Representation in Neuroscience

Recent research suggests that human memories are stored not between neurons as synaptic weights, but within individual neurons themselves. This opens the possibility to replace the dominant paradigm of brain function – neural networks – with a new one. In this article, I explore how “identified neurons” could explain how memories are stored, and how human traits are implemented in the brain.

Many of us consider it uncontroversial that information processing is a natural function of the brain. Since functions in biology are only won through empirical investigation, there should be a significant body of unambiguous evidence that supports this functional claim. Before we can interpret the evidence, however, we must ask what it means for a biological system to process information. Although a concept of information is generally accepted in the neurosciences without critique, in other biological sciences applications of information, despite (...) careful analysis, remain controversial. In this work I will review classical stimulus-response studies in neuroscience and use Claude Shannon’s mathematical information theory as a starting point to interpret information processing as a function of the brain. I will illustrate a disanalogy between Shannon’s communication model (source, encode, channel, receiver, decode) and neural systems, and will argue that the neural code is not very code-like in comparison to genetic and engineered codes. I suggest that we have conflated the act of representing neuroscientific facts—which we do to summarize and communicate our findings with others—with taking experimental facts to be representations. (shrink)

Many cognitive scientists, neuroscientists, and philosophers of science consider it uncontroversial that the brain processes information. In this work we broadly consider the types of experimental evidence that would support this claim, and find that although physical features of specific brain areas selectively covary with external stimuli or abilities, there is no direct evidence supporting an information processing function of any particular brain area.

Formal representations drawn from rational choice theory have been used in a variety of ways to fruitfully model the way in which actual agents are approximately rational. This analysis requires bridging between ideal normative theory, in which the mechanisms, representations, and other such internal parts are in an important sense interchangeable, and descriptive psychological theory, in which understanding the internal workings of the agent is often the main goal of the entire inquiry. In this paper, I raise a problem brought (...) on by this gap: for almost every theory of approximate rationality, there will be an empirically indistinguishable alternative that individuates the parts of the agent in a significantly different way. (shrink)

Talk of ”mental representations” is ubiquitous in the philosophy of mind, psychology, and cognitive science. A slogan common to many different approaches says that representations ”stand in for” the things they represent. This slogan also attaches to most talk of "internal models" in cognitive science. We argue that this slogan is either false or uninformative. We then offer a new slogan that aims to do better. The new slogan ties the role of representations to the cognitive role played by the (...) deliverances of perception. After clarifying the new slogan and warding off some misunderstandings, we discuss how the new slogan still captures the seed of truth in the old, point to some specific misunderstandings that can be avoided, and then suggest some ways that the new slogan is useful in the project of giving a satisfying philosophical theory of representation. (shrink)

According to a standard view in psychology and neuroscience, there are multiple memory systems in the brain. Philosophers and scientists of memory rely on the idea that there are multiple memory systems in the brain to infer that procedural memory is not a cognitive form of memory. As a result, memory is considered to be a disunified capacity. In this paper, I evaluate two criteria used by Michaelian to demarcate between cognitive and non-cognitive memory systems: appeal to stored content and (...) retrieval flexibility. By considering several empirical cases I argue that the criteria offered ultimately fail to distinguish between memory systems. The procedural memory the system is neither contentless nor inflexible. (shrink)

A constitutive theory of representation must address two challenges. The content determination challenge requires specifying why a particular state has a given content. The job description challenge requires spelling out the explanatory role that representational notions play in that theory. Recently, Nicholas Shea has advanced *varitel semantics* as a hybrid approach to representation to answer those challenges, supplementing teleosemantics with non-historical features –namely, exploitable relations and robustness. In this paper, I critically assess the hybrid theory’s answers to both challenges, arguing (...) that their hybrid nature undermines their merits. In each case, I will show that it is hard to establish how the alleged complementariness of the hybrid account components works. I will conclude that internal problems beset Shea’s theory of representation. (shrink)

