The sceptical positions philosophers have adopted with respect to neuroimaging data are based on detailed evaluations of subtraction, which is one of many data analysis techniques used with neuroimaging data. These positions are undermined when the epistemic implications of the use of a diversity of data analysis techniques are taken into account. I argue that different data analysis techniques reveal different patterns in the data. Through the use of multiple data analysis techniques, researchers can produce results that are locally robust. (...) Thus, the epistemology of neuroimaging must take into consideration the details of the different data analysis techniques that are used to evaluate neuroimaging data, and the specific theoretical aims those techniques are deployed towards. _1_ Introduction _2_ Scepticism about Neuroimaging _3_ Data Analysis and Evidence _4_ Deconvolution and Pattern Classification Analysis _4.1_ Deconvolution analysis _4.2_ Region of interest selection _4.3_ Pattern classification analysis _5_ The Strength of Multiple Analyses _6_ Conclusion. (shrink)

We argue that one important aspect of the “cognitive neuroscience revolution” identified by Boone and Piccinini :1509–1534. doi: 10.1007/s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both “from below” and “from above”—that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves, and from a greater appreciation of the problems (...) solved by bio-cognitive systems, chiefly regulation and prediction. We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of dynamical models as a central postulate in neurocognitive research. (shrink)

Representations in cognitive neuroscience Explanations in terms of representations are ubiquitous in cognitive neuroscience. In this paper I will show that the question of who is using the representation is of crucial importance, but not often explicitly answered. Two possible users, the scientist and the cognitive system are theoretically strictly distinct, but the distinction is in practice often blurred. It is tempting to jump from ‘representations to the scientist’ to ‘representations to the system’. This step, however, is unwarranted. I will (...) show that representations to the scientist are not in themselves problematic, and can even be useful, but can lead to wrong conclusions. The problems with representations for the system are more fundamental. (shrink)

Romantic love is universally observed in human communities, and the manner in which a person chooses a long-term romantic partner has been a central question in studies on close relationships. Numerous empirical psychological studies have demonstrated that facial attractiveness greatly impacts initial romantic attraction. This close link was further investigated by neuroimaging studies showing that both viewing attractive faces and having romantic thoughts recruit the reward system. However, it remains unclear how our brains integrate perceived facial attractiveness into initial romantic (...) attraction. In addition, it remains unclear how our brains shape a persistent attraction to a particular person through interactions; this persistent attraction is hypothesized to contribute to a long-term relationship. After reviewing related studies, I introduce methodologies that could help address these questions. (shrink)

Similarity-based cognition is commonplace. It occurs whenever an agent or system exploits the similarities that hold between two or more items—e.g., events, processes, objects, and so on—in order to perform some cognitive task. This kind of cognition is of special interest to cognitive neuroscientists. This paper explicates how similarity-based cognition can be understood through the lens of radical enactivism and why doing so has advantages over its representationalist rival, which posits the existence of structural representations or S-representations. Specifically, it is (...) argued that there are problems both with accounting for the content of S-representations and with understanding how neurally-based structural similarities can work as representations in guiding intelligent behavior. Finally, with these clarifications in place, it is revealed how radical enactivism can commit to an account of similarity-based cognition in its understanding of neurodynamics. (shrink)

Similarity-based cognition is commonplace. It occurs whenever an agent or system exploits the similarities that hold between two or more items—e.g., events, processes, objects, and so on—in order to perform some cognitive task. This kind of cognition is of special interest to cognitive neuroscientists. This paper explicates how similarity-based cognition can be understood through the lens of radical enactivism and why doing so has advantages over its representationalist rival, which posits the existence of structural representations or S-representations. Specifically, it is (...) argued that there are problems both with accounting for the content of S-representations and with understanding how neurally-based structural similarities can work as representations in guiding intelligent behavior. Finally, with these clarifications in place, it is revealed how radical enactivism can commit to an account of similarity-based cognition in its understanding of neurodynamics. (shrink)

