CNSP: And
CNSP: oh, I just forgot that we hadn't recorded yet. People who are learning. So this is great.
CNSP: Surprisingly, there's no one from industry. And I'm wondering, Mick, did you not fill out the form? Actually, this is not true, because the last registrant we got is from industry. So now I think we have
CNSP: 2 industry people. Where are you from? We're from all over the world.
CNSP: There's a nice concentration of folks in Europe. So here's a little zoom in on that.
CNSP: But generally we're from all over a few people in America, South America, Asia, and Australia.
CNSP: Sadly, no one from Africa or Antarctica.
CNSP: We also asked you
CNSP: what tools you use to analyze your data. And just like last year, most people are using Matlab Python or R, you've got a nice number of people in Matlab, followed by Python, and then R.
CNSP: The sport. One poor soul speaks assembly.
CNSP: I mean, I don't even really know assembly, but he's by himself, or she's by himself. So
CNSP: for them.
CNSP: We also asked if people spoke lisp, I mean, I don't. I don't know, but no one does.
CNSP: We also asked a little bit about your interest and your research directions. And so I compute, compiled all of those words into a word cloud, and the usual suspects show up
CNSP: auditory attention, speech, and eeg.
CNSP: It's pretty much the same every year, and this is not really a surprise, but some of the fringe
CNSP: words. Here are a bit more interesting to me because they illustrate that we are taking the Trf a bit further beyond the domains of speech and music to things that are, you know, like memory and emotion, and then actually using them to do
CNSP: to understand brain responses to movement which I think is really interesting.
CNSP: Maybe sometime in the future we'll have someone that can talk to us about how we can trf movements.
CNSP: I also learned that my analysis for this was not very good. It broke up some words that should not have been broken up.
CNSP: Sorry about that.
CNSP: So finally.
CNSP: 5 people that are on the call that you're here to see. We've got 2 great keynotes from Lucas Para and Micah Halbron. We'll have an update today from our 2,022 Cns project award Giorgio, and then tomorrow we'll have 2 tutorials from 2 great people, Marley and Ola.
CNSP: So here's how today we'll go. Lucas will give his keynote. Speak in just a moment, followed by Joe and Georgia. Georgia. and then we'll break today around 5 40 Dublin time
CNSP: and then we'll come back a few minutes later for the second session. Today the tutorial in between there, there'll be some time to have a moment. Get a drink of water, have a break, or you can stay on the call and ask some questions. If you're having problems getting your Cnsp resources set up
CNSP: tomorrow will be much of the same, the same sort of schedule. We'll start again at the same time. 11 Am. Sorry. 4 PM. Dublin time
CNSP: we'll do a two-hour session as normal, break around the same time and then come back for the second tutorial session around the same time
CNSP: for that session. 4. You'll choose which session to go to. You'll either be going to track one or track 2 to see Marley or Ola.
CNSP: So today.
CNSP: We have Lucas Para.
CNSP: He is the Harold Shames Professor of Biomedical Engineering at the City College of New York.
CNSP: He's got an impressive resume, with hundreds of papers and tens of thousands of citations.
CNSP: But here's what I actually appreciate about Lucas's work. Here's his most cited paper
CNSP: convoluted blind separation of non-stationary sources. I don't actually really know what that means, but I know that it's complicated, and I know that it's probably a really good and analysis technique.
CNSP: His scholar profile is littered with titles like this, involving complex things like joint decoration, inter subject correlation, correlated components, analysis, canonical components, analysis.
CNSP: Sometimes sometimes people end up missing the forest for the trees. Well, Lucas is in the weeds here.
CNSP: and he doesn't miss the force, and this is evident from some of his later talk, or from some of his later papers. Papers. Conscious processing of narrative stimuli synchronizes heart rate between individuals.
CNSP: and also from the title of this talk. Today narratives synchronize the body and mind. So I will stop sharing here and turn it over to Lucas.
CNSP: who will?
CNSP: tell us about narrative synchronized body and mind.