Some philosophers have argued that we do not hear sounds as located in the environment. Others have objected that this straightforwardly contradicts the phenomenology of auditory experience. And from this they draw metaphysical conclusions about the nature of sounds—that they are events or properties of vibrating surfaces rather than waves or sensations. I argue that there is a minimal, but recognizable, notion of audition to which this phenomenal objection does not apply. While this notion doesn’t correspond to our ordinary notion (...) of auditory experience, it does—in conjunction with our lack of an uncontroversial individuation of the senses and recent interest in distinctively multisensory features of perceptual experiences—raise the possibility of more expansive notions of audition, including some that do plausibly count as corresponding to our everyday notion of audition, that lack the spatial phenomenology cited in the objection. Until this possibility is ruled out, the phenomenal objection and metaphysical conclusions drawn from it remain inconclusive. (shrink)

Cognitive representations are typically analysed in terms of content, vehicle and format. While current work on formats appeals to intuitions about external representations, such as words and maps, in this paper we develop a computational view of formats that does not rely on intuitions. In our view, formats are individuated by the computational profiles of vehicles, i.e., the set of constraints that fix the computational transformations vehicles can undergo. The resulting picture is strongly pluralistic, it makes space for a variety (...) of different formats, and is intimately tied to the computational approach to cognition in cognitive science and artificial intelligence. (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)

We explore the contribution made by oscillatory, synchronous neural activity to representation in the brain. We closely examine six prominent examples of brain function in which neural oscillations play a central role, and identify two levels of involvement that these oscillations take in the emergence of representations: enabling (when oscillations help to establish a communication channel between sender and receiver, or are causally involved in triggering a representation) and properly representational (when oscillations are a constitutive part of the representation). We (...) show that even an idealized informational sender–receiver account of representation makes the representational status of oscillations a non-trivial matter, which depends on rather minute empirical details. (shrink)

We distinguish between different problems of “aboutness”: the “hard” problem of explaining the everyday phenomenon of intentionality and three less challenging “easy” sets of problems concerning the posits of folk psychology, the notions of representation invoked in the mind‐brain sciences, and the intensionality (with an “s”) of mental language. The problem of intentionality is especially hard in that, as is the case with the hard problem of phenomenal consciousness, there is no clear path to a solution using current methods. We (...) argue that naturalistic theories of mental representation do not address the hard problem—either they are only intended to address the easy problems, or the claims they make help address the problem of intentionality only under undefended and prima facie implausible assumptions to the effect that the hard problem reduces to some combination of the easy problems. We offer a positive account of what would be required to properly face up to the problem of intentionality. (shrink)

Levenstein et al. aptly highlight some of the foundational issues in theoretical neuroscience, such as the role of abstraction and idealization in providing scientific explanations and understanding, and distinguishing under which conditions neuroscientific models provide genuine explanations, or mere descriptions, predictions, or control. -/- The authors rightly emphasize that philosophers of science can also gain valuable insights from the vast body of neuroscience literature, by employing methods of digital humanities, such as text mining, in line with the cognitive metascience approach (...) (Miłkowski, 2023). -/- Conversely, neuroscientists can benefit from the latest research in philosophy of science, particularly on the epistemic norms of diverse kinds of explanations in neuroscience, e.g., topological (Kostić, 2020), computational (Miłkowski, 2013; Chirimuuta, 2018), dynamical (Favela, 2020), and functional (Egan, 2017). Understanding the diverse goals of theories is conducive to fruitful exploratory and explanatory scientific practice. Some forms of scientific understanding are not always grounded in explanations, but also in diverse theoretical representations, e.g., diagrams, taxonomies, and diagnostic manuals (Miłkowski, 2023). Philosophical theories of understanding can also integrate different kinds of explanations in a single framework (Khalifa et al., 2022). -/- The need for these interdisciplinary connections is particularly vivid in the case of network models that are increasingly being used in neuroscience (Kostić et al., 2020), and especially when it comes to evaluating their explanatory power (Kostić, 2020; Kostić and Khalifa, 2023). -/- In conclusion, we strongly support Levenstein et al.'s call for greater collaboration between philosophers of science and neuroscientists on the foundational issues in neuroscience. Embracing this interdisciplinary approach is essential for advancing our knowledge of the brain and its function. (shrink)

Peter Godfrey-Smith recently introduced the idea of representational ‘organization’. When a collection of representations form an organized family, similar representational vehicles carry similar contents. For example, where neural firing rate represents numerosity (an analogue magnitude representation), similar firing rates represent similar numbers of items. Organization has been elided with structural representation, but the two are in fact distinct. An under-appreciated merit of representational organization is the way it facilitates computational processing. Representations from different organized families can interact, for example to (...) perform addition. Their being organized allows them to implement a useful computation. Many of the cases where organization has seemed significant, but which fall short of structural representation, are cases where representational organization underpins a computationally useful processing structure. (shrink)

