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Time is something that most fields don't get to until they sort of, you know, gain enough courage or craziness to tackle.

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But it's so, so fundamental to the broad principles of organization of the brains.

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I'm John Foxe, director of the Del Monte Institute for Neuroscience here at the University of Rochester.

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I'd like to welcome you to another episode of Neuroscience Perspectives.

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I'm absolutely thrilled today to introduce my guest, Dr. Kia Nobre.

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Dr. Nobre is the director of the Center for Neurocognition and Behavior at the Wu Tsai Institute at Yale University.

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She recently returned to her alma mater after spending the majority of her career at Oxford University,

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where she held several major leadership roles,

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including the director of the Oxford Center for Human Brain Activity.

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She is an international member of the National Academy of Sciences and a fellow of the British Academy,

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and she has received numerous honors, including the Lifetime Mentor Award from the Association for Psychological Science,

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the MRC Suffrage Science Award, the Broadbent Prize from the European Society for Cognitive Psychology,

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and the D. Carvajal-Hernéken Prize for Cognitive Neuroscience.

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Her discoveries have literally revolutionized our understanding of human brain and behavior.

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So thank you, Dr. Nobrey, for being here in Neuroscience Perspectives with us.

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Let's dive in. Let's dive straight in with your research.

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We've known each other quite some time and actually we do stuff in the same kind of vein.

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So I have been a huge fan and I followed your work for, I shouldn't say decades, but it is decades.

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We've been around a while, unfortunately.

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So, and, you know, I always think of you as an attention researcher,

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but you've really, you've really span the gamut looking at sort of the role of cognition in determining behavior.

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And one specific area is this business of time and attention or cognition in time and frameworks of time.

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So why time and why is it so important?

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I'm like voraciously curious about the brain, always have been as long as I can remember.

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And, you know, when I was a graduate student, I didn't know what I was going to do.

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Am I going to do cellular, molecular, systems level human stuff?

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So I'm really curious about the whole breadth of it.

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And so I kind of found a trick, which is to study some things that are relevant to everything.

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So that I can kind of dabble in understanding, you know,

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ultimately trying to pick up the broad principles of organization of the brain.

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So in my sort of way of thinking about attention, it's, you know, really maybe not about our

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colloquial definition of it, but it's really about how the brain picks out the relevant

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information out in the world and how it does that proactively, dynamically as we're moving around the world.

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And, you know, meanwhile, how it's putting all those signals together, how it's putting away other things.

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And all of this is also happening through time. And, you know, and I think the time is something

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that most fields don't get to until they sort of, you know, gain enough courage or craziness to tackle.

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But it's so, so fundamental to the broad principles of organization of the brain.

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So for me, like studying attention and studying attention in a dynamic,

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temporally structured, proactive way is a way of kind of carving out the core principles of brain organization.

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It goes back to this idea of a limited resource, right? There's only so much you can take on at any given time,

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which is kind of how I think most of the field think about it. I'm going to attend over here,

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not over there, I'm going to tend to red and not blue and so on. And those are in instantaneous time.

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And so you've said, I need to understand this as it plays out across an episode or a period.

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You know, I think that framing that you gave is like the textbook framing of the field.

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You know, like we have limited resources, there's bottlenecks.

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And it kind of emphasizes sort of the limitations of our mental capacity in our brain.

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I like to think about it a different way. I think that actually the brain is pretty clever

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of trying to pick out the right things in order to plan for adaptive behavior.

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So it's kind of always ahead of the game and picking out things to do, you know,

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as long as far as it can tell what's going to be the right thing next.

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So I kind of think of like this idea of like picking out information as a positive, constructive,

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proactive thing for guiding behavior, rather than because we have these limited resources.

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And I also think that, you know, that kind of framing of the limited resource doesn't really

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tell us anything about how it works. So I think it's true that there are some limitations in the

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brain, but we should think about how do they come about? What kind of nature are they?

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And how does that? So I think a lot of it is, you know, we pick up the world through, you know,

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our different, you know, quite restricted sensors. Then we split out that information into lots and

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lots of different features are all rumbling around the brain at different, you know, times

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and looping around. And somehow we have to put that all together. So I think the constraints

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are really about like when there's overlap of information on hitting on the same cells,

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how do the cells know who to connect to? And it's not about that. There's just too much.

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It's like it's the problem of stitching it all together again, that I think is the tough thing.

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Does it go hand in hand with the so theories come and go in our field and we're all about

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predictive coding at the moment. This idea of having like a model and projecting out into the

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future. Is it related to that? Yeah, I mean, I think, you know, attention research has been

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looking at that stuff for a long time. And I think predictive coding, so I don't want a bad mouth,

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predictive coding. I love predictive coding colleagues, but, you know, predictive coding

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has come in as almost like a new framework. But it's an old idea. Yeah, and I think, you know,

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the two fields could do better at finding the points of conciliation, convergence, mutual

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information. So I think a lot of the predictive coding literature has emphasized the fact that,

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you know, if you already know something, then you don't actually need to process it because you

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kind of filter it out and you just process the things you don't know. Whereas the attention

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literature tells you exactly the opposite. It says, if you know things, you can put them to

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really good use and do that thing to the best of your ability. Like if you're in a tennis game or,

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you know, marathon and having to figure out. I think that both of those things are probably

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correct. And they happen probably in different circumstances within different mechanisms,

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possibly within different circuits. But I think, you know, sometimes a predictive coding has kind

