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Philosophy of Cognitive Science :: Philosophy of Neuroscience

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7.3a Imaging and Localization

See also: 7.2b. Modularity, 7.3f. Philosophy of Neuroscience, Misc, 8.1b. Neural Correlates of Consciousness, 8.2a. Neural Correlates of Visual Consciousness, 8.2c. Visual Pathways.

Anderson, Michael L. (2007). Massive redeployment, exaptation, and the functional integration of cognitive operations. Synthese 159 (3).   (Google | More links | Edit)
Abstract: Abstract: The massive redeployment hypothesis (MRH) is a theory about the functional topography of the human brain, offering a middle course between strict localization on the one hand, and holism on the other. Central to MRH is the claim that cognitive evolution proceeded in a way analogous to component reuse in software engineering, whereby existing components-originally developed to serve some specific purpose-were used for new purposes and combined to support new capacities, without disrupting their participation in existing programs. If the evolution of cognition was indeed driven by such exaptation, then we should be able to make some specific empirical predictions regarding the resulting functional topography of the brain. This essay discusses three such predictions, and some of the evidence supporting them. Then, using this account as a background, the essay considers the implications of these findings for an account of the functional integration of cognitive operations. For instance, MRH suggests that in order to determine the functional role of a given brain area it is necessary to consider its participation across multiple task categories, and not just focus on one, as has been the typical practice in cognitive neuroscience. This change of methodology will motivate (even perhaps necessitate) the development of a new, domain-neutral vocabulary for characterizing the contribution of individual brain areas to larger functional complexes, and direct particular attention to the question of how these various area roles are integrated and coordinated to result in the observed cognitive effect. Finally, the details of the mix of cognitive functions a given area supports should tell us something interesting not just about the likely computational role of that area, but about the nature of and relations between the cognitive functions themselves. For instance, growing evidence of the role of “motor” areas like M1, SMA and PMC in language processing, and of “language” areas like Broca’s area in motor control, offers the possibility for significantly reconceptualizing the nature both of language and of motor control
Anderson, Michael L. (2007). The massive redeployment hypothesis and the functional topography of the brain. Philosophical Psychology 21 (2):143-174.   (Cited by 2 | Google | More links | Edit)
Abstract: This essay introduces the massive redeployment hypothesis, an account of the functional organization of the brain that centrally features the fact that brain areas are typically employed to support numerous functions. The central contribution of the essay is to outline a middle course between strict localization on the one hand, and holism on the other, in such a way as to account for the supporting data on both sides of the argument. The massive redeployment hypothesis is supported by case studies of redeployment, and compared and contrasted with other theories of the localization of function
Avison, M. J. (2002). Functional brain mapping: What is it good for? Absolutely nothing. Brain and Mind 3:367-73.   (Google | Edit)
Bechtel, William P. (2001). Decomposing and localizing vision: An exemplar for cognitive neuroscience. In William P. Bechtel, Pete Mandik, Jennifer Mundale & Robert S. Stufflebeam (eds.), Philosophy and the Neurosciences: A Reader. Blackwell.   (Cited by 10 | Google | Edit)
Bechtel, William P. (2002). Decomposing the brain: A long term pursuit. Brain and Mind 3 (1):229-242.   (Cited by 15 | Google | More links | Edit)
Abstract: This paper defends cognitive neuroscience’s project of developing mechanistic explan- ations of cognitive processes through decomposition and localization against objections raised by William Uttal in The New Phrenology. The key issue between Uttal and researchers pursuing cognitive neuroscience is that Uttal bets against the possibility of decomposing mental operations into component elementary operations which are localized in distinct brain regions. The paper argues that it is through advancing and revising what are likely to be overly simplistic and incorrect decompositions that the goals of cognitive neuroscience are likely to be achieved
Bechtel, William P. & Stufflebeam, Robert S. (1997). PET: Exploring the myth and the method. Philosophy Of Science 64 (4).   (Google | Edit)
Bogen, James (2002). Experiment and observation. In The Blackwell Guide to the Philosophy of Science. Cambridge: Blackwell.   (Google | Edit)
Bogen, James (2002). Epistemological custard pies from functional brain imaging. Philosophy of Science 69 (3):S59-S71.   (Google | More links | Edit)
Bogen, James (2001). Functional imaging evidence: Some epistemic hotspots. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press.   (Cited by 1 | Google | Edit)