The idea that the brain is a representational organ has roots in the nineteenth century, when neurologists began drawing conclusions about what the brain represents from clinical and experimental studies. One of the earliest controversies surrounding representation in the brain was the “muscles versus movements” debate, which concerned whether the motor cortex represents complex movements or rather fractional components of movement. Prominent thinkers weighed in on each side: neurologists John Hughlings Jackson and F.M.R. Walshe in favor of complex movements, neurophysiologist (...) Charles Sherrington and neurosurgeon Wilder Penfield in favor of movement components. This essay examines these and other brain scientists’ evolving notions of representation during the first eighty years of the muscles versus movements debate (c. 1873–1954). Although participants agreed about many of the superficial features of representation, their inferences reveal deep-seated disagreements about its inferential role. Divergent epistemological commitments stoked conflicting conceptions of what representational attributions imply and what evidence supports them. (shrink)

_Abstract_: The significance of neuroscientific findings for the analysis of central problems in cognitive science has long been a matter of debate. Recent developments in cognitive neuroscience have reignited this discussion, especially with regard to the study of cognitive representations and cognitive processes. The present paper focuses on multivariate analyses, a class of neuroscientific methods that promises to shed new light on the neural bases of cognitive representations. Multivariate approaches are both powerful and increasingly used. Yet, we argue that their (...) successful application in neuroscience requires significant theoretical and methodological clarification. After providing a preliminary assessment of the pros and cons of multivariate methods, we claim that their successful application crucially depends on how we conceptualize the relationships between representations, cognitive processes, and neural data, in other words, on the cognitive ontology we use to describe the human mind. Our discussion also highlights some general strengths and weaknesses of neuroscientific contributions to the program of classical cognitive science. _Keywords_: Cognitive Process; fMRI; Multivariate Analysis; Marr’s Three Levels of Analysis; Cognitive Ontology _Rappresentazioni e processi: quale ruolo per i metodi multivariati nelle neuroscienze cognitive?_ _Riassunto_: La rilevanza dei risultati neuroscientifici per quanto riguarda i problemi centrali delle scienze cognitive è motivo di discussione. Recenti sviluppi nelle neuroscienze cognitive hanno rianimato tale dibattito, in particolare rispetto allo studio delle rappresentazioni e dei processi cognitivi. Il presente articolo si focalizza sull’analisi multivariata, un insieme di metodi neuroscientifici che si promettono di studiare le basi neurali delle rappresentazioni cognitive. Nonostante le potenzialità e l’uso pervasivo degli approcci multivariati, in questo lavoro sosteniamo che prima di poter valutare il loro effettivo contributo nello studio delle rappresentazioni cognitive sia necessaria una chiarificazione teorica e metodologica. Dopo una discussione preliminare dei vantaggi e degli svantaggi dei metodi multivariati, evidenziamo come una loro efficace applicazione dipenda in maniera sostanziale da come viene intesa la relazione tra rappresentazioni, processi cognitivi e dati neurali o, in altre parole, dall’ontologia cognitiva che impieghiamo per descrivere la mente umana. Il presente lavoro affronta inoltre i generali punti di forza e gli elementi critici rispetto al contributo della ricerca neuroscientifica nel programma delle scienze cognitive classiche. _Parole chiave_: Cognitive Process; fMRI; Multivariate Analysis; Marr’s Three Levels of Analysis; Cognitive Ontology. (shrink)

Representations seem to play a major role in many neuroscientific explanations. Philosophers have long attempted to properly define what it means for a neural state to be a representation of a specific content. Teleosemantic theories of content which characterize representations, in part, by appealing to a historical notion of function, are often regarded as our best path towards an account of neural representations. This paper points to the anti-representationalist consequences of these accounts. I argue that assuming such teleosemantic views will (...) deprive representations of their explanatory role in computational explanations. My argument rests on the claim that many explanations in cognitive neuroscience are entirely independent of any historical considerations. In making this claim, I will also offer an adapted version of the famous Swampperson thought experiment, which is better suited to discussions of subpersonal neural representations. (shrink)