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of swept the attention field under the carpet. And I think it would be much better if we bring

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all those threads together and build a better tapestry out of it. Tell me something, tell me

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something that you've learned about a mechanism that keeps track or divvies out my resources

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in this temporal way over time. Would you have a simple answer to that? Or is that an impossible

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question? I'm not sure what you mean, but I mean, I think we can read out the fact that the brain

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is pre-playing and pre-playing things in time. We can read these things out, you know, we can

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read out like what you're focusing in mind based on what you're going to have to retrieve all the

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way to your eyeballs by measuring your pupil. We can look at how the brain is selectively focused

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on the information is going to need to retrieve at a given moment. We can measure that from your eyes,

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for example, which is pretty crazy. So seeing the signals in neuron circuits using electrophysiology

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or imaging and saying, yeah, this area, even physiological signals all the way to the periphery.

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Yeah. Right. Right. And is that like to oscillations, for example, like, you know, as

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part of a temporal way to hold on to time. Yeah. Is that a player in this?

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There are a lot of potential players in time processing at the moment. And I think that I

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don't think we have a consensus, broad understanding of which of the many potential mechanisms that

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might be contributing to this. So for example, there are oscillations. Oscillations have been

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proposed as like, you know, keeping beats and keeping tempos and possibly structuring a bit

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of our active sensing of the world. There are also activity in neurons that ramp up to anticipated

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moments. There are dynamical systems that are, you know, very dynamical and that at given moments of

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time, if something happens, that's important. You get a plasticity event, which then consolidates

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this, that state, which intrinsically carries time information in it. So there are lots and lots of

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different types of potential time contributing mechanisms. And I think a really big challenge

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for the field is going to be to try to understand like, do all of these happen? Are some of them

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incompatible with each other? Do some serve certain purposes or others? And I don't think

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that we'll, you know, John, you and I, we're not going to be able to do this only with the human

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methodology. So I think this is going to take a concerted effort of colleagues working across

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scales, you know, all the way to some of the cellular mechanisms, but also, you know, very

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importantly, like the animal models, circuit models to try to put it all together. Very good. So well,

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let's pull back up a little bit. I recently saw you giving the Fred Cavley Distinguished Contributors

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Award lecture at the cognitive neuroscience meeting in Toronto. I was out there in the adoring crowd

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and I was really struck by what you did there was, you know, you really, you pulled back and

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did one of those 30,000 foot sort of career things. And a number of things you said there

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struck me. One was you talked about us as a young, immature field. And another thing you said was,

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you know, we've gone down many cul-de-sacs, you know, having a sort of front row view from really

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the very early days of cognitive neuroscience. So two things, two questions for you there.

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Are we still young? Are we maturing? Is there a lesson to be learned about ways that going forward

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we don't track ourselves down these cul-de-sacs? Do you have an insight on that? You know, now that

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you're in this really enormous leadership role? Yeah, thank you for that question. First of all,

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I really agonized about whether I should give that kind of talk or whether I should just talk about,

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you know, the trajectory of my own science. I feel like some of those messages are important enough

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to me that I thought, you know, maybe it was important to say them out loud and not just to

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my people in my own research group. Right, right. I know the audience appreciates that. Maybe that

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actually goes to the immaturity pieces. We need the foundational people in the field to step up

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down and really take that longer view and say, like, what are we doing here? And are we really

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approaching these questions in the right way? I mean, I think, you know, I think we are in an

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amazingly exciting time. I mean, in our careers, we've seen all of the human neuroimaging

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methodology emerge and it keeps, you know, it keeps transforming and advancing at a relentless pace.

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And we've learned a lot really very, very fast. And I think the thing which, you know,

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naturally we're swept away by how much we've learned and how smart we are and how much smarter

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we are than we were 10 years ago and stuff. But I would say that, yeah, we are crawling as a

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science. You know, first of all, a point that I made only I just mentioned this and I didn't talk

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about it in my talk is that our science was had a really tough genesis. You know, people did not

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believe that you should or could understand the subjective mind. You know, you can understand the

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brain, the organ, but actually understanding cognition was not something that people thought

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should happen necessarily in the 1800s and 1700s. So if you read some of the original papers of some

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of those pioneers, like, you know, Wundt, for example, or Helmholtz, those people were fighting

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deep prejudice to sort of, you know, create, you know, psychophysics and psychophysiology and all

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these different approaches that we have today. So we started late. We started much later than most

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other sciences. And yeah, the mind is hard, you know, it's subjective. It's subjective.

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It's crazy stuff to try to get a hold of. We have to triangulate it. You have to get it through

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behavior, through brain, you know, you can't, you can't measure it necessarily directly. So you have

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to kind of approach it. So it's, it's, it's a hard science. And I think if you look at the state of

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our field, we still kind of, you know, really the the grounding start point for most people are is

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just intuition. How do we think, oh, we make a decision, we have this choice or that choice, we,

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you know, it's very much folk psychology intuition still grounding us. We don't have like a formal

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language of the field. And, and, you know, I think we're doing really well. I don't think we're

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immature. I think we're young. Yeah. So would you mean things like, you know, right, we have these,

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uh, this sort of taxonomy of terms that, you know, we don't even have a good taxonomy yet,

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you know, attention. Yeah. And I know you think we may need to really, really even re-examine

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those concepts. Absolutely. I think we're gonna, I think, constructs. I think many of those things

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will probably, you know, if we're successful, many of those things are going to be radically