Similarity-based cognition is commonplace. It occurs whenever an agent or system exploits the similarities that hold between two or more items—e.g., events, processes, objects, and so on—in order to perform some cognitive task. This kind of cognition is of special interest to cognitive neuroscientists. This paper explicates how similarity-based cognition can be understood through the lens of radical enactivism and why doing so has advantages over its representationalist rival, which posits the existence of structural representations or S-representations. Specifically, it is (...) argued that there are problems both with accounting for the content of S-representations and with understanding how neurally-based structural similarities can work as representations in guiding intelligent behavior. Finally, with these clarifications in place, it is revealed how radical enactivism can commit to an account of similarity-based cognition in its understanding of neurodynamics. (shrink)

Functional neuroimaging is sometimes criticized as showing only where in the brain things happen, not how they happen, and thus being unable to inform us about questions of mental and neural representation. Novel analytical methods increasingly make clear that imaging can give us access to constructs of interest to psychology. In this paper I argue that neuroimaging can give us an important, if limited, window into the large-scale structure of neural representation. I describe Representational Similarity Analysis, increasingly used in neuroimaging (...) studies, and lay out desiderata for representations in general. In that context I discuss what RSA can and cannot tell us about neural representation. I compare RSA with fMRI to a different experimental paradigm which has been embraced as being indicative of representation in psychology, and argue that it compares favorably. (shrink)

Since its introduction, multivariate pattern analysis, or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is (...) a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. (shrink)

Since its introduction, multivariate pattern analysis, or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the dictum, arguing that it is false: decodability is (...) a poor guide for revealing the content of neural representations. However, we also suggest how the dictum can be improved on, in order to better justify inferences about neural representation using MVPA. 1Introduction 2A Brief Primer on Neural Decoding: Methods, Application, and Interpretation 2.1What is multivariate pattern analysis? 2.2The informational benefits of multivariate pattern analysis 3Why the Decoder’s Dictum Is False 3.1We don’t know what information is decoded 3.2The theoretical basis for the dictum 3.3Undermining the theoretical basis 4Objections and Replies 4.1Does anyone really believe the dictum? 4.2Good decoding is not enough 4.3Predicting behaviour is not enough 5Moving beyond the Dictum 6Conclusion. (shrink)

Neurons repeatedly exposed to similar perceptual experiences fire together and wire together to form ‘meaning kernels’ of concepts. Pulvermueller argues that abstract concepts may be devoid of meaning kernels, because the perceptual experiences that construct abstract concepts are subject to great variation and share few common features. Abstract concept are therefore grounded in the brain through features that belong to ‘meaning halos’, rather than to ‘meaning kernels’.

The Significance Argument (SA) for the irreducibility of consciousness is based on a series of new puzzle-cases that I call multiple candidate cases. In these cases, there is a multiplicity of physical-functional properties or relations that are candidates to be identified with the sensible qualities and our consciousness of them, where those candidates are not significantly different. I will argue that these cases show that reductive materialists cannot accommodate the various ways in which consciousness is significant and must allow massive (...) vagueness in conscious experience. I also will argue that a nonreductive theory of the conscious-of relation can easily provide a very satisfying, unified explanation of the ways in which this relation is significant. (shrink)

A novel account of semantic information is proposed. The gist is that structural correspondence, analysed in terms of similarity, underlies an important kind of semantic information. In contrast to extant accounts of semantic information, it does not rely on correlation, covariation, causation, natural laws, or logical inference. Instead, it relies on structural similarity, defined in terms of correspondence between classifications of tokens into types. This account elucidates many existing uses of the notion of information, for example, in the context of (...) scientific models and structural representations in cognitive science. It is poised to open a new research program concerned with various kinds of semantic information, its functions, and its measurement. (shrink)

