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.

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)

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.

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)

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)

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)

This essay proposes and defends a pluralistic theory of conceptual embodiment. Our concepts are represented in at least two ways: (i) through sensorimotor simulations of our interactions with objects and events and (ii) through sensorimotor simulations of natural language processing. Linguistic representations are “dis-embodied” in the sense that they are dynamic and multimodal but, in contrast to other forms of embodied cognition, do not inherit semantic content from this embodiment. The capacity to store information in the associations and inferential relationships (...) among linguistic representations extends our cognitive reach and provides an explanation of our ability to abstract and generalize. This theory is supported by a number of empirical considerations, including the large body of evidence from cognitive neuroscience and neuropsychology supporting a multiple semantic code explanation of imageability effects. (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.

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)

We criticize the lack of neuroanatomical precision in the Grodzinsky target article. We propose a more precise neuroanatomical characterization of syntactic processing and suggest that syntactic procedures are supported by the left frontal operculum in addition to the anterior part of the superior temporal gyrus, which appears to be associated with syntactic knowledge representation.

Simulation theories of social cognition abound in the literature, but it is often unclear what simulation means and how it works. The discovery of mirror neurons, responding both to action execution and observation, suggested an embodied approach to mental simulation. Over the last years this approach has been hotly debated and alternative accounts have been proposed. We discuss these accounts and argue that they fail to capture the uniqueness of embodied simulation (ES). ES theory provides a unitary account of basic (...) social cognition, demonstrating that people e their own mental states or processes represented with a bodily format in functionally attributing them to others. (shrink)

The first use of the term “information” to describe the content of nervous impulse occurs in Edgar Adrian's The Basis of Sensation (1928). What concept of information does Adrian appeal to, and how can it be situated in relation to contemporary philosophical accounts of the notion of information in biology? The answer requires an explication of Adrian's use and an evaluation of its situation in relation to contemporary accounts of semantic information. I suggest that Adrian's concept of information can be (...) to derive a concept of arbitrariness or semioticity in representation. This in turn provides one way of resolving some of the challenges that confront recent attempts in the philosophy of biology to restrict the notion of information to those causal connections that can in some sense be referred to as arbitrary or semiotic. (shrink)

The first use of the term "information" to describe the content of nervous impulse occurs 20 years prior to Shannon`s (1948) work, in Edgar Adrian`s The Basis of Sensation (1928). Although, at least throughout the 1920s and early 30s, the term "information" does not appear in Adrian`s scientific writings to describe the content of nervous impulse, the notion that the structure of nervous impulse constitutes a type of message subject to certain constraints plays an important role in all of his (...) writings throughout the period. The appearance of the concept of information in Adrian`s work raises at least two important questions: (i) what were the relevant factors that motivated Adrian`s use of the concept of information? (ii) What concept of information does Adrian appeal to, and how can it be situated in relation to contemporary philosophical accounts of the notion of information in biology? The first question involves an account of the application of communications technology in neurobiology as well as the historical and scientific background of Adrian`s major scientific achievement, which was the recording of the action potential of a single sensory neuron. The response to the second question involves an explication of Adrian`s concept of information and an evaluation of how it may be situated in relation to more contemporary philosophical explications of a semantic concept of information. I suggest that Adrian`s concept of information places limitations on the sorts of systems that are referred to as information carriers by causal and functional accounts of information. (shrink)

A connectionist vehicle theory of consciousness needs to disambiguate its criteria for identifying the relevant vehicles. Moreover, a vehicle theory may appear entirely arbitrary in sorting between what are typically thought of as conscious and unconscious processes.

