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To me, the two biggest questions, what is the nature of the universe?

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I'm not going to do that.

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You have to be a theoretical physicist to get there.

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But the other one is, what is it that makes us human?

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It really became clear to me that an understanding of the brain

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was the way to most address that second question.

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The human brain is the most complex structure in the known universe.

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And we are in the middle of a scientific revolution

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to understand its inner workings.

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Join us for a conversation with world-renowned neuroscientists

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as they visit Rochester.

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I am Dr. John Foxe, Director of the Del Monte Institute for Neuroscience

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at the University of Rochester.

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And you are listening to Neuroscience Prospectives.

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We are almost 30 years into the functional neuroimaging revolution.

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And if you asked anybody in our field to name the top 10 people

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who have been influential in the field of functional neuroimaging,

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Steve Peterson's name would be on everybody's list.

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So Steve, it's really great to have you here today.

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Well, thanks for the introduction.

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I'm happy to be here.

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That's great.

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So I have a few hard questions for you today.

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OK.

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All right, you ready?

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I'm ready.

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I'm very interested, first of all, actually, in how did you end up

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as a neuroscientist?

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What's the story?

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Well, I started out as an anthropology major as an undergraduate.

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It was a long time ago.

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And there were a lot of people talking about the evolution of the brain.

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But it was very obvious that they didn't know anything about the brain.

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So I decided I was going to be the anthropologist that

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goes and learns about the brain and brings it back to anthropology.

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And I knew two names.

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A small goal.

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A small goal.

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And built of great naivety, I knew two names in neuroscience.

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Roger Sperry, who did the split brain work, and Jim Olds, who's

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been lost to the sands of time a little bit.

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But he was the first guy that began self-stimulation experiments

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where the rats will push a key because they're stimulating their brain

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and getting reinforcement.

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And they were both at Caltech.

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So I applied to a bunch of anthropology programs.

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And I applied to Caltech, the most selective place in the country.

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I got into no anthropology programs.

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And I got into Caltech.

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So that's how I got into neuroscience.

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I went to Caltech.

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And there was a guy, John Olman, who also had an anthropology degree

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and was doing monkey vision physiology.

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And so I went into his lab for my graduate work.

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Could you imagine yourself doing anything else?

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Was it the right decision?

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I didn't know that it was right.

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But it seemed very cool at the time.

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I mean, I didn't know much about neurophysiology or about vision.

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But I learned a tremendous amount there over the five years that I was there.

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Now, you've watched neuroimaging from its genesis, really,

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to the explosion that it is today.

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I was going to say cottage industry.

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But it's a military industrial complex, essentially.

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If you had to pick out the top one or two things

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that we've learned from neuroimaging, is there something you could point to?

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Or is it much broader than that?

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I think it's much broader than that.

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I think the idea that you could take pictures of the human brain at work,

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that's just totally amazing.

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How long ago I got into this is PET didn't even exist at the time

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I started graduate school.

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It developed over the years I was in graduate school.

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And functional MRI was not even a twinkle in people's eyes at that point.

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But no, it's covered everything.

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It's changed neurology.

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It's changed psychiatry.

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And it's changed cognitive neuroscience, our understanding of how people think.

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So it's hard to go back and pick out one or two.

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I had a little birdie tell me that one of your favorite things to do

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if you're not doing neuroscience involves cards.

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Yes.

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Are you going to tell us a little bit about that?

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And we'll get back to science afterwards.

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All right.

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Well, I love poker.

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Being able to see what the people have and being able to play out

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their thought process, I got really enamored of it.

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I played in graduate school a little bit, drinking and playing

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for Nicodemus Quarter.

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But going to the casino and playing against other people, it's really fun.

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And you get to be mathematical.

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And you get to try to read other people's minds.

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So it's really a fun game.

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So it's leveraging some of the skill set that a neuroscientist has.

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I think also it's deeply competitive.

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And I picked it up in my 50s.

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And so things that were really competitive for me, like sports,

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they were on the big time ebb.

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And so it also gave me a place to put the competitiveness in.

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Let's go back to science.

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I think one of the things that scientists often struggle with,

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apart from just generally communicating what can be complex thoughts

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to folks, the folks that pay for our work, the taxpayer.

