Representation in Neuroscience

La autora presenta una critica a la concepcion clasica de los sentidos asumida por la mayoria de autores naturalistas que pretenden explicar el contenido mental. Esta crítica se basa en datos neurobiologicos sobre los sentidos que apuntan a que estos no parecen describir caracteristicas objetivas del mundo, sino que actuan de forma ʼnarcisita', es decir, representan informacion en funcion de los intereses concretos del organismo.El articulo se encuentra también en: Bechtel, et al., Philosophy and the Neuroscience.

The theory put forward by Cohen Kadosh & Walsh (CK&W) proposing that semantic representations of numerical magnitude in the parietal cortex are format-specific, does not specify how these representations might be constructed over the course of learning and development. The developmental predictions of the non-abstract theory are discussed and the need for a developmental perspective on the abstract versus non-abstract question highlighted.

The contribution that neurobiological data provide us to comprehend the psychological aspects of economic decision-making is critically examined. First, different kinds of correspondences between neural events and mental activities are identified. On the basis of the distinctions made, some recent studies are selected, each of which focuses on a different stage of decision-making and employs a different set of neurobiological data. The thorough analysis of each study suggests that neuro-mental correspondences do not have an evidentiary function but rather a heuristic (...) function, since they can point to the presence of specific differences between phenomena considered homogeneous or to the existence of a relationship between two mental activities which, however, have to be tested by psychological investigations. (shrink)

Stable neuronal assemblies are generally regarded as neural correlates of mental representations. Their temporal sequence corresponds to the experience of a direction of time, sometimes called the psychological time arrow. We show that the stability of particular, biophysically motivated models of neuronal assemblies, called coupled map lattices, is supported by causal interactions among neurons and obstructed by non-causal or anti-causal interactions among neurons. This surprising relation between causality and stability suggests that those neuronal assemblies that are stable due to causal (...) neuronal interactions, and thus correlated with mental representations, generate a psychological time arrow. Yet this impact of causal interactions among neurons on the directed sequence of mental representations does not rule out the possibility of mentally less efficacious non-causal or anti-causal interactions among neurons. (shrink)

The dynamics of neuronal systems, briefly neurodynamics, has developed into an attractive and influential research branch within neuroscience. In this paper, we discuss a number of conceptual issues in neurodynamics that are important for an appropriate interpretation and evaluation of its results. We demonstrate their relevance for selected topics of theoretical and empirical work. In particular, we refer to the notions of determinacy and stochasticity in neurodynamics across levels of microscopic, mesoscopic and macroscopic descriptions. The issue of correlations between neural, (...) mental and behavioral states is also addressed in some detail. We propose an informed discussion of conceptual foundations with respect to neurobiological results as a viable step to a fruitful future philosophy of neuroscience. (shrink)

A categorical, higher dimensional algebra and generalized topos framework for Łukasiewicz–Moisil Algebraic–Logic models of non-linear dynamics in complex functional genomes and cell interactomes is proposed. Łukasiewicz–Moisil Algebraic–Logic models of neural, genetic and neoplastic cell networks, as well as signaling pathways in cells are formulated in terms of non-linear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable ‘next-state functions’ is extended to a Łukasiewicz–Moisil (...) Topos with an n-valued Łukasiewicz–Moisil Algebraic Logic subobject classifier description that represents non-random and non-linear network activities as well as their transformations in developmental processes and carcinogenesis. The unification of the theories of organismic sets, molecular sets and Robert Rosen’s (M,R)-systems is also considered here in terms of natural transformations of organismal structures which generate higher dimensional algebras based on consistent axioms, thus avoiding well known logical paradoxes occurring with sets. Quantum bionetworks, such as quantum neural nets and quantum genetic networks, are also discussed and their underlying, non-commutative quantum logics are considered in the context of an emerging Quantum Relational Biology. (shrink)

The framework within which Tsuda proposes his solution for transitory dynamics between attractor states is flawed from a neurological perspective. We present a more genuine framework and discuss the roles that external input and synaptic modulations play in the evolution of the dynamics of neuronal systems. Chaotic itinerancy, it is argued, is not necessary for transitory dynamics.