A large body of research in computational vision science stems from the pioneering work of David Marr. Recently, Patricia Kitcher and others have criticized this work as depending upon optimizing assumptions, assumptions which are held to be inappropriate for evolved cognitive mechanisms just as anti-adaptationists (e.g., Lewontin and Gould) have argued they are inappropriate for other evolved physiological mechanisms. The paper discusses the criticism and suggests that it is, in part, misdirected. It is further suggested that the criticism leads to (...) interesting questions about how one formulates constraints--across "levels of organization" and disciplinary boundaries--on one's models of complex systems, such as human vision. (shrink)

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in (...) parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language. Key Words: efference copies; emulation theory of representation; forward models; Kalman filters; motor control; motor imagery; perception; visual imagery. (shrink)

I argue against a growing radical trend in current theoretical cognitive science that moves from the premises of embedded cognition, embodied cognition, dynamical systems theory and/or situated robotics to conclusions either to the effect that the mind is not in the brain or that cognition does not require representation, or both. I unearth the considerations at the foundation of this view: Haugeland's bandwidth-component argument to the effect that the brain is not a component in cognitive activity, and arguments inspired by (...) dynamical systems theory and situated robotics to the effect that cognitive activity does not involve representations. Both of these strands depend not only on a shift of emphasis from higher cognitive functions to things like sensorimotor processes, but also depend on a certain understanding of how sensorimotor processes are implemented - as closed-loop control systems. I describe a much more sophisticated model of sensorimotor processing that is not only more powerful and robust than simple closed-loop control, but for which there is great evidence that it is implemented in the nervous system. The is the emulation theory of representation, according to which the brain constructs inner dynamical models, or emulators, of the body and environment which are used in parallel with the body and environment to enhance motor control and perception and to provide faster feedback during motor processes, and can be run off-line to produce imagery and evaluate sensorimotor counterfactuals. I then show that the emulation framework is immune to the radical arguments, and makes apparent why the brain is a component in the cognitive activity, and exactly what the representations are in sensorimotor control. (shrink)

I examine one of the conceptual cornerstones of the field known as computational neuroscience, especially as articulated in Churchland et al. (1990), an article that is arguably the locus classicus of this term and its meaning. The authors of that article try, but I claim ultimately fail, to mark off the enterprise of computational neuroscience as an interdisciplinary approach to understanding the cognitive, information-processing functions of the brain. The failure is a result of the fact that the authors provide no (...) principled means to distinguish the study of neural systems as genuinely computational/information-processing from the study of any complex causal process. I then argue for two things. First, that in order to appropriately mark off computational neuroscience, one must be able to assign a semantics to the states over which an attempt to provide a computational explanation is made. Second, I show that neither of the two most popular ways of trying to effect such content assignation -- informational semantics and 'biosemantics' -- can make the required distinction, at least not in a way that a computational neuroscientist should be happy about. The moral of the story as I take it is not a negative one to the effect that computational neuroscience is in principle incapable of doing what it wants to do. Rather, it is to point out some work that remains to be done. (shrink)

b>: In this article I outline, apply, and defend a theory of natural representation. The main consequences of this theory are: i) representational status is a matter of how physical entities are used, and specifically is not a matter of causation, nomic relations with the intentional object, or information; ii) there are genuine (brain-)internal representations; iii) such representations are really representations, and not just farcical pseudo-representations, such as attractors, principal components, state-space partitions, or what-have-you;and iv) the theory allows us to (...) sharply distinguish those complex behaviors which are genuinely cognitive from those which are merely complex and adaptive. (shrink)

"Cognitive psychology," "cognitive neuroscience," and "philosophy of mind" are names for three very different scientific fields, but they label aspects of the same scientific goal: to understand the nature of mental phenomena. Today, the three disciplines strongly overlap under the roof of the cognitive sciences. The book's purpose is to present views from the different disciplines on one of the central theories in cognitive science: the theory of mental models. Cognitive psychologists report their research on the representation and processing of (...) mental models in human memory. Cognitive neuroscientists demonstrate how the brain processes visual and spatial mental models and which neural processes underlie visual and spatial thinking. Philosophers report their ideas about the role of mental models in relation to perception, emotion, representation, and intentionality. The single articles have different and mutually complementing goals: to introduce new empirical methods and approaches, to report new experimental results, and to locate competing approaches for their interpretation in the cross-disciplinary debate. The book is strongly interdisciplinary in character. It is especially addressed to researchers in any field related to mental models theory as both a reference book and an overview of present research on the topic in other disciplines. However, it is also an ideal reader for a specialized graduate course. (shrink)