Why is it rational for scientists to pursue multiple models of a phenomenon at the same time? The literatures on mechanistic inquiry and scientific pursuit each develop answers to a version of this question which is rarely discussed by the other. The mechanistic literature suggests that scientists pursue different complementary models because each model provides detailed insights into different aspects of the phenomenon under investigation. The pursuit literature suggests that scientists pursue competing models because alternative models promise to solve outstanding (...) empirical and conceptual problems. Looking into research on visual processing as a case study, we suggest an integrated account of why it is rational for scientists to pursue both complementary and competing models of the same mechanism in scientific practice. (shrink)

Neuroclassicism is the view that cognition is explained by “classical” computing mechanisms in the nervous system that exhibit a clear demarcation between processing machinery and read–write memory. The psychologist C. R. Gallistel has mounted a sophisticated defense of neuroclassicism by drawing from ethology and computability theory to argue that animal brains necessarily contain read–write memory mechanisms. This argument threatens to undermine the “connectionist” orthodoxy in contemporary neuroscience, which does not seem to recognize any such mechanisms. In this paper I argue (...) that the neuroclassicist critique rests on a misunderstanding of how computability theory constrains theorizing about natural computing mechanisms. (shrink)

Circularity is the core dynamic in Nature.

Do perceptual experiences always inherit the content of their neural correlates? Most scientists and philosophers working on perception say ‘yes’. They hold the view that an experience’s content just is (i.e. is identical to) the content of its neural correlate. This paper presses back against this view, while trying to retain as much of its spirit as possible. The paper argues that type-2 blindsight experiences are plausible cases of experiences which lack the content of their neural correlates. They are not (...) experiences of the stimuli or stimulus properties prompting them, but their neural correlates represent these stimulus properties. The argument doesn’t depend on any special view of what it is for an experience to be of a stimulus or stimulus property. The upshot is that, even assuming there is a deep relationship between experiential content and neural content, that relationship is more complex than simple identity. (shrink)

Over the last 30 years, representationalist and dynamicist positions in the philosophy of cognitive science have argued over whether neurocognitive processes should be viewed as representational or not. Major scientific and technological developments over the years have furnished both parties with ever more sophisticated conceptual weaponry. In recent years, an enactive generalization of predictive processing – known as active inference – has been proposed as a unifying theory of brain functions. Since then, active inference has fueled both representationalist and dynamicist (...) campaigns. However, we believe that when diving into the formal details of active inference, one should be able to find a solution to the war; if not a peace treaty, surely an armistice of a sort. Based on an analysis of these formal details, this paper shows how both representationalist and dynamicist sensibilities can peacefully coexist within the new territory of active inference. (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)

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)

Structural representations are increasingly popular in philosophy of cognitive science. A key virtue they seemingly boast is that of meeting Ramsey's job description challenge. For this reason, structural representations appear tailored to play a clear representational role within cognitive architectures. Here, however, I claim that structural representations do not meet the job description challenge. This is because even our most demanding account of their functional profile is satisfied by at least some receptors, which paradigmatically fail the job description challenge. Hence, (...) the functional profile typically associated with structural representations does not identify representational posits. After a brief introduction, I present, in the second section of the paper, the job description challenge. I clarify why receptors fail to meet it and highlight why, as a result, they should not be considered representations. In the third section I introduce what I take to be the most demanding account of structural representations at our disposal, namely Gładziejewski's account. Provided the necessary background, I turn from exposition to criticism. In the first half of the fourth section, I equate the functional profile of structural representations and receptors. To do so, I show that some receptors boast, as a matter of fact, all the functional features associated with structural representations. Since receptors function merely as causal mediators, I conclude structural representations are mere causal mediators too. In the second half of the fourth section I make this conclusion intuitive with a toy example. I then conclude the paper, anticipating some objections my argument invites. (shrink)

Our access to computer-generated worlds changes the way we feel, how we think, and how we solve problems. In this review, we explore the utility of different types of virtual reality, immersive or non-immersive, for providing controllable, safe environments that enable individual training, neurorehabilitation, or even replacement of lost functions. The neurobiological effects of virtual reality on neuronal plasticity have been shown to result in increased cortical gray matter volumes, higher concentration of electroencephalographic beta-waves, and enhanced cognitive performance. Clinical application (...) of virtual reality is aided by innovative brain-computer interfaces, which allow direct tapping into the electric activity generated by different brain cortical areas for precise voluntary control of connected robotic devices. Virtual reality is also valuable to healthy individuals as a narrative medium for redesigning their individual stories in an integrative process of self-improvement and personal development. Future upgrades of virtual reality-based technologies promise to help humans transcend the limitations of their biological bodies and augment their capacity to mold physical reality to better meet the needs of a globalized world. (shrink)