Page's target article presents an argument for the use of localist, connectionist models in future psychological theorising. The “manifesto” marshalls a set of arguments in favour of localist connectionism and against distributed connectionism, but in doing so misses a larger argument concerning the level of psychological explanation that is appropriate to a given domain.

2. Daugman, J. G. Brain metaphor and brain theory 3. Mundale, J. Neuroanatomical Foundations of Cognition: Connecting the Neuronal Level with the Study of Higher Brain Areas.

Under shift, caused for example by eye movement, or by relative movement of the subject or object of perception, the cortical representation undergoes very large changes in “size” and “shape.” Space-variance of cortical representation rules out models that fundamentally require linear interpolation between shifted patterns (e.g., Edelman's model) or rigid shift of an invariant retinal stimulus corresponding to shift at the cortex (e.g., the shifter theory of van Essen). Recently, a computational solution of “quasi-shift” invariance for space-variant mappings has been (...) constructed (Bonmassar & Schwartz 1997a; 1997b). (shrink)

Experimental evidence and mathematical/computational models show that in many cases chaotic, nonregular oscillations are adequate to describe the dynamical behaviour of neural systems. Further work is needed to understand the meaning of this dynamical regime for modelling information processing in the brain.

We argue that neural networks for semantic cognition, as proposed by Rogers & McClelland (R&M), do not acquire semantics and therefore cannot be the basis for a theory of semantic cognition. The reason is that the neural networks simply perform statistical categorization procedures, and these do not require any semantics for their successful operation. We conclude that this has severe consequences for the semantic cognition views of R&M.

Following the concept of internal representations, signal processing in a neuronal system has to be evaluated exclusively on the basis of internal system characteristics. Thus, this approach omits the external observer as a control function for sensory integration. Instead, the configuration of the system and its computational performance are the effects of endogeneous factors. Such self-referential operation is due to a strictly local computation in a network. Thereby, computations follow a set of rules that constitutes the emergent behaviour of the (...) system. Because these rules can be demonstrated to correspond to a "logic" intrinsic to the system, it can be shown that the concept of internal representation provides the basis for neurosemantics. (shrink)

This volume provides an up to date and comprehensive overview of the philosophy and neuroscience movement, which applies the methods of neuroscience to traditional philosophical problems and uses philosophical methods to illuminate issues in neuroscience. At the heart of the movement is the conviction that basic questions about human cognition, many of which have been studied for millennia, can be answered only by a philosophically sophisticated grasp of neuroscience's insights into the processing of information by the human brain. Essays in (...) this volume are clustered around five major themes: data and theory in neuroscience; neural representation and computation; visuomotor transformations; color vision; and consciousness. (shrink)

Philosophers have been talking about brain states for almost 50 years and as of yet no one has articulated a theoretical account of what one is. In fact this issue has received almost no attention and cognitive scientists still use meaningless phrases like 'C-fiber firing' and 'neuronal activity' when theorizing about the relation of the mind to the brain. To date when theorists do discuss brain states they usually do so in the context of making some other argument with the (...) result being that any discussion of what brain states are has a distinct en passant flavor. In light of this it is a goal of mine to make brain states the center of attention by providing some general discussion of them. I briefly look at the argument of Bechtel and Mundale, as I think that they expose a common misconception philosophers had about brain states early on. I then turn to briefly examining Polger's argument, as I think he offers an intuitive account of what we expect brain states to be as well as a convincing argument against a common candidate for knowledge about brain states that is currently "on the scene." I then introduce a distinction between brain states and states of the brain: Particular brain states occur against background states of the brain. I argue that brain states are patterns of synchronous neural firing, which reflects the electrical face of the brain; states of the brain are the gating and modulating of neural activity and reflect the chemical face of the brain. (shrink)

Good old-fashioned cognitive science characterizes human thinking as symbol manipulation qua computation and therefore emphasizes the processing of symbolic representations as a necessary if not sufficient condition for “general intelligent action.” Recent alternative conceptions of human thinking tend to deemphasize if not altogether eschew the notion of representation. The present paper shows how classical American pragmatist conceptions of human thinking can successfully avoid either of these extremes, replacing old-fashioned conceptions of representation with one that characterizes both representatum and representans in (...) externalist terms. (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.