This book attempts to marry truth-conditional semantics with cognitive linguistics in the church of computational neuroscience. To this end, it examines the truth-conditional meanings of coordinators, quantifiers, and collective predicates as neurophysiological phenomena that are amenable to a neurocomputational analysis. Drawing inspiration from work on visual processing, and especially the simple/complex cell distinction in early vision (V1), we claim that a similar two-layer architecture is sufficient to learn the truth-conditional meanings of the logical coordinators and logical quantifiers. As a prerequisite, (...) much discussion is given over to what a neurologically plausible representation of the meanings of these items would look like. We eventually settle on a representation in terms of correlation, so that, for instance, the semantic input to the universal operators (e.g. and, all)is represented as maximally correlated, while the semantic input to the universal negative operators (e.g. nor, no)is represented as maximally anticorrelated. On the basis this representation, the hypothesis can be offered that the function of the logical operators is to extract an invariant feature from natural situations, that of degree of correlation between parts of the situation. This result sets up an elegant formal analogy to recent models of visual processing, which argue that the function of early vision is to reduce the redundancy inherent in natural images. Computational simulations are designed in which the logical operators are learned by associating their phonological form with some degree of correlation in the inputs, so that the overall function of the system is as a simple kind of pattern recognition. Several learning rules are assayed, especially those of the Hebbian sort, which are the ones with the most neurological support. Learning vector quantization (LVQ) is shown to be a perspicuous and efficient means of learning the patterns that are of interest. We draw a formal parallelism between the initial, competitive layer of LVQ and the simple cell layer in V1, and between the final, linear layer of LVQ and the complex cell layer in V1, in that the initial layers are both selective, while the final layers both generalize. It is also shown how the representations argued for can be used to draw the traditionally-recognized inferences arising from coordination and quantification, and why the inference of subalternacy breaks down for collective predicates. Finally, the analogies between early vision and the logical operators allow us to advance the claim of cognitive linguistics that language is not processed by proprietary algorithms, but rather by algorithms that are general to the entire brain. Thus in the debate between objectivist and experiential metaphysics, this book falls squarely into the camp of the latter. Yet it does so by means of a rigorous formal, mathematical, and neurological exposition – in contradiction of the experiential claim that formal analysis has no place in the understanding of cognition. To make our own counter-claim as explicit as possible, we present a sketch of the LVQ structure in terms of mereotopology, in which the initial layer of the network performs topological operations, while the final layer performs mereological operations. The book is meant to be self-contained, in the sense that it does not assume any prior knowledge of any of the many areas that are touched upon. It therefore contains mini-summaries of biological visual processing, especially the retinocortical and ventral /what?/ parvocellular pathways computational models of neural signaling, and in particular the reduction of the Hodgkin-Huxley equations to the connectionist and integrate-and-fire neurons Hebbian learning rules and the elaboration of learning vector quantization the linguistic pathway in the left hemisphere memory and the hippocampus truth-conditional vs. image-schematic semantics objectivist vs. experiential metaphysics and mereotopology. All of the simulations are implemented in MATLAB, and the code is available from the book’s website. • The discovery of several algorithmic similarities between visison and semantics. • The support of all of this by means of simulations, and the packaging of all of this in a coherent theoretical framework. (shrink)

In this article, I link the empirical hypothesis that neural representations of sensory stimulation near the body involve a unique motor component to the idea that the perceptual field is structured by skillful bodily activity. The neurophenomenological view that emerges is illuminating in its own right, though it may also have practical consequences. I argue that recent experiments attempting to alter the scope of these near space sensorimotor representations are actually equivocal in what they show. I propose resolving this ambiguity (...) by treating these representations as responsive to the development or degeneration of know-how—which can be isolated as an appropriate object for scientific investigation. (shrink)