Lucas Parra: Very good. Thank you for the introduction. Thanks for organizing this event. It's a lovely group of people from all over the world. 68 participants. I was wondering how they help you invite all of these people to come to an online event. Welcome, everybody.
Lucas Parra: Okay. So I wanna do something a little different. Hopefully, it's an experiment.
Lucas Parra: I like to interact with you guys.
Lucas Parra: So I'm gonna stop a lot and give you opportunity to ask questions. I want you to interrupt me. I'll show a little bit of code. So hopefully, this is not going to be the usual
Lucas Parra: endless
Lucas Parra: sequence of slides that put everybody to sleep and sent them running for their email and messages. So
Lucas Parra: so I'll try to keep you engaged, which, in fact, is the topic of the presentation.
Lucas Parra: I love to see people's faces. So if you're not shy about turning on your video, that'd be great. That's the way I find out that you're with me. So don't be shy. Turn on the video. I'll be. I'll be I'll be looking at you. There you go.
Lucas Parra: I got some takers very nice.
Lucas Parra: so let's get started, while you all alternative faces and look at each other and say, Hello,
Lucas Parra: When you have questions, raise your hand. Somebody will be monitoring
Lucas Parra: when you raised your hand. It means that you want to ask a question, and then you can unmute yourself and and I'll hear your question, otherwise I won't know that you have a question. If you're feeling very shy, you can also type your question into the chat.
Lucas Parra: and then I think Aaron will read it. That's how we're gonna do it. So don't be shy about asking questions.
Lucas Parra: so let me share the screen
Lucas Parra: to set up my screen a little bit. This can stay here.
Lucas Parra: Let's see, this is gonna move over there.
Lucas Parra: and this is going to be.
Lucas Parra: or on a place.
Lucas Parra: Alright. So
Lucas Parra: no, that's the wrong presentation. I'm sorry about that.
Lucas Parra: Pieces were not starting. Well. if I'm sharing the screen, yeah, I am sharing the screen right? Okay.
Lucas Parra: You guys see the the title that says narrative synchronize this body of mind. I hope if not, somebody will tell me so. I'm at the City College of New York. Beautiful campus here.
Lucas Parra: The work that I'll be talking about today is from a few students. Jens, Matson, Samantha, go on. And Jason Key that I put this together a couple of years. There's a few more people mentioned along the way.
Lucas Parra: so I was very inspired. I don't know if you know the work from Yuri Hasan and David Heeger back in in 2,004 I heard a talk of theirs. It was fascinating. They had people watch movies
Lucas Parra: and they were recording brain activity. This was actually with Fmri. And the basic observation is that people's brain synchronize. So they in this case Fmri signal as a as a sort of function on time, ended up correlating between 2 individuals.
Lucas Parra: I was fascinated by that work. And since I do, eg. We try to reproduce something like that with electroencephalography. So we we measure something that's called inter subject correlation. So we we have multiple people say, watch a movie or listen to a story, and then we record, and all of them
Lucas Parra: the brain activity. And then we compare the brains of 2 subjects and correlate it with one another. And that way we measure how correlated that. That is so. This is work from Yasek Demachovsky back in 2,012,
Lucas Parra: and the first observation is that this metric of inter subject correlation is very strongly modulated by attention.
Lucas Parra: So this was activity recorded while watching a Hitchcock clip. I don't have time to actually show you too many movies today.
Lucas Parra: And the the basic observation was, so each dot here. This cross is a person watching this Hitchcock clip about 5 min worth of of movie and we measure how similar is that person spraying activity to that of the group. The other participants, while they are regularly attending, or when they're distracted. Our usual manipulation for distraction is
Lucas Parra: count in your head in steps of 7 backwards. So that's a very difficult task. Meanwhile you're still looking at the video. You're still listening to sound. But evidently your brain is not with it anymore, because the brain activity is then no longer correlated to that this subject. So we get this precipitous drop in inter subject correlation. So this person here was very correlated to the group, and one distracted was very
Lucas Parra: I guess, uncorrelated from the group, and the effect is so strong that we can draw a line and perfectly discriminate the person attending or not attending. So II don't know about you guys, but I've worked on electrostoplography for many, many years, and I have never seen an effect, this strong on any metric. So this is much stronger than alpha activity, much stronger than anything I've ever seen before. In terms of measuring attention.