Cleeremans, Axel & Maia, Tiago V. (2005). Consciousness: Converging insights from connectionist modeling and neuroscience. Trends in Cognitive Sciences 9 (8):397-404.   (Google | More links | Edit)
Abstract: Over the past decade, many findings in cognitive about the contents of consciousness: we will not address neuroscience have resulted in the view that selective what might be called the ‘enabling factors’ for conscious- attention, working memory and cognitive control ness (e.g. appropriate neuromodulation from the brain- stem, etc.). involve competition between widely distributed rep-
Cleeremans, Axel (2006). Computational Correlates of Consciousness. In Steven Laureys (ed.), The Boundaries of Consciousness: Neurobiology and Neuropathology: Progress in Brain Research. Elsevier.   (Cited by 7 | Google | More links | Edit)
Abstract: Over the past few years numerous proposals have appeared that attempt to characterize consciousness in terms of what could be called its computational correlates: Principles of information processing with which to characterize the differences between conscious and unconscious processing. Proposed computational correlates include architectural specialization (such as the involvement of specific regions of the brain in conscious processing), properties of representations (such as their stability in time or their strength), and properties of specific processes (such as resonance, synchrony, interactivity, or information integration). In exactly the same way as one can engage in a search for the neural correlates of consciousness, one can thus search for the computational correlates of consciousness. The most direct way of doing is to contrast models of conscious versus unconscious information processing. In this paper, I review these developments and illustrate how computational modeling of specific cognitive processes can be useful in exploring and in formulating putative computational principles through which to capture the differences between conscious and unconscious cognition. What can be gained from such approaches to the problem of consciousness is an understanding of the function it plays in information processing and of the mechanisms that subtend it. Here, I suggest that the central function of consciousness is to make it possible for cognitive agents to exert flexible, adaptive control over behavior. 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 properties of representation, and (2) the implication of systems of meta-representations
Cranford, Ronald E. & Killpatrick, Barbara (1981). Tests in the diagnosis of brain death: The role of the radioisotope brain scan. Bioethics Quarterly 3:67-72.   (Google | More links | Edit)
Hardcastle, Valerie Gray & Stewart, C. Matthew (2005). Localization in the brain and other illusions. In Andrew Brook (ed.), Cognition and the Brain. Cambridge: Cambridge University Press.   (Google | Edit)
Hardcastle, Valerie Gray & Stewart, C. Matthew (2004). Neuroscience and the art of single-cell recordings. Biology and Philosophy 18:195-208.   (Cited by 1 | Google | More links | Edit)
Hardcastle, Valerie Gray & Stewart, C. Matthew (2002). What do brain data really show? Philosophy of Science 69 (3):572-582.   (Cited by 3 | Google | More links | Edit)
Landreth, Anthony & Richardson, Robert C. (2004). Localization and the new phrenology: A review essay on William Uttal's the new phrenology. Philosophical Psychology 17 (1):107-123.   (Google | More links | Edit)
Abstract: William Uttal's The new phrenology is a broad attack on localization in cognitive neuroscience. He argues that even though the brain is a highly differentiated organ, "high level cognitive functions" should not be localized in specific brain regions. First, he argues that psychological processes are not well-defined. Second, he criticizes the methods used to localize psychological processes, including imaging technology: he argues that variation among individuals compromises localization, and that the statistical methods used to construct activation maps are flawed. Neither criticism is compelling. First, as we illustrate, there are behavioral measures which offer at least weak constraints on psychological attribution. Second, though imaging does face methodological difficulties associated with variation among individuals, these are broadly acknowledged; moreover, his specific criticisms of the imaging work, and in particular of fMRI, misrepresent the methodology. In concluding, we suggest a way of framing the issues that might allow us to resolve differences between localizationist models and more distributed models empirically
Leo, John R. & Cohen, D. (2003). Broken brains or flawed studies? A critical review of ADHD neuroimaging research. Journal of Mind and Behavior 24 (1):29-55.   (Google | Edit)
Lloyd, Dan (2002). Studying the mind from the inside out. Brain and Mind 3 (1):243-59.   (Cited by 2 | Google | More links | Edit)
Lloyd, Dan (2000). Terra cognita: From functional neuroimaging to the map of the mind. Brain and Mind 1 (1):93-116.   (Cited by 15 | Google | More links | Edit)
Mole, Christopher; Kubatzky, Corey; Plate, Jan; Waller, Rawdon; Dobbs, Marilee & Nardone, Marc (2007). Faces and brains: The limitations of brain scanning in cognitive science. Philosophical Psychology 20 (2):197 – 207.   (Google | More links | Edit)