This paper investigates how "representation" is actually used in some areas in cognitive neuroscience. It is argued that recent philosophy has largely ignored an important kind of representation that differs in interesting ways from the representations that are standardly recognized in philosophy of mind. This overlooked kind of representation does not represent by having intentional contents; rather members of the kind represent by displaying or instantiating features. The investigation is not simply an ethnographic study of the discourse of neuroscientists. If (...) there are indeed two different kinds of representations, and the non-standard ones are the ones referred to in some areas of cognitive neuroscience, then we will have to give up the idea that appealing to inner representations with intentional contents is the defining distinction between cognitive neuroscience and behaviorist psychology (Montgomery, 1995). Further, if the conclusions of this paper are correct, many general accounts of how neural states represent are either false or theoretically ill-motivated. (shrink)

Attentional selection biases the processing of higher visual areas to particular parts of a scene. Recent experiments show how stimulation of neurons in the frontal eye fields can mimic this process.

A wide range of systems appear to perform computation: what common features do they share? I consider three examples, a digital computer, a neural network and an analogue route finding system based on soap-bubbles. The common feature of these systems is that they have autonomous dynamics — their states will change over time without additional external influence. We can take advantage of these dynamics if we understand them well enough to map a problem we want to solve onto them. Programming (...) consists of arranging the starting state of a system so that the effects of the system''s dynamics on some of its variables corresponds to the effects of the equations which describe the problem to be solved on their variables. The measured dynamics of a system, and hence the computation it may be performing, depend on the variables of the system we choose to attend to. Although we cannot determine which are the appropriate variables to measure in a system whose computation basis is unknown to us I go on to discuss how grammatical classifications of computational tasks and symbolic machine reconstruction techniques may allow us to rule out some measurements of a system from contributing to computation of particular tasks. Finally I suggest that these arguments and techniques imply that symbolic descriptions of the computation underlying cognition should be stochastic and that symbols in these descriptions may not be atomic but may have contents in alternative descriptions. (shrink)

This paper explores several paths a distinctive third wave of extended cognition might take. In so doing, I address a couple of shortcomings of first- and second-wave extended cognition associated with a tendency to conceive of the properties of internal and external processes as fixed and non-interchangeable. First, in the domain of cognitive transformation, I argue that a problematic tendency of the complementarity model is that it presupposes that socio-cultural resources augment but do not significantly transform the brain’s representational capacities (...) during diachronic development. In this paper I show that there is available a much more dynamical explanation—one taking the processes of the brain’s enculturation into patterned practices as transforming the brain’s representational capacities. Second, in the domain of cognitive assembly, I argue that another problematic tendency is an individualistic notion of cognitive agency, since it overlooks the active contribution of socio-cultural practices in the assembly process of extended cognitive systems. In contrast to an individualistic notion of cognitive agency, I explore the idea that it is possible to decentralize cognitive agency to include socio-cultural practices. (shrink)

A popular view has it that the mental representations underlying human pretense are not beliefs, but are “belief-like” in important ways. This view typically posits a distinctive cognitive attitude (a “DCA”) called “imagination” that is taken toward the propositions entertained during pretense, along with correspondingly distinct elements of cognitive architecture. This paper argues that the characteristics of pretense motivating such views of imagination can be explained without positing a DCA, or other cognitive architectural features beyond those regulating normal belief and (...) desire. On the present “Single Attitude” account of imagination, propositional imagining just is a form of believing. The Single Attitude account is also distinguished from “metarepresentational” accounts of pretense, which hold that both pretending and recognizing pretense in others require one to have concepts of mental states. It is argued, to the contrary, that pretending and recognizing pretense require neither a DCA nor possession of mental state concepts. (shrink)

(1) Reaction time (RT) studies give only a partial picture of language processing, hence it may be risky to use the output of the computational model to inspire neurophysiological investigations instead of seeking further neurophysiological data to adjust the RT based theory. (2) There is neurophysiological evidence for differences in the cortical representation of different word categories; this could be integrated into a future version of the Levelt model. (3) EEG/MEG coherence analysis allows the monitoring of synchronous electrical activity in (...) large groups of neurons in the cortex; this is especially interesting for activation based network models. (shrink)

Surveys theories in contemporary neuroscience, exploring many of its methodological techniques and problems.