Lucas Parra: In fact, this this metric of interstopch correlation. Samantha, go and did an experiment where she asked people about the content of the movie they just saw 3 weeks later. So the 3 weeks later, people got online a quiz basically about the content of the movie. They saw. And we looked at the brain activity
Lucas Parra: during the video and what they recalled afterwards. So the percent correct answer, 0 is the mean of the group, by the way, so people that were more correlated to the group than the average. Also remember the story from 3 weeks ago. Better, so essentially, during encoding, we can measure how well people will 3 weeks later. Recall the content of a story. And it's a pretty robust effect. We've replicated it a couple of times.
Lucas Parra: So how do we do this? How? How do we measure actually inter subject correlation? So we measure this with components. So if if you guys work with electron photography or Meg, perhaps the concept of a component is not new to.
Lucas Parra: So, for instance, independent components something that people are very familiar with. So we take the activities, say from subject one, which is, you know, samples and time. This only a cartoon brain that Ega activities look like this. But you have multiple sensors and multiple samples. And then we combine the sensors with a waiting matrix. So say, sensor. One gets one value sensor, 2 gets another one, and so on, so that we get a single combined time course from subject.
and we do the same waiting for subject 2. So we get another time course from Subject 2.
Lucas Parra: And the objective is for this weights to be such that the red curve and the blue curve are similar as possible. By similar we mean correlated, so we will find weights, components. W. That will make the connectivity between 2 subjects as similar as possible. I hope this makes sense. I'm gonna pause to give people opportunity to ask questions. So if there's already question, let me know. Raise your hand.
Lucas Parra: Really
Lucas Parra: 68 people. Nobody has any questions so far.
CNSP: You're 72 now, and if you're
CNSP: if you're shy, please type in the chat.
Stephanie Haro: Alright, why do you think about whether you have questions or not? Let me switch to some code?
Camila Zugarramurdi: Yeah. Hi, hi. So when you say that you adjust the weight to get subject one and subject to to be as similar as possible. Do you do that paradise for the whole group? Or how do you find weights that are just
Lucas Parra: similarity for a larger group than to be wonderful question. So you can do it. There's 2 ways of doing it. Everybody gets their own weight.
Lucas Parra: so I had to adjust my brain. Activity would be as similar as as possible as others. Also, each one with their own weights, or you can share the way. So the same way for everybody.
Lucas Parra: it turns out. And this is perhaps no surprise that if you use the same way for everybody, you get much smoother responses, much less noisy results, but somewhat weaker correlation. If everybody gets their own weights, then you need a lot more data to get similarly smooth results. But yes, you can do both.
Lucas Parra: It's different algorithms at the end. One is called canonical correlation, the other is correlated components, which is the one that I'll talk about.
Lucas Parra: But a wonderful question. So it turns out doing this business with a single way gives a little equation like this, which, if you have learned eigenvalues in linear algebra. At some point you might have seen an equation like that. This is an eigenvalue equation. It's called
Lucas Parra: and principal component does the same thing. There's versions of this for independent components so this, this sort of equation with eigenvalue shows up a lot. In this case, we're trying to maximize the between subject covariance
Lucas Parra: divided by the within subject covariance. So if you do that and you look at the weights you get components sort of like this. So this is the component in the G that during video watching captured most of the correlated activity between subjects. So this is the case where we're sharing the weights.
Lucas Parra: there's a second component that looks like this, and a third component that looks like that. And frankly, a fourth one that I'm not showing. That's just like the third. But symmetric so turned out to the other way around. And let me show you some code in case you feel compelled to do this at home.
Lucas Parra: So if you downloaded this.
