An Introduction to Model-Based Cognitive Neuroscience

The main goal of this edited collection is to promote the integration of cognitive modeling and cognitive neuroscience. Experts in the field provide tutorial-style chapters that explain particular techniques and highlight their usefulness through concrete examples and numerous case studies. The book also includes a thorough list of references pointing the reader toward additional literature and online resources. The second edition of Introduction to Model-Based Cognitive Neuroscience explores important new advances in the field including joint modeling and applications in areas such as computational psychiatry, neurodegenerative diseases, and social decision-making.

Accessible to both neuroscientists and mathematical modelers Presents several tutorial chapters Features applications emphasizing the value of models for neuroscience

Autorentext

Birte Forstmann is a Professor for Cognitive Neurosciences at the University of Amsterdam as well as honorary professor at the University of Leiden. She earned her PhD in 2006 at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. After completing her postdoc in 2008 at the University of Amsterdam, she became tenured Research Fellow at the Cognitive Science Center Amsterdam with the focus of model-based cognitive neurosciences. Since then she has contributed to a range of topics in cognitive neuroscience, experimental psychology, mathematical psychology, and lately also in quantitative neuroanatomy.

Brandon M. Turner is a Professor in the Psychology Department at The Ohio State University. He received a B.S. from Missouri State University in mathematics and psychology in 2008, a MAS in statistics from The Ohio State University in 2010, and a Ph.D. from The Ohio State University in 2011. He then spent one year as a postdoctoralresearcher at University of California, Irvine, and two years as a postdoctoral fellow at Stanford University. His research interests include dynamic models of cognition, perceptual decision making, selective attention and learning, efficient methods for performing likelihood-free and likelihood-informed Bayesian inference, and unifying behavioral and neural explanations of cognition.

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