A set of images can be considered as meaningfully different for an observer if they can be distinguished phenomenally from one another. Each phenomenal difference must be supported by some neurophysiological differences. Differentiation analysis aims to quantify neurophysiological differentiation evoked by a given set of stimuli to assess its meaningfulness to the individual observer. As a proof of concept using high-density EEG, we show increased neurophysiological differentiation for a set of natural, meaningfully different images in contrast to another set of (...) artificially generated, meaninglessly different images in nine participants. Stimulus-evoked neurophysiological differentiation (over 257 channels, 800 ms) was systematically greater for meaningful vs. meaningless stimulus categories both at the group level and for individual subjects. Spatial breakdown showed a central-posterior peak of differentiation, consistent with the visual nature of the stimulus sets. Temporal breakdown revealed an early peak of differentiation around 110 ms, prominent in the central-posterior region; and a later, longer-lasting peak at 300–500 ms that was spatially more distributed. The early peak of differentiation was not accompanied by changes in mean ERP amplitude, whereas the later peak was associated with a higher amplitude ERP for meaningful images. An ERP component similar to visual-awareness-negativity occurred during the nadir of differentiation across all image types. Control stimulus sets and further analysis indicate that changes in neurophysiological differentiation between meaningful and meaningless stimulus sets could not be accounted for by spatial properties of the stimuli or by stimulus novelty and predictability. (shrink)

This paper argues, contrary to Fodor’s well-known position, that the iconic and discursive modes of representation are not mutually exclusive categories. It is argued that there exists at least a third kind of representation which blends the semantic properties of icons and the syntactic properties of discourses. We reason that this iconic-discursive genus behaves differently from other representational formats, such as distributed representations or maps, previously put forward as challenging Fodor’s basic distinction. A reflection follows about how this kind of (...) representation impacts on the Fodorian issues for which the original dual distinction was argumentatively instrumental, namely, the kinds of codes and possible inter-code relations accessible to the representational mind. The suggestion is put forward that iconic-discursive representations may facilitate trade-offs between the world and the representational mind, as well as between the differently complex levels of representation that mediate between percepts and concepts. We conclude that such aspects of the computational mind, which until now appeared to be stubbornly resistant with respect to a conciliation of Hume’s empiricism and Fodor’s computationalism, may be more easily accessed and understood taking advantage of the biosemiotics perspective and acknowledging the richness of the biosemiotics codes accessible to cognition. (shrink)

This paper argues, contrary to Fodor’s well-known position, that the iconic and discursive modes of representation are not mutually exclusive categories. It is argued that there exists at least a third kind of representation which blends the semantic properties of icons and the syntactic properties of discourses. We reason that this iconic-discursive genus behaves differently from other representational formats, such as distributed representations or maps, previously put forward as challenging Fodor’s basic distinction. A reflection follows about how this kind of (...) representation impacts on the Fodorian issues for which the original dual distinction was argumentatively instrumental, namely, the kinds of codes and possible inter-code relations accessible to the representational mind. The suggestion is put forward that iconic-discursive representations may facilitate trade-offs between the world and the representational mind, as well as between the differently complex levels of representation that mediate between percepts and concepts. We conclude that such aspects of the computational mind, which until now appeared to be stubbornly resistant with respect to a conciliation of Hume’s empiricism and Fodor’s computationalism, may be more easily accessed and understood taking advantage of the biosemiotics perspective and acknowledging the richness of the biosemiotics codes accessible to cognition. (shrink)

The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate pattern analysis (...) and philosophy of science can help to illuminate the conditions under which we can use artificial neural networks to better understand neural mechanisms. This paper considers a specific technique for MVPA called representational similarity analysis. I develop a theoretically-informed account of RSA that draws on early connectionist research and work on idealization in the philosophy of science. By bringing a philosophical account of cognitive modelling in conversation with RSA, this paper clarifies the practices of neuroscientists and provides a generalizable framework for using artificial neural networks to study neural mechanisms in the brain. (shrink)

According to the popular Grounded Cognition Model (GCM), the sensory and motor features of concepts, including word meanings, are stored directly within neural systems for perception and action. More precisely, the core claim is that these concrete conceptual features reuse some of the same modality-specific representations that serve to categorize experiences involving the relevant kinds of objects and events. Research in semantic typology, however, has shown that word meanings vary significantly across the roughly 6500 languages in the world. I argue (...) that this crosslinguistic semantic diversity has significant yet previously unrecognized theoretical consequences for the GCM. In particular, to accommodate the typological data, the GCM must assume that the concrete features of word meanings are not merely stored within sensory/motor brain systems, but are represented there in ways that are, to a nontrivial degree, language-specific. Moreover, it must assume that these conceptual representations are also activated during the nonlinguistic processing of the relevant kinds of objects and events (e.g., during visual perception and action planning); otherwise, they would not really be grounded, which is to say, embedded inside sensory/motor systems. Crucially, however, such activations would constitute what is traditionally called linguistic relativity—that is, the influence of language-specific semantic structures on other forms of cognition. The overarching aim of this paper is to elaborate this argument more fully and explore its repercussions. To that end, I discuss in greater detail the key aspects of the GCM, the evidence for crosslinguistic semantic diversity, pertinent work on linguistic relativity, the central claim that the GCM entails linguistic relativity, some initial supporting results, and some important limitations and future directions. (shrink)

