it is important not to conflate them with predictive coding.

Some proponents of structural representations (henceforth, structuralists) claim that no other theory of representation can legitimatize the explanatory appeals that cognitive science makes to mental content. Because other naturalistic approaches to representation purportedly posit an arbitrary relation between representing vehicles and representational content, these approaches must appeal to the role played by a representation, i.e., how it is used by the system in which it is embedded, to ground its content. This is in supposed contrast to structural representations, in which the relation of resemblance results in a non-arbitrary relationship between vehicles and content. Structuralists argue that, as a result, approaches that posit structural representations can, and alternative approaches cannot, explain how representational content can be causally relevant in the production of behavior. In this paper, I will argue that structural representations are susceptible to the very same critiques that proponents level against what they sometimes refer to as “use” theories. This, I contend, is not surprising given that a theory of structural representations is, in fact, just as much a use-theory as alternative approaches.

This paper develops a theory of analog representation. We first argue that the mark of the analog is to be found in the nature of a representational system’s interpretation function, rather than in its vehicles or contents alone. We then develop the rulebound structure theory of analog representation, according to which analog systems are those that use interpretive rules to map syntactic structural features onto semantic structural features. The theory involves three degree-theoretic measures that capture three independent ways in which a system can be more or less analog. We explain how our theory improves upon prior accounts of analog representation, provides plausible diagnoses for novel challenge cases, extends to hybrid systems that are partially analog and partially symbolic, and accounts for some of the advantages and disadvantages of representing analogically versus symbolically.

Computational physical systems may exhibit indeterminacy of computation (IC). Their identified physical dynamics may not suffice to select a unique computational profile. We consider this phenomenon from the point of view of cognitive science and examine how computational profiles of cognitive systems are identified and justified in practice, in the light of IC. To that end, we look at the literature on the underdetermination of theory by evidence and argue that the same devices that can be successfully employed to confirm physical hypotheses can also be used to rationally single out computational profiles, notwithstanding IC.

Extended cognition brings with it a particular phenomenology. It has been argued that when an artifact is integrated into an agent’s cognitive system, it becomes transparent in use to the cognizing subject. In this paper, I challenge some of the assumptions underlying how the transparency of artifacts is described in extended cognition theory. To this end, I offer two arguments. First, I make room for some forms of conscious thought and attention within extended cognitive routines, and I question the close association drawn between attention and effort. Second, I vindicate the importance of paying careful attention to individual differences and the diverse ways in which bodies and technologies can be experienced. I end by offering some hints toward an alternative, and more accurate, account of the phenomenology of extended cognition.

_Abstract_: Fueled by the rapid development of neuroscientific tools and techniques, some scholars consider the shift from traditional cognitive psychology toward cognitive neuroscience to be a _revolution_ (most notably Boone and Piccinini). However, the term “revolution” in philosophy of science can easily be construed as involving a paradigm shift in the sense of Kuhn’s _The Structure of Scientific Revolutions_. Is a Kuhnian account sound in the case at hand? To answer this question, we consider heuristic indicators of two features of paradigm shifts: the incommensurability of ontologies; and a gap between scientific communities. Based on our evidence, we argue that no revolution has occurred (at least, not yet). _Keywords_: Cognitive Neuroscience; Cognitive Psychology; Philosophy of Science; Thomas Kuhn; Scientometrics _La “rivoluzione delle neuroscienze cognitive” non è una rivoluzione (in senso kuhniano). Evidenze scientometriche _ _Riassunto_:_ _Complice il rapido sviluppo di strumenti e tecniche in neuroscienze, alcuni studiosi (in particolare Boone e Piccinini) intendono il passaggio dalla psicologia cognitiva classica alla neuroscienza cognitiva nei termini di una _rivoluzione_. Tuttavia, il termine ‘rivoluzione’ in filosofia della scienza è strettamente associato alla nozione di successione di paradigmi esposta da Kuhn ne _La struttura delle rivoluzioni scientifiche_. Obiettivo di questo lavoro è capire se effettivamente la concezione kuhniana offra una corretta descrizione di questa dinamica storica. In particolare, prenderemo in esame due indicatori euristici delle rivoluzioni kuhniane: l’incommensurabilità ontologica trai due paradigmi e la diversa composizione demografica delle comunità scientifiche. Sulla base delle evidenze scientometriche che prenderemo in esame, affermeremo che non è avvenuta nessuna rivoluzione (almeno per ora). _Parole chiave_: Neuroscienze cognitive; Psicologia cognitiva; Filosofia della scienza; Thomas Kuhn; Scientometria.

