Abstract: 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

Abstract: It is no secret that scientists argue. They argue about theories. But even more, they argue about the evidence for theories. Is the evidence itself trustworthy? This is a bit surprising from the perspective of traditional empiricist accounts of scientific methodology according to which the evidence for scientific theories stems from observation, especially observation with the naked eye. These accounts portray the testing of scientific theories as a matter of comparing the predictions of the theory with the data generated by these observations, which are taken to provide an objective link to reality

Abstract: 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

Abstract: The aim of cognitive neuropsychology is to articulate the functional architecture underlying normal cognition, on the basis of congnitive performance data involving brain-damaged subjects. Throughout the history of the subject, questions have been raised as to whether the methods of neuropsychology are adequate to its goals. The question has been reopened by Glymour [1994], who formulates a discovery problem for cognitive neuropsychology, in the sense of formal learning theory, concerning the existence of a reliable methodology. It appears that the discovery problem may be insoluble in principle! I propose a modified formulation of Glymour's discovery problem and argue that a sceptical conclusion about the possiblity of cognitive neuropsychology as an empirical science is not warranted

Abstract: For the uninitiated, there are two major tendencies in the modeling of human cognition. The older, tradtional school believes, in essence, that full human cognition can be modeled by dividing the world up into distinct entities -- called __symbol s__-- such as “dog”, “cat”, “run”, “bite”, “happy”, “tumbleweed”, and so on, and then manipulating this vast set of symbols by a very complex and very subtle set of rules. The opposing school claims that this system, while it might be good at concluding that Paris is the capital of France or that there must be blood flowing in the left-rear leg of a cow, can never capture the full measure -- indeed, the essence -- of human cognition. For them, the essential features of cognition emerge from the combined effects of myriad, tiny actions far below the surface of consciousness. This is the camp to which Paul Churchland belongs

Abstract: It is a common assumption in contemporary cognitive neuroscience that discovering a putative realized kind to be dissociably realized (i.e., to be realized in each instance by two or more distinct realizers) mandates splitting that kind. Here I explore some limits on this inference using two deceptively similar examples: the dissociation of declarative and procedural memory and Ramachandran's argument that the self is an illusion

Abstract: Bennett and Hacker’s _Philosophical Foundations of Neuroscience_ (Blackwell, 2003), a collaboration between a philosopher (Hacker) and a neuroscientist (Bennett), is an ambitious attempt to reformulate the research agenda of cognitive neuroscience by demonstrating that cognitive scientists and other theorists, myself among them, have been bewitching each other by misusing language in a systematically “incoherent” and conceptually “confused” way. In both style and substance, the book harks back to Oxford in the early 1960's, when Ordinary Language Philosophy ruled, and Ryle and Wittgenstein were the authorities on the meanings of our everyday mentalistic or psychological terms. I myself am a product of that time and place (as is Searle, for that matter), and I find much to agree with in their goals and presuppositions, and before turning to my criticisms, which will be severe, I want to highlight what I think is exactly right in their approach–the oft-forgotten lessons of Ordinary Language Philosophy

Abstract: The “top-down” and “bottom-up” approaches have been thought to exhaust the possibilities for doing cognitive neuroscience. We argue that neither approach is likely to succeed in providing a theory that enables us to understand how cognition is achieved in biological creatures like ourselves. We consider a promising third way of doing cognitive neuroscience, what might be called the “neural dynamic systems” approach, that construes cognitive neuroscience as an autonomous explanatory endeavor, aiming to characterize in its own terms the states and processes responsible for brain-based cognition. We sketch the basic motivation for the approach, describe a particular version of the approach, so-called ‘Dynamic Causal Modeling’ (DCM), and consider a concrete example of DCM. This third way, we argue, has the potential to avoid the problems that afflict the other two approaches

Abstract: Keywords: computational neuroscience, neural coding, brain function, neural modeling, cognitive modeling, computation, representation, neuroscience, neuropsychology, semantics, theoretical psychology, theoretical neuroscience

Abstract: To have a fully integrated understanding of neurobiological systems, we must address two fundamental questions: 1. What do brains do (what is their function)? and 2. How do brains do whatever it is that they do (how is that function implemented)? I begin by arguing that these questions are necessarily inter-related. Thus, addressing one without consideration of an answer to the other, as is often done, is a mistake. I then describe what I take to be the best available approach to addressing both questions. Specifically, to address 2, I adopt the Neural Engineering Framework (NEF) of Eliasmith & Anderson [Neural engineering: Computation representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press, 2003] which identifies implementational principles for neural models. To address 1, I suggest that adopting statistical modeling methods for perception and action will be functionally sufficient for capturing biological behavior. I show how these two answers will be mutually constraining, since the process of model selection for the statistical method in this approach can be informed by known anatomical and physiological properties of the brain, captured by the NEF. Similarly, the application of the NEF must be informed by functional hypotheses, captured by the statistical modeling approach