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One difficulty can be sometimes to explain to folks why you're not

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necessarily working on a specific disease model.

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You're not working on autism or schizophrenia or stroke.

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You're just working on the basics of how the brain works.

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Do you have a way, for example, to think about that, to explain to folks

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why it's really important to be able to do really basic stuff?

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To me, the two biggest questions that scientists, philosophers,

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and intellectual people in general, the two big things are,

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what is the nature of the universe?

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And I'm not going to do that.

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You have to be a theoretical physicist to get there.

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But the other one is, what is it that makes us human?

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And to me, what makes us human is our ability to think.

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And that's what, as I was doing anthropology, it's why I studied

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anthropology, it really became clear to me that an understanding of the brain

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was the way to most address that second question, is what is the nature of humanity?

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And we get way down in the weeds and sometimes very far from that.

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But I always hope that ultimately we're trying to ask and answer questions

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about our basic humanity.

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So I don't feel very apologetic that I'm not studying a disease.

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I also think that many of the ways that we've come at solving diseases

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is by having really good basic science.

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So it just flows out of our understanding, our basic understanding.

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And I think that's true about the brain as well.

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And I think some of our recent failures probably

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are a little bit losing sight of the human aspects of some of these things.

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Right, right.

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So you've watched the system for a few decades.

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A while.

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A while.

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And you've seen how it operates.

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You've seen, for example, how the National Institute of Health works,

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how the National Science Foundation works, essentially the economics

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of the scientific enterprise.

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And I don't think there's too many people would argue that in the US

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we have the best science engine.

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But if you were given the magic wand, or I gave you the scepter in the morning,

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said, Steve, what would you do to reorganize?

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You're the new CEO of the whole scientific enterprise.

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I know that's a big question.

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Can I just do biomedical research?

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Biomedical research.

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I meant that.

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Sorry.

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So I think the NIH has gone down some bad roads.

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I would change things in probably three-ish ways.

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So the first thing is, it's hard for somebody to get into the system.

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So the first thing I would do is make it much easier for people

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to get into the system.

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Now, I don't know what the age is this year, but it keeps going up.

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Time to your first R01, people are in mid-40s.

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And that's totally ridiculous.

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Absolutely.

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I wasn't the fastest person through graduate school and post-doc,

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but I got my first grant at 35.

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And it changes your life.

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And that should not be another decade for everybody on average.

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So the system should be much more permissive to people at the front end.

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And I think that's merely a matter of will.

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You get good people who've had good training,

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and you make damn sure that half of them get funded as fast as possible.

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The second thing I would do is not make the funding one-zero.

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So now you get your first grant, you go along, you try to get it renewed.

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If you don't get it renewed, you have no funding.

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And the lab that you've built up can disappear overnight.

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It should be more docile to the person so that you look and go,

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well, you're doing pretty well.

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We're going to maintain you at this level.

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You're not doing so well.

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We're going to cut you back.

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This is your warning.

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If you're doing better, we're going to give you more.

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And that presupposes the third thing I would do,

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which is make the funding more retrospective.

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The best predictor of future success is past success.

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So the idea that your grant goes into a study section,

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and it may get turned down because somebody doesn't like the control

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on your 10th experiment is just crazy.

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You've been incredibly productive.

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The first grant that I did not get, I had just probably the most productive period

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of my life, and I had to sit and wait for funding

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because one of the reviewers didn't like a couple of the things

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I said on a couple of experiments.

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And to me, that was one, it was really irritating, but it's debilitating.

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And so people have worked for years to build a lab group and technicians

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and administrative help and a series of students and post-docs.

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And now all of a sudden, you're just without.

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Because of the serendipity or lack of serendipity in a review,

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I think it's just a really bad system.

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It's crazy to me because the NIH pretty much has the system I just described

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for their intramural program.

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And I've served as a reviewer and a counselor for the intramural program.

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It's much easier to do reviews when what you're deciding about

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is how productive the person has been.

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And the reviewers pretty much all agree there's

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never a huge disagreements about things because it's not

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so focused on such small issues.

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The other worst thing about the NIH is the number,

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because it's difficult to get through the system,

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people are throwing more and more grants at less and less money.

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And so the funding percentages go down.

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You could solve all of those, all of that with the ideas I just portrayed.