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transformed and we will have a different understanding. You know, it could be, you know,

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that it's more of an evolutionarily rooted thing. It could be, you know, that we focus more on the

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senses and sensory motor anchors or I don't know what it's going to look like, but I think we're

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still carrying a lot of folk psychological baggage with us. Right. Um, so I think, you know, I think

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we are in the stage, like kind of the Aristotelian slash Newtonian phase of like, you know, we kind

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of use common sense observation intuition, but yeah, we have amazing measuring tools and they

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keep getting better and better. We can quantify and measure and relate, but we haven't kind of

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gotten to that core theoretical principles. We're not like where some of the quantum physics and

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some of the other more advanced sciences are. And would you say in some ways those are advanced

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science because the questions themselves are more tractable? Does it go back to that business of

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complexity? I think that's part of it. You know, obviously the universe is also very complex and

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the mind is only, you know, one part of it, but I think complexity matters. And also the fact that

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yeah, the mind is, is still, I would say the biggest, you know, mystery in the universe still.

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Yeah, certainly. So I'm looking forward to things getting really crazy, you know, so, and I think

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you said that, you know, how do we avoid cul-de-sacs? I think the other thing is, you know, one thing our

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psychology has been really good at is kind of revealing to us what social animals we are. So,

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you know, everything we do is social, political, you know, and we work as kind of tribes, you know,

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our labs and things. So I say, oh, look, John Fox is doing this. Oh, I don't think he used the right

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interval. I'm going to do it right. So we end up kind of, you know, creating little cottage industries

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of working on. We're working on these, you know, there's a big territory out there. It can get a little esoteric sometimes, you know, arguing over intervals.

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Yeah, it gets esoteric. We build like very crowded neighborhoods of research and then other

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vast interesting landscapes are left open. So the thing I try to do is just always like know your stuff, you know,

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like bring some scholarship along, but just look at it with really open eyes, you know, curiosity, look at it for

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what's out there, you know, is this question everything. So I think, you know, I don't, it doesn't always work, but it kind of helps you getting, you know, stuck in like, you know, fashion corners of the field.

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Yeah, absolutely. So and then projecting out, like if you were giving some advice to the younger, obviously you do this all the time with your graduate students and trainees and that, you know, what do you say to them about that? Like getting on stock or framing out the questions in a way that will make them meaningful and important?

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Yeah, I sometimes feel for people in my lab because, you know, I really push them hard to make their own decisions and really to think like, is this really interesting to you? Do you care about this? Do you, you know, what do you think is going to, you know, how is this going to change the way you think about things? Is this going to change our understanding? I really try to operate at that very basic low level where I think things are really important. So I really try and I think, you know,

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there are many ways to be a successful mentor, but sort of my approach is really to try to help each individual find meaning and joy and their own, you know, pathway of the balance of productivity for themselves. So I try not, you know, I'm not one of those things like everyone has to do this every month and here we do, you know, it's all very, it's kind of intense and organic.

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By working on personhood as well as just like, you know, this is a great project to do or something.

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And not just the person as a scientist, but the whole person, you know, like, I mean, I think you're only going to do great science if you're really happy and if it fits in your life.

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Yeah, that's very good. And of course, you had some extraordinary mentors. I had the great privilege to know two of your foundational mentors, Greg McCarthy and Truett Allison.

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Can we talk a bit about that, like the mentorship in your life and maybe some people that lifted you up and framed the way you approach things?

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Absolutely. I mean, I think I've, you know, I've been so lucky. I've had pretty much only support and, you know, championing and enthusiasm from people I've worked with.

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Of course, you know, everybody's weird and, you know, quirky from close up and you have to embrace the quirks of your mentor.

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Especially in our business.

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Yeah, absolutely. So, you know, I just always try to take the best out of people. So Truett Allison, who, you know, as you know, has now passed, I mean, he was one of the very modest giants in the field.

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You know, I remember him singing, you know, country music and I can't actually sing some of the songs because they're not repeatable in public.

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Super funny guy and he was part of the beatnik generation, you know, traveling around in his convertible in the 50s.

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But yeah, you know, his mentor and he were the people who turned squiggly lines into ERPs, event related potentials.

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And then, you know, Greg, super methodical, brilliant, like he was really cared about the big conceptual questions and trying to get the best out of the methods and really always pushing the methods.

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He was, you know, at Yale at the time when there was like the rumblings of the big MR revolution, he was the one who really initiated like the first cognitive study with using, you know, noninvasive MRI.

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And he had made, you know, all the connections with all the biophysics and the biochemistry and the physics guys and brought the monkey people together.

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And, you know, he had that kind of entrepreneurship and initiative, methodological initiative, which he still has.

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So actually the one of the best things is a bit of a tangent.

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But going back to Yale after 30 years at Oxford is reuniting with Greg because Greg also had left and now he's back there.

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The other thing that Truett was amazing about is, I mean, he didn't do this explicitly.

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It was just him. He just created such an amazing culture.

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So we all had lunch together all the time and he had funny championships that he would put up and competitions, you know, all about, you know, non scientific things sometimes.

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And then he had he had the secret Santa party, which he had.

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We all gave each other silly anonymous gifts and teased each other.

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We had he created diplomas for people who left the lab, which were kind of based on people's features and traits, which I still do.

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I still carry those traditions till now.

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And so, like, yeah, so actually when I left Oxford now, my whole lab gave me my Oxford diploma and they're all just poking fun at each other and not taking ourselves too seriously.