Lucas Parra: you you might have gone to this website. You can. You can do this right now. So if you go to parallap, org
Lucas Parra: and click on the on resources so you can, I'll I'll give you some time where it says inter subject correlation here on the resources. You click code and test data. You need come to this page
Lucas Parra: and you can download this sip file.
Lucas Parra: So I'll give you a second to do that, you can download that. So if you're a Matlab user, and if you have Matlab on your computer, I already downloaded this. So if you don't have time to do that right now, that's quite all right. I'm going to change to.
Lucas Parra: It's not what I wanted to do. I wanted to change, to
Lucas Parra: let me try this
CNSP: while you're doing that I'm gonna ask a question was on the chat. It was, why don't you just calculate the coherence between corresponding channels. So I guess, maybe like, what's the difference between intercept correlation?
Lucas Parra: That's a very good question. While I fiddle with this, I will try to answer it. So why don't I just measure the coherence between individual channels? The reason for that is, I can correlate one channel with a corresponding channel
Lucas Parra: of the other person.
Lucas Parra: or I could correlate the channel of one person with a different channel of another person. So right there it gets combinatorial right all the possible correlations you can measure coherence. You could measure.
Lucas Parra: I'm having a hard time. So I'm going to exit out of this and switch into Matlab.
Lucas Parra: Do you guys still see my screen? Is is Matlab on the screen? Yes, yep, wonderful.
Lucas Parra: so
Lucas Parra: so I hope I answered that question. So if if I have.
Lucas Parra: if I don't do some, the composition of components, I have to basically commit which electrode with which electrode to compare and just comparing the corresponding electrodes, might might not really capture everything that's in it. Instead, we're gonna try to capture components. That is sort of virtual sensors. Think of it as a combination of electrodes that are virtual sensor.
Lucas Parra: So here, here, I'll give you some examples. So I loaded already this this data file. If we look what's in memory for this data file, it's got a one big file variable called X, which is number of samples in this case typical for, eg, is 50,000 samples. Let's see, what's the sampling rate? Here is 2 56. So this is maybe a few minutes of video, maybe 5 min or so. I have 70 channels. So this is 64 channels, plus some
Lucas Parra: I eog electrodes electrodes on the eyes. And then I have 18 subjects. So they're nicely time aligned. II arranged them so that this first sample in subject one is the same moment in the video as in subject 2, 3 and 4. So they're nicely time aligned across subject. And all this thing takes is basically a volume. If you know that some channels and these subjects are bad. You can tell their list of channels that are not that good, and you can tell that the list of channels that are for the eye movements.
Lucas Parra: And that's it. So I ran this code already. You can just type in. If you're doing this at home, you can just type ICEG. And we'll use this data from Samantha Cohen from 2,016.
Lucas Parra: It will generate this figure. But you see over here. So what are we seeing. So up above, you see the components. And this is what in in in imaging, eg. Sometimes called the forward model. So how does that activity that's shared by subject looked on the surface. So this is the first one. You see, ocipital activity is not surprising. Why? Because, these are
Lucas Parra: participants watching movies. So it's obvious that you might find some hospital cortex involvement in here. You get this symmetric distribution, which we think is mostly auditory, and then other stuff. Okay, what you see down in this panel is the level of correlation of each subject in each component. So the first component by definition, just like in Pca captures most of the variance, this component and and this algorithm, which is called correlated component captures.
Lucas Parra: Most of the covariance between subject. So it's component one and 2, and each.is a person. So how similar was that person to the group? Essentially. And over here we have the same thing, resolved in time for each one of these 3 components. So there are moments in time like over here, where the correlation was pretty high that we interpret to mean that people were
Lucas Parra: pretty similarly responding, and I will tell you later that, or our interpretation is that people were very attentive.
Lucas Parra: So the code. If you just want to know what it spits out, it spits exactly these things that I've mentioned. It measures the overall level of inter subject correlation. It gives you a number per subject.