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I'd love to say that these are only my ideas,

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but lots of people have very similar ideas about this.

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Good practical solutions.

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Now, there's the funding aspect of it, but then the unit of productivity

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for a scientist is the paper, a published paper.

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And you've served as a senior editor on one of our great publications

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in the field, Cervo Cortex, for many years now.

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I know you've edited some of my own papers.

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Thank you.

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Hopefully, you were handled well.

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Well, they were always handled well, I have to say.

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But I know you have strong thoughts as well about where

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we are with the output and how we publish our papers.

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And there's a revolution now in the field with regard to open access.

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And there are more than 300 journals in the neuroscience space.

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What's going to happen here?

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Where is it going?

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What do you see?

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What do you like?

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What don't you like?

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Well, boy, this is a much, to me, NIH is simple

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compared to scientific publishing.

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I think the difficulty comes from a similar problem,

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is there's more and more papers chasing publishing utility.

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So people tend to want their paper to be in Nature or Science,

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the absolute top journals.

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And then they step down and they want to be in Nature Neuroscience.

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And then each phase that they go through till they get to the journal

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that they like, there are reviews and reviewers that have to be obtained.

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So I know you're also editing and see hundreds of papers,

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new papers come in a year that you have to handle.

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And it often takes six or eight requests for review

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before you can get two reviewers.

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The sheer number of people that you have to get to do this

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overwhelms the system.

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There aren't enough good reviewers to review papers well.

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I don't see an answer to this.

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I think this will just continue to be a real bottleneck

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for useful publication.

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I thought you were going to go the other way and say,

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what are the metrics for good publications?

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Well, how about that?

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What are the metrics for good publications?

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Well, I have one that I like that other people just really seem to not like.

251
00:14:34,680 --> 00:14:40,680
So I think the H index for somebody's scientific productivity is pretty good.

252
00:14:40,680 --> 00:14:42,280
Absolutely, yeah.

253
00:14:42,280 --> 00:14:46,720
So maybe just for our audience, a quick explanation of the H index.

254
00:14:46,720 --> 00:14:51,840
So the H index, it sounds more complicated than it is.

255
00:14:51,840 --> 00:14:56,480
It's the number of papers that you have that are cited greater

256
00:14:56,480 --> 00:14:58,400
than the number of your publications.

257
00:14:58,400 --> 00:15:04,880
So if you have 10 papers with 10 or more citations, then your H index is 10.

258
00:15:04,880 --> 00:15:11,360
And if you look within a field, the H index actually varies quite a bit

259
00:15:11,360 --> 00:15:12,760
by the field that you're in.

260
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So imaging, people tend to have higher H indexes, so you can't indices.

261
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You can't judge people sort of outside their field.

262
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So somebody in single unit physiology, they just don't publish as much

263
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because it's so much more demanding to reach a publishable unit.

264
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But if you look within a field and you pick out the people that you think

265
00:15:37,560 --> 00:15:41,760
intuitively are the best people, in a great number of cases,

266
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they will have the higher H index.

267
00:15:44,360 --> 00:15:46,720
And so I think it's intuitively a good one.

268
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Right.

269
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The only argument there is it works very well when you get to mid-career

270
00:15:53,200 --> 00:15:58,280
and above because it's an index of your history.

271
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And of course, the more history you have, the better chance you have

272
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to have this thing represent your real productivity.

273
00:16:04,080 --> 00:16:07,120
But for a postdoc who's had a couple of papers and is just coming out,

274
00:16:07,120 --> 00:16:09,480
the H index hasn't really kicked in as well.

275
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And I don't think there is a good metric.

276
00:16:12,800 --> 00:16:17,600
At the postdoctoral level, I've had truly awesome postdocs

277
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who don't have very many publications and not many citations.

278
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At the time when they're looking for their job,

279
00:16:25,920 --> 00:16:31,000
and then it's the man, the fan, and the plan.

280
00:16:31,000 --> 00:16:35,000
So that's where the mentors are incredibly important.

281
00:16:35,000 --> 00:16:39,720
And that's where the person having a clear idea of where they want to go

282
00:16:39,720 --> 00:16:44,560
and being able to articulate it to the people who may or may not hire them.