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All about that sort of generating a culture in the lab that makes people.

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I know there's a lot of buzzwords these days like safe and and and secure.

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But a culture that doesn't make people feel like they have to produce something positive or positive results we talk about in that.

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But that you're there to to be part of an adventure to get to the truth.

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Yeah, absolutely. I mean, that's one thing that was the most scary thing for me moving over after 30 years at Oxford is whether I would be able to reestablish the kind of lab that I still have.

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Some people are still in Oxford now. The lab that I have is like amazing.

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And it's every first of all, it's flat. It's everybody counts.

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Everybody is as valuable as anybody else in the lab.

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Obviously, everyone will have different contributions, different levels of knowledge, different skills.

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So we kind of respect each other's qualities and also our defects.

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We all have defects as well.

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But it is a lab where I try to create a space where we can be honest scientists.

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We can kind of, you know, tell each other like when something is amazing, when something is not quite there yet and just be open.

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And when you know, where I always celebrate like unexpected results and mistakes, because that's where we learn from.

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So and we we have a very safe lab, but safe in a very different way, I think, than how people tend to use it.

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Now, it's not like we're not challenged. We challenge ourselves all the time, but we challenge ourselves in a supportive, you know, honest environment where we're all just trying to be the best we can.

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You're an electrophysiologist first, right? And so you're a squiggly lines and high tempered precision.

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And then along came FMRI and you and I both had the great good fortune of being right there when this all happened.

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So I often wonder actually about our youngsters, you know, are they going to have the same opportunity?

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There is a big happenstance component to it.

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But when you see all this work now using functional imaging, where they're talking about temporal correlations between areas and we're really still looking at the brain plumbing,

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do you think about this, you know, you know, the tension between what you know about timing, given the time is so important to you from electrophysiology and the way this is talked about with the functional imaging folks?

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Absolutely. And I think I touched on that in that Kavli lecture. And that was one of the things I was hesitant to mention.

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I tried to couch it in a friendly way. But yeah, I think, you know, ultimately the the brain is working at a very, you know, maybe not super fast way because it's still biology, you know, not compared to electricity and stuff.

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But, you know, things are happening in the tens and hundreds of millisecond scales. The MR based and PET based imaging are much, much slower.

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You know, they're kind of looking at the multiple downstream consequences of that neural activity and not even in a necessarily one to one way.

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Right. So we don't even know exactly what's all contributing to the to the hemodynamic response.

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So the thing that baffles me is that people actually use imaging to say, like, what is this area doing? You can't ask that question.

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Because, you know, what you're going to see in an MR image is all the activity that's going through that area as part of the network that this area is in.

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And you don't know what the contribution of that area is or what's the feedback to that area, what's the reentrant stuff, what's the lateral connection.

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So that really, you know, worries me. And I've actually and that's I would say now I think people are using MR also for many other different types of questions,

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which some of which I think are great and fine. But I think the bulk of the MR work is still about, you know, what's this area doing?

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And not only do we now do, you know, just look at an activation as we also like throw in really complex, you know,

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computational equations to understand exactly what factor of this process is.

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And I think that's even more crazy because, you know, I think so I actually worry that, you know,

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we are creating a sort of a hallucinogenic kind of view of what the brain is doing based on the same way,

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because in the end of the day, you go back to first principles, we're looking at fluid dynamics in a in a tissue system, you know,

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compartmentalized tissue system, but they by definition are slower than the actual events of interest to us.

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And there is no amount of speeding up of the capture of the data that's going to change that fact.

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The signal is the signal. Yeah. Yeah. Yeah.

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So that does worry me. I mean, I still think there are lots of amazing things that MR can do.

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So I think, you know, if you're just trying to understand in general, our areas correlated with each other,

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that's good, because it doesn't really matter the fine grain, so you can kind of get the broad networks, maybe.

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I think if you want to say, you know, if you want to like, say, I can measure,

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I can see that the brain is coding this kind of information or some area has that kind of information by using decoding methods or compare

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this kind of representation versus that representation or the transformations of representation.

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I think that's OK, as long as you're not trying to pin it down to a specific.

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So I think there are lots of good questions that people can ask.

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But I think as usual, you know, having that respect for the limitations of the methods often falls short.

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It also falls short in our area. I mean, to be honest, I think, you know, when I remember when I went to Oxford,

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initially, I was trying to set up the sort of first EEG and ERP lab there.

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And what everybody wanted to do is like, oh, I want to know what this area is doing at what point in time.

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I said, well, you can't really ask that question with EEG, you know, because we don't have that kind of spatial resolution or pinpointing.

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So, again, like we can ask lots of interesting questions, but there are some that are not as well answered.

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And beyond the resolution and those for some reason, because I guess ultimately humans are a phrenologist at heart,

256
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maybe phrenology and space and time. Those are the things that they want to ask.

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And the methods don't don't really allow us to ask those things.

258
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Yeah, exactly. Yeah.

259
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So so let's let's go back a bit then. Let's go way back.

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You're not from these shores. You were born in Brazil.

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There's a couple of things I want to ask about that.

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So how does a girl from Brazil end up in the US?

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And how does a girl growing up the way you did end up thinking, I want to be a neuroscientist?

264
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Yeah. So I think, you know, life is full of coincidences and circumstances.

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And I think those make a huge difference to people's lives.

