This is a wonderful overview. I especially appreciated the notes about possible biases in each study.
My expectation is that the “mental health tech” field is also worth keeping an eye on, although it’s often characterized by big claims and not a lot of supporting data. I’m cautiously optimistic that an app like UpLift (Spencer Greenberg et. al) might be able to improve upon existing self-administered CBT options.
There have also been a lot of promising developments in neuroscience and ‘applied philosophy of mind’, and if there are ways of turning these into technology, it seems plausible we could start to see some “10x results”. Better ways to understand what’s going on in brains will lead to better tools to fix them when they break.
The two paradigms I find most intriguing here are
the predictive coding / free energy paradigm (primary work by Karl Friston, Anil K. Seth, Andy Clark; for a nice summary see SSC’s book review of Surfing Uncertainty and ‘toward a predictive theory of depression’ - also, Adam Safron is an EA who really knows his stuff here, and would be a good person to talk to about how predictive coding models could help inform mental health interventions)
the connectome-specific harmonic wave paradigm (primary work by Selen Atasoy; for a nice summary see this video&transcript—this has informed much of QRI’s thinking about mental health)
I’d also love to survey other peoples’ intuitions on what neuroscience work they think could lead to a ’10x breakthrough’ in mental health tech.
Two areas I think are most promising off the top of my head (held lightly)
Continuing connectome work with advanced meditators. This kind of research has been ongoing at various institutes for the last decade. It would be nice to get a consistent pipeline of funding to enable less stop-start.
Triaging of people into mental health interventions. By paying too much attention to mean effect size in aggregates of treatment populations we are potentially ignoring large effect sizes in restricted treatment populations. Gathering data on outcome distribution shapes and attempting to do some hypothesis exploration on what hidden features are making certain people high responders to certain interventions could be incredibly high returns.
This is a wonderful overview. I especially appreciated the notes about possible biases in each study.
My expectation is that the “mental health tech” field is also worth keeping an eye on, although it’s often characterized by big claims and not a lot of supporting data. I’m cautiously optimistic that an app like UpLift (Spencer Greenberg et. al) might be able to improve upon existing self-administered CBT options.
There have also been a lot of promising developments in neuroscience and ‘applied philosophy of mind’, and if there are ways of turning these into technology, it seems plausible we could start to see some “10x results”. Better ways to understand what’s going on in brains will lead to better tools to fix them when they break.
The two paradigms I find most intriguing here are
the predictive coding / free energy paradigm (primary work by Karl Friston, Anil K. Seth, Andy Clark; for a nice summary see SSC’s book review of Surfing Uncertainty and ‘toward a predictive theory of depression’ - also, Adam Safron is an EA who really knows his stuff here, and would be a good person to talk to about how predictive coding models could help inform mental health interventions)
the connectome-specific harmonic wave paradigm (primary work by Selen Atasoy; for a nice summary see this video&transcript—this has informed much of QRI’s thinking about mental health)
I’d also love to survey other peoples’ intuitions on what neuroscience work they think could lead to a ’10x breakthrough’ in mental health tech.
Two areas I think are most promising off the top of my head (held lightly)
Continuing connectome work with advanced meditators. This kind of research has been ongoing at various institutes for the last decade. It would be nice to get a consistent pipeline of funding to enable less stop-start.
Triaging of people into mental health interventions. By paying too much attention to mean effect size in aggregates of treatment populations we are potentially ignoring large effect sizes in restricted treatment populations. Gathering data on outcome distribution shapes and attempting to do some hypothesis exploration on what hidden features are making certain people high responders to certain interventions could be incredibly high returns.
I would definitely endorse these.