Cognitive Regime Shift II - When/why/how the Brain Breaks/JackGallant
Notes by user Jack Gallant (UC Berkeley) for Cognitive Regime Shift II - When/why/how the Brain Breaks
1+ paragraphs on any combination of the following:
- Presentation highlights
- Open questions that came up
- How your perspective changed
- Impact on your own work
- e.g. the discussion on [A] that we are having reminds me of [B] conference/[C] initiative/[D] funding call-for-proposal/[E] research group
I have three brief comments:
(1) Regarding brain explanations. I agree with others that we should seek to explain behavior, at a fine-grained level, in terms of measurable brain functions. However, it is important to acknowledge that broad analogies involving architectures or cost functions will NOT do this. Those kinds of findings are interesting and possibly necessary, but not sufficient for explaining behavior except in the broadest strokes. What is needed is rich mathematical models that link brain measurements to behavior. When such models are available they can be translated into whatever expressive system is most useful for the purpose.
(2) Regarding brain measurement. Neuroscience is currently strongly measurement-limited. We have a wide variety of tools, but each tool is limited in spatial resolution, temporal resolution or coverage, and most tools cannot be used in humans. Given this, the best that we can do is to use our measurements as efficiently as we can given our modeling/prediction goals. In the end, all brain measurements are merely different views of the same system, so they will all be correlated with one another to some extent and in the end they should all converge on the same explanation.
(3) Regarding brain dynamics. The brain is a spatially distributed nonlinear dynamical system. To understand such a system requires that we recover the whole trajectory of the system through space-time. However, as noted above we are measurement-limited. We can recover the spatial marginal alone (e.g., in fMRI) , or the temporal marginal alone (e.g., in EEG), but we can't recover both simultaneously (except in very reduced systems or in very special local cases). The fact that we cannot recover the space-time trajectory of the system inevitably limits the provisional explanations and models that we can construct; it limits how well one can answer different kinds of questions; and it limits the usefulness of dynamical tools for analyzing and modeling our data today.
Reference material notes
- Here is [A] database on [B] that I pull data from to do [C] analysis that might be of interest to this group (insert link).
- Here is a free tool for calculating [ABC] (insert link)
- This painting/sculpture/forms of artwork is emblematic to our discussion on [X]!
- Schwartz et al. 2017 offers a review on [ABC] migration as relate to climatic factors (add the reference as well).
- Poeppel D. 2012 nicely lays out one of the central challenges of using brain data to understand mind and behavior: the elements of psychological models are incommensurate with brain measurements. Failure to recognize this problem has hobbled cognitive neuroscience and its applications to medicine.
- Huth et al. 2016 (from the Gallant group) shows how high-dimensional functional mapping can be performed in single individuals, and how we can predict individualized functional maps using a statistical model that reflects the variance and covariance of brain anatomy and brain function across individuals.
|Title||Author name||Source name||Year||Citation count From Scopus. Refreshed every 5 days.||Page views||Related file|
|The maps problem and the mapping problem: Two challenges for a cognitive neuroscience of speech and language||David Poeppel||Cognitive Neuropsychology||2012||0||13|| Download (Encrypted)
|Natural speech reveals the semantic maps that tile human cerebral cortex||Alexander G. Huth, Wendy A. De Heer, Thomas L. Griffiths, Frédéric E. Theunissen, Jack L. Gallant||Nature||2016||0||9|| Download (Encrypted)
|Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning||0||1|