Lucas Parra: It gives you a number per second, and then it gives you what we call a backward and a forward model. So this W is what gives you the component and A is the corresponding forward model that's displayed here, so that you have an idea of what this activity is
Lucas Parra: so this was a lot of information. I will pause again to see if people have questions.
Lucas Parra: No questions
CNSP: I've got. One
Lucas Parra: might be a little a little out of.
CNSP: I don't know, anyway. Is there any way to yoke this to the stimulus
CNSP: like the way that
CNSP: one of the things that I like about it is that it's not yoke to the stimulus, and that you don't have to like input like an explicit thing. And you like, if you want to see something that's like common between 2 different like experiments, maybe that should be one thing you can do that, and without any like.
Lucas Parra: it's in a way implicitly to the stimulus as in it's aligned to the stimulus. So without align this to the stimulus, this correlate activity would have nothing to do with the stimulus, both subjects or all subjects being aligned to the stimulus. We're only extracting activity that's evoked by the stimulus. So this is stimulus evoked activity
Lucas Parra: and only stimulus about activity, because otherwise there wouldn't have anything in common.
Lucas Parra: So in that sense, it's yoked, it's not yoked in the sense that we're extracting components that have something do with specific features of the stimulus. This could anything about the stimulus, in fact, could be any nonlinear property of the stimulus. This doesn't have to unlike the temporal response function approach. That's a linear relationship between that stimulus feature and the brain activities could be any nonlinear property of the of the stimulus that we capture, which is kind of a nice thing.
Lucas Parra: It is disappointing in the sense that we don't know what these components are, and this is most the critique that I've received for this kind of work is well, what is this component? Well, like? What does it mean? Does it have to do with with what? And so that's this thing that's perhaps a little
Lucas Parra: unsettling about it. So therefore I'll show you a different way of doing it. So the other way of doing it, which which we conjecture is. So we argue that the stimulus and I'll show you a video game just soon enough.
Lucas Parra: The stimulus drives the brain in, both individuals say, and therefore it's correlated. What about measuring? Just simply how correlated the brain activities with the stimulus itself. And so this approach we call stimulus, response, correlation, or tracking. This is what people in speech would call speech tracking. If the stimulus was speech.
Lucas Parra: So we have our own particular way of doing this, and I'll show you this. We've done with video for the first time. So I'll show you a video.
Lucas Parra: So this is, participants are playing this video game. This is like Mario Cards. And you see all these arrows, these red arrows. They're not really seeing these red arrows. This is something we extract from the stimulus.
Lucas Parra: So people that do speech processing, they dish. They extract maybe the sound envelope we for video extract the optic flow. These errors are the optic flow like, how is, think, are things locally moving on the screen? You also see eye tracking by the way, those 2 circles in the middle is eye tracking
Lucas Parra: so we will then correlate the brain activity to how strong these red arrows are, so kind of like the volume, if you will, of the of the visuals.
Lucas Parra: how much movement is there.
Lucas Parra: and what we find is that when people are playing this game, so this is the stimulus response correlation. Now, so this is yoked to this stimulus, if you will specifically optic flow. So if you look at people actively playing this game, you get pretty high correlation. So these are not small numbers, by the way, with the EG. Activity and this optic flow. Now, this is the total optic flow anywhere on the screen.
Lucas Parra: And then this is when they're passively viewing.
Lucas Parra: Now, we also did another thing, which is, we told them that they're controlling the video game with their brain activity. Of course that was not true.
Lucas Parra: They were just told that that's what they're doing. So this is brain computer interface. They thought, Oh, my, this works really? Well, it actually, we're playing back a previous game. And they had no idea that they're not actually controlling this this screen. So we call this sham play.
Lucas Parra: And sure enough, people that thought they're controlling the brain, the the video game had brain activity, those just as correlated, if not more, to the optic flow in the screen as the people that were actively playing my point being that this metric of correlation really captures how engaged the audience is with the game, even though they're not really controlling the the input.