283
00:16:44,560 --> 00:16:46,240
The man, the fan, and the plan.

284
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I like that.

285
00:16:46,760 --> 00:16:47,240
That's very good.

286
00:16:47,240 --> 00:16:51,400
Or it could be the woman, the fan, and the plan.

287
00:16:51,400 --> 00:16:54,520
Offline, we were talking about a couple of papers.

288
00:16:54,520 --> 00:17:00,960
And you mentioned a paper that you did, one I remember quite well,

289
00:17:00,960 --> 00:17:02,200
looking at perceptual closure.

290
00:17:02,200 --> 00:17:04,920
And we won't bore people with what perceptual closure is.

291
00:17:04,920 --> 00:17:07,560
But you were on the hunt for the seat of consciousness.

292
00:17:07,560 --> 00:17:08,280
I was.

293
00:17:08,280 --> 00:17:10,520
That really brings up a question.

294
00:17:10,520 --> 00:17:11,280
Is it possible?

295
00:17:11,280 --> 00:17:12,480
Can we find it with our tools?

296
00:17:12,480 --> 00:17:13,160
I don't know.

297
00:17:13,160 --> 00:17:16,560
I thought the experiment was really good.

298
00:17:16,560 --> 00:17:18,480
I just don't trust the outcome.

299
00:17:18,480 --> 00:17:24,480
I have to say where this idea came from is I

300
00:17:24,480 --> 00:17:28,560
was at the cognitive neuroscience meeting with two people that I knew.

301
00:17:28,560 --> 00:17:31,160
And we were closing a bar.

302
00:17:31,160 --> 00:17:35,840
And we were having the deep intellectual conversation

303
00:17:35,840 --> 00:17:38,680
that you have after you've had too many drinks.

304
00:17:38,680 --> 00:17:41,560
Drawing pictures on napkins.

305
00:17:41,560 --> 00:17:46,000
And basically, every time we've done this, you go up to your room.

306
00:17:46,000 --> 00:17:47,240
You have the napkins with you.

307
00:17:47,240 --> 00:17:52,160
You get up in the morning and go, either I have no idea what this is about,

308
00:17:52,160 --> 00:17:55,760
or that's the stupidest idea I've ever had.

309
00:17:55,760 --> 00:17:59,320
And in this case, we saw each other the next day and go,

310
00:17:59,320 --> 00:18:03,320
I don't think this is a bad experiment to try.

311
00:18:03,320 --> 00:18:06,800
And it was basically a hangman experiment.

312
00:18:06,800 --> 00:18:12,280
So you basically have people play a game of hangman

313
00:18:12,280 --> 00:18:15,920
where you have blanks and you fill in serially.

314
00:18:15,920 --> 00:18:18,920
And all of a sudden, you go, the word.

315
00:18:18,920 --> 00:18:24,720
So it's blank H, blank T, blank something.

316
00:18:24,720 --> 00:18:31,040
And you get along and you go, oh, rhythm, which is a great hangman word.

317
00:18:31,040 --> 00:18:36,760
And so that moment in which you have real recognition, the aha moment

318
00:18:36,760 --> 00:18:40,080
and the magical moment of recognition, and we

319
00:18:40,080 --> 00:18:43,440
thought we could take a picture of that with imaging.

320
00:18:43,440 --> 00:18:48,000
So you get a picture of what your brain does at the moment of aha.

321
00:18:48,000 --> 00:18:49,280
And we ran the experiment.

322
00:18:49,280 --> 00:18:52,520
And we got stuff.

323
00:18:52,520 --> 00:18:56,880
I'm just not convinced that we got the magical moment of recognition.

324
00:18:56,880 --> 00:19:00,400
The problem, of course, with consciousness is it's totally internal

325
00:19:00,400 --> 00:19:04,400
and it's mine and mine alone.

326
00:19:04,400 --> 00:19:07,320
But I thought experiments like that.

327
00:19:07,320 --> 00:19:09,200
Getting out of innocence.

328
00:19:09,200 --> 00:19:11,320
Yeah, a little bit close to it.

329
00:19:11,320 --> 00:19:13,720
I suppose there are philosophical arguments

330
00:19:13,720 --> 00:19:16,200
about the difference between awareness and consciousness.

