An Introduction to Model-Based Cognitive Neuroscience (2nd Ed., 2nd ed. 2024)
Coordonnateurs : Forstmann Birte U., Turner Brandon M.
Tutorial chapters
1. General Introduction (BU Forstmann & B Turner)
2. Mathematical cognitive modeling (A Heathcote)
3. Cognitive neurosciences (G Ashby)
4. Model-Based Cognitive Neuroscience (B Turner & BU Forstmann)
Cognitive modeling
5. Reinforcement learning (RL) models (J O’Doherty)6. Diffusion Decision models (DDM) (R Ratcliff & P Smith)
7. ACT-R (R Anderson)
Cognitive neurosciences
8. Single cell recordings (M Shadlen)
9. (Ultra-high field) MRI (P-L Bazin)
10. EEG/MEG (B van Wijk)Joint modeling
11. Joint modeling of brain and behavior (B Turner & T Palmeri & BU Forstmann)
12. Joint DDM & EEG/fMRI (S Brown & M Steyvers)
13. Joint RL & EEG/fMRI (M Frank)
15. Computational Psychiatry (N Daw)
16. Neurodegenerative diseases (J Rowe & P Brown)
17. Social decision making (M Crocket)
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 postdoctoral researcher 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.
Accessible to both neuroscientists and mathematical modelers
Presents several tutorial chapters
Features applications emphasizing the value of models for neuroscience
Date de parution : 04-2024
Ouvrage de 377 p.
15.5x23.5 cm