According to the popular Grounded Cognition Model (GCM), the sensory and motor features of concepts, including word meanings, are stored directly within neural systems for perception and action. More precisely, the core claim is that these concrete conceptual features reuse some of the same modality-specific representations that serve to categorize experiences involving the relevant kinds of objects and events. Research in semantic typology, however, has shown that word meanings vary significantly across the roughly 6500 languages in the world. I argue (...) that this crosslinguistic semantic diversity has significant yet previously unrecognized theoretical consequences for the GCM. In particular, to accommodate the typological data, the GCM must assume that the concrete features of word meanings are not merely stored within sensory/motor brain systems, but are represented there in ways that are, to a nontrivial degree, language-specific. Moreover, it must assume that these conceptual representations are also activated during the nonlinguistic processing of the relevant kinds of objects and events (e.g., during visual perception and action planning); otherwise, they would not really be grounded, which is to say, embedded inside sensory/motor systems. Crucially, however, such activations would constitute what is traditionally called linguistic relativity—that is, the influence of language-specific semantic structures on other forms of cognition. The overarching aim of this paper is to elaborate this argument more fully and explore its repercussions. To that end, I discuss in greater detail the key aspects of the GCM, the evidence for crosslinguistic semantic diversity, pertinent work on linguistic relativity, the central claim that the GCM entails linguistic relativity, some initial supporting results, and some important limitations and future directions. (shrink)

According to the popular Grounded Cognition Model (GCM), the sensory and motor features of concepts, including word meanings, are stored directly within neural systems for perception and action. More precisely, the core claim is that these concrete conceptual features reuse some of the same modality-specific representations that serve to categorize experiences involving the relevant kinds of objects and events. Research in semantic typology, however, has shown that word meanings vary significantly across the roughly 6500 languages in the world. I argue (...) that this crosslinguistic semantic diversity has significant yet previously unrecognized theoretical consequences for the GCM. In particular, to accommodate the typological data, the GCM must assume that the concrete features of word meanings are not merely stored within sensory/motor brain systems, but are represented there in ways that are, to a nontrivial degree, language-specific. Moreover, it must assume that these conceptual representations are also activated during the nonlinguistic processing of the relevant kinds of objects and events (e.g., during visual perception and action planning); otherwise, they would not really be grounded, which is to say, embedded inside sensory/motor systems. Crucially, however, such activations would constitute what is traditionally called linguistic relativity—that is, the influence of language-specific semantic structures on other forms of cognition. The overarching aim of this paper is to elaborate this argument more fully and explore its repercussions. To that end, I discuss in greater detail the key aspects of the GCM, the evidence for crosslinguistic semantic diversity, pertinent work on linguistic relativity, the central claim that the GCM entails linguistic relativity, some initial supporting results, and some important limitations and future directions. (shrink)

This paper deals with Gärdenfors’ theory of conceptual spaces. Let \({\mathcal {S}}\) be a conceptual space consisting of 2-type fuzzy sets equipped with several kinds of metrics. Let a finite set of prototypes \(\tilde{P}_1,\ldots,\tilde{P}_n\in \mathcal {S}\) be given. Our main result is the construction of a classification algorithm. That is, given an element \({\tilde{A}}\in \mathcal {S},\) our algorithm classifies it into the conceptual field determined by one of the given prototypes \(\tilde{P}_i.\) The construction of our algorithm uses some physical analogies (...) and the Newton potential plays a significant role here. Importantly, the resulting conceptual fields are not convex in the Euclidean sense, which we believe is a reasonable departure from the assumptions of Gardenfors’ original definition of the conceptual space. A partitioning algorithm of the space \(\mathcal {S}\) is also considered in the paper. In the application section, we test our classification algorithm on real data and obtain very satisfactory results. Moreover, the example we consider is another argument against requiring convexity of conceptual fields. (shrink)