Often, sensory input underdetermines perception. One such example is the perception of illusory contours. In illusory contour perception, the content of the percept includes the presence of a contour that is absent from the informational content of the sensation. (By “sensation” I mean merely information-bearing events at the transducer level. I intend no further commitment such as the identification of sensations with qualia.) I call instances of perception underdetermined by sensation “underdetermined perception.” The perception of illusory contours is just one (...) kind of underdetermined perception. The focus of this chapter is another kind of underdetermined perception: what I shall call "active perception". Active perception occurs in cases in which the percept, while underdetermined by sensation, is determined by a combination of sensation and action. The phenomenon of active perception has been used by several to argue against the positing of representations in explanations of sensory experience, either by arguing that no representations need be posited or that far fewer than previously thought need be posited. Such views include, but are not limited to those of Gibson (1966, 1986), Churchland. (shrink)

In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and (...) anorganism. My approach in the current paper isto explore this and other key themes inneurosemantics through the use of computermodels of neural networks embodied and evolvedin virtual organisms. The models allow for thelaying bare of the causal economies of entireyet simple artificial organisms so that therelations between the neural bases of, forinstance, representation in perception andmemory can be regarded in the context of anentire organism. On the basis of thesesimulations, I argue for an account ofneurosemantics adequate for the solution of theeconomy problem. (shrink)

Computation and philosophy intersect three times in this essay. Computation is considered as an object, as a method, and as a model used in a certain line of philosophical inquiry concerning the relation of mind to matter. As object, the question considered is whether computation and related notions of mental representation constitute the best ways to conceive of how physical systems give rise to mental properties. As method and model, the computational techniques of artificial life and embodied evolutionary connectionism are (...) used to conduct prosthetically enhanced thought experiments concerning the evolvability of mental representations. Central to this essay is a discussion of the computer simulation and evolution of three-dimensional synthetic animals with neural network controllers. The minimally cognitive behavior of finding food by exhibit- ing positive chemotaxis is simulated with swimming and walking creatures. These simulations form the basis of a discussion of the evolutionary and neurocomputa- tional bases of the incremental emergence of more complex forms of cognition. Other related work has been used to attack computational and representational theories of cognition. In contrast, I argue that the proper understanding of the evolutionary emergence of minimally cognitive behaviors is computational and representational through and through. (shrink)

Page has done connectionist researchers a valuable service in this target article. He points out that connectionist models using localized representations often work as well or better than models using distributed representations. I point out that models using distributed representations are difficult to understand and often lack parsimony and plausibility. In conclusion, I give an example – the case of the missing fundamental in music – that can easily be explained by a model using localist representations but can be explained (...) only with great difficulty and implausibility by a model using distributed representations. (shrink)

In the book, I argue that the mind can be explained computationally because it is itself computational—whether it engages in mental arithmetic, parses natural language, or processes the auditory signals that allow us to experience music. All these capacities arise from complex information-processing operations of the mind. By analyzing the state of the art in cognitive science, I develop an account of computational explanation used to explain the capacities in question.

