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In contrast, I was underwhelmed. Neuroscientists typically try to develop tasks that create specific contrasts that highlight aspects of specific sorts of compu

In contrast, I was underwhelmed. Neuroscientists typically try to develop tasks that create specific contrasts that highlight aspects of specific sorts of computations. They didn't do anything like that. For example, in the field I work in, we very very reliably get signal that track reward prediction errors in the striatum (e.g. BOLD response in fmri) during reward learning tasks.

eli_gottlieb 7y ago
>For example, in the field I work in, we very very reliably get signal that track reward prediction errors in the striatum (e.g. BOLD response in fmri) during reward learning tasks. Yeah, but that's a property of how the BOLD response correlates with the task structure, not a map of what computations the brain actually does. Yeah, ok, I'm shortening things by a lot, but neuroimaging really is quite fraught in terms of what sorts of computational-level inferences we can draw from task-based experiments.
- jonnycomputer 7y ago
  In these experiments, the BOLD signal reliably scales with reward prediction error, which is a computation, the difference between the reward expected and received. In short, we fit reinforcement learning models to in-task behavior, then correlate parameters of that model to neuroimaging acquired during that behavior. fMRI does have quite a few limitations; BOLD signal is believed to reflect the amount of work being done, loosely, speaking, but it is not a computational signal in of itself; for reinforcement learning models we'd like to measure dopamine itself. Two decades ago, the pioneering work of Peter Dayan and Read Montague established that dopamine neurons report a prediction error signal (see: http://static.vtc.vt.edu/media/documents/SchultzDayanMontague1997Science.pdf http://static.vtc.vt.edu/media/documents/SchultzDayanMontagu...), but recording of neurotransmitter release in humans is hard, and frankly, long thought impossible (given ethical constraints). However, recent work by Read Montague has done just that. See: https://www.pnas.org/content/113/1/200 https://www.pnas.org/content/113/1/200 Keep tuned.
  - mattkrause 7y ago
    Do you have any insight into why it took so long to do this in hmans? Fast-scan cyclic voltammeter was done in animals ~15 years ago by Wightman's group.
    - jonnycomputer 7y ago
      ethics, mainly. unlike macaques (etc.) we don't just stick probes in people. however, Parkinson's and epilepsy patients are getting implants for deep brain stimulation; Montague's group is working with hospitals performing these procedures. https://psychcentral.com/news/2015/11/29/new-insights-into-dopamine-function-in-parkinsons-patients/95495.html https://psychcentral.com/news/2015/11/29/new-insights-into-d...
      - mattkrause 7y ago
        I wasn't sure if there was some advance in voltammeter techniques, or if it just took forever to find a willing patient and convince ethics to green-light it. DBS has been FDA-approved since the late 1990s (and even longer under IRB/IND stuff), so there have been lots of opportunities to stick probes in humans' heads.
        jonnycomputer 7y ago
        There actually is a technical advance here; I just don't know enough about it to discuss intelligently (sorry). I would reach out to the researchers involved if interested.
df 7y ago
Here is a fun one: https://www.cs.cmu.edu/~tom/pubs/science2008.pdf https://www.cs.cmu.edu/~tom/pubs/science2008.pdf -- "Predicting Human Brain Activity Associated with the Meanings of Nouns" "We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus,with highly significant accuracies over the 60 nouns for which we currently have fMRI data." This is a frequently cited paper - I stumbled across it recently as a drive-by mention in this blog post about word embeddings: http://www.offconvex.org/2015/12/12/word-embeddings-1/ http://www.offconvex.org/2015/12/12/word-embeddings-1/ Anyway - totally tangential to the topic of the OP. But maybe some interesting food for thought.

eli_gottlieb 7y ago

>For example, in the field I work in, we very very reliably get signal that track reward prediction errors in the striatum (e.g. BOLD response in fmri) during reward learning tasks. Yeah, but that's a property of how the BOLD response correlates with the task structure, not a map of what computations the brain actually does. Yeah, ok, I'm shortening things by a lot, but neuroimaging really is quite fraught in terms of what sorts of computational-level inferences we can draw from task-based experiments.

jonnycomputer 7y ago
In these experiments, the BOLD signal reliably scales with reward prediction error, which is a computation, the difference between the reward expected and received. In short, we fit reinforcement learning models to in-task behavior, then correlate parameters of that model to neuroimaging acquired during that behavior. fMRI does have quite a few limitations; BOLD signal is believed to reflect the amount of work being done, loosely, speaking, but it is not a computational signal in of itself; for reinforcement learning models we'd like to measure dopamine itself. Two decades ago, the pioneering work of Peter Dayan and Read Montague established that dopamine neurons report a prediction error signal (see: http://static.vtc.vt.edu/media/documents/SchultzDayanMontague1997Science.pdf http://static.vtc.vt.edu/media/documents/SchultzDayanMontagu...), but recording of neurotransmitter release in humans is hard, and frankly, long thought impossible (given ethical constraints). However, recent work by Read Montague has done just that. See: https://www.pnas.org/content/113/1/200 https://www.pnas.org/content/113/1/200 Keep tuned.
- mattkrause 7y ago
  Do you have any insight into why it took so long to do this in hmans? Fast-scan cyclic voltammeter was done in animals ~15 years ago by Wightman's group.
  - jonnycomputer 7y ago
    ethics, mainly. unlike macaques (etc.) we don't just stick probes in people. however, Parkinson's and epilepsy patients are getting implants for deep brain stimulation; Montague's group is working with hospitals performing these procedures. https://psychcentral.com/news/2015/11/29/new-insights-into-dopamine-function-in-parkinsons-patients/95495.html https://psychcentral.com/news/2015/11/29/new-insights-into-d...
    - mattkrause 7y ago
      I wasn't sure if there was some advance in voltammeter techniques, or if it just took forever to find a willing patient and convince ethics to green-light it. DBS has been FDA-approved since the late 1990s (and even longer under IRB/IND stuff), so there have been lots of opportunities to stick probes in humans' heads.
      - jonnycomputer 7y ago
        There actually is a technical advance here; I just don't know enough about it to discuss intelligently (sorry). I would reach out to the researchers involved if interested.

df 7y ago

Here is a fun one: https://www.cs.cmu.edu/~tom/pubs/science2008.pdf https://www.cs.cmu.edu/~tom/pubs/science2008.pdf -- "Predicting Human Brain Activity Associated with the Meanings of Nouns" "We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus,with highly significant accuracies over the 60 nouns for which we currently have fMRI data." This is a frequently cited paper - I stumbled across it recently as a drive-by mention in this blog post about word embeddings: http://www.offconvex.org/2015/12/12/word-embeddings-1/ http://www.offconvex.org/2015/12/12/word-embeddings-1/ Anyway - totally tangential to the topic of the OP. But maybe some interesting food for thought.