In this paper I will present three reservations about the claims made by Hutto and Satnet. First of all, though TNOC is presented as drawing on teleological theories of mental content for a conception of Ur-Intentionaltiy, what is separated out after objectionable claims are removed from teleological accounts may not retain enough to give us directed intelligence. This problem raises a question about what we need in a naturalistic basis for an account of the mental. Secondly, I think that the (...) authors’ objections to cognitive science’s supposed notion of a mental representation in fact attack a philosophers’ invention, one that sprung into vibrant being in the mid-1970’s. It is not employed in cognitive neuroscience to any significant extent and it isn’t needed by other disciplines involved in cognitive science. Finally, I worry that the account of the origin of content – namely, explanatory narrative - presents us with a too limited view of human discourse, particularly as it is needed for a social theory of mind. (shrink)

A large number of papers in Computational Cognitive Neuroscience are developing and testing novel analysis methods using one specific neuroimaging dataset and problematic experimental stimuli. Publication bias and confirmatory exploration will result in overfitting to the limited available data. We highlight the problems with this specific dataset and argue for the need to collect more good quality open neuroimaging data using a variety of experimental stimuli, in order to test the generalisability of current published results, and allow for more robust (...) results in future work. (shrink)

Some philosophers search for the mark of the cognitive: a set of individually necessary and jointly sufficient conditions identifying all instances of cognition. They claim that the mark of the cognitive is needed to steer the development of cognitive science on the right path. Here, I argue that, at least at present, it cannot be provided. First (§2), I identify some of the factors motivating the search for a mark of the cognitive, each yielding a desideratum the mark is supposed (...) to satisfy (§2.1). I then (§2.2) highlight a number of tensions in the literature on the mark of the cognitive, suggesting they’re best resolved by distinguishing two distinct programs. The first program (§3) is that of identifying a mark of the cognitive capturing our everyday notion of cognition. I argue that such a program is bound to fail for a number of reasons: it is not clear whether such an everyday notion exists; and even if it existed, it would not be able to spell out individually necessary and jointly sufficient conditions for cognition; and even if it were able to spell them out, these conditions won’t satisfy the desiderata a mark of the cognitive should satisfy. The second program is that of identifying a mark of the cognitive spelling out a genuine scientific kind. But the current state of fragmentation of cognitive science, and the fact that it is splintered in a myriad of different research traditions, prevent us from identifying such a kind. And we have no reason to think that these various research traditions will converge, allowing us to identify a single mark. Or so, at least, I will argue in (§4). I then conclude the paper (§5) deflecting an intuitive objection, and exploring some of the consequences of the thesis I have defended. (shrink)

Neural structural representations are cerebral map- or model-like structures that structurally resemble what they represent. These representations are absolutely central to the “cognitive neuroscience revolution”, as they are the only type of representation compatible with the revolutionaries’ mechanistic commitments. Crucially, however, these very same commitments entail that structural representations can be observed in the swirl of neuronal activity. Here, I argue that no structural representations have been observed being present in our neuronal activity, no matter the spatiotemporal scale of observation. (...) My argument begins by introducing the “cognitive neuroscience revolution” (Sect. 1 ) and sketching a prominent, widely adopted account of structural representations (Sect. 2 ). Then, I will consult various reports that describe our neuronal activity at various spatiotemporal scales, arguing that none of them reports the presence of structural representations (Sect. 3 ). After having deflected certain intuitive objections to my analysis (Sect. 4 ), I will conclude that, in the absence of neural structural representations, representationalism and mechanism can’t go together, and so the “cognitive neuroscience revolution” is forced to abandon one of its commitments (Sect. 5 ). (shrink)