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)

This article distinguishes three archetypal ways of articulating spatial cognition: (1) via metric representation of objective geometry, (2) via somatosensory constitution of the peripersonal environment, and (3) via pragmatic comprehension of the finalistic sense of action. The last one is documented by neuroscientific studies concerning mirror neurons. Bio-robotic experiments implementing mirror functions confirm the constitutive role of goal-oriented actions in spatial processes.

We focus on Karmiloff-Smith's Representational redescription model, arguing that it poses some problems concerning the architecture of a redescribing system. To discuss the topic, we consider the implicit/explicit dichotomy and the relations between natur al language and the language of thought. We argue that the model regards how knowledge is employed rather than how it is represented in the system.

A formalism is introduced to represent the connective organization of an evolving neuronal network and the effects of environment on this organization by stabilization or degeneration of labile synapses associated with functioning. Learning, or the acquisition of an associative property, is related to a characteristic variability of the connective organization: the interaction of the environment with the genetic program is printed as a particular pattern of such organization through neuronal functioning. An application of the theory to the development of the (...) neuromuscular junction is proposed and the basic selective aspect of learning emphasized. (shrink)

Cognitive science has always included multiple methodologies and theoretical commitments. The philosophy of cognitive science should embrace, or at least acknowledge, this diversity. Bechtel's (2009a) proposed philosophy of cognitive science, however, applies only to representationalist and mechanist cognitive science, ignoring the substantial minority of dynamically-oriented cognitive scientists. As an example of non-representational, dynamical cognitive science, we describe strong anticipation as a model for circadian systems (Stepp and Turvey 2009). We then propose a philosophy of science appropriate to non-representational, dynamical cognitive (...) science. (shrink)

This paper outlines the functional capacities of a novel scheme for cognitive representation and computation, and it explores the possible implementation of this scheme in the massively parallel organization of the empirical brain. The suggestion is that the brain represents reality by means of positions in suitably constitutes phase spaces; and the brain performs computations on these representations by means of coordinate transformations from one phase space to another. This scheme may be implemented in the brain in two distinct forms: (...) (1) as a phase-space sandwich, which may explain certain laminar structures, such as cerebral cortex and the superior colliculus; and (2) as a neural matrix, which may explain other structures, such as the beautifully orthogonal architecture of the cerebellum. (shrink)

The first question concerns a fundamental assumption of most researchers who theorize about the brain. Do neural systems exploit classical compositional and systematic representations, distributed representations, or no representations at all? The question is not easily answered. Connectionism, for example, has been criticised for both holding and challenging representational views. The second quesútion concerns the crucial methodological issue of how results emerging from the various brain sciences can help to constrain cognitive scientific models. Finally, the third question focuses attention on (...) a major challenge to contemporary cognitive science: the challenge of understanding the mind as a controller of embodied and environmentally embedded action. (shrink)

While the study of implicit learning is nothing new, the field as a whole has come to embody — over the last decade or so — ongoing questioning about three of the most fundamental debates in the cognitive sciences: The nature of consciousness, the nature of mental representation (in particular the difficult issue of abstraction), and the role of experience in shaping the cognitive system. Our main goal in this chapter is to offer a framework that attempts to integrate current (...) thinking about these three issues in a way that specifically links consciousness with adaptation and learning. Our assumptions about this relationship are rooted in further assumptions about the nature of processing and of representation in cognitive systems. When considered together, we believe that these assumptions offer a new perspective on the relationships between conscious and unconscious processing and on the function of consciousness in cognitive systems. (shrink)

