Introduction to the Series on Computational Psychiatry

Anticevic A. Murray J. D. Barch D. M. (2015). Bridging levels of understanding in schizophrenia through computational modeling. Clinical Psychological Science, 3, 433–459.

Brodersen K. H. Deserno L. Schlagenhauf F. Lin Z. Penny W. D. Buhmann J. M. Stephan K. E. (2014). Dissecting psychiatric spectrum disorders by generative embedding. NeuroImage: Clinical, 4, 98–111. doi:10.1016/j.nicl.2013.11.002

Daunizeau J. Adam V. Rigoux L. (2014). VBA: A probabilistic treatment of nonlinear models for neurobiological and behavioural data. PLOS Computational Biology, 10, e1003441. doi:10.1371/journal.pcbi.1003441

Daw N. D. (2011). Trial-by-trial data analysis using computational models. In Delgado M. R. Phelps E. A. Robbins T. W. (Eds.), Decision making, affect, and learning: Attention & performance XXIII (pp.). Oxford, England: Oxford University Press. doi:10.1093/acprof:oso/9780199600434.003.0001

Friston K. J. Stephan K. E. Montague R. Dolan R. J. (2014). Computational psychiatry: The brain as a phantastic organ. Lancet Psychiatry, 1, 148–158. doi:10.1016/S2215-0366(14)70275-5

Huys Q. J. M. (2013). Computational psychiatry. In Jaeger D. Jung R. (Eds.), Encyclopedia of computational neuroscience (pp.). New York, NY: Springer. doi:10.1007/978-1-4614-7320-6_501-2

Huys Q. J. M. Guitart-Masip M. Dolan R. J. Dayan P. (2015). Decision-theoretic psychiatry. Clinical Psychological Science, 3, 400–421.

Klöppel S. Abdulkadir A. Jack C. R. Koutsouleris N. Mourão-Miranda J. Vemuri P. (2012). Diagnostic neuroimaging across diseases. NeuroImage, 61, 457–463. doi:10.1016/j.neuroimage.2011.11.002

Lu J. Kishida K. Asis-Cruz J. De Lohrenz T. Treadwell-Deering D. Beauchamp M. Montague P. R. (2015). Single-stimulus functional MRI produces a neural individual difference measure for autism spectrum disorder. Clinical Psychological Science, 3, 422–432.

Maia T. V. Cano-Colino M. (2015). The role of serotonin in orbitofrontal function and obsessive-compulsive disorder. Clinical Psychological Science, 3, 460–482.

Maia T. V. Frank M. J. (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience, 14, 154–162. doi:10.1038/nn.2723

Montague P. R. Dolan R. J. Friston K. J. Dayan P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16, 72–80. doi:10.1016/j.tics.2011.11.018

O’Reilly R. C. Munakata Y. Frank M. J. Hazy T. E. (2014). Computational cognitive neuroscience. Retrieved from http://ccnbook.colorado.edu

Rigoux L. Stephan K. E. Friston K. J. Daunizeau J. (2014). Bayesian model selection for group studies—Revisited. NeuroImage, 84, 971–985. doi:10.1016/j.neuroimage.2013.08.065

Stephan K. E. Mathys C. (2014). Computational approaches to psychiatry. Current Opinion in Neurobiology, 25, 85–92. doi:10.1016/j.conb.2013.12.007

Stephan K. E. Penny W. D. Daunizeau J. Moran R. J. Friston K. J. (2009). Bayesian model selection for group studies. NeuroImage, 46, 1004–1017. doi:10.1016/j.neuroimage.2009.03.025

Wang X.-J. Krystal J. H. (2014). Computational psychiatry. Neuron, 84, 638–654. doi:10.1016/j.neuron.2014.10.018

Wiecki T. V. Poland J. Frank M. J. (2015). Model-based cognitive neuroscience approaches to computational psychiatry: Clustering and classification. Clinical Psychological Science, 3, 378–399.