In his novel Remembrance of Things Past, Marcel Proust argues that conventional descriptions of the phenomenology of consciousness are incomplete because they focus too much on the highly-salient sensory information that dominates each moment of awareness and ignore the network of associations that lies in the background. In this paper, I explicate Proust’s theory of conscious experience and show how it leads him directly to a theory of aesthetic perception. Proust’s division of awareness into two components roughly corresponds to William (...) James’ division of the stream of thought into a “nucleus” and “fringe.” Proust argues that the function of art is to evoke the underlying associative network indirectly in the mind of the observer by using carefully chosen sensory surfaces to control the stream of thought. I propose a possible neural basis for this Proustian/Jamesian phenomenology, and argue that the general principles of Proustian aesthetics can be applied to all forms of art. I conclude that a scientific theory of art should follow in a straightforward manner from a scientific theory of consciousness. (shrink)

Conceptual spaces have become an increasingly popular modeling tool in cognitive psychology. The core idea of the conceptual spaces approach is that concepts can be represented as regions in similarity spaces. While it is generally acknowledged that not every region in such a space represents a natural concept, it is still an open question what distinguishes those regions that represent natural concepts from those that do not. The central claim of this paper is that natural concepts are represented by the (...) cells of an optimally designed similarity space. (shrink)

Especialistas em desenvolvimento de Inteligência Artificial prevêem que o avanço no desenvolvimento de sistemas e agentes inteligentes irá remodelar áreas vitais em nossa sociedade. Contudo, se tal avanço não for realizado de maneira prudente e crítico-reflexiva, pode resultar em desfechos negativos para a humanidade. Por este motivo, diversos pesquisadores na área têm desenvolvido uma concepção de IA robusta, benéfica e segura para a preservação da humanidade e do meio-ambiente. Atualmente, diversos dos problemas em aberto no campo de pesquisa em IA (...) advêm da dificuldade de evitar comportamentos indesejados de agentes e sistemas inteligentes, e ao mesmo tempo especificar o que realmente queremos que tais sistemas façam, especialmente quando prospectamos a possibilidade de agentes inteligentes atuarem em vários domínios ao longo prazo. É de suma importância que agentes inteligentes artificiais tenham seus valores alinhados com os valores humanos, dado ao fato de que não podemos esperar que uma IA desenvolva valores morais humanos por conta de sua inteligência, conforme é discutido na Tese da Ortogonalidade. Talvez tal dificuldade venha da maneira que estamos abordando o problema de expressar objetivos, valores e metas, utilizando de métodos cognitivos representacionais. Uma solução para este problema seria a abordagem dinâmica proposta por Dreyfus, que com base na filosofia fenomenológica mostra que a experiência humana do ser-no-mundo em diversos aspectos não é bem representada pelo método cognitivo simbólico ou conexionista, especialmente na questão de aprendizagem de valores. Uma possível abordagem para esse problema seria a utilização de modelos téoricos como SED para abordar o porblema de aprendizagem de valores em IA. (shrink)

In this paper we investigate whether one of the most common uses of the concept of representation is justifiable by suggesting the conditions under which it can be accepted and how it can be related to mental states. We present mental states in terms of private experiences and public events. We argue that a representation is a relation involving three main elements as well as the user of the representation, and defend that the conditions in which we can conceive neural (...) activity as representational are set by the context of observing a correlation between public events and patterns of neural activity. We aim at demonstrating that neural activity can be seen as both representational and non-representational - but rather constitutive - depending upon if we are considering public events under the perspective of the observer, or if we are considering private experiences under the subjective perspective. (shrink)