In this chapter, I sketch a conceptual framework which takes it as a starting point that conscious and unconscious cognition are rooted in the same set of interacting learning mechanisms and representational systems. On this view, the extent to which a representation is conscious depends in a graded manner on properties such as its stability in time or its strength. Crucially, these properties are accrued as a result of learning, which is in turn viewed as a mandatory process that always (...) accompanies information processing. From this perspective, consciousness is best characterized as involving (1) a graded continuum defined over “quality of representation”, such that availability to consciousness and to cognitive control correlates with quality , and (2) the implication of systems of metarepresentations. A first implication of these ideas is that the main function of consciousness is to make flexible, adaptive control over behavior possible. A second, much more speculative implication, is that we learn to be conscious. This I call the “radical plasticity thesis” — the hypothesis that consciousness emerges in systems capable not only of learning about their environment, but also about their own internal representations of it. (shrink)

I defend a theory of mental representation that satisfies naturalistic constraints. Briefly, we begin by distinguishing (i) what makes something a representation from (ii) given that a thing is a representation, what determines what it represents. Representations are states of biological organisms, so we should expect a unified theoretical framework for explaining both what it is to be a representation as well as what it is to be a heart or a kidney. I follow Millikan in explaining (i) in terms (...) of teleofunction, explicated in terms of natural selection. -/- To explain (ii), we begin by recognizing that representational states do not have content, that is, they are neither true nor false except insofar as they both “point to” or “refer” to something, as well as “say” something regarding whatever it is they are about. To distinguish veridical from false representations, there must be a way for these separate aspects to come apart; hence, we explain (ii) by providing independent theories of what I call f-reference and f-predication (the ‘f’ simply connotes ‘fundamental’, to distinguish these things from their natural language counterparts). -/- Causal theories of representation typically founder on error, or on what Fodor has called the disjunction problem. Resemblance or isomorphism theories typically founder on what I’ve called the non-uniqueness problem, which is that isomorphisms and resemblance are practically unconstrained and so representational content cannot be uniquely determined. These traditional problems provide the motivation for my theory, the structural preservation theory, as follows. F-reference, like reference, is a specific, asymmetric relation, as is causation. F-predication, like predication, is a non-specific relation, as predicates typically apply to many things, just as many relational systems can be isomorphic to any given relational system. Putting these observations together, a promising strategy is to explain f-reference via causal history and f-predication via something like isomorphism between relational systems. -/- This dissertation should be conceptualized as having three parts. After motivating and characterizing the problem in chapter 1, the first part is the negative project, where I review and critique Dretske’s, Fodor’s, and Millikan’s theories in chapters 2-4. Second, I construct my theory about the nature of representation in chapter 5 and defend it from objections in chapter 6. In chapters 7-8, which constitute the third and final part, I address the question of how representation is implemented in biological systems. In chapter 7 I argue that single-cell intracortical recordings taken from awake Macaque monkeys performing a cognitive task provide empirical evidence for structural preservation theory, and in chapter 8 I use the empirical results to illustrate, clarify, and refine the theory. (shrink)

Despite the impressive amount of financial resources invested in carrying out large-scale brain simulations, it is controversial what the payoffs are of pursuing this project. The present paper argues that in some cases, from designing, building, and running a large-scale neural simulation, scientists acquire useful knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. What this means, why it is not a trivial lesson, and how it advances the literature on (...) the epistemology of computer simulation are the three preoccupations addressed by the paper. (shrink)

