Book contents
- The Cambridge Handbook of Research Methods in Clinical Psychology
- The Cambridge Handbook of Research Methods in Clinical Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgments
- Part I Clinical Psychological Science
- Part II Observational Approaches
- Part III Experimental and Biological Approaches
- Part IV Developmental Psychopathology and Longitudinal Methods
- Part V Intervention Approaches
- Part VI Intensive Longitudinal Designs
- Part VII General Analytic Considerations
- Index
- References
Part III - Experimental and Biological Approaches
Published online by Cambridge University Press: 23 March 2020
- The Cambridge Handbook of Research Methods in Clinical Psychology
- The Cambridge Handbook of Research Methods in Clinical Psychology
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Acknowledgments
- Part I Clinical Psychological Science
- Part II Observational Approaches
- Part III Experimental and Biological Approaches
- Part IV Developmental Psychopathology and Longitudinal Methods
- Part V Intervention Approaches
- Part VI Intensive Longitudinal Designs
- Part VII General Analytic Considerations
- Index
- References
Summary
- Type
- Chapter
- Information
- Publisher: Cambridge University PressPrint publication year: 2020
References
References
References
References
References
Reference
Further Reading
For an in-depth treatment of reinforcement learning, we recommend Sutton and Barto’s recently updated classic book, Reinforcement Learning: An Introduction (2018). The Oxford Handbook of Computational and Mathematical Psychology introduces the reader to cognitive modeling and contains Gureckis and Love’s superb chapter on reinforcement learning (2015). Excellent computational neuroscience texts include Miller’s Introductory Course in Computational Neuroscience (2018) and Dayan and Abbott’s Theoretical Neuroscience (2005). Miller covers useful preliminary material, including mathematics, circuit physics and even computing and MATLAB (much of existing code for reinforcement learning modeling is written in MATLAB, but R and Python are becoming increasingly popular). Dayan and Abbot treat conditioning and reinforcement learning in greater detail. A more detailed treatment of model-based cognitive neuroscience can be found in An Introduction to Model-Based Cognitive Neuroscience (Forstmann & Wagenmakers, 2015).