An Introduction to Model-Based Cognitive Neuroscience

Author:   Birte U. Forstmann ,  Brandon M. Turner
Publisher:   Springer International Publishing AG
Edition:   2nd ed. 2024
ISBN:  

9783031452703


Pages:   388
Publication Date:   31 March 2024
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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An Introduction to Model-Based Cognitive Neuroscience


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Author:   Birte U. Forstmann ,  Brandon M. Turner
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2nd ed. 2024
ISBN:  

9783031452703


ISBN 10:   3031452704
Pages:   388
Publication Date:   31 March 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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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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