According to John Haugeland, the capacity for “authentic intentionality” depends on a commitment to constitutive standards of objectivity. One of the consequences of Haugeland’s view is that a neurocomputational explanation cannot be adequate to understand “authentic intentionality”. This paper gives grounds to resist such a consequence. It provides the beginning of an account of authentic intentionality in terms of neurocomputational enabling conditions. It argues that the standards, which constitute the domain of objects that can be represented, reflect the statistical structure (...) of the environments where brain sensory systems evolved and develop. The objection that I equivocate on what Haugeland means by “commitment to standards” is rebutted by introducing the notion of “florid, self-conscious representing”. Were the hypothesis presented plausible, computational neuroscience would offer a promising framework for a better understanding of the conditions for meaningful representation. (shrink)

The concept of “information” is virtually ubiquitous in contemporary cognitive science. It is claimed to be “processed” (in cognitivist theories of perception and comprehension), “stored” (in cognitivist theories of memory and recognition), and otherwise manipulated and transformed by the human central nervous system. Fred Dretske's extensive philosophical defense of a theory of informational content (“semantic” information) based upon the Shannon-Weaver formal theory of information is subjected to critical scrutiny. A major difficulty is identified in Dretske's equivocations in the use of (...) the concept of a “signal” bearing informational content. Gibson's alternative conception of information (construed as analog by Dretske), while avoiding many of the problems located in the conventional use of “signal”, raises different but equally serious questions. It is proposed that, taken literally, the human CNS does not extract or process information at all; rather, whatever “information” is construed as locatable in the CNS is information only for an observer-theorist and only for certain purposes. (shrink)

Language is a spontaneously evolved emergent adaptation, not a formal computational system. Its structure does not derive from either innate or social instruction but rather self-organization and selection. Its quasi-universal features emerge from the interactions among semiotic constraints, neural processing limitations, and social transmission dynamics. The neurological processing of sentence structure is more analogous to embryonic differentiation than to algorithmic computation. The biological basis of this unprecedented adaptation is not located in some unique neurologieal structure nor the result of any (...) single mutation, but is vested in the synergistic interaction of numerous coevolved neurological biases and social dynamics. (shrink)

The Hebbian view of word representation is challenged by findings of task (level of processing)-dependent, event-related potential patterns that do not support the notion of a fixed set of neurons representing a given word. With cross-language phonological reliability encoding more asymmetrical left hemisphere activity is evoked than with word comprehension. This suggests a dynamical view of the brain as a self-organizing, connectivity-adjusting system.

In ‘Of Sensory Systems and the “Aboutness” of Mental States’, Kathleen Akins (1996) argues against what she calls ‘the traditional view’ about sensory systems, according to which they are detectors of features in the environment outside the organism. As an antidote, she considers the case of thermoreception, a system whose sensors send signals about how things stand with themselves and their immediate dermal surround (a ‘narcissistic’ sensory system); and she closes by suggesting that the signals from many sensory systems may (...) not in any familiar sense be about anything at all. Her presentation of the issues, however, overlooks resources available to ‘the traditional view’—or so I shall argue. Akins’s own thumbnail sketch of what is wrong with the traditional view is that it asks, concerning a given sensory system, ‘what is it detecting?’, when we should instead be asking ‘what is it doing?’ (352). Her point is that on the traditional view the function of a sensory system—what it's ‘for’—is to detect or indicate (values of) features of the outside environment. But at least on one version of the traditional view—namely Ruth Millikan’s—this would never be the sole or main proper function of a sensory system. (Akins does not list Millikan as a traditionalist, but Millikan fits squarely Akins’s description of them, since she believes in a naturalistic theory of aboutness and thinks it should begin with the senses.) For Millikan (1989, 1993), the proper function of a sensory system is in the first instance enabling behavioural systems—in the simplest case, motor routines—to perform their proper function. This they do, roughly, by switching on and steering the behavioural routines. Where features of the outside environment come in is as Normal (= assumed-by-the-design) conditions for the successful performance of the sensory system's proper function. That is, the only strategy for switching on and steering that is simple enough for evolution to have hit upon it, and reliable enough for evolution to have liked it, is a strategy which gears the steering to (values of) features of the outside environment. But as soon as one starts fleshing out the details of this story, one notices that they are probably quite different in the case of thermoreception from how they are with ‘distance’ senses such as vision and olfaction--a point which Akins overlooks.. (shrink)

Questions concerning the nature of representation and what representations are about have been a staple of Western philosophy since Aristotle. Recently, these same questions have begun to concern neuroscientists, who have developed new techniques and theories for understanding how the locus of neurobiological representation, the brain, operates. My dissertation draws on philosophy and neuroscience to develop a novel theory of representational content.