The focus of this commentary is what Andy Clark takes to be the most groundbreaking of the philosophical import of the ‘bidirectional hierarchical model of brain functions’, namely, the claim that perceptual representations represent probabilities. This is what makes his account Bayesian and this is a philosophical or theoretical conclusion that neuroscientists and psychologists are also quick and happy to draw. My claim is that nothing in the ‘bidirectional hierarchical models of brain functions’ implies that perceptual representations are probabilistic, or (...) that they represent or ‘encode probability density distributions’. There is a much more parsimonious way of describing the representations in the bidirectional hierarchical model of brain functions: they attribute properties to objects (or to the perceived scene) that are not fully determinate. (shrink)

This paper reviews the fate of the central ideas behind the complementary learning systems (CLS) framework as originally articulated in McClelland, McNaughton, and O’Reilly (1995). This framework explains why the brain requires two differentially specialized learning and memory systems, and it nicely specifies their central properties (i.e., the hippocampus as a sparse, pattern-separated system for rapidly learning episodic memories, and the neocortex as a distributed, overlapping system for gradually integrating across episodes to extract latent semantic structure). We review the application (...) of the CLS framework to a range of important topics, including the following: the basic neural processes of hippocampal memory encoding and recall, conjunctive encoding, human recognition memory, consolidation of initial hippocampal learning in cortex, dynamic modulation of encoding versus recall, and the synergistic interactions between hippocampus and neocortex. Overall, the CLS framework remains a vital theoretical force in the field, with the empirical data over the past 15 years generally confirming its key principles. (shrink)

We present a neuro-geometrical model for generating the shape of Kanizsa's modal subjective contours which is based on the functional architecture of the primary areas of the visual cortex. We focus on V1 and its pinwheel structure and model it as a discrete approximation of a continuous fibration π: R × P → P with base space the space of the retina R and fiber the projective line P of the orientations of the plane. The horizontal cortico-cortical connections of V1 (...) implement what the geometers call the contact structure of the fibration π, and defines therefore an integrability condition which can be shown to correspond to Field's, Hayes', and Hess' psychophysical concept of association field. We present then a variational model of curved modal illusory contours (in the spirit of previous models due to Ullman, Horn, and Mumford) based on the idea that virtual contours are “geodetic” integral curves of the contact structure. (shrink)

It is worthwhile to search for forms of coding, processing, and learning common to various cortical regions and cognitive functions. Local cortical processors may coordinate their activity by maximizing the transmission of information coherently related to the context in which it occurs, thus forming synchronized population codes. This coordination involves contextual field (CF) connections that link processors within and between cortical regions. The effects of CF connections are distinguished from those mediating receptive field (RF) input; it is shown how CFs (...) can guide both learning and processing without becoming confused with the transmission of RF information. Simulations explore the capabilities of networks built from local processors with both RF and CF connections. Physiological evidence for synchronization, CFs, and plasticity of the RF and CF connections is described. Coordination via CFs is related to perceptual grouping, the effects of context on contrast sensitivity, amblyopia, implicit influences of color in achromotopsia, object and word perception, and the discovery of distal environmental variables and their interactions through self-organization. Cortical computation could thus involve the flexible evaluation of relations between input signals by locally specialized but adaptive processors whose activity is dynamically associated and coordinated within and between regions through specialized contextual connections. Key Words: cell assemblies; cerebral cortex; context; coordination; dynamic binding; epistemology; functional specialization; learning; neural coding; neural computation; neuropsychology; object recognition; perception; reading; self-organization; synaptic plasticity; synchronization. (shrink)

In the target article Pylyshyn revives the spectre of the “little green man,” arguing for a largely symbolic representation of visual imagery. To clarify this problem, we provide precise definitions of the key term “picture,” present some examples of our definition, and outline an information-theoretic analysis suggesting that the problem of addressing data in the brain requires a partially analogue and partially symbolic solution. This is made concrete in the ventral stream of object recognition, from V1 to IT cortex.