Abstract: The use of brain scanning now dominates the cognitive sciences, but important questions remain to be answered about what, exactly, scanning can tell us. One corner of cognitive science that has been transformed by the use of neuroimaging, and that a scanning enthusiast might point to as proof of scanning's importance, is the study of face perception. Against this view, we argue that the use of scanning has, in fact, told us rather little about the information processing underlying face perception and that it is not likely to tell us much more
Mundale, Jennifer (2002). Concepts of localization: Balkanization in the brain. Brain and Mind 3 (3):313-30.   (Cited by 3 | Google | More links | Edit)
Mundale, Jennifer (2001). Neuroanatomical foundations of cognition: Connecting the neuronal level with the study of higher brain areas. In William P. Bechtel, Pete Mandik, Jennifer Mundale & Robert S. Stufflebeam (eds.), Philosophy and the Neurosciences: A Reader. Blackwell.   (Google | Edit)
Opie, Jonathan & O'Brien, Gerard (1999). A connectionist theory of phenomenal experience. Behavioral and Brain Sciences 22:127-148.   (Google | More links | Edit)
Abstract: When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches _vehicle_ and _process_ theories of consciousness, respectively. However, while there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are _dissociable_, and on the other, by the _classical_ computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of _connectionism; _and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: _phenomenal experience consists in the explicit_ _representation of information in neurally realized PDP networks_..
Opie, Jonathan (1998). Consciousness: A Connectionist Perspective. Dissertation, University of Adelaide   (Cited by 2 | Google | More links | Edit)
Abstract: To my father, who got me thinking, and to Tricia, who provided the love, support, and encouragement that enabled me to see this through
Opie, Jonathan & O'Brien, Gerard (1999). Putting content into a vehicle theory of consciousness. Behavioral and Brain Sciences 22:175-192.   (Google | More links | Edit)
Abstract: The connectionist vehicle theory of phenomenal experience in the target article identifies consciousness with the brain’s explicit representation of information in the form of stable patterns of neural activity. Commentators raise concerns about both the conceptual and empirical adequacy of this proposal. On the former front they worry about our reliance on vehicles, on representation, on stable patterns of activity, and on our identity claim. On the latter front their concerns range from the general plausibility of a vehicle theory to our specific attempts to deal with the dissociation studies. We address these concerns, and then finish by considering whether the vehicle theory we have defended has a coherent story to tell about the active, unified subject to whom conscious experiences belong
Schutter, D.; van Honk, J. & Panksepp, Jaak (2004). Introducing transcranial magnetic stimulation (TMS) and its property of causal inference in investigating brain-function relationships. Synthese 141 (2):155-73.   (Google | Edit)
Abstract:   Transcranial magnetic stimulation (TMS) is a method capable of transiently modulating neural excitability. Depending on the stimulation parameters information processing in the brain can be either enhanced or disrupted. This way the contribution of different brain areas involved in mental processes can be studied, allowing a functional decomposition of cognitive behavior both in the temporal and spatial domain, hence providing a functional resolution of brain/mind processes. The aim of the present paper is to argue that TMS with its ability to draw causal inferences on function and its neural representations is a valuable neurophysiological tool for investigating the causal basis of neuronal functions and can provide substantive insight into the modern interdisciplinary and (anti)reductionist neurophilosophical debates concerning the relationships between brain functions and mental abilities. Thus, TMS can serve as a heuristic method for resolving causal issues in an arena where only correlative tools have traditionally been available
Stufflebeam, Robert S. & Bechtel, William P. (1997). PET: Exploring the myth and the method. Philsophy of Science 64 (4):95-106.   (Cited by 13 | Google | More links | Edit)
Uttal, William R. (2002). Functional brain mapping: What is it good for? Plenty, but not everything. Brain and Mind 3:375-79.   (Google | Edit)
Uttal, William R. (2002). Response to Bechtel and Lloyd. Brain and Mind 3 (1):261-273.   (Cited by 2 | Google | More links | Edit)
Uttal, William R. (2001). The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain. MIT Press.   (Cited by 181 | Google | More links | Edit)
van Orden, G. C. (1997). Functional neuroimages fail to discover pieces of mind in the parts of the brain. Philosophy of Science Supplement 64 (4):85-94.   (Google | Edit)
Zawidski, Tadeusz & Bechtel, William P. (2004). Gall's legacy revisited: Decomposition and localization in cognitive neuroscience. In Christina E. Erneling & David Martel Johnson (eds.), Mind As a Scientific Object. Oxford University Press.   (Google | Edit)

