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  1. The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain.William R. Uttal - 2001 - MIT Press.
  • Knowledge and the Flow of Information.Fred I. Dretske - 1981 - MIT Press.
    This book presents an attempt to develop a theory of knowledge and a philosophy of mind using ideas derived from the mathematical theory of communication developed by Claude Shannon. Information is seen as an objective commodity defined by the dependency relations between distinct events. Knowledge is then analyzed as information caused belief. Perception is the delivery of information in analog form for conceptual utilization by cognitive mechanisms. The final chapters attempt to develop a theory of meaning by viewing meaning as (...)
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  • Knowledge and the Flow of Information.Barry Loewer - 1981 - Philosophy of Science 49 (2):297-300.
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  • Knowlegde and the Flow of Information.F. Dretske - 1989 - Trans/Form/Ação 12:133-139.
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  • The Multiplicity of Experimental Protocols: A Challenge to Reductionist and Non-Reductionist Models of the Unity of Neuroscience.Jacqueline Anne Sullivan - 2008 - Synthese 167 (3):511-539.
    Descriptive accounts of the nature of explanation in neuroscience and the global goals of such explanation have recently proliferated in the philosophy of neuroscience and with them new understandings of the experimental practices of neuroscientists have emerged. In this paper, I consider two models of such practices; one that takes them to be reductive; another that takes them to be integrative. I investigate those areas of the neuroscience of learning and memory from which the examples used to substantiate these models (...)
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  • Wimsatt and the Robustness Family: Review of Wimsatt’s Re-Engineering Philosophy for Limited Beings. [REVIEW]Brett Calcott - 2011 - Biology and Philosophy 26 (2):281-293.
    This review of Wimsatt’s book Re-engineering Philosophy for Limited Beings focuses on analysing his use of robustness, a central theme in the book. I outline a family of three distinct conceptions of robustness that appear in the book, and look at the different roles they play. I briefly examine what underwrites robustness, and suggest that further work is needed to clarify both the structure of robustness and the relation between it various conceptions.
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  • Images Are Not the Evidence in Neuroimaging.Colin Klein - 2010 - British Journal for the Philosophy of Science 61 (2):265-278.
    fMRI promises to uncover the functional structure of the brain. I argue, however, that pictures of ‘brain activity' associated with fMRI experiments are poor evidence for functional claims. These neuroimages present the results of null hypothesis significance tests performed on fMRI data. Significance tests alone cannot provide evidence about the functional structure of causally dense systems, including the brain. Instead, neuroimages should be seen as indicating regions where further data analysis is warranted. This additional analysis rarely involves simple significance testing, (...)
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  • Saving Subtraction: A Reply to Van Orden and Paap.A. L. Roskies - 2010 - British Journal for the Philosophy of Science 61 (3):635-665.
    Van Orden and Paap argue that subtractive functional neuroimaging is fundamentally flawed, unfalsifiable, and cannot bear upon the nature of mind. In this they are mistaken, although their criticisms interestingly illuminate the scientific problems we confront in investigating the material basis of mind. Here, I consider the criticisms of Van Orden and Paap and discuss where they are mistaken and where justified. I then consider the picture of imaging science that Van Orden and Paap seem to espouse and sketch an (...)
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  • Representational Geometry: Integrating Cognition, Computation, and the Brain.Nikolaus Kriegeskorte & Rogier A. Kievit - 2013 - Trends in Cognitive Sciences 17 (8):401-412.
  • Functional Neuroimages Fail to Discover Pieces of Mind in the Parts of the Brain.Guy Van Orden & Kenneth Papp - 1997 - Philosophy of Science 64 (Supplement):S85-S94.
    The method of positron emission tomography illustrates the circular logic popular in subtractive neuroimaging and linear reductive cognitive psychology. Both require that strictly feed-forward, modular, cognitive components exist, before the fact, to justify the inference of particular components from images after the fact. Also, both require a "true" componential theory of cognition and laboratory tasks, before the fact, to guarantee reliable choices for subtractive contrasts. None of these possibilities are likely. Consequently, linear reductive analysis has failed to yield general, reliable, (...)
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  • The Philosophy of Exploratory Data Analysis.I. J. Good - 1983 - Philosophy of Science 50 (2):283-295.
    This paper attempts to define Exploratory Data Analysis (EDA) more precisely than usual, and to produce the beginnings of a philosophy of this topical and somewhat novel branch of statistics. A data set is, roughly speaking, a collection of k-tuples for some k. In both descriptive statistics and in EDA, these k-tuples, or functions of them, are represented in a manner matched to human and computer abilities with a view to finding patterns that are not "kinkera". A kinkus is a (...)
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  • Objectivity in Experimental Inquiry: Breaking Data-Technique Circles.Sylvia Culp - 1995 - Philosophy of Science 62 (3):438-458.
    I respond to H. M. Collins's claim (1985, 1990, 1993) that experimental inquiry cannot be objective because the only criterium experimentalists have for determining whether a technique is "working" is the production of "correct" (i.e., the expected) data. Collins claims that the "experimenters' regress," the name he gives to this data-technique circle, cannot be broken using the resources of experiment alone. I argue that the data-technique circle, can be broken even though any interpretation of the raw data produced by techniques (...)
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  • Philosophical Issues in Neuroimaging.Colin Klein - 2010 - Philosophy Compass 5 (2):186-198.
