An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and Bayesian models; and models of social interaction. All mathematical concepts are introduced gradually, with no background in advanced topics required. Hints and solutions for exercises and a glossary follow the main text. All code in the book is Python, with the Spyder editor in the Anaconda environment. A GitHub repository with Python files enables readers to access the computer code used and start programming themselves. The book is suitable as an introduction to modeling cognitive processes for students across a range of disciplines and as a reference for researchers interested in a broad overview.

This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the computational cognitive neuroscience. The goal of computational cognitive neuroscience is to understand how the brain embodies the mind by using biologically based computational models comprising networks of neuronlike units. This text, based on a course taught by Randall O'Reilly and Yuko Munakata over the past several years, provides an in-depth introduction to the main ideas in the field. The neural units in the simulations use equations based directly on the ion channels that govern the behavior of real neurons, and the neural networks incorporate anatomical and physiological properties of the neocortex. Thus the text provides the student with knowledge of the basic biology of the brain as well as the computational skills needed to simulate large-scale cognitive phenomena. The text consists of two parts. The first part covers basic neural computation mechanisms: individual neurons, neural networks, and learning mechanisms. The second part covers large-scale brain area organization and cognitive phenomena: perception and attention, memory, language, and higher-level cognition. The second part is relatively self-contained and can be used separately for mechanistically oriented cognitive neuroscience courses. Integrated throughout the text are more than forty different simulation models, many of them full-scale research-grade models, with friendly interfaces and accompanying exercises. The simulation software (PDP++, available for all major platforms) and simulations can be downloaded free of charge from the Web. Exercise solutions are available, and the text includes full information on the software.

"For the neuroscientist or psychologist who cringes at the sight of mathematical formulae and whose eyes glaze over at terms like differential equations, linear algebra, vectors, matrices, Bayes’ rule, and Boolean logic, this book just might be the therapy needed." - Anjan Chatterjee, Professor of Neurology, University of Pennsylvania "Anderson provides a gentle introduction to computational aspects of psychological science, managing to respect the reader’s intelligence while also being completely unintimidating. Using carefully-selected computational demonstrations, he guides students through a wide array of important approaches and tools, with little in the way of prerequisites...I recommend it with enthusiasm." - Asohan Amarasingham, The City University of New York This unique, self-contained and accessible textbook provides an introduction to computational modelling neuroscience accessible to readers with little or no background in computing or mathematics. Organized into thematic sections, the book spans from modelling integrate and firing neurons to playing the game Rock, Paper, Scissors in ACT-R. This non-technical guide shows how basic knowledge and modern computers can be combined for interesting simulations, progressing from early exercises utilizing spreadsheets, to simple programs in Python. Key Features include: Interleaved chapters that show how traditional computing constructs are simply disguised versions of the spread sheet methods. Mathematical facts and notation needed to understand the modelling methods are presented at their most basic and are interleaved with biographical and historical notes for contex. Numerous worked examples to demonstrate the themes and procedures of cognitive modelling. An excellent text for postgraduate students taking courses in research methods, computational neuroscience, computational modelling, cognitive science and neuroscience. It will be especially valuable to psychology students.

An essential reference for the new discipline of evolutionary cognitive neuroscience that defines the field's approach of applying evolutionary theory to guide brain-behavior investigations. Since Darwin we have known that evolution has shaped all organisms and that biological organs—including the brain and the highly crafted animal nervous system—are subject to the pressures of natural and sexual selection. It is only relatively recently, however, that the cognitive neurosciences have begun to apply evolutionary theory and methods to the study of brain and behavior. This landmark reference documents and defines the emerging field of evolutionary cognitive neuroscience. Chapters by leading researchers demonstrate the power of the evolutionary perspective to yield new data, theory, and insights on the evolution and functional modularity of the brain. Evolutionary cognitive neuroscience covers all areas of cognitive neuroscience, from nonhuman brain-behavior relationships to human cognition and consciousness, and each section of Evolutionary Cognitive Neuroscience addresses a different adaptive problem. After an introductory section that outlines the basic tenets of both theory and methodology of an evolutionarily informed cognitive neuroscience, the book treats neuroanatomy from ontogenetic and phylogenetic perspectives and explores reproduction and kin recognition, spatial cognition and language, and self-awareness and social cognition. Notable findings include a theory to explain the extended ontogenetic and brain development periods of big-brained organisms, fMRI research on the neural correlates of romantic attraction, an evolutionary view of sex differences in spatial cognition, a theory of language evolution that draws on recent research on mirror neurons, and evidence for a rudimentary theory of mind in nonhuman primates. A final section discusses the ethical implications of evolutionary cognitive neuroscience and the future of the field. Contributors: C. Davison Ankney, Simon Baron-Cohen, S. Marc Breedlove, William Christiana, Michael Corballis, Robin I. M. Dunbar, Russell Fernald, Helen Fisher, Jonathan Flombaum, Farah Focquaert, Steven J.C. Gaulin, Aaron Goetz, Kevin Guise, Ruben C. Gur, William D. Hopkins, Farzin Irani, Julian Paul Keenan, Michael Kimberly, Stephen Kosslyn, Sarah L. Levin, Lori Marino, David Newlin, Ivan S. Panyavin, Shilpa Patel, Webb Phillips, Steven M. Platek, David Andrew Puts, Katie Rodak, J. Philippe Rushton, Laurie Santos, Todd K. Shackelford, Kyra Singh, Sean T. Stevens, Valerie Stone, Jaime W. Thomson, Gina Volshteyn, Paul Root Wolpe

This volume describes the new field of cognitive neuroscience - the study of what happens in the brain when we perceive, think, reason, remember, and act. Focusing on the human brain, Passingham looks at the most recent research in the field, the modern brain imaging technologies, and what the images can and can't tell us.