7.3b Representation in Neuroscience

See also: 2.5. Representation, 6.3b. Representation in Connectionism, 7.3c. Explanation in Neuroscience, 7.3e. Neurophilosophy, 7.3f. Philosophy of Neuroscience, Misc.

Breidbach, Olaf (1999). Internal representations--a prelude for neurosemantics. Journal of Mind and Behavior 20 (4):403-419.   (Cited by 1 | Google | Edit)
Churchland, Patricia S. & Sejnowski, Terrence J. (1989). Neural representation and neural computation. In L. Nadel (ed.), Neural Connections, Mental Computations. MIT Press.   (Cited by 78 | Annotation | Google | More links | Edit)
Churchland, Paul M. (1986). Cognitive neurobiology: A computational hypothesis for laminar cortex. Biology and Philosophy 1 (1):25-51.   (Cited by 8 | Google | More links | Edit)
Abstract:   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
Eliasmith, Chris (2000). How Neurons Mean. Dissertation, Washington University in St. Louis   (Cited by 6 | Google | More links | Edit)
Abstract: 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
Garson, James W. (2003). The introduction of information into neurobiology. Philosophy of Science 70 (5):926-936.   (Cited by 2 | Google | More links | Edit)
Abstract: 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
Grush, Rick (2003). In defense of some "cartesian" assumption concerning the brain and its operation. Biology and Philosophy 18 (1):53-92.   (Google | Edit)
Abstract:   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
Grush, Rick (2001). The semantic challenge to computational neuroscience. In Peter K. Machamer, Peter McLaughlin & Rick Grush (eds.), Theory and Method in the Neurosciences. University of Pittsburgh Press.   (Cited by 11 | Google | More links | Edit)
Kayser, Christoph & Logothetis, Nicos (2006). Vision: Stimulating your attention. Current Biology 16 (15):R581-R583.   (Google | More links | Edit)
Keeley, Brian L. (1999). Fixing content and function in neurobiological systems: The neuroethology of electroreception. Biology and Philosophy 14 (3):395-430.   (Cited by 11 | Google | More links | Edit)
Mandik, Pete (2003). Varieties of representation in evolved and embodied neural networks. Biology and Philosophy 18 (1):95-130.   (Cited by 6 | Google | More links | Edit)
Abstract:   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
Stufflebeam, Robert S. (2001). Brain matters: A case against representations in the brain. In William P. Bechtel, P. M, Valerie , Jennifer Mundale & Robert S. Stufflebeam (eds.), Philosophy and the Neurosciences: A Reader. Blackwell.   (Cited by 1 | Google | Edit)

7.3c Explanation in Neuroscience

See also: 1.2c. The Explanatory Gap, 7.3b. Representation in Neuroscience, 7.3d. Interlevel Relations, 7.3f. Philosophy of Neuroscience, Misc, 7.4b. Psychological Explanation, 7.4c. Explanation in Cognitive Science.

Bechtel, William P. (online). Mental mechanisms: What are the operations?   (Google | More links | Edit)
Abstract: trying to explain these reactions in terms of changes in ele- began trying to characterize physiological processes in
Bechtel, William P. (2005). The challenge of characterizing operations in the mechanisms underlying behavior. Journal of the Experimental Analysis of Behavior 84:313-325.   (Google | More links | Edit)
Abstract: Neuroscience and cognitive science seek to explain behavioral regularities in terms of underlying mechanisms. An important element of a mechanistic explanation is a characterization of the operations of the parts of the mechanism. The challenge in characterizing such operations is illustrated by an example from the history of physiological chemistry in which some investigators tried to characterize the internal operations in the same terms as the overall physiological system while others appealed to elemental chemistry. In order for biochemistry to become successful, researchers had to identify a new level of operations involving operations over molecular groups. Existing attempts at mechanistic explanation of behavior are in a situation comparable to earlier approaches to physiological chemistry, drawing their inspiration either from overall psychology activities or from low-level neural processes. Successful mechanistic explanations of behavior require the discovery of the appropriate component operations. Such discovery is a daunting challenge but one on which success will be beneficial to both behavioral scientists and cognitive and neuroscientists