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I was I was a small kid in Rio, in Rio de Janeiro and Brazil.

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And my parents were very young.

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So they were sort of in their early 20s when they had me.

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And my mom convinced my dad that he should go and do a course.

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Let's go and travel abroad. And, you know, why don't you go and do a course in the States?

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So, you know, so that was kind of unheard of at that time in Rio and stuff.

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And, you know, they didn't come from very rich families or anything.

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So my dad was a very good student and he got a fellowship and we ended up

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he spent a little bit of time at Princeton and NYU doing some he was doing law.

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And that's when I was like in my real formative years, I was four, four to six.

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So, you know, that's kind of where I'm put into the into school.

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Yeah, I went into an international school, like the United Nations School,

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which was amazing because I met, you know, friends from all over the world.

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I had like my best friends were from Kenya and Peru and Canada.

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And so we all had our little flags, you know, in our different countries.

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That was a blast. That was amazing.

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It was also during, you know, the hippie years.

283
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So you go outside, everyone's like dropping LSD and, you know,

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there was the Black Panther movement.

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It was a really crazy time in New York City.

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So those were very vivid memories.

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When I when I went back, when we went back to Brazil,

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my parents realized that I had this great gift that I spoke English, you know,

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perfectly, and they thought that this would be a good thing for me.

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So they kind of convinced the then the American school there

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to let me in on a scholarship

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to continue studying and in being able to speak English.

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And then that school, you know, was, you know, it's a great school,

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again, with people from all over the world landed in Rio.

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You can imagine, you know, people from all kinds of corners,

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from the Netherlands and Cape Town and everywhere.

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And so I grew up in this very international community.

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And but the school was very good at sort of preparing us.

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And, you know, their aim was to get us to the American,

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you know, Ivy League's kind of thing.

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And and at that point, I had a really big young life crisis,

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you know, because I I wanted to go to the States.

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My dad was like, you can't do this.

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If you leave Brazil now, you're never going to come back.

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And we'd have these like, you know, many hours long every day

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discussions about this and stuff.

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And in the end, I had decided that I wasn't going to come to the states.

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And I like I, you know, didn't actually.

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I threw out a lot of my college applications,

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but I had applied to some places still.

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So I got into those places and then I ended up in the most bizarre

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place for me, which was a wonderful place.

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But I ended up in Williams College, which is probably the other side

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of the universe from Rio de Janeiro, which is very, you know,

315
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at the time, very homogeneous, very conservative, very small. Right.

316
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So coming from, you know, the the whole world on the Rio Beach

317
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to Williamstown in the snow was a bit like a mind bend.

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Yeah. But but good education and a good education was amazing.

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Actually, I think because I was a bit of a misfit there, you know, I was it.

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It was good in that sense.

321
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I you know, I did a lot of soul searching and I studied like hell.

322
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You know, like I, I just like I know, I'm supposed to take like four courses.

323
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I was always taking six courses at a time just to keep myself from trying to,

324
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you know, cope with a bigger growing up issues or something.

325
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Right, right, right. And then what happened after Williams?

326
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Did you go straight into PhD studies?

327
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No, I took a year back in Brazil after that.

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I wanted to go back.

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There were just stuff going on with family and things.

330
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I also wanted to be there for some difficult times in the family.

331
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So I spent a year there.

332
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But at that point, I had I had applied to graduate school and I just deferred

333
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for a year and then I went to I went to Yale. Yeah.

334
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The jump from here to Oxford, 1994.

335
00:31:15,960 --> 00:31:18,040
Yeah. Yeah. Just a couple of years back.

336
00:31:18,040 --> 00:31:20,040
Yeah. That was a big jump as well. Right.

337
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And what was it?

338
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What was your experience with England and Britain before that?

339
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I mean, I had visited Europe, but I mean, I really was, you know,

340
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I was I'm very much a New World girl from Rio.

341
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And so, you know, it wasn't really part of my DNA in any way. Right.

342
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I ended up in in England again for, you know, vagaries of happenstance in life.

343
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You know, I had a I had a boyfriend at the time who was English

344
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and from London and wanted to go back.

345
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And so I thought, like, oh, it'd be fun to do a, you know, a fellowship there

346
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for a while and I'll see how it goes.

347
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So I ended up in Oxford, broke up with a boyfriend within the month, you know.

348
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And and yeah.

349
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And then life is peculiar like that.

350
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Very peculiar. And divergences.

351
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And yeah. And it was it was, you know, I think the thing that I

352
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one of the things that I most love in life is just learning new things,

353
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obviously in science, but also in terms of, you know, meaning of life

354
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and why are we here and perspectives.

355
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And and so England was so different in many ways.

356
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And also the the tone of the science was different.

357
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I think people were at that time more interested in some of the theoretical

358
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questions rather than the the data productivity.

359
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And that resonated with me.

360
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So it was like it's been forever a trajectory of learning and change.

361
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And and I've really enjoyed it there.

362
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And I can't really say I lived in England because I lived in Oxford.

363
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Oxford is another right. Another small crucible of the universe.

364
00:32:52,760 --> 00:32:54,800
Yeah. And I love that place.

365
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So going back home to Yale, a kind of a home, it's been it's been a good experience

366
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and navigating the reentry into the US system.

367
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Yeah, it's I was very much like Oxford establishment, as you were kind of implying.

368
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And I, you know, I have like all my great friends there

369
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and I kind of gave my blood and soul to that institution.

370
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And I still love it dearly.