Lucas Parra: so just like with a temporal response approach, that you guys are familiar, we extract components. So rather than doing this, one electrode at a time we do it again with components. So this is the first, second, and third component. And this are the corresponding filters. So so how's this whole thing work, actually, and here's a cartoon, and this cartoon is already too complicated. So I'm actually going to
Lucas Parra: stop, screen, share
Lucas Parra: and give people the opportunity ask. And instead, I'm gonna show you something on the board to explain what we just did.
Lucas Parra: You guys see the board?
Lucas Parra: Yep. any questions so far, by the way.
Lucas Parra: No, alright. So the the approach that maybe some of you are familiar with is that there's some that some stimulus feature that's coming in. Let's call it
Lucas Parra: X of T. So this could be. If you've been working with sound, this might be the spectrogram over time, or it might be the speech volume over time, or something like that.
Lucas Parra: And I'll that goes into
Lucas Parra: a conclusion.
Lucas Parra: We call this a linear system right? This is the the so-called temporal
Lucas Parra: temporal response
Lucas Parra: function
Lucas Parra: more conventionally. By the way, this is called the impose response. So I
Lucas Parra: I resent
Lucas Parra: at Lalo for calling it what it should not calling it what it should be, but at any rate, temp response. Sign by me, it's impose response. And then the output.
Lucas Parra: There's another thing, namely, the brain activity.
Lucas Parra: Okay? And so this is this is essentially the model of the temporal response function. And people call this model. So y of T
Lucas Parra: is H combol with X of T. They call this encoding.
Lucas Parra: This is an encoding model. How does the brain signal encode the feature of the stimulus? So that's that some of you are familiar with.
Lucas Parra: Another approach is very popular in
Lucas Parra: also an eg. But also an fmri is to decode the brain activity. So a lot of what they use. Articles pick up about brain reading works, kinda like what I'm just about to draw, which is, you have many brain signals.
Lucas Parra: Maybe you think so?
Lucas Parra: And so, yeah, at every moment. In time, you have a whole vector let's call this Y vector, y of T,
Lucas Parra: okay, and that vector, y of T,
Lucas Parra: it's multiplied with a matrix.
Lucas Parra: and out comes the stimulus feature. X of T. So the equation here is something like
Lucas Parra: X is W. Times.
Lucas Parra: Why? Okay? And of course, these things can also depend on time.
Lucas Parra: This could also be multiple features. And this can also depend on time. And so this is typically called decoding brain decoding.
Lucas Parra: This is a brain reading, right? So all of the news articles you see about. Oh, my God! We can decode speech or music, or whatever else
Lucas Parra: from brain activity or some flavor of this decoding. So what we did is what you just saw. But we live with that video game and other applications is sort of a hybrid thing. It's a bit above. So we take the stimulus
Lucas Parra: and pass it through an encoder with a convolution like. So
Lucas Parra: so this is again X of T, that generates a signal COT. And on the other end
Lucas Parra: we're gonna decode. But we have the brain activity
Lucas Parra: measure here.
Lucas Parra: That's Y of T
hmm.
Lucas Parra: makes another signal. See COT. And the goal is that they're as correlated as possible. So this measures correlation. We call this stimulus
Lucas Parra: stimulus
Lucas Parra: response correlation.
Lucas Parra: Okay? So let's say, approach. And and this is sort of a hybrid between encoding and decoding alright. So that's that's the that's
Lucas Parra: that's the approach that we have taken with the results that you just saw on video games. We've done this also with speech and a whole bunch of other things. So let me go back to screen share. So I'll show you some more results. Any questions so far.
Lucas Parra: Yeah, there was one in the chat and then another that might want to ask you in person, in the chat? Can one move from sensor space to source space with the metric just presented to quantify interpersonal correlation.
Lucas Parra: Yeah. So to go back to this.
Lucas Parra: to this picture.
Lucas Parra: That's
Lucas Parra: so, this picture here. This would be the this would be the source
Lucas Parra: right, and this will be the sensor.
Lucas Parra: And yes, you can go back and forth between the 2. So they.
Lucas Parra: going backwards is what's called the forward model. So this is a forward model. And this is the backward model.