331
00:19:16,200 --> 00:19:20,440
And maybe this sounds like an awareness of the relevance of something

332
00:19:20,440 --> 00:19:23,680
or a piece of information as it enters.

333
00:19:23,680 --> 00:19:28,040
So my inspiration for the experiment was actually

334
00:19:28,040 --> 00:19:32,280
a bunch of experiments that were done by John Duncan.

335
00:19:32,280 --> 00:19:38,480
And it's called, it's the moment of the past.

336
00:19:38,480 --> 00:19:41,080
So you can have a bunch of stimuli on the screen

337
00:19:41,080 --> 00:19:44,680
and be monitoring them for something to happen.

338
00:19:44,680 --> 00:19:52,800
And then at the moment that there is a target at one of the locations,

339
00:19:52,800 --> 00:19:55,920
you get this bottleneck of attention.

340
00:19:55,920 --> 00:19:58,360
And the argument that Duncan made, and I think it's a good one,

341
00:19:58,360 --> 00:20:04,040
is when that target comes, it is passed into consciousness.

342
00:20:04,040 --> 00:20:07,880
And that's your total perception of what's going on,

343
00:20:07,880 --> 00:20:12,280
because that T all of a sudden is there out of nowhere

344
00:20:12,280 --> 00:20:15,080
and it captures your attention.

345
00:20:15,080 --> 00:20:17,520
So the idea of this perceptual closer experiment

346
00:20:17,520 --> 00:20:19,400
was to try to take advantage of that.

347
00:20:19,400 --> 00:20:22,080
And the interesting thing about that moment

348
00:20:22,080 --> 00:20:24,960
is other stuff can't get in.

349
00:20:24,960 --> 00:20:28,800
It blocks out all the other things that could be going on

350
00:20:28,800 --> 00:20:32,360
so that if something else happened at one of the other locations,

351
00:20:32,360 --> 00:20:33,760
you wouldn't see it.

352
00:20:33,760 --> 00:20:38,040
You wouldn't have consciousness of that or awareness of that.

353
00:20:38,040 --> 00:20:41,160
So I think it gets close to what I think of as sort

354
00:20:41,160 --> 00:20:43,880
of phenomenal consciousness.

355
00:20:43,880 --> 00:20:48,440
And so that's what got me going down this magical moment

356
00:20:48,440 --> 00:20:51,840
kind of experimental line.

357
00:20:51,840 --> 00:20:52,720
Now that sounds like it.

358
00:20:52,720 --> 00:20:53,720
That's a great experiment.

359
00:20:53,720 --> 00:20:55,320
I love that John Duncan paper.

360
00:20:55,320 --> 00:20:57,040
It's a fantastic paper.

361
00:20:57,040 --> 00:20:59,440
Do you have a favorite paper?

362
00:20:59,440 --> 00:21:02,080
You have a massive CV of papers.

363
00:21:02,080 --> 00:21:05,960
Is there one that just makes you happy to think about?

364
00:21:05,960 --> 00:21:08,480
Oh, boy.

365
00:21:08,480 --> 00:21:13,040
I have probably a small number of them for different reasons.

366
00:21:13,040 --> 00:21:17,520
So the very first big imaging paper

367
00:21:17,520 --> 00:21:23,280
was a nature paper done using PET in, I think, 1988.

368
00:21:23,280 --> 00:21:25,440
So it's a while back.

369
00:21:25,440 --> 00:21:26,880
I love that paper.

370
00:21:26,880 --> 00:21:29,920
One, because it was like a breakthrough paper.

371
00:21:29,920 --> 00:21:32,800
It was written up by John Marshall at the front of Nature

372
00:21:32,800 --> 00:21:34,760
that time.

373
00:21:34,760 --> 00:21:36,720
And it has kind of an interesting history.

374
00:21:36,720 --> 00:21:39,920
So Nature actually asked us for that paper.

375
00:21:39,920 --> 00:21:42,240
Somebody at a cocktail party had said,

376
00:21:42,240 --> 00:21:45,840
I saw this preprint of this paper, which we didn't know where

377
00:21:45,840 --> 00:21:47,920
we were going to send because it was so huge.

378
00:21:47,920 --> 00:21:51,520
And Nature said, well, have them contact us.

