Hacker News Reader

Comment by nancykanwisher | Hacker News Reader

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

View on HN

Nancy, one thing I love about your style is that once in a while you'll stop amidst the lecture and teach about the scientific method, critical thinking, being humble and open-minded in your findings - the whole enterprise of science. It is very inspiring.

Of course, the content is insanely interesting. I am hooked like the first episode of Breaking Bad.

I am about 4 hours in and still into it! Thank you for sharing the lectures.

Just finished the series on Face Recognition. I was wondering if the fusiform face area (FFA) get activated while thinking of someone's face instead of actually looking at one? Might also be interesting to explore the memory of faces in persons with prosopagnosia?

Haven't actuallly googled for these yet. Will do so in a bit.. after the next series of lectures may be.

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

PLEASE DON'T STOP POSTING THEM! PRETTY PLEASE? At least, future generations may appreciate it, right?! :P

What? Negative reviews? Why would anyone do that? Get fMRIs of their brains. That lump of entitlement is pressing down on important parts of the brain.

These lectures are fantastic, please don't stop!

You mentioned David Marr's book. Given its age, I assume some parts have stood the test of time better than others - would you recommend reading it all or would you focus just on the first part you mentioned in the lecture? And are there other books you would recommend? (I haven't got very far though the lectures yet so apologies if you mention some later on.)

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.

Hi, as a recent undergraduate at an engineering school in the Boston area, I can truthfully say these are some of the best lectures that I’ve been exposed to on a technical topic. Thanks for publishing them to the public.

Students are so needlessly cruel on course evals!

Students in a typical undergrad environment are replicating Calhoun's rats in a cage experiments. In an explicitly competetive environment no less.

Aberrant behaviour should be expected.

Oh, please do don't stop, please. These are extremely intriguing. I haven't binge watched anything like this recently.

Personally I love the way you describe the brain in the simplest terms possible. You're describing what has the potential to be a very scary hard to learn topic in the simplest terms possible. I'd liken this to how Andrew Ng manages to explain ML/DL in a very calm simple manner.

So often lecturers hide behind terminology to make a topic seem more impressive / complex. So thank you. Plus great to see that you're on here too!

Awesome lectures! Did cogsci undergrad many years ago. If you had to estimate how much of the brain the field of neuroscience truly understands, what would that percentage be around?

I remember being shocked how little we actually know about the brain and wonder if we can unlock most of its secrets in our lifetimes. Thanks for being a part of the HN community :)

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

I've watched the first two videos. You're an engaging lecturer and this is a fascinating subject. Thanks for putting them online.

I have an MA in philosophy and your friendly dig at philosophers' penchant for stuff like "undetached rabbit parts" made me lol.

Thanks for all of this Nancy. I'm just a software engineer but I find the connection between technology and the brain intriguing. The brain seems incredibly complex but I'm glad we have content like yours to at least guide us in understanding it.

Thanks for the videos! I took intermediate and advanced physiology, so this is more of a refresher. Is there videos for the more advanced courses available too?

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

I love the way you opened the course with a personal story.

Hey, I just wanted to say that the first video was one of the best introductions to a new topic that I've ever seen. Thanks so much for these.

Hey Nancy, thanks for doing the lectures. Coolest Online course I ever came across/studied. Love from India.

Hey,I'm at lecture 2.6,you're talking about the recognition of faces and it's relationship with the holistic image view/image processing.

So, can I go ahead and abstract out the ability of mind being discussed? Basically, given a category, this vision processing module in brain is processing different features of the image(here feature in the machine learning sense). And these categories can be hierarchical. Like faces, humans, creatures, this can be a hierarchy that the brain may be referring to when it is trying to identify a face and switches to the mode where it needs a holistic image view rather than some isolated parts of brain. I understand that imagining how this happens biologically(physiologically) is hard for me.

My question is, am I correct in the above inference? I want to suggest an experiment now :D :P

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.

Interesting. In the past, I've read researchers commenting that "neural networks" was mostly unfortunate terminology because it implied the similarities between the physical patterns of the brain and connections between nodes in a neural network were a surface similarity that probably didn't offer insights into how the brain really worked.

But you're saying that there may be more similarity than we thought. I remember way back when there was some evidence of things like horizontal- and vertical-feature detection. It sounds as if there is still some evidence of this but perhaps more plastic than was once imagined.

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

Thanks! I look forward to diving into this.

I work with neural networks for complex scene processing and object detection on the roads. Best of part of my job is watching a network "learn/train itself" to classify various object categories.

Are there any good theories on what happens during the training process of the brain (for example, while learning a new skill or something very basic/simple) and how individual neurons get affected by this "learning" process? So, I understand from a psychological perspective, we see the brain as this beautiful system but I am asking from physiological perspective. What kind of changes can we observe in neurons when we learn something new?

P.S: Thanks a lot for your replies. Means a lot. :)

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...

Thanks, both of them look really relevant. Will read. :)

Hi Nancy,

Any chance you've got material covering motor systems instead of the visual sensory systems?

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

Ah well, thanks. I'm still gonna enjoy the perception material!

Oh man I'm watching the first video and am already hooked, thank you so much for this.

Just wanted to say thanks--I'm really enjoying these videos!

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?

I don't think pieces are necessarily what web viewers like, but it increases the potential virality of the video by breaking a broad lecture into tweetable, sharable soundbites, thus videos of this format become the de facto web video format.

I personally prefer long lectures, but if social media growth is what you are after, I'd keep it as it is.

Perhaps it is my internet addled reward circuits, but I personally prefer the shorter videos for learning because it helps me compartmentalize and break down topics for understanding.

Yes, the pieces are just perfect. Any good lecture series on major sites like Coursera have it that way. 1.5 hour lecture give me a good sleep.

> I am so psyched... Heh, I see what you did there XD

Reply on news.ycombinator.com