The discovery of the engram, the physical substrate of memory, is a central challenge for the sciences of memory. Following the application of optogenetics to the neurobiological study of memory, scientists and philosophers claim that the engram has been found. In this paper, I evaluate the implications of applying optogenetic tools to the localization of the engram. I argue that conceptions of engram localization need to be revised to be made consistent with optogenetic studies of the engram. I distinguish between (...) challenges to vehicle and content localization. First, I consider the silent engram hypothesis. According to this hypothesis, optogenetic studies indicate that synaptic efficacy, the traditional engram-bearing vehicle, is important merely for retrieval. I argue that this interpretation rests upon a misunderstanding of accessibility. Second, I argue that optogenetic-based strategies and findings conflict with preservationist and constructivist views on memory storage. There is an enduring trace, but stored content may change over time and experience, resulting in doubt about what constitutes a single engram. (shrink)

Cryptocurrency is just the tip of a never-melting iceberg…because everything in Nature is connected to everything else by an always-conserved (and uber-simple) circle. Giving us, finally, an explanation (and, technically, a use-case, and proof) for a 'self.'.

Philosophical proponents of predictive processing cast the novelty of predictive models of perception in terms of differences in the functional role and information content of neural signals. However, they fail to provide constraints on how the crucial semantic mapping from signals to their informational contents is determined. Beyond a novel interpretative gloss on neural signals, they have little new to say about the causal structure of the system, or even what statistical information is carried by the signals. That means that (...) the predictive framework for perception can be relabeled in traditional, non-predictive terms, with no empirical consequences relevant to existing or future data. To the extent that neuroscientific research based on predictive processing is both innovative and productive, it will be due to the framework’s suggestive heuristic effects, or perhaps auxiliary empirical claims about implementation, rather than a difference in the information-processing structure that it describes. (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)

Although there is a substantial philosophical literature on dynamical systems theory in the cognitive sciences, the same is not the case for neuroscience. This paper attempts to motivate increased discussion via a set of overlapping issues. The first aim is primarily historical and is to demonstrate that dynamical systems theory is currently experiencing a renaissance in neuroscience. Although dynamical concepts and methods are becoming increasingly popular in contemporary neuroscience, the general approach should not be viewed as something entirely new to (...) neuroscience. Instead, it is more appropriate to view the current developments as making central again approaches that facilitated some of neuroscience’s most significant early achievements, namely, the Hodgkin–Huxley and FitzHugh–Nagumo models. The second aim is primarily critical and defends a version of the “dynamical hypothesis” in neuroscience. Whereas the original version centered on defending a noncomputational and nonrepresentational account of cognition, the version I have in mind is broader and includes both cognition and the neural systems that realize it as well. In view of that, I discuss research on motor control as a paradigmatic example demonstrating that the concepts and methods of dynamical systems theory are increasingly and successfully being applied to neural systems in contemporary neuroscience. More significantly, such applications are motivating a stronger metaphysical claim, that is, understanding neural systems as being dynamical systems, which includes not requiring appeal to representations to explain or understand those phenomena. Taken together, the historical claim and the critical claim demonstrate that the dynamical hypothesis is undergoing a renaissance in contemporary neuroscience. (shrink)

Nick Shea’s Representation in Cognitive Science commits him to representations in perceptual processing that are about probabilities. This commentary concerns how to adjudicate between this view and an alternative that locates the probabilities rather in the representational states’ associated “attitudes”. As background and motivation, evidence for probabilistic representations in perceptual processing is adduced, and it is shown how, on either conception, one can address a specific challenge Ned Block has raised to this evidence.