Cohen Kadosh & Walsh (CK&W) present convincing evidence indicating the existence of notation-specific numerical representations in parietal cortex. We suggest that the same conclusions can be drawn for a particular type of numerical representation: the representation of time. Notation-dependent representations need not be limited to number but may also be extended to other magnitude-related contents processed in parietal cortex (Walsh 2003).

We propose that a top priority of the cerebral cortex must be the discovery and explicit representation of the environmental variables that contribute as major factors to environmental regularities. Any neural representation in which such variables are represented only implicitly (thus requiring extra computing to use them) will make the regularities more complex and therefore more difficult, if not impossible, to learn. The task of discovering such important environmental variables is not an easy one, since their existence is only indirectly (...) suggested by the sensory input patterns the cortex receives – these variables are “hidden.” We present a candidate computational strategy for (1) discovering regularity-simplifying environmental variables, (2) learning the regularities, and (3) using regularities in perceptual and decision-making tasks. The SINBAD computational model discovers useful environmental variables through a search for different, but nevertheless highly correlated functions of any kind over non-overlapping subsets of the known variables, this being indicative of some important environmental variable that is responsible for the correlation. We suggest that such a search is performed in the neocortex by the dendritic trees of individual pyramidal cells. According to the SINBAD model, the basic function of each pyramidal cell is (1) to discover and represent one of the regularity-simplifying environmental variables, and (2) to learn to infer the state of its variable from the states of other variables, represented by other pyramidal cells. A network of such cells – each cell just attending to representation of its variable – can function as a sophisticated and useful inferential model of the outside world. (shrink)

The concept of representation has been a key element in the scientific study of mental processes, ever since such studies commenced. However, usage of the term has been all but too liberal—if one were to adhere to common use it remains unclear if there are examples of physical systems which cannot be construed in terms of representation. The problem is considered afresh, taking as the starting point the notion of activity spaces—spaces of spatiotemporal events produced by dynamical systems. It is (...) argued that representation can be analyzed in terms of the geometrical and topological properties of such spaces. Several attributes and processes associated with conceptual domains, such as logical structure, generalization and learning are considered, and given analogues in structural facets of activity spaces, as are misrepresentation and states of arousal. Based on this analysis, representational systems are defined, as is a key concept associated with such systems, the notion of representational capacity. According to the proposed theory, rather than being an all or none phenomenon, representation is in fact a matter of degree—that is can be associated with measurable quantities, as is behooving of a putative naturalistic construct. (shrink)

A standing challenge for the science of mind is to account for the datum that every mind faces in the most immediate – that is, unmediated – fashion: its phenomenal experience. The complementary tasks of explaining what it means for a system to give rise to experience and what constitutes the content of experience (qualia) in computational terms are particularly challenging, given the multiple realizability of computation. In this paper, we identify a set of conditions that a computational theory must (...) satisfy for it to constitute not just a sufficient but a necessary, and therefore naturalistic and intrinsic, explanation of qualia. We show that a common assumption behind many neurocomputational theories of the mind, according to which mind states can be formalized solely in terms of instantaneous vectors of activities of representational units such as neurons, does not meet the requisite conditions, in part because it relies on inactive units to shape presently experienced qualia and implies a homogeneous representation space, which is devoid of intrinsic structure. We then sketch a naturalistic computational theory of qualia, which posits that experience is realized by dynamical activity-space trajectories (rather than points) and that its richness is measured by the representational capacity of the trajectory space in which it unfolds. (shrink)