379
00:21:51,520 --> 00:21:52,920
We can't do a nature paper.

380
00:21:52,920 --> 00:21:54,720
And they said, yes, you can.

381
00:21:54,720 --> 00:21:58,240
And so we put the paper together and send it to Nature.

382
00:21:58,240 --> 00:22:02,600
And I love that paper partly because it's a breakthrough

383
00:22:02,600 --> 00:22:04,400
cognitive imaging paper.

384
00:22:04,400 --> 00:22:07,880
People think that's among the early ones,

385
00:22:07,880 --> 00:22:10,480
depending on how you counted.

386
00:22:10,480 --> 00:22:14,520
And also, that paper, I started giving talks everywhere

387
00:22:14,520 --> 00:22:15,600
because of that paper.

388
00:22:15,600 --> 00:22:17,240
So I got a lot of pub.

389
00:22:17,240 --> 00:22:20,080
I got on newspapers.

390
00:22:20,080 --> 00:22:23,960
So I was exposed to the press for the first time

391
00:22:23,960 --> 00:22:29,320
and learned some kind of bad things you should do and not do.

392
00:22:29,320 --> 00:22:32,680
And it was just, and I think it's fair to say,

393
00:22:32,680 --> 00:22:36,080
that paper got me the Society for Neuroscience Young

394
00:22:36,080 --> 00:22:39,800
Investigator Award a couple of years later,

395
00:22:39,800 --> 00:22:43,360
which was great because I got in a time in my life

396
00:22:43,360 --> 00:22:46,160
where we had no money, I got $5,000.

397
00:22:46,160 --> 00:22:46,800
Excellent.

398
00:22:46,800 --> 00:22:47,760
Paid off cars.

399
00:22:47,760 --> 00:22:51,400
So I love that paper for that.

400
00:22:52,800 --> 00:22:57,280
I love the paper Maurizio Corbetta and several of us

401
00:22:57,280 --> 00:22:59,880
did where we showed that paying attention

402
00:22:59,880 --> 00:23:04,000
to different aspects of visual information,

403
00:23:04,000 --> 00:23:07,680
so whether if you're trying to make a color decision

404
00:23:07,680 --> 00:23:10,560
or a movement decision or a shape decision,

405
00:23:10,560 --> 00:23:13,760
it actually changed the pictures in your brain.

406
00:23:13,760 --> 00:23:15,960
So it wasn't what you were seeing.

407
00:23:15,960 --> 00:23:18,360
Or how you were responding.

408
00:23:18,360 --> 00:23:20,080
It was what you were thinking about.

409
00:23:20,080 --> 00:23:22,480
And I thought that was really cool.

410
00:23:22,480 --> 00:23:24,200
That paper launched many careers.

411
00:23:24,200 --> 00:23:27,240
The dorsal ventral potential activation paper.

412
00:23:27,240 --> 00:23:28,040
Yes.

413
00:23:28,040 --> 00:23:29,480
I love that paper.

414
00:23:33,480 --> 00:23:37,320
And I like this new stuff.

415
00:23:37,320 --> 00:23:40,400
I know it hasn't really quite captured

416
00:23:40,400 --> 00:23:43,600
people's fancy in the sense that it's

417
00:23:43,600 --> 00:23:47,560
people's fancy in the same kind of romantic way.

418
00:23:47,560 --> 00:23:50,040
But I love this network stuff.

419
00:23:50,040 --> 00:23:51,320
I think it's really fun.

420
00:23:51,320 --> 00:23:57,120
I was getting tired of doing the next task, FMRI.

421
00:23:57,120 --> 00:23:59,600
What does this kind of stimulus do to your brain?

422
00:24:02,200 --> 00:24:05,880
And so it kind of revitalized my energy

423
00:24:05,880 --> 00:24:08,280
because it was so different from all the stuff

424
00:24:08,280 --> 00:24:09,240
that we'd done before.

425
00:24:09,240 --> 00:24:12,560
So that, I don't know which paper I'd pick.

426
00:24:12,560 --> 00:24:16,000
Maybe the first Jonathan Power Network one.

427
00:24:16,000 --> 00:24:19,320
That sort of started that phase off.

428
00:24:19,320 --> 00:24:21,960
So that was really fun.

429
00:24:21,960 --> 00:24:22,880
It's really fun.