The notion of “hierarchy” is one of the most commonly posited organizational principles in systems neuroscience. To this date, however, it has received little philosophical analysis. This is unfortunate, because the general concept of hierarchy ranges over two approaches with distinct empirical commitments, and whose conceptual relations remain unclear. We call the first approach the “representational hierarchy” view, which posits that an anatomical hierarchy of feed-forward, feed-back, and lateral connections underlies a signal processing hierarchy of input-output relations. Because the representational (...) hierarchy view holds that unimodal sensory representations are subsequently elaborated into more categorical and rule-based ones, it is committed to an increasing degree of abstraction along the hierarchy. The second view, which we call “topological hierarchy", is not committed to different representational functions or degrees of abstraction at different levels. Topological approaches instead posit that the hierarchical level of a part of the brain depends on how central it is to the pattern of connections in the system. Based on the current evidence, we argue that three conceptual relations between the two approaches are possible: topological hierarchies could substantiate the traditional representational hierarchy, conflict with it, or contribute to a plurality of approaches needed to understand the organization of the brain. By articulating each of these possibilities, our analysis attempts to open a conceptual space in which further neuroscientific and philosophical reasoning about neural hierarchy can proceed. (shrink)

Menary OpenMIND, MIND Group, Frankfurt am Main, 2015) has argued that the development of our capacities for mathematical cognition can be explained in terms of enculturation. Our ancient systems for perceptually estimating numerical quantities are augmented and transformed by interacting with a culturally-enriched environment that provides scaffolds for the acquisition of cognitive practices, leading to the development of a discrete number system for representing number precisely. Numerals and the practices associated with numeral systems play a significant role in this process. (...) However, the details of the relationship between the ancient number system and the discrete number system remain unclear. This lack of clarity is exacerbated by the problem of symbolic estrangement and the fact that unique features of how numeral systems represent require our ancient number system to play a dual role. These issues highlight that Dehaene’s From monkey brain to human brain, MIT Press, Cambridge, pp 133–157, 2005) neuronal recycling hypothesis may be insufficient to explain the neural mechanisms underlying the process of enculturation. In order to explain mathematical enculturation, and enculturation more generally, it may be necessary to adopt Anderson’s :245–266, 2010; After phrenology: neural reuse and the interactive brain, MIT Press, Cambridge, 2014) theory of neural reuse. (shrink)

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation (...) and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies. (shrink)

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)

Neural systems process information. This platitude contains an interesting ambiguity between multiple senses of the term “information.” According to a popular thought, the ambiguity is best resolved by reserving semantic concepts of information for the explication of neural activity at a high level of organization, and quantitative concepts of information for the explication of neural activity at a low level of organization. This article articulates the justification behind this view, and concludes that it is an oversimplification. An analysis of the (...) meaning of claims about Shannon information rates in the spiking activity of neurons is then developed. On the basis of that analysis, it is shown that quantitative conceptions of information are more intertwined with semantic concepts than they seem to be, and, partially for that reason, are also more philosophically interesting. (shrink)

Os realistas diretos sobre a memória episódica alegam que um sujeito que lembra está em contato direto com um evento passado. No entanto, como seria possível estar em contato direto com um evento que deixou de existir? Este é o assim-chamado problema da cotemporalidade. A solução padrão para este problema, a qual foi proposta por Sven Bernecker, consiste em distinguir entre, por um lado, a ocorrência de um evento, e, por outro lado, a existência de um evento, de modo que (...) um evento deixa de ocorrer sem deixar de existir – esta é a solução eternista para o problema da cotemporalidade. No entanto, alguns filósofos da memória – notadamente Kourken Michaelian – alegam que a adoção de uma metafísica eternista do tempo seria um preço metafísico muito alto a ser pago para se defender as intuições dos realistas diretos sobre a memória. Ainda que eu concorde com essas críticas, buscarei mostrar duas coisas. Primeiro, que este tipo de “argumento do senso comum” não é decisivo. Segundo, que a proposta de Bernecker permanece sendo a melhor solução para o problema da cotemporalidade. (shrink)

Mental representation is one of core theoretical constructs within cognitive science and, together with the introduction of the computer as a model for the mind, is responsible for enabling the ‘cognitive turn’ in psychology and associated fields. Conceiving of cognitive processes, such as perception, motor control, and reasoning, as processes that consist in the manipulation of contentful vehicles representing the world has allowed us to refine our explanations of behavior and has led to tremendous empirical advancements. Despite the central role (...) that the concept plays in cognitive science, there is no unanimously accepted characterization of mental representation. Technological and methodological progress in the cognitive sciences has produced numerous computational models of the brain and mind, many of which have introduced mutually incompatible notions of mental representation. This proliferation has led some philosophers to question the metaphysical status and explanatory usefulness of the notion. This book contains state-of-the-art chapters on the topic of mental representation, assembling some of the leading experts in the field and allowing them to engage in meaningful exchanges over some of the most contentious questions. The collection gathers both proponents and critics of the concept of mental representation, allowing them to engage with topics such as the ontological status of representations, the possibility of formulating a general account of mental representation which would fit our best explanatory practices, and the possibility of delivering such an account in fully naturalistic terms. (shrink)

