nancykanwisher

Sorry, I did not cover the motor system in the course.

Here is one cool example: https://www.sciencedirect.com/science/article/pii/S089662731... And this one I have not read but is by reputable people in a good journal and looks fun: https://www.sciencedirect.com/science/article/pii/S089662731...

As the Marr intro chapter explains so beautifully, there are many levels of analysis in cognitive science and cognitive neuroscience. Units in deep nets are very different from actual neurons, and the backprop methods used to train deep nets have no resemblance to how human brains get wired up. But for the case of object recognition at the level of representation, there are striking similarities between deep nets optimized for invariant object recognition, and parts of the primate brain that carry out this task. See this brilliant and seminal paper: http://www.pnas.org/content/111/23/8619.long

It is really the intro and first chapter of Marr's book that is still totally current. The rest of the book is brilliant but less representative of current thinking.

It depends what you mean by "understand" :-) On many interpretations, we understand none of it!

So glad to hear it, thank you. Do let me know if you encounter bits that are not clear or if you see ways the lectures could be improved. BTW the lectures were all originally 1 hour and 25 minutes - I just broke them into pieces because everyone tells me that is what web viewers like. Is that working for you guys, or would you prefer to watch a whole 1.5 hour lecture not broken into bits?

This is not a course per se, but there are a lot of good, cutting edge videos by major players in the field here: https://cbmm.mit.edu/videos

Sounds like you got it, more or less. Current views of how object recognition works in the brain are a lot like current deep net models of object recognition (e.g. Alexnet and beyond): a heirarchical series of processing steps in which units at successive processing stages get more selective for specific things, and more invariant to image variation (size, position, lighting, etc). One view of holistic face perception is that it is just the natural consequence of having units tuned to whole faces (or to large portions of the face). But why this should be implemented in humans as a specific category-selective patch of the brain is an open and fascinating question, that I am now hopeful network modeling may inform.

Thank you! It means so much to me that some people are appreciating these lectures. I knocked myself out all spring preparing this course, and it was quite a gut punch to read my course evaluations a few days ago, which ere pretty negative. I was so dispirited, I was actually thinking of stopping posting the lectures, but if you guys/gals are into it, that is awesome,I will keep at it! To answer your question: yes we showed long ago that if you close your eyes and imagine a face you turn on the face area, and if you imagine a place you turn on the place area - here is the article: http://web.mit.edu/bcs/nklab/media/pdfs/OCravenKanwisherJOCN... We and others have shown that in developmental prosopagnosia, the deficit is not just in remembering faces, but also in perceiving them. Nancy

Hi all- I am so psyched to see that some are finding the videos interesting! Do send comments if you have them. cheers, Nancy