Computational Psychiatry is hosting a 1.5-day course which aims to introduce the tools and methods of this new approach to mental function and dysfunction, in Washington D.C. (USA) on November 10 and 11. As the time of this special event approaches, we check in with Dr. Peter Dayan and Dr. Read Montague, both of whom are the helms of Computational Psychiatry. They answered a few questions for us about this upcoming conference.
Why is Computational Psychiatry holding this event?
The field of Computational Psychiatry concerns the “application, analysis, or invention of theoretical, computational and statistical approaches to mental function and dysfunction” – to quote from the Journal’s press release. Thus, Computational Psychiatry seeks to foster “brain modeling over multiple scales and levels of analysis, and the use of these models to understand psychiatric dysfunction, its remediation, and the sustenance of healthy cognition through the lifespan.” The leadership of the National Institutes of Mental Health is very supportive, perhaps because it sees the prospect of what new multidisciplinary thinking might provide, and recognises the underexploited opportunities afforded by dramatic advances in computational methods and thinking.
Despite deep roots in many disciplines, that portion of Computational Psychiatry currently in view and having a direct impact remains modest. For the field to reach its potential, the field must spread the evolving word to the wider group of clinicians and scientists across many disciplines who can contribute and benefit. The substantial enthusiasm for existing annual courses in London, Zurich and beyond shows the demand; co-locating the event with the premier neuroscience conference in the world eases the path for a further group of investigators to join in.
The Journal is hosting this event to help build, nurture and educate this community.
What are the goals of the event?
The course aims to introduce the tools, methods and sample applications of this new approach to mental function and dysfunction. It has been designed so that no prior experience in either psychiatry or computational modeling is required – we hope to provide sufficient detail for researchers and practitioners steeped in particular camps to gain an appreciation and understanding of what those in other camps do, why they do it, and how this contributes to the greater whole.
We also hope that, facilitated by a growing common language, the informal interactions among the attendees will be highly and perhaps unexpectedly productive. This will inelcutably lead to articles for submission (this is a hint to the ambitious).
Who is the ideal audience for the special course?
From the breadth of the ‘mission statement’ above, it is apparent that we are casting our net very wide. The ideal audience would include scientists doing computationally-informed research who are interested in the application to mental health; those doing experimental work in areas such as affective decision-making who want to formalize their findings and link them to dysfunction; clinical scientists interested, for instance, in providing quantitative ties between results in their populations of patients and neural substrates; and beyond. In fact, the broader the audience, the more that the course, and its community, will benefit.
During this 1.5-day course, what will be included, and why?
We will cover a range of core topics, including Bayesian decision theory, reinforcement learning and network science, and describe a set of applications in areas such as depression, addiction, and interpersonal interactions.
The core topics reflect some of the current major foci of work in the field, which grew partly out of the application to dysfunctional affective decision-making of some decades of work on normal and normative choice.
Of most importance: there will be ample opportunity for questions and discussions. The organizers look forward to learning as much as the students.