Are there representations in the brain? It depends on what you mean by representations, and it depends on what you want them to do for you—both in terms of the causal role they play in the system, and in terms of their explanatory value. But ideally, we would like an account of representation that allows us to assign a representational role and content to the appropriate mechanistic precursors of behavior that in fact play that role and conversely, search for the (...) mechanistic realizers of representational roles that are posited by our models of behavior and cognition. Such an account would be methodologically valuable in neuroscience. Lately, people have started asking similar questions about deep neural networks. What representations do they learn and use, and what is the relation between those representations and the sometimes impressive capacities these networks exhibit? More philosophically puzzling, perhaps, is the question of why the internal activities or dispositions labeled as such in neuroscience and AI research should count as representations at all. I think we can give a unified answer to both sets of questions, in the form of a kind of representational pragmatism. What makes something a representation just is that we can identify and re-identify it as such, and moreover, that we can manipulate it effectively to do whatever it is that we think representations ought to do for us in that particular context. For the first condition, the idea is that so long as we have some probe that allows us to pick out the relevant causally effective candidate, we should consider that to be a candidate representation-relative-to-that-probe. The second condition can be understood as a kind of anti-gerrymandering constraint: one ought to be able to intervene on the candidate, and see effects of that intervention consistent with the functional role the representation is supposed to play. Naturally the details need to be spelled out for different contexts—and I will articulate them for a few illustrative cases—which effectively allows for a kind of pluralism about representation depending on the setting and the kinds of questions being asked. With the extra components filled in, representational pragmatism can make sense of existing practices in neuroscience and AI, as well as their relationship to naturalistic theories of representation in philosophy. (shrink)

The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented opportunities to study the human brain in action as language is read and understood. Recent contextualized language models seem to be able to capture homonymic meaning variation (“bat”, in a baseball vs. a vampire context), as (...) well as more nuanced differences of meaning—for example, polysemous words such as “book”, which can be interpreted in distinct but related senses (“explain a book”, information, vs. “open a book”, object) whose differences are fine‐grained. We study these subtle differences in lexical meaning along the concrete/abstract dimension, as they are triggered by verb‐noun semantic composition. We analyze functional magnetic resonance imaging (fMRI) activations elicited by Italian verb phrases containing nouns whose interpretation is affected by the verb to different degrees. By using a contextualized language model and human concreteness ratings, we shed light on where in the brain such fine‐grained meaning variation takes place and how it is coded. Our results show that phrase concreteness judgments and the contextualized model can predict BOLD activation associated with semantic composition within the language network. Importantly, representations derived from a complex, nonlinear composition process consistently outperform simpler composition approaches. This is compatible with a holistic view of semantic composition in the brain, where semantic representations are modified by the process of composition itself. When looking at individual brain areas, we find that encoding performance is statistically significant, although with differing patterns of results, suggesting differential involvement, in the posterior superior temporal sulcus, inferior frontal gyrus and anterior temporal lobe, and in motor areas previously associated with processing of concreteness/abstractness. (shrink)

In this chapter, Pautz raises a puzzle about spatial experience for phenomenal internalists like Ned Block. If an accidental, lifelong brain-in-the-void (BIV) should have all the same experiences as you, it would have an experience as of items having various shapes, and be able to acquire concepts of those shapes, despite being cut off from real things with the shapes. Internalists cannot explain this by saying that BIV is presented with Peacocke-style visual field regions having various shapes, because these would (...) have to be non-physical sense data. They might instead explain this by saying that BIV “phenomenally represents” various shape properties. But since BIV lacks any interesting physical relations to shapes, this would imply that phenomenally representation is an irreducible relation. (shrink)

In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue that (...) MVPA does not inherently provide a new answer to classical worries about reverse inference, and that the method faces pervasive interpretive problems of its own. Further, the epistemic setting of MVPA and other decoding methods contributes to a potentially worrisome shift towards prediction and away from explanation in fundamental neuroscience. (shrink)

At the beginning of the 20th century, Gestalt psychologists put forward the concept of psychoneural isomorphism, which was meant to replace Fechner’s obscure notion of psychophysical parallelism and provide a heuristics that may facilitate the search for the neural correlates of the mind. However, the concept has generated much confusion in the debate, and today its role is still unclear. In this contribution, I will attempt a little conceptual spadework in clarifying the concept of psychoneural isomorphism, focusing exclusively on conscious (...) visual perceptual experience and its neural correlates. Firstly, I will outline the history of our concept, and its alleged metaphysical and epistemic roles. Then, I will clarify the nature of isomorphism and rule out its metaphysical role. Finally, I will review some epistemic roles of our concept, zooming in on the work of Jean Petitot, and suggest that it does not play a relevant heuristic role. I conclude suggesting that psychoneural isomorphism might be an indicator of robustness for certain mathematical descriptions of perceptual content. (shrink)