What is the relationship between information and representation? Dating back at least to Dretske (1981), an influential answer has been that information is a rung on a ladder that gets one to representation. Representation is information, or representation is information plus some other ingredient. In this paper, I argue that this approach oversimplifies the relationship between information and representation. If one takes current probabilistic models of cognition seriously, information is connected to representation in a new way. It enters as a (...) property of the represented content as well as a property of the vehicles that carry that content. This offers a new, conceptually and logically distinct way in which information and representation are intertwined in cognition. (shrink)

It’s often claimed in the philosophical and scientific literature on temporal representation that there is no such thing as a genuine sensory system for time. In this paper, I argue for the opposite—many animals, including all mammals, possess a genuine sensory system for time based in the circadian system. In arguing for this conclusion, I develop a semantics and meta-semantics for explaining how the endogenous rhythms of the circadian system provide organisms with a direct information link to the temporal structure (...) of their environment. In doing so, I highlight the role of sensory systems in an information processing architecture. (shrink)

In the 1980s, a number of philosophers argued that perception is analog. In the ensuing years, these arguments were forcefully criticized, leaving the thesis in doubt. This paper draws on Weber’s Law, a well-entrenched finding from psychophysics, to advance a new argument that perception is analog. This new argument is an adaptation of an argument that cognitive scientists have leveraged in support of the contention that primitive numerical representations are analog. But the argument here is extended to the representation of (...) non-numerical magnitudes, such as luminance and distance, and shown to apply to perception and not just cognition. The relevant sense of ‘analog’ is also clarified, and two powerful objections are addressed. Finally, the question whether perception’s analog vehicles are located in conscious experience is explored and related to a well-known controversy within psychophysics. (shrink)

Joint actions often require agents to track others’ actions while planning and executing physically incongruent actions of their own. Previous research has indicated that this can lead to visuomotor interference effects when it occurs outside of joint action. How is this avoided or overcome in joint actions? We hypothesized that when joint action partners represent their actions as interrelated components of a plan to bring about a joint action goal, each partner’s movements need not be represented in relation to distinct, (...) incongruent proximal goals. Instead they can be represented in relation to a single proximal goal – especially if the movements are, or appear to be, mechanically linked to a more distal joint action goal. To test this, we implemented a paradigm in which participants produced finger movements that were either congruent or incongruent with those of a virtual partner, and either with or without a joint action goal (the joint flipping of a switch, which turned on two light bulbs). Our findings provide partial support for the hypothesis that visuomotor interference effects can be reduced when two physically incongruent actions are represented as mechanically interdependent contributions to a joint action goal. (shrink)

Introduction: The objective of this investigation is to analyse the informational circuits of the brain connections with the body from neurologic and neuroscience point of view, on the basis of the concepts of information promoted by the Informational Model of Consciousness. Analysis: Distinguishing between the virtual and matter-related information promoted by the Informational Model of Consciousness, the main specific features of consciousness are analyzed from the informational perspective, showing that the informational architecture of consciousness consists in seven groups of specific (...) activities, defined as cognitive centres, each of them with specific distinct tasks, but correlated each other: centre of acquisition and storing of information (memory), centre of decision and command (decision), centre of the emotional states (emotions), centre of the body maintenance (power and health), centre of the genetic elaboration/transmission (reproduction) and the info-genetic generator, inherited from the parents (predispositions, talents and skills). A special centre, dedicated to the connectivity with some extra-power properties of the mind is also introduced, assuring an intimate supra-sensitive detection of the world to explain the associated phenomena of the near-death experiences. Result: The activity of all these centres should be supported neurologically by the brain neuro-connectivity to the body and to external and internal info-signals. On the basis of the informational analysis, the neuro-connections of the brain regions associated to the main characteristics of the cognitive centres are highlighted, showing the anatomic and neuro-functional relation between the distinct components of the brain and the specific operating body regions. These connections describe in terms of information the brain-body neuro-activities as informational specific circuits, composed by the info-operational subsystems managed by the brain, and sensors, transducer and execution elements. Conclusion: The components and connections mind-body stipulated by the Informational Model of Consciousness are supported by the neurologic/neuroscience evidence. (shrink)