If the cortex is an associative memory, strongly connected cell assemblies will form when neurons in different cortical areas are frequently active at the same time. The cortical distributions of these assemblies must be a consequence of where in the cortex correlated neuronal activity occurred during learning. An assembly can be considered a functional unit exhibiting activity states such as full activation (“ignition”) after appropriate sensory stimulation (possibly related to perception) and continuous reverberation of excitation within the assembly (a putative (...) memory process). This has implications for cortical topographies and activity dynamics of cell assemblies forming during language acquisition, in particular for those representing words. Cortical topographies of assemblies should be related to aspects of the meaning of the words they represent, and physiological signs of cell assembly ignition should be followed by possible indicators of reverberation. The following postulates are discussed in detail: (1) assemblies representing phonological word forms are strongly lateralized and distributed over perisylvian cortices; (2) assemblies representing highly abstract words such as grammatical function words are also strongly lateralized and restricted to these perisylvian regions; (3) assemblies representing concrete content words include additional neurons in both hemispheres; (4) assemblies representing words referring to visual stimuli include neurons in visual cortices; and (5) assemblies representing words referring to actions include neurons in motor cortices. Two main sources of evidence are used to evaluate these proposals: (a) imaging studies focusing on localizing word processing in the brain, based on stimulus-triggered event-related potentials (ERPs), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI), and (b) studies of the temporal dynamics of fast activity changes in the brain, as revealed by high-frequency responses recorded in the electroencephalogram (EEG) and magnetoencephalogram (MEG). These data provide evidence for processing differences between words and matched meaningless pseudowords, and between word classes, such as concrete content and abstract function words, and words evoking visual or motor associations. There is evidence for early word class-specific spreading of neuronal activity and for equally specific high-frequency responses occurring later. These results support a neurobiological model of language in the Hebbian tradition. Competing large-scale neuronal theories of language are discussed in light of the data summarized. Neurobiological perspectives on the problem of serial order of words in syntactic strings are considered in closing. Key Words: associative learning; cell assembly; cognition; cortex; ERP; EEG; fMRI; language; lexicon; MEG; PET; word category. (shrink)

I comment on two problems in Glover's account. First, semantic representations are not always available to awareness. Second, some functional properties, the affordances of objects, should be encoded in the dorsal system. Then I argue that the existence of Glover's two types of representations is supported by studies on “object-centered” attention. Furthermore, it foreshadows a nondescriptive causal reference fixing process.

A commentary on "Whatever next? Predictive brains, situated agents, and the future of cognitive science" by Clark, A. (in press). Behav. Brain Sci. -/- We will argue that the context sensitivity of action-oriented processing is not adequately recognized in Clark’s target article. The ecological notion of a niche(e.g., Gibson, 1979) is for instance central in Friston (2011) account of embodied cognition, but we find it curiously absent in the account Clark gives of action.

Discriminating behavior depends on neural representations in which the sensory activity patterns guiding different responses are decorrelated from one another. Visual information can often be parsimoniously transformed into these behavioral bridge-locus representations within neuro-computational visuo-spatial maps. Isomorphic inverse-optical world representation is not the goal. Nevertheless, such useful transformations can involve neural filling-in. Such a subpersonal representation of information is consistent with personal-level vision theory.

Chapter 1: Discussion of my a posteriori strategy and the nature of representation in models. Chapter 2: Presentation of the SINBAD theory of the cerebral cortex. Chapter 3: Demonstration of how SINBAD networks develop into genuinely representational models. Application of the theory to equivocal representation, misrepresentation, empty representation, and twin cases. Chapter 4: Representata as sources of correlation, a solution to the problem of teleological indeterminacy, useful vs. true representation, objectivity. Chapter 5: The non-representational use of representations in the brain (...) in occurrent belief (i.e. judgement) and occurrent desire, Swampman, propositional content, the non-occurrent attitudes, inference and psychological explanation. Chapter 6: SINBAD representation as a kind of mental representation, summary & conclusion. (shrink)