Daniel Dennett's distinction between personal and subpersonal explanations was fundamental in establishing the philosophical foundations of cognitive science. Since it was first introduced in 1969, the personal/subpersonal distinction has been adapted to fit different approaches to the mind. In one example of this, the ‘Pittsburgh school’ of philosophers attempted to map Dennett's distinction onto their own distinction between the ‘space of reasons’ and the ‘space of causes’. A second example can be found in much contemporary philosophy of psychology, where Dennett's distinction has been presumed to be equivalent to Stephen Stich's distinction between doxastic and subdoxastic states. Both these interpretations of the personal/subpersonal distinction, and also Dennett's own philosophical views of the mind, go beyond the personal/subpersonal distinction itself. They each involve supplementing the distinction between personal and subpersonal explanations with metaphysical claims about the relationship between the two kinds of explanation and the entities they posit.

Whilst the topic of representations is one of the key topics in philosophy of mind, it has only occasionally been noted that representations and representational features may be gradual. Apart from vague allusions, little has been said on what representational gradation amounts to and why it could be explanatorily useful. The aim of this paper is to provide a novel take on gradation of representational features within the neuroscientific framework of predictive processing. More specifically, we provide a gradual account of two features of structural representations: structural similarity and decoupling. We argue that structural similarity can be analysed in terms of two dimensions: number of preserved relations and state space granularity. Both dimensions can take on different values and hence render structural similarity gradual. We further argue that decoupling is gradual in two ways. First, we show that different brain areas are involved in decoupled cognitive processes to a greater or lesser degree depending on the cause (internal or external) of their activity. Second, and more importantly, we show that the degree of decoupling can be further regulated in some brain areas through precision weighting of prediction error. We lastly argue that gradation of decoupling (via precision weighting) and gradation of structural similarity (via state space granularity) are conducive to behavioural success.

Pain is often used as the paradigmatic example of a phenomenal kind with a phenomenal quality common and unique to its instantiations. Philosophers have intensely discussed the relation between the subjective feeling, which unites pains and distinguishes them from other experiences, and the phenomenal properties of sensory, affective, and evaluative character along which pains typically vary. At the center of this discussion is the question whether the phenomenal properties prove necessary and/or sufficient for pain. In the empirical literature, sensory, affective, and evaluative properties have played a decisive role in the investigation of psychophysical correspondence and clinical diagnostics. This paper addresses the outlined philosophical and empirical issues from a new perspective by constructing a multidimensional phenomenal space for pain. First, the paper will construe the phenomenal properties of pains in terms of a property space whose structure reflects phenomenal similarities and dissimilarities by means of spatial distance. Second, philosophical debates on necessary and sufficient properties are reconsidered in terms of whether there is a phenomenal space formed of dimensions along which all and only pains vary. It is concluded that there is no space of this kind and, thus, that pain constitutes a primitive phenomenal kind that cannot be analyzed entirely in terms of its varying phenomenal properties. Third, the paper addresses the utility of continued reference to pain and its phenomenal properties in philosophical and scientific discourses. It is argued that numerous insights into the phenomenal structure of pain can be gained that have thus far received insufficient attention.

In their attempt to connect the workings of the human mind with their neural realizers, cognitive neuroscientists often bracket out individual differences to build a single, abstract model that purportedly represents (almost) every human being’s brain. In this paper I first examine the rationale behind this model, which I call ‘Platonic Brain Model’. Then I argue that it is to be surpassed in favor of multiple models allowing for patterned inter-individual differences. I introduce the debate on legitimate (and illegitimate) ways of mapping neural structures and cognitive functions, endorsing a view according to which function-structure mapping is context-sensitive. Building on the discussion of the ongoing debate on the function(s) of the so-called Fusiform “Face” Area, I show the necessity of indexing function-structure mappings to some populations of subjects, clustered on the basis of factors such as their expertise in a given domain.