    Functional neuroimaging (NI) technologies like Positron Emission Tomography and functional Magnetic Resonance Imaging (fMRI) have revolutionized neuroscience, and provide crucial tools to link cognitive psychology and traditional neuroscientific models. A growing discipline of 'neurophilosophy' brings fMRI evidence to bear on traditional philosophical issues such as weakness of will, moral psychology, rational choice, social interaction, free will, and consciousness. NI has also attracted critical attention from psychologists and from philosophers of science. I review debates over the evidential status of fMRI, including (...)
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  • What Do Brain Data Really Show?Valerie Gray Hardcastle & C. Matthew Stewart - 2002 - Philosophy of Science 69 (3):572-582.
    There is a bias in neuroscience toward localizing and modularizing brain functions. Single cell recording, imaging studies, and the study of neurological deficits all feed into the Gallian view that different brain areas do different things and the things being done are confined to particular processing streams. At the same time, there is a growing sentiment that brains probably don’t work like that after all; it is better to conceive of them as fundamentally distributed units, multi‐tasking at every level. This (...)
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  • Localization and the New Phrenology: A Review Essay on William Uttal's the New Phrenology. [REVIEW]Anthony Landreth & Robert C. Richardson - 2004 - Philosophical Psychology 17 (1):107-123.
    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 (...)
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  • Saving the Phenomena.James Bogen & James Woodward - 1988 - Philosophical Review 97 (3):303-352.
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  • What Do Brain Data Really Show?Valerie Hardcastle & C. Stewart - 2002 - Philosophy of Science 69 (S3):72-82.
    There is a bias in neuroscience toward localizing and modularizing brain functions. Single cell recording, imaging studies, and the study of neurological deficits all feed into the Gallian view that different brain areas do different things and the things being done are confined to particular processing streams. At the same time, there is a growing sentiment that brains probably don’t work like that after all; it is better to conceive of them as fundamentally distributed units, multi‐tasking at every level. This (...)
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  • A Critique of Multi-Voxel Pattern Analysis.Michael L. Anderson - unknown
    Multi-voxel pattern analysis (MVPA) is a popular analytical technique in neuroscience that involves identifying patterns in fMRI BOLD signal data that are predictive of task conditions. But the technique is also frequently used to make inferences about the regions of the brain that are most important to the tasks in question, and our analysis shows that this is a mistake. MVPA does not provide a reliable guide to what information is being used by the brain during cognitive tasks, nor where (...)
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  • Evidence as Passing Severe Tests: Highly Probable Versus Highly Probed Hypotheses.Deborah G. Mayo - 2005 - In P. Achinstein (ed.), Scientific Evidence: Philosophical Theories & Applications. The Johns Hopkins University Press. pp. 95--128.
  • Severe Tests in Neuroimaging: What We Can Learn and How We Can Learn It.Emrah Aktunc - 2014 - Philosophy of Science 81 (5):961-973.
    Considerable methodological difficulties abound in neuroimaging, and several philosophers of science have recently called into question the potential of neuroimaging studies to contribute to our knowledge of human cognition. These skeptical accounts suggest that functional hypotheses are underdetermined by neuroimaging data. I apply Mayo’s error-statistical account to clarify the evidential import of neuroimaging data and the kinds of inferences it can reliably support. Thus, we can answer the question “What can we reliably learn from neuroimaging?” and make sense of how (...)
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  • Functional Neuroimages Fail to Discover Pieces of Mind in the Parts of the Brain.Guy C. Orden & Kenneth R. Paap - 1997 - Philosophy of Science 64 (S1):S85 - S94.
    The method of positron emission tomography (PET imaging) illustrates the circular logic popular in subtractive neuroimaging and linear reductive cognitive psychology. Both require that strictly feed-forward, modular, cognitive components exist, before the fact, to justify the inference of particular components from images (or other observables) after the fact. Also, both require a "true" componential theory of cognition and laboratory tasks, before the fact, to guarantee reliable choices for subtractive contrasts. None of these possibilities are likely. Consequently, linear reductive analysis has (...)
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  • Functional Neuroimages Fail to Discover Pieces of Mind in the Parts of the Brain.Guy C. Ordevann & Kenneth R. Paap - 1997 - Philosophy of Science 64 (S1):S85-.
    The method of positron emission tomography illustrates the circular logic popular in subtractive neuroimaging and linear reductive cognitive psychology. Both require that strictly feed-forward, modular, cognitive components exist, before the fact, to justify the inference of particular components from images after the fact. Also, both require a "true" componential theory of cognition and laboratory tasks, before the fact, to guarantee reliable choices for subtractive contrasts. None of these possibilities are likely. Consequently, linear reductive analysis has failed to yield general, reliable, (...)
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  • Functional Neuroimages Fail to Discover Pieces of Mind in the Parts of the Brain.G. C. van Orden - 1997 - Philosophy of Science Supplement 64 (4):85-94.
    The method of positron emission tomography illustrates the circular logic popular in subtractive neuroimaging and linear reductive cognitive psychology. Both require that strictly feed-forward, modular, cognitive components exist, before the fact, to justify the inference of particular components from images after the fact. Also, both require a "true" componential theory of cognition and laboratory tasks, before the fact, to guarantee reliable choices for subtractive contrasts. None of these possibilities are likely. Consequently, linear reductive analysis has failed to yield general, reliable, (...)
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