Lucas Parra: So the algorithm really extracts the backward model. But you can always reconstruct a forward model from it. And, in fact, what I was showing you before was the forward model. So going from source space to sensor space. But you can also go that way. In fact, that's what the algorithm really does. By the way, the algorithm to find these things is called canonical correlation analysis was developed by hopling in the fifties. So it's a really old algorithm. It's not something we invented. What we invented is
Lucas Parra: this sort of idea of doing both decoding and coding at the same time.
Lucas Parra: Alright other questions.
Chelsea Blankenship: Yeah, I had a quick question about the type of stimuli that you can use. So I'm more familiar with auditory evoked responses, thinking of like bond or something repeated over and over. Can you do this kind of analysis on that, using that type of stimuli? Or does it need to be continuous stimuli?
Lucas Parra: No, I. Anything works now, if you have events like the ones you have the conventional approaches event related potentials. It turns out that doing something like this, or even the temporal response function without. By the way, this this thing here, what I drew here. If you get rid of this, you basically back here. And this is what
Lucas Parra: if you get rid of this sort of complication of extracting components, but you do it electrode at a time, one electrode at a time. You get the conventional temporal response function. So does a temporal response function, or this one work for events like the ones you have. And the answer is, Yes.
Lucas Parra: in the, in the impulse response you find here here the temporal response function you will call and evoke response
Lucas Parra: or an event related potential. So absolutely, you can do with either.
Okay?
Chelsea Blankenship: And then other questions. My other question I had is, you're just displaying 3 components. Is that just because of those those are the 3 largest? Or is it like Ica, where, you know, if you have 128 channels, you get 128 components 128 components here, too, but they they're nicely sorted by relevance. The first one captures most of the correlation. The last one captures the least.
Lucas Parra: and mostly you're in the noise at the end. So unlike Ica, that doesn't have a preference for one component or another. This the composition technique gives you a sorting for the one that explains most of the stimulus, and then the second and the third, and so on and between each other. They are actually uncorrelated. So you can think of this as doing temporal response function, and some sort of blank decomposition at the same time.
Lucas Parra: Ica in front, because you're doing it here.
CNSP: Sorry I had a question but I just totally forgot it. When you were saying that last part, I'll ask it again if I think of it.
Lucas Parra: Okay. we're good. I'm happy to chat. not show any more slides
Lucas Parra: Ed Lallor in the house. Very nice high, Dad.
Lucas Parra: I have a question. Lucas. Yeah.
Giovanni Di Liberto: Do. Do you think in terms of number of subjects would there be. and upper bound to what you can get like? Is it demand a case that if you keep adding subjects, it will keep getting better.
Lucas Parra: or what? Which are the 2 techniques? The inter subject correlation, I suppose. Sorry. I'm talking about the the Cca style, the the inter subject correlation.
Lucas Parra: So you know. The more subjects you have, the more robust it becomes in a way.
Lucas Parra: but also it gets blurrier. So you might argue that eventually too many is too many.
Lucas Parra: usually we get nice results with this starting at 15 and upwards.
Lucas Parra: you can. By the way. Along the javanier. He has done it
Lucas Parra: with extracting components for each individual subject. Use some similar things. so you can mix and match whatever you want, either single setup components for all subjects, or each one gets your own.
Lucas Parra: It's a usual sort of parameter number of parameters issue.
Lucas Parra: Would you ever mix different types of recordings? What do you mean by different types.
Giovanni Di Liberto: even.
Giovanni Di Liberto: you know, starting from simple to, you know data recorded from here in my lab and recorded in your lab still E jiva, or even eg. Mg.
Lucas Parra: if they are, if they are in the same sensor space. So if they have the same location for electorate, I think that's quite all right. If you are combining activity from different sensors altogether, I would have to think a bit about it, but I think you could potentially play games and combine.
Lucas Parra: But I have to think about it. Certainly not.