371
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So I think when I, you know, when we decided to move,

372
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my husband and I, it was kind of a shock to everyone.

373
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You know, like it's like, what?

374
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And but it was it was, you know, it was really interesting because,

375
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you know, I'd been there for 30 years and I loved it.

376
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But I knew what was ahead, you know, and as I said, I'm like a voracious learner

377
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and stuff. And when this opportunity came, I mean, other opportunities had come back.

378
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But it was either something that maybe was interesting for me,

379
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maybe was interesting for my husband.

380
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But, you know, we always kind of said, now we're happy where we are.

381
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But when when Yale called, I was like, you know, this was a special place for me.

382
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These are I'm the scientist I am because they really invested me

383
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in at that those graduate student years, you know, those such important years.

384
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And I had that loyalty to the institution.

385
00:34:06,960 --> 00:34:10,800
And then Luchando, my husband, who's a digital ethicist, you know, he was like,

386
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I don't know, Yale, not really in the on the planet in my area.

387
00:34:15,160 --> 00:34:18,240
But it turns out that this was a huge priority area for them.

388
00:34:18,240 --> 00:34:22,320
So he was able to build all this stuff, you know, and lead.

389
00:34:22,600 --> 00:34:25,400
So it was a really interesting opportunity for both of us.

390
00:34:25,400 --> 00:34:26,640
And we kind of thought about it.

391
00:34:26,640 --> 00:34:29,200
And we're a little bit past our prime year.

392
00:34:29,280 --> 00:34:30,600
Are we really going to do this?

393
00:34:30,600 --> 00:34:32,000
And we're like, let's do it.

394
00:34:32,000 --> 00:34:35,120
Let's just do something new and adventurous.

395
00:34:35,120 --> 00:34:37,120
And then getting there has been amazing.

396
00:34:37,120 --> 00:34:41,680
You know, but it's I had to say to people because everybody who knows I've been there

397
00:34:41,680 --> 00:34:43,200
is like, oh, you've been here before.

398
00:34:43,200 --> 00:34:45,040
And I was like, it's not like that.

399
00:34:45,040 --> 00:34:49,160
It's like I was walking around in Oxford and I opened the portal

400
00:34:49,160 --> 00:34:53,600
and I ended up in some other, you know, parallel future.

401
00:34:53,840 --> 00:34:57,160
In a slightly weird New Haven that wasn't the same as the one I left.

402
00:34:57,160 --> 00:34:59,640
Right. I had quite some time in the past.

403
00:34:59,680 --> 00:35:01,560
Yeah, it's been 30 years.

404
00:35:01,560 --> 00:35:03,280
You were. Yeah, I was a different person.

405
00:35:03,280 --> 00:35:05,440
New Haven, you know, has been much gentrified.

406
00:35:05,440 --> 00:35:08,280
Yeah, luckily, it still has some rough edges, but it was like,

407
00:35:08,560 --> 00:35:10,600
you know, it's a very different place than it used to be.

408
00:35:10,600 --> 00:35:15,280
So it feels in a way familiar and another way really eerie and different.

409
00:35:15,280 --> 00:35:18,120
You know, but the colleagues have been amazing.

410
00:35:18,160 --> 00:35:21,440
People have been like so.

411
00:35:21,440 --> 00:35:24,480
Welcoming and generous and collaborative, it's been really fun.

412
00:35:24,480 --> 00:35:27,440
Do you? I mean, maybe again, this might be a little unfair.

413
00:35:27,440 --> 00:35:31,640
Is there do you see a difference now 30 years later between,

414
00:35:31,640 --> 00:35:35,640
you know, the system in Europe or in Britain, particularly and what you've

415
00:35:35,640 --> 00:35:41,240
come back to or emerged back in re-entered here in the US?

416
00:35:41,520 --> 00:35:42,880
Are the systems set up different?

417
00:35:42,880 --> 00:35:44,160
Are the motivations different?

418
00:35:44,160 --> 00:35:46,840
Is there are the good and bad components to that?

419
00:35:47,680 --> 00:35:52,360
Yeah, I think they're, you know, they're in a way academia is the same everywhere.

420
00:35:52,400 --> 00:35:56,320
You know, we're a bit conservative, a bit entrenched.

421
00:35:56,760 --> 00:36:00,440
You know, we we kind of have creative and whatever souls,

422
00:36:00,440 --> 00:36:02,960
but we kind of get stuck in the academic machinery.

423
00:36:02,960 --> 00:36:05,120
And that's kind of the same everywhere.

424
00:36:05,120 --> 00:36:08,560
But of course, there are like a lot of really interesting differences as well.

425
00:36:10,560 --> 00:36:13,400
Yeah, I think I think one of the things

426
00:36:13,400 --> 00:36:15,960
well, one of the things that's very similar between Oxford and Yale,

427
00:36:15,960 --> 00:36:19,480
which I like, is that there is kind of a

428
00:36:20,920 --> 00:36:22,600
a collaborative spirit.

429
00:36:22,600 --> 00:36:25,320
So people across the they're very loyal to the institution.

430
00:36:25,320 --> 00:36:29,000
And there's this idea of like doing things together and doing it as a team.

431
00:36:29,000 --> 00:36:32,800
It's a little bit. I think Yale has that reputation among the Ivy's,

432
00:36:32,800 --> 00:36:36,200
or at least Yale thinks it has that reputation, I don't know, from the outside,

433
00:36:36,840 --> 00:36:39,240
compared to some other institutions of really like

434
00:36:39,920 --> 00:36:43,240
doing things for each other together as a team and collaboratively.