430
00:24:22,880 --> 00:24:24,640
Excellent.

431
00:24:24,640 --> 00:24:28,920
Question for you from an electrophysiologist.

432
00:24:28,920 --> 00:24:31,680
So the brain's an electrical device.

433
00:24:31,680 --> 00:24:33,960
And those of us who, yes, you've heard.

434
00:24:33,960 --> 00:24:36,280
There's a rumor going around.

435
00:24:36,280 --> 00:24:39,640
So those of us who measure the primary signal sometimes

436
00:24:39,640 --> 00:24:43,360
get a little bit suspicious of you plumbers measuring

437
00:24:43,360 --> 00:24:46,280
the hemodynamics in there.

438
00:24:46,280 --> 00:24:49,400
How much should we worry about the direct coupling

439
00:24:49,400 --> 00:24:52,080
of blood flow and the blood flow measures,

440
00:24:52,080 --> 00:24:54,720
perfusion measures to the electrical activity?

441
00:24:54,720 --> 00:24:59,320
And where are we on solving that coupling issue

442
00:24:59,320 --> 00:25:01,680
or understanding?

443
00:25:01,680 --> 00:25:11,440
So I think it's very easy to overdo the bold signal

444
00:25:11,440 --> 00:25:14,560
as being information carrying.

445
00:25:14,560 --> 00:25:16,640
But I think it does certain things.

446
00:25:16,640 --> 00:25:19,840
I think you can say this.

447
00:25:19,840 --> 00:25:23,440
I think the bold signal, if it's handled properly,

448
00:25:23,440 --> 00:25:26,600
is monotonically related to the bulk amount

449
00:25:26,600 --> 00:25:29,920
of synaptic activity that takes place in one

450
00:25:29,920 --> 00:25:33,640
of the little image elements.

451
00:25:33,640 --> 00:25:37,880
I think going further than that is overstating it.

452
00:25:37,880 --> 00:25:40,600
And I think particularly in the cortex, where

453
00:25:40,600 --> 00:25:44,680
you have all this complex circuitry, excitation,

454
00:25:44,680 --> 00:25:50,480
inhibition, I think it gives you at least a probably

455
00:25:50,480 --> 00:25:56,240
monotonically related signal of the amount of information

456
00:25:56,240 --> 00:26:01,640
processing that goes on in that volume element.

457
00:26:01,640 --> 00:26:05,840
I think you can go a long way with that.

458
00:26:05,840 --> 00:26:08,480
And I think we and other people have shown

459
00:26:08,480 --> 00:26:12,680
that you can play temporal games like the Hangman experiment

460
00:26:12,680 --> 00:26:20,560
to try to slow down all this incredibly fast, very malleable

461
00:26:20,560 --> 00:26:22,760
electrical activity.

462
00:26:22,760 --> 00:26:25,640
You can play some games to get some temporal access

463
00:26:25,640 --> 00:26:26,560
to what's going on.

464
00:26:26,560 --> 00:26:29,400
But it's very easy to over-represent how deeply we

465
00:26:29,400 --> 00:26:32,000
can get a representation of that.

466
00:26:32,000 --> 00:26:34,760
But I think just with what I said,

467
00:26:34,760 --> 00:26:37,240
I think you can learn a lot.

468
00:26:37,240 --> 00:26:40,000
But I think we have to be humble that there

469
00:26:40,000 --> 00:26:45,080
was a whole universe of time scales and spatial scales

470
00:26:45,080 --> 00:26:48,400
that we're just not addressing with imaging.

471
00:26:48,400 --> 00:26:53,640
Steve, I know I speak for a lot of people in the field

472
00:26:53,640 --> 00:26:57,040
to say that it is a really good thing that this anthropologist

473
00:26:57,040 --> 00:26:58,360
became a neuroscientist.

474
00:26:58,360 --> 00:27:01,240
And your contributions to the field have been enormous.

475
00:27:01,240 --> 00:27:05,000
And you've had a massive impact on many of us.

476
00:27:05,000 --> 00:27:06,600
Thank you for being in Rochester with us.

477
00:27:06,600 --> 00:27:07,760
Thank you for having me.

478
00:27:07,760 --> 00:27:24,060
Thank you.