By appointment. Please contact Prof. Gershman (firstname.lastname@example.org).
Grading will be based on the following elements:
(i) Reading responses, due 9pm on the night before class. Each reading response should consist of one paragraph for each reading.
(ii) Each student must lead the discussion of at least one paper. The student should read all the reading responses and synthesize a set of questions to guide the discussion. They should also begin each discussion with a brief summary of the paper.
(iii) Psychology graduate students who wish to receive credit towards their degree requirements must additionally write a 10-page final paper on a topic of their choosing.
You are expected to submit your own, original work for the exam and the final paper. Any misconduct will be reported, as is required by the college. Discussing your ideas with others and getting feedback on your work is encouraged, but you are required to cite any and all ideas that are not your own, and ensure that any assignments you turn in are your own writing and the result of your own research.
Any student needing academic adjustments or accommodations is requested to present their letter from the Accessible Education Office (AEO) and speak with the professor by the end of the second week of the term, (specific date). Failure to do so may result in the Course Head’s inability to respond in a timely manner. All discussions will remain confidential, although AEO may be consulted to discuss appropriate implementation.
Class 1: Introduction and overview
Huys, Q.J.M., Maia, T., & Frank, M.J. (2016). Computational psychiatry as a bridge from neuroscience to clinical applications. Nature Neuroscience, 19, 404-413.
Class 2: Schizophrenia, part 1
Braver, T.S., Barch, D.M., & Cohen, J.D. (1999). Cognition and control in
schizophrenia: a computational model of dopamine and prefrontal function.
Biological Psychiatry, 46, 312-328.
Presenter: Kristen Fang
Maia, T.V. & Frank, M.J. (2016). An integrative perspective on the role of dopamine in schizophrenia. Biological Psychiatry, 81, 52-66.
Presenter: Harry Fu
Class 3: Schizophrenia, part 2
Presenters: T'Ajmal Hogue
Stephan, K.E., Baldeweg, T., & Friston, K.J. (2006). Synaptic plasticity and disconnection in schizophrenia. Biological Psychiatry, 59, 929–39.
Presenters: Philip Thomsen
Class 4: Hallucinations and delusions
Corlett, P. R., Horga, G., Fletcher, P. C., Alderson-Day, B., Schmack, K., & Powers III, A. R. (2018). Hallucinations and strong priors. Trends in Cognitive Sciences, 23, 114-127.
Presenters: Molly Sacks
Coltheart, M., Menzies, P., & Sutton, J. (2010). Abductive inference and delusional belief. Cognitive Neuropsychiatry, 15, 261-287.
Presenters: Josh Stern
Class 5: Attention-deficit hyperactivity disorder
Frank, M.J., Santamaria, A., O’Reilly, R.C., & Willcutt, E. (2007). Testing computational models of dopamine and noradrenaline dysfunction in attention deficit/hyperactivity disorder. Neuropsychopharmacology, 32, 1583–1599.
Presenters: Kayla Johnson, Jackie Walzer
Hauser, T. U., Fiore, V. G., Moutoussis, M., & Dolan, R. J. (2016). Computational psychiatry of ADHD: neural gain impairments across Marrian levels of analysis. Trends in neurosciences, 39, 63-73.
Presenters: Kevin Dai, Chloe Close
Class 6: Obsessive-compulsive disorder
Rolls, E. T., Loh, M., & Deco, G. (2008). An attractor hypothesis of obsessive–compulsive disorder. European Journal of Neuroscience, 28, 782-793.
Presenters: Emma Rogge
Vaghi, M. M., Luyckx, F., Sule, A., Fineberg, N. A., Robbins, T. W., & De Martino, B. (2017). Compulsivity reveals a novel dissociation between action and confidence. Neuron, 96, 348-354.
Presenters: Zach Diamandis
Class 7: Autism
Pellicano, E., & Burr, D. (2012). When the world becomes “too real”: A Bayesian explanation of autistic perception. Trends in Cognitive Sciences, 16, 504–510.
Presenters: Annabelle Tao, Marisa Trapani
Rosenberg, A., Patterson, J.S., & Angelaki, D.E. (2015). A computational perspective on autism. Proceedings of the National Academy of Sciences, 112, 9158–9165.
Presenters: Christine Jou
Class 8: Depression
Huys, Q.M., Daw, N.D., & Dayan, P. (2015). Depression: a decision-theoretic analysis. Annual Review of Neuroscience, 38, 1-23.
Presenters: Luke Sajer
Presenters: Jeanne Khang
Class 9: Mood and anxiety
Raymond, J. G., Steele, J. D., & Seriès, P. (2017). Modeling trait anxiety: From computational processes to personality. Frontiers in psychiatry, 8, 1.
Presenters: Evelyn Wong
Mkrtchian, A., Aylward, J., Dayan, P., Roiser, J. P., & Robinson, O. J. (2017). Modeling avoidance in mood and anxiety disorders using reinforcement learning. Biological psychiatry, 82, 532-539.
Presenters: Isabel Ruehl, Amanda Westort
Class 10: Addiction, part 1
Simon, D. A., & Daw, N. D. (2012). Dual-system learning models and drugs of abuse. In Computational neuroscience of drug addiction (pp. 145-161). Springer, New York, NY.
Presenters: Michael Wornow, Jake Cui
Redish, A.D., Jensen, S., Johnson, A. (2008). A unified framework for addiction: vulnerabilities in the decision process. Behavioral and Brain Sciences, 31, 415–37.
Presenters: Olivia Chapman
Class 11: Addiction, part 2
Gutkin, B.S., Dehaene, S., & Changeux, J.-P. (2006). A neurocomputational hypothesis for nicotine addiction. Proceedings of the National Academy of Sciences, 103, 1106–1111.
Presenters: Antonio Moreno, Kendall Zhu
Dayan, P. (2009). Dopamine, reinforcement learning, and addiction. Pharmacopsychiatry, 42, 56–65.
Presenters: Katharine Pan
Class 12: Transdiagnostic approaches
Gillan, C. M., Kosinski, M., Whelan, R., Phelps, E. A., & Daw, N. D. (2016). Characterizing a psychiatric symptom dimension related to deficits in goal-directed control. Elife, 5, e11305.
Presenters: Josie Wolf
Cramer, A. O., Waldorp, L. J., Van Der Maas, H. L., & Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and brain sciences, 33, 137-150.
Presenters: Joyce Clanon
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