Reductive, naturalistic psychosemantic theories do not have a good track record when it comes to accommodating the representation of kinds. In this paper, I will suggest a particular teleosemantic strategy to solve this problem, grounded in the neurocomputational details of the cerebral cortex. It is a strategy with some parallels to one that Ruth Millikan has suggested, but to which insufficient attention has been paid. This lack of attention is perhaps due to a lack of appreciation for the severity of (...) the problem, so I begin by explaining why the situation is indeed a dire one. One of the main tasks for a naturalistic psychosemantic theory is to describe how the extensions of mental representations are determined. (Such a theory may also attempt to account for other aspects of the “meaning” of mental representations, if there are any.) Some mental representations, e.g. the concept of water, denote kinds (I shall be assuming this is non-negotiable). How is this possible? Unfortunately, I haven’t the space to canvass all the theories out there and show that each one fails to accommodate the representation of kinds, but I will point out the major types of problems that arise for the kinds of theories that, judging by the literature, are considered viable contenders.1 In general, the theories either attempt and fail to account for the representation of kinds, or they fall back on something like an intention to refer to a kind – not exactly the most auspicious move for a reductive theory. There are a number of problems that prevent non-teleosemantic theories from explaining how it is possible to represent kinds. A concept of a kind K must.. (shrink)

Introduction There are some exceptions, which we shall see below, but virtually all theories in psychology and cognitive science make use of the notion of representation. Arguably, folk psychology also traffics in representations, or is at least strongly suggestive of their existence. There are many different types of things discussed in the psychological and philosophical literature that are candidates for representation-hood. First, there are the propositional attitudes – beliefs, judgments, desires, hopes etc. (see Chapters 9 and 17 of this volume). (...) If the propositional attitudes are representations, they are person-level representations – the judgment that the sun is bright pertains to John, not a subpersonal part of John. By contrast, the representations of edges in V1 of the cerebral cortex that neuroscientists talk about and David Marr’s symbolic representations of “zero-crossings” in early vision (Marr 1982) are at the “sub-personal” level – they apply to parts or states of a person (e.g. neural parts or computational states of the visual system). Another important distinction is often made among perceptual, cognitive, and action-oriented representations (e.g. motor commands). Another contrast lies between “stored representations” (e.g. memories) and “active representations” (e.g. a current perceptual state). Related to this is the distinction between “dispositional representations” and “occurrent representations.” Beliefs that are not currently being entertained are dispositional, e.g. your belief that the United States is in North America - no doubt you had this belief two minutes ago, but you were not consciously accessing it until you read this sentence. Occurrent representations, by contrast, are active, conscious thoughts or perceptions. Which leads us to another important distinction: 1 between conscious and non-conscious mental representations, once a bizarre-sounding distinction that has become familiar since Freud (see Chapter 4 of this volume). I mention these distinctions at the outset to give you some idea of the range of phenomena we will be considering, and to set the stage for our central “problem of representation”: what is a mental representation, exactly, and how do we go about deciding whether there are any? We know there are public representations of various kinds: words, maps, and pictures, among others.. (shrink)

The ability to predict is the most importantability of the brain. Somehow, the cortex isable to extract regularities from theenvironment and use those regularities as abasis for prediction. This is a most remarkableskill, considering that behaviourallysignificant environmental regularities are noteasy to discern: they operate not only betweenpairs of simple environmental conditions, astraditional associationism has assumed, butamong complex functions of conditions that areorders of complexity removed from raw sensoryinputs. We propose that the brain's basicmechanism for discovering such complexregularities is implemented in (...) the dendritictrees of individual pyramidal cells in thecerebral cortex. Pyramidal cells have 5–8principal dendrites, each of which is capableof learning nonlinear input-to-outputtransfer functions. We propose that eachdendrite is trained, in learning its transferfunction, by all the other principal dendritesof the same cell. These dendrites teach eachother to respond to their separate inputs with matching outputs. Exposed to differentbut related information about the sensoryenvironment, principal dendrites of the samecell tune to functions over environmentalconditions that, while different, are correlated . As a result, the cell as awhole tunes to the source of the regularitiesdiscovered by the cooperating dendrites,creating a new representation. When organizedinto feed-forward/feedback layers, pyramidalcells can build their discoveries on thediscoveries of other cells, graduallyuncovering nature's hidden order. Theresulting associative network is powerfulenough to meet a troubling traditionalobjection to associationism: that it is toosimple an architecture to implement rationalprocesses. (shrink)