Mainstream teleosemantics is the view that mental representation should be understood in terms of biological functions, which, in turn, should be understood in terms of selection processes. One of the traditional criticisms of teleosemantics is the problem of novel contents: how can teleosemantics explain our ability to represent properties that are evolutionarily novel? In response, some have argued that by generalizing the notion of a selection process to include phenomena such as operant conditioning, and the neural selection that underlies it, (...) we can resolve this problem. Here, we do four things: we develop this suggestion in a rigorous way through a simple example, we draw on recent neurobiological research to support its empirical plausibility, we defend the move from a host of objections in the literature, and we sketch how the picture can be extended to help us think about more complex “conceptual” representations and not just perceptual ones. (shrink)

The concept of mental representation has long been considered to be central concept of philosophy of mind and cognitive science. But not everyone agrees. Neo-behaviorists aim to explain the mind without positing any representations. My aim here is not to assess the merits and demerits of neo-behaviorism, but to take their challenge seriously and ask the question: What justifies the attribution of representations to an agent? Both representationalists and neo-behaviorists tend to take it for granted that the real question about (...) representations is whether we should be realist about the theory of representationalism. This paper is an attempt to shift the emphasis from the debate concerning realism about theories to the one concerning realism about entities. My claim is that regardless of whether we are realist about representational theories of the mind, we have compelling reasons to endorse entity realism about mental representations. (shrink)

In recent years, a number of different disciplines have begun to investigate the fundamental role context appears to play in a number of cognitive phenomena. Traditionally, linguistics, and the fields of communication and pragmatics in particular, have been the areas that have focused the most on contextual effects. Context has increasingly been studied for its role in influencing mental concepts, for some scholars being considered constitutive for most – if not all – concepts. Cognitive neuroscience is now starting to consider (...) in a systematic way how context interacts with neural responses, although this research is still scattered and concentrated in a small number of specific cases only. In this chapter, we attempt to tie these three levels together, since only from their integration can a comprehensive explanation of how context affects cognition be constructed. The way context drives language comprehension depends on the effects of context on the conceptual scaffolding of the listener, which in turn, is the result of his neural responses in combination to context. These neural responses derive from learning throughout the history of experiences of the individual, and the association between possible contexts and heard utterances. The road we take to accomplishing the multi-level integration between what appear to be distant domains, is a computational one. This approach meets with the mechanistic framework of explanation, which is currently held as the most appropriate way of approaching cognitive phenomena that is often characterized by a multiplicity of levels, as is the case with context. The core underlying concept of the neurocomputational framework here proposed, is an account of neural representation, based on structural similarity. Structural representations are still the best option on the market in cognitive science, but in their traditional form, derived from classical measurement theory, are affected by a number of serious drawbacks, including not being able to account for context. We suggest a different account of structural similarity, one informed by current neuroscience, where the homomorphic relations required for structural similarity are derived from neural population coding. In a preliminary mathematical sketch, we indicate how this approach can construct neural aggegations that are sensitive to context. (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)

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)

Barsalou has recently argued against the strategy of identifying amodal neural representations by using their cross-modal responses (i.e., their responses to stimuli from different modalities). I agree that there are indeed modal structures that satisfy this “cross-modal response” criterion (CM), such as distributed and conjunctive modal representations. However, I argue that we can distinguish between modal and amodal structures by looking into differences in their cross-modal responses. A component of a distributed cell assembly can be considered unimodal because its responses (...) to stimuli from a given modality are stable, whereas its responses to stimuli from any other modality are not (i.e., these are lost within a short time, plausibly as a result of cell assembly dynamics). In turn, conjunctive modal representations, such as superior colliculus cells in charge of sensory integration, are multimodal because they have a stable response to stimuli from different modalities. Finally, some prefrontal cells constitute amodal representations because they exhibit what has been called ‘adaptive coding’. This implies that their responses to stimuli from any given modality can be lost when the context and task conditions are modified. We cannot assign them a modality because they have no stable relation with any input type. (shrink)