Abstract: Contemporary cognitive neuropsychology attempts to infer unobserved features of normal human cognition, or ?cognitive architecture?, from experiments with normals and with brain-damaged subjects in whom certain normal cognitive capacities are altered, diminished, or absent. Fundamental methodological issues about the enterprise of cognitive neuropsychology concern the characterization of methods by which features of normal cognitive architecture can be identified from such data, the assumptions upon which the reliability of such methods are premised, and the limits of such methods?even granting their assumptions?in resolving uncertainties about that architecture. With some idealization, the question of the capacities of various experimental designs in cognitive neuropsychology to uncover cognitive architecture can be reduced to comparatively simple questions about the prior assumptions investigators are willing to make. This paper presents some of simplest of those reductions. 1Research for this paper was made possible by a fellowship from the John Simon Guggenheim Memorial Foundation and by grant number SBE-9212264 from the National Science Foundation. I thank Martha Farah for teaching me what little I know of cognitive neuropsychology, Jeffrey Bub for stimulating me to think about these issues and for commenting on drafts of this paper, and Peter Slezak for additional comments. Alfonso Caramazza and Michael McCloskey provided very helpful comments on a second draft

Abstract: This article examines how scientists move from physical measurementsto actual observation of single-cell recordings in the brain. We highlight how easy it is to change the fundamental nature of ourobservations using accepted methodological techniques for manipulatingraw data. Collecting single-cell data is thoroughly pragmatic. Weconclude that there is no deep or interesting difference betweenaccounting for observations by measurements and accounting forobservations by theories

Abstract: Different cognitive functions recruit a number of different, often overlapping, areas of the brain. Theories in cognitive and computational neuroscience are beginning to take this kind of functional integration into account. The contributions to this special issue consider what functional integration tells us about various aspects of the mind such as perception, language, volition, agency, and reward. Here, I consider how and why functional integration may matter for the mind; I discuss a general theoretical framework, based on generative models, that may unify many of the debates surrounding functional integration and the mind; and I briefly introduce each of the contributions

Abstract: William James has been characterized as “the major whipping boy of the later Wittgenstein,” and the currency of this impression of the relation between James and Wittgenstein is understandable. Reading Wittgenstein and his commentators can leave one with the impression that James was a badly muddled “exponent of the tradition in the philosophy of mind that [Wittgenstein] was opposing.” There have been recent attempts to resist this trend, but even these tend to focus on the affinities between the two philosophers, still accepting the prevailing view that Wittgenstein was often critical of James, and that in such cases Wittgenstein was always right and James was always wrong. By contrast, by focusing on Wittgenstein’s discussion of James’s “if-feeling”, it will be argued that Wittgenstein’s criticisms of James are often not as damaging, or even as extensive, as has often been assumed

Abstract: In this study I propose an epistemological discussion of multiple spatio-temporal scales in neuroscience. Are such scales merely convenient levels of description of structure and function, or do they correspond to irreducible levels of brain organization? What criteria should we employ in order to reduce one level to another, or to identify levels that are not reducible to others? Should we think of these criteria as based on empirical and/or theoretical reasons? Beginning with an empirical criterion – the necessity of different experimental methodologies for the measurement of different phenomena in the same system – I summarize spatial and temporal scales currently used in neuroscience and discuss the possibility of a more general theoretical criterion. I conclude that multiscaling should be recognized as a central concept in the epistemology of neuroscience

Abstract: A movement dedicated to applying neuroscience to traditional philosophical problems and using philosophical methods to illuminate issues in neuroscience began about twenty-five years ago. Results in neuroscience have affected how we see traditional areas of philosophical concern such as perception, belief-formation, and consciousness. There is an interesting interaction between some of the distinctive features of neuroscience and important general issues in the philosophy of science. And recent neuroscience has thrown up a few conceptual issues that philosophers are perhaps best trained to deal with. After sketching the history of the movement, we explore the relationships between neuroscience and philosophy and introduce some of the specific issues that have arisen

Abstract:   Despite its significance in neuroscience and computation, McCulloch and Pitts's celebrated 1943 paper has received little historical and philosophical attention. In 1943 there already existed a lively community of biophysicists doing mathematical work on neural networks. What was novel in McCulloch and Pitts's paper was their use of logic and computation to understand neural, and thus mental, activity. McCulloch and Pitts's contributions included (i) a formalism whose refinement and generalization led to the notion of finite automata (an important formalism in computability theory), (ii) a technique that inspired the notion of logic design (a fundamental part of modern computer design), (iii) the first use of computation to address the mind–body problem, and (iv) the first modern computational theory of mind and brain