435
00:36:43,240 --> 00:36:47,560
And I think Oxford also has that, you know, people don't see it

436
00:36:47,560 --> 00:36:51,240
because everything is like there's everything so distributed in all the colleges,

437
00:36:51,240 --> 00:36:53,000
the institutes, the centers of things.

438
00:36:53,000 --> 00:36:55,920
But ultimately, if you ask anyone, hey, do you want to work on this project?

439
00:36:55,920 --> 00:36:56,560
They're like, yes.

440
00:36:56,560 --> 00:36:59,360
You know, if you if you manage to get the connections going,

441
00:36:59,640 --> 00:37:00,840
things really synergize.

442
00:37:00,840 --> 00:37:03,520
And I think that's true in both places.

443
00:37:03,520 --> 00:37:06,960
But then I think the institutions are conservative in really different ways.

444
00:37:06,960 --> 00:37:12,720
Like the thing that has struck me in the US is like the

445
00:37:13,320 --> 00:37:15,680
yeah, the agony that people put into the

446
00:37:16,760 --> 00:37:20,880
recruitment, promotion of of faculty,

447
00:37:20,880 --> 00:37:23,680
you know, which is so cutthroat, so involved

448
00:37:23,680 --> 00:37:27,160
to an extent that, you know, is probably beyond what's

449
00:37:28,480 --> 00:37:30,880
what's necessary or even optimal.

450
00:37:31,280 --> 00:37:35,680
And I think it causes a lot of angst and and work for everybody.

451
00:37:35,720 --> 00:37:38,400
Incentive structure, that's a little anti-science, maybe a little

452
00:37:38,400 --> 00:37:41,640
anti-intellectual. Well, yeah, I think I mean, that's one of the things that

453
00:37:42,480 --> 00:37:46,000
was exciting to me about coming to this particular institute at Yale.

454
00:37:46,000 --> 00:37:50,880
It's an institute that sits, you know, outside of any of the schools

455
00:37:50,880 --> 00:37:53,760
or any of the departments, it's kind of a free floating institute

456
00:37:53,760 --> 00:37:55,720
right under the provost's office.

457
00:37:55,720 --> 00:37:57,200
So it has a lot of freedom there.

458
00:37:57,200 --> 00:38:00,800
And I think one of the things that I would love to do is kind of to kind of,

459
00:38:01,080 --> 00:38:05,120
you know, obviously push gently and nudge, see if we can change

460
00:38:05,120 --> 00:38:07,600
some of these academic incentives and help link

461
00:38:08,880 --> 00:38:12,160
our science to the world better.

462
00:38:12,160 --> 00:38:15,800
You know, we have we do a lot of cool science in our labs.

463
00:38:15,800 --> 00:38:20,240
But if we think about the science of human behavior and science of the human mind,

464
00:38:20,240 --> 00:38:23,960
a lot of it is happening in industry and companies and tech companies

465
00:38:23,960 --> 00:38:26,320
and car companies collecting all these data and stuff.

466
00:38:26,600 --> 00:38:30,400
We need to find ways to partner and work together across academia

467
00:38:30,400 --> 00:38:32,200
and non academic sectors.

468
00:38:32,200 --> 00:38:35,240
So that's one of the things that I'm, you know, I think is

469
00:38:36,120 --> 00:38:39,000
kind of pushing against that conservatism is going to be hard.

470
00:38:39,000 --> 00:38:41,120
But it's something I'm really determined to try to do.

471
00:38:41,120 --> 00:38:41,760
That's the next challenge.

472
00:38:41,760 --> 00:38:43,760
Yeah, I'll just be annoying people that way.

473
00:38:43,760 --> 00:38:45,640
Well, I'm going to ask you one more question.

474
00:38:45,640 --> 00:38:47,800
I really appreciate all the time you've taken with us.

475
00:38:47,800 --> 00:38:50,880
I mean, you brought up Luciano, your husband.

476
00:38:51,240 --> 00:38:54,360
And I would normally ask about somebody's significant other.

477
00:38:54,360 --> 00:38:57,720
But but it's it's very meaningful in this case because he's also an academic

478
00:38:57,720 --> 00:39:01,920
and he's a philosopher working in this digital ethics.

479
00:39:02,960 --> 00:39:06,240
And I've been certainly I have a sort of fascination about like,

480
00:39:06,240 --> 00:39:08,880
you know, what what's the conversation at the dinner table like, you know?

481
00:39:09,400 --> 00:39:12,760
Have you found that, you know, being married to a philosopher

482
00:39:12,760 --> 00:39:15,240
or has he found being married to a neuroscientist that this

483
00:39:15,240 --> 00:39:17,880
this been cross infection of the way you guys think about things?

484
00:39:18,000 --> 00:39:20,760
Totally. Yeah, I think that's been so

485
00:39:22,040 --> 00:39:23,840
mutually enriching both ways.

486
00:39:23,840 --> 00:39:28,080
You know, I mean, first we kind of like we were a bit at loggerheads

487
00:39:28,080 --> 00:39:31,280
that coming, you know, at things from really different perspectives.

488
00:39:31,800 --> 00:39:35,480
But yeah, I think Luciano has

489
00:39:37,760 --> 00:39:40,720
I mean, first of all, he has kind of a really deep knowledge of like,

490
00:39:40,720 --> 00:39:44,120
you know, history of science, but also history of thought and philosophy and stuff.

