An Introduction to Model-Based Cognitive Neuroscience

1. Front Matter

   Pages i-xi

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2. Tutorials

   1. Front Matter

      Pages 1-1

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   2. An Introduction to Cognitive Modeling

      • Simon Farrell, Stephan Lewandowsky

      Pages 3-24

   3. An Introduction to Good Practices in Cognitive Modeling

      • Andrew Heathcote, Scott D. Brown, Eric-Jan Wagenmakers

      Pages 25-48

   4. An Introduction to the Diffusion Model of Decision Making

      • Philip L. Smith, Roger Ratcliff

      Pages 49-70

   5. An Introduction to Human Brain Anatomy

      • Birte U. Forstmann, Max C. Keuken, Anneke Alkemade

      Pages 71-89

   6. An Introduction to fMRI

      • F. Gregory Ashby

      Pages 91-112

   7. An Introduction to Neuroscientific Methods: Single-cell Recordings

      • Veit Stuphorn, Xiaomo Chen

      Pages 113-137

   8. Model-Based Cognitive Neuroscience: A Conceptual Introduction

      • Birte U. Forstmann, Eric-Jan Wagenmakers

      Pages 139-156

3. How Cognitive Models Inform the Cognitive Neurosciences

   1. Front Matter

      Pages 157-157

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   2. Linking Across Levels of Computation in Model-Based Cognitive Neuroscience

      • Michael J. Frank

      Pages 159-177

   3. Bayesian Models in Cognitive Neuroscience: A Tutorial

      • Jill X. O’Reilly, Rogier B. Mars

      Pages 179-197

   4. Constraining Cognitive Abstractions Through Bayesian Modeling

      • Brandon M. Turner

      Pages 199-220

   5. Predictive Coding in Sensory Cortex

      • Peter Kok, Floris P. de Lange

      Pages 221-244

   6. Using Human Neuroimaging to Examine Top-down Modulation of Visual Perception

      • Thomas C. Sprague, John T. Serences

      Pages 245-274

4. How the Cognitive Neurosciences Inform Cognitive Models

   1. Front Matter

      Pages 275-275

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   2. Distinguishing Between Models of Perceptual Decision Making

      • Jochen Ditterich

      Pages 277-290

   3. Optimal Decision Making in the Cortico-Basal-Ganglia Circuit

      • Rafal Bogacz

      Pages 291-302

   4. Inhibitory Control in Mind and Brain: The Mathematics and Neurophysiology of the Underlying Computation

      • Gordon D. Logan, Jeffrey D. Schall, Thomas J. Palmeri

      Pages 303-320

   5. Reciprocal Interactions of Computational Modeling and Empirical Investigation

      • William H. Alexander, Joshua W. Brown

      Pages 321-338

Two recent innovations, the emergence of formal cognitive models and the addition of cognitive neuroscience data to the traditional behavioral data, have resulted in the birth of a new, interdisciplinary field of study: model-based cognitive neuroscience.  Despite the increasing scientific interest in model-based cognitive neuroscience, few active researchers and even fewer students have a good knowledge of the two constituent disciplines. The main goal of this edited collection is to promote the integration of cognitive modeling and cognitive neuroscience. Experts in the field will provide tutorial-style chapters that explain particular techniques and highlight their usefulness through concrete examples and numerous case studies.  The book will also include a thorough list of references pointing the reader towards additional literature and online resources.

• cognitive neuroscience
• computational neuroscience
• decision making
• mathematical models
• model-based neuroscience

• Cognitive Science Center, University of Amsterdam, Amsterdam, The Netherlands

  Birte U. Forstmann

• Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands

  Eric-Jan Wagenmakers

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. 

Eric-Jan (EJ) Wagenmakers is a professor at the Psychological Methods Unit of the University of Amsterdam. His current work concerns Bayesian inference, philosophy of science, mathematical models of cognition, and model-based cognitive neuroscience. His studies in cognitive neuroscience are guided by the conviction that mathematical process models can provide useful structure and constraint for the analysis and interpretation of brain data.

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