SUMMER PROGRAM IN COMPUTATIONAL PSYCHIATRY EDUCATION
Applications are now open!
Please submit your applications here: Application Form
Deadline: March 15, 2023
What is SPICE?
The Summer Program in Computational Psychiatry Education provides an opportunity for high-school and college students aged 16 and up to work alongside computational psychiatry researchers at Icahn School of Medicine at Mount Sinai, and assist in cutting-edge basic and clinical research.
SPICE is a 8-week research program offered to high school and college students at the Center for Computational Psychiatry at Icahn School of Medicine at Mount Sinai. Prospective trainees should be independent, motivated, and inquistive and demonstrate a strong interest in psychiatric or neuroscience research. Over the course of the program, trainees will undergo a 2-week intensive lecture series about the basics of neuroscience, psychiatry, and computational sciences, and spend the remaining time completing a basic research project under the supervision of CCN graduate students, post-doctoral fellows, and/or faculty.
SPICE will take place in the summer starting June, meeting Monday-Friday from 9:30am-4:30pm. The lecture series will be held in the first two weeks, see Syllabus below a list of covered topics. Training sessions will be highly interactive, with students learning about the latest neuroscientific literature, designing their own research questions, and presenting interesting journal articles to classmates.
Frequently Asked Questions
Will the program be virtual this year?
We are excited to have the program in-person this year! Trainees will be expected to be available during work hours (9:30am - 4:30pm) during the full 8-week program. Trainees who are fully vaccinated and close to the NYC area are encouraged to visit the lab and work in person!
When does the program take place?
SPICE takes place in summer 2023 starting in late June and ending in early August. Student should plan to be available on weekdays from 9:30am - 4:30pm. Final dates will be announced shortly.
What are the eligibility criteria?
Students should be at least 16 years old to apply for this program, and at least sophomores in high school. Additionally, it is highly recommended for local students attend SPICE in-person, if possible.
Where is the Center for Computational Psychiatry at Icahn School of Medicine at Mount Sinai?
Our center is located in Harlem at 55 W 125st St, New York, NY 10027. The main Mount Sinai campus is located further south in East Harlem at 1492 Madison Ave, New York, NY 10029.
What does a typical day at SPICE look like?
For the first two weeks of the program, trainees will have morning lecture sessions about the basics of neuroscience/psychiatry and computational science (see Syllabus). Here, they will receive the necessary prerequisite training to begin working on their independent project. Afternoon sessions will be spent completing homework, and brainstorming and designing research questions. In the remaining six weeks, trainees will spend their morning and afternoon sessions completing their independent research projects under the supervision of their research mentor.
Is there homework? Are there tests or grades?
During the first two weeks, trainees should expect to complete homework that will help solidify their knowledge of important neuroscience and computational science concepts. There are no grades and no tests, but trainees should expect to be challenged with reading research articles, conducting experiments, and preparing a final presentation at the end of the program. Punctuality and daily attendance is mandatory.
For the application - How do I write a cover letter and/or resume?
If you've never written a cover letter or resume before, don't stress! We just want to see what your interests and goals are, and what (if any!) experience you've had with neuroscience/biology/psychology/coding. This helps us see if the program is a good fit for your goals and helps us pair you with a mentor. Here are some recommended resources:
Bishop, S. J., & Gagne, C. (2018). Anxiety, depression, and decision making: a computational perspective. Annual review of neuroscience, 41, 371-388.
Blohm, G., Kording, K. P., & Schrater, P. R. (2020). A How-to-Model Guide for Neuroscience. eNeuro, 7(1), 1–12.
Crockett, M. J. (2013). Models of morality. Trends in Cognitive Sciences, 17(8), 363-366.
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. https://doi.org/10.7554/eLife.11305.
Hartley, C. A., Nussenbaum, K., & Cohen, A. O. (2021). Interactive Development of Adaptive Learning and Memory. Annual Review of Developmental Psychology, 3, 59-85.
Huys, Quentin J M, Tiago V Maia, and Michael J Frank. “Computational Psychiatry as a Bridge from Neuroscience to Clinical Applications.” Nature Neuroscience 19, no. 3 (2016): 404–13. https://doi.org/10.1038/nn.4238.
Lockwood, P. L., Apps, M. A. J., & Chang, S. W. C. (2020). Is there a “social” brain? Implementations and algorithms. Trends in Cognitive Sciences, 1–12.
Sharot, T., & Garrett, N. (2016). Forming beliefs: Why valence matters. Trends in cognitive sciences, 20(1), 25-33.
Montague, P. Read, Raymond J. Dolan, Karl J. Friston, and Peter Dayan. “Computational Psychiatry.” Trends in Cognitive Sciences, Special Issue: Cognition in Neuropsychiatric Disorders, 16, no. 1 (2012): 72–80. https://doi.org/10.1016/j.tics.2011.11.018.
Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. eLife, 8, e49547.
Relevant Lab Publications:
Berner, L. A., Fiore, V. G., Chen, J. Y., Krueger, A., Kaye, W. H., Viranda, T., & de Wit, S. (2023). Impaired belief updating and devaluation in adult women with bulimia nervosa. Translational Psychiatry, 13(1), 1–9. https://doi.org/10.1038/s41398-022-02257-6
Gu, X., & Filbey, F. (2017). A Bayesian Observer Model of Drug Craving. JAMA Psychiatry, 74(4), 419–420. https://doi.org/10.1001/jamapsychiatry.2016.3823
Gu, X., FitzGerald, T. H., & Friston, K. J. (2019). Modeling subjective belief states in computational psychiatry: Interoceptive inference as a candidate framework. Psychopharmacology, 236(8), 2405–2412. https://doi.org/10.1007/s00213-019-05300-5
Na, S., Chung, D., Hula, A., Jung, J., Fiore, V. G., Dayan, P., & Gu, X. (2019). Humans Use Forward Thinking to Exert Social Control. BioRxiv, 737353. https://doi.org/10.1101/737353
Radulescu, A., Niv, Y., & Ballard, I. (2019). Holistic reinforcement learning: the role of structure and attention. Trends in cognitive sciences, 23(4), 278-292.
Saez, I., & Gu, X. (2022). Invasive Computational Psychiatry. Biological Psychiatry. https://doi.org/10.1016/j.biopsych.2022.09.032
Schafer, M., & Schiller, D. (2018). Navigating Social Space. Neuron, 100(2), 476–489. https://doi.org/10.1016/j.neuron.2018.10.006
Please feel free to email Sarah or Shawn if you need access to any of these papers!
Graduate Student and Postdoc Leadership:
Shawn Rhoads: shawn.rhoads (at) mssm.edu
Sarah Banker: sarah.banker (at) icahn.mssm.edu
Xiaosi Gu: xiaosi.gu (at) icahn.mssm.edu