Journal ArticleDOI
Innateness and the Brain
TLDR
The philosophical innateness debate has long relied onpsychological evidence as discussed by the authors, however, a parallel debate hastaken place within neuroscience, and it is argued that the majority of natural systems are in fact innate.Abstract:
The philosophical innateness debate has long relied onpsychological evidence. For a century, however, a parallel debate hastaken place within neuroscience. In this paper, I consider theimplications of this neuroscience debate for the philosophicalinnateness debate. By combining the tools of theoretical neurobiologyand learning theory, I introduce the ``problem of development'' that alladaptive systems must solve, and suggest how responses to this problemcan demarcate a number of innateness proposals. From this perspective, Isuggest that the majority of natural systems are in fact innate. Lastly,I consider the acquistion strategies implemented by the human brain andsuggest that there is a rigorous way of characterizing these ``neuralconstructivist'' strategies as not being strongly innate. Alternatives toinnateness are thus both rigorously definable and empirically supported.read more
Citations
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The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95
TL;DR: The Computational Brain this paper provides a broad overview of neuroscience and computational theory, followed by a study of some of the most recent and sophisticated modeling work in the context of relevant neurobiological research.
Book
Adapting Minds: Evolutionary Psychology and the Persistent Quest for Human Nature
TL;DR: David Buller argues that the authors' minds are not adapted to the Pleistocene, but, like the immune system, are continually adapting, over both evolutionary time and individual lifetimes.
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Boundaries of the Mind: The Individual in the Fragile Sciences - Cognition
TL;DR: Genes and the Agents of Life as discussed by the authors explores the theme in the biological sciences and provides a unique blend of traditional philosophical analysis, cognitive science, and the history of psychology and the human sciences.
References
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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
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Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
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Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
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Genetic Programming: On the Programming of Computers by Means of Natural Selection
TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.