491
00:39:44,120 --> 00:39:48,720
And that's this it's a great, great to have like a gold mine like that.

492
00:39:48,720 --> 00:39:50,600
We can bounce ideas off.

493
00:39:50,600 --> 00:39:51,680
I usually have a great idea.

494
00:39:51,680 --> 00:39:55,720
And he says, oh, you know, Epitetu said that in what I'm like, oh, OK, of course.

495
00:39:55,880 --> 00:39:57,400
There's always someone who's right.

496
00:39:57,400 --> 00:39:59,720
But that's that's just really fun and useful.

497
00:40:00,680 --> 00:40:03,200
But I think we have

498
00:40:03,200 --> 00:40:07,320
we've converged and and and and changed a lot together.

499
00:40:07,560 --> 00:40:10,320
Sounds wonderful. The usual the morning conversation.

500
00:40:10,320 --> 00:40:11,440
I'm not a morning person.

501
00:40:11,440 --> 00:40:14,960
So the morning conversation usually goes that Luciano brings me coffee.

502
00:40:14,960 --> 00:40:16,800
He brings me coffee in bed.

503
00:40:16,800 --> 00:40:20,280
And he knows that for about 30 minutes, he can just, you know, say all these

504
00:40:20,280 --> 00:40:24,840
like heavy philosophical things, because I'm not ready to fight back.

505
00:40:25,560 --> 00:40:28,160
So he would wake up and says, Kea, you know, I think

506
00:40:28,640 --> 00:40:33,440
I think I finally figured out a way to separate meaning from truth.

507
00:40:34,200 --> 00:40:37,200
And I'll go, oh, OK.

508
00:40:37,200 --> 00:40:38,600
Where's the coffee?

509
00:40:38,600 --> 00:40:40,080
Where's the coffee?

510
00:40:40,080 --> 00:40:45,000
But yeah, so it's the conversations are they go all over the place.

511
00:40:45,000 --> 00:40:48,600
And it's been but, you know, he's I know that this is not like

512
00:40:48,600 --> 00:40:51,600
the most important contribution, but as scientists,

513
00:40:52,200 --> 00:40:55,840
we're sometimes quite sloppy at how we use concepts and words and things.

514
00:40:55,840 --> 00:41:00,960
And and I think having someone who thinks about high degree of precision.

515
00:41:00,960 --> 00:41:04,120
Yeah. He used words is like very, very sharp tools and things

516
00:41:04,120 --> 00:41:05,880
really helps the thinking as well.

517
00:41:05,880 --> 00:41:06,960
So it's been. Yeah.

518
00:41:06,960 --> 00:41:09,240
And we actually just published our first paper together.

519
00:41:09,240 --> 00:41:10,800
Very good. Right.

520
00:41:10,800 --> 00:41:13,160
I don't know. I think it's just out two weeks ago or something.

521
00:41:13,320 --> 00:41:15,480
Excellent. On everybody's reading this.

522
00:41:15,480 --> 00:41:19,160
Now, I said I had one more question, but I do have one more burning question,

523
00:41:19,160 --> 00:41:22,400
which I happen to know is that the question that the cognitive

524
00:41:22,400 --> 00:41:24,720
neuroscience community wants to know the answer to.

525
00:41:25,440 --> 00:41:27,640
You are Anna Christina Nobre.

526
00:41:27,920 --> 00:41:29,200
Where the hell does Kea come from?

527
00:41:29,200 --> 00:41:31,160
Oh, OK. Yeah, that's easy.

528
00:41:31,160 --> 00:41:35,160
So in Brazil, you know, maybe like in Ireland, everybody is Anna

529
00:41:35,160 --> 00:41:37,320
something or Mary something. Yes.

530
00:41:37,320 --> 00:41:39,120
Very Christian, including the boys.

531
00:41:39,120 --> 00:41:40,600
Yeah, exactly.

532
00:41:40,960 --> 00:41:43,280
So the Anna didn't really count.

533
00:41:43,600 --> 00:41:47,000
So it was like Christina is like was my and then Christina in

534
00:41:48,000 --> 00:41:50,920
in Portuguese is Christina is quite hard to pronounce.

535
00:41:50,920 --> 00:41:54,800
And so it's like it's not a word like so it has a hard K on the.

536
00:41:55,080 --> 00:41:58,680
She's seen it, you know, it's like so I think it was like the first child

537
00:41:58,680 --> 00:42:02,040
who was born after me couldn't like would just call me Kea, Kea, Kea.

538
00:42:02,040 --> 00:42:03,600
And so there was just like a family name.

539
00:42:03,600 --> 00:42:04,920
It just so that's very clear for me.

540
00:42:04,920 --> 00:42:07,240
OK, so now we know now we know the origin of it.

541
00:42:07,240 --> 00:42:11,960
Yeah, but yeah, my name is causes endless problems with Anna Christina.

542
00:42:12,160 --> 00:42:15,000
I do you think I should change my name to Kea Nobre?

543
00:42:15,360 --> 00:42:19,400
No, I think I think you've got the mystique of the Anna

544
00:42:19,400 --> 00:42:22,600
Christina and Kea is very important.

545
00:42:22,600 --> 00:42:25,280
Thank you so much for being here. We really appreciate it.

546
00:42:25,280 --> 00:42:37,840
Thanks for all the hard questions.