Lucas Parra: off the shelf code. So let me show you some code for this. So enter. This is stimulus response correlation. This is the same website and the resources you click here. It brings you to Yosex Github. You download the code here download. I did this already. So let me change to Matlab to show you
Lucas Parra: what that looks like.
CNSP: Are you doing that? I remembered my question. Are all the components that come out? Are they all nonlinear? Or do you have, like all the linear parts of the relationship in the first component likes?
CNSP: Cca, I think it is
Lucas Parra: so. No, th, this is. This is now back to the linear world. So here you're extracting linear relationship between the stimulus feature and the brain activity. So this is very much like the temporal response function. You're limited to linear response. There's no nothing nonlinear here.
Lucas Parra: So let me demo the the code that II hope I answered it. This is the code that I have from. That's this demo. Let's see, let's run this thing.
Lucas Parra: Demo.
Lucas Parra: That's not the demo. Give me some sort of map left, Demo. That's not the one I wanna run.
Lucas Parra: I have to change here now again.
Lucas Parra: So these are the correlations in the first component
Lucas Parra: so so these are the 3 components that they extracted and they co correspond corresponding impulse responses. This is on data of a single subject. So these are a little noisy. If we had run this on more, say 10 or 15 would have come out a lot smoother. But what do you see here? The first one, at least resembles the figures in
Lucas Parra: in the paper.
Lucas Parra: Which is this figure here, where we see that that the the activity. So this was again, this was the sham play versus passive viewing. We see that the activity around 400 ms was weaker in the passive viewing, and that was predominantly in the electrodes in the back of the head. As indicated by this first component.
Lucas Parra: Can we do this with other stimuli? So so we've done it with a lot with video. We've done it also with the usual things like speech envelope, and even the semantic dissimilarity signal, and and a whole bunch of things. So you can use this
Lucas Parra: pretty much where you would usually use it in speech. One example that I thought was kind of cool. We did this with the optic flow in different locations of the screen. So we looked at how much correlation, so stimulus, response, correlation. So how correlated is optic flow between a location and the screen and the brain.
Lucas Parra: And so this gives us essentially a map in this video game where our subjects well responding to, I would argue, attending to. And, interestingly enough, during active play, they're paying attention to the horizon, the upcoming buildings
Lucas Parra: presumably cause they're making an attempt not to crash into them. And we don't see that as clearly in the passive viewing. By the way, the eyes are always peeled to the center of the screen. So that's not related to eye movements.
Lucas Parra: So let me just summarize what I've told you so far. It's a sort of a partial summary of this talk.
Lucas Parra: natural stimuli drive the brain, but only when the mind is engaged. So basically, when you're not attending, we had seen that with inter subject correlation, the inter subject correlation drops protect precipitously. Or in this video game, we saw that when they stimulus, when the brain is no longer correlated to the stimulus, then we know that people aren't paying attention and aren't really engaged. And these are basically 2 metrics that we've used
Lucas Parra: to measure this.
Lucas Parra: So so this sort of concludes the part, the main, the main title of my talk, though, is not just about the brain, but about the body.
Lucas Parra: So I'm going to switch topic on us. Unless there are questions
Lucas Parra: so far.
Lucas Parra: So now we're gonna start working with not just the brain signals. But we started looking at the heart specifically the heart rate we capture now, respiration
Lucas Parra: we are going to measure gauge position the pupil size in addition to dg, and we do this with audio only, or with audio visual or stimuli. So videos or continue speech audio books, that kind of thing. So this is work that Uhson has been doing over the last several years, I would say.
Lucas Parra: and we had some really interesting results. So here, I'm gonna show you some
Lucas Parra: basic signals. This was collected while people are watching educational videos, you'll get to see one of them in a minute. So this is, eg.
Lucas Parra: The 2 colors are 2 participants this is the heart rate. So you see how the hard rates going up and down here.
Lucas Parra: This is over several seconds. So this little bar here is 1 s
Lucas Parra: gaze position. You see, these 2 subjects are Fo pretty much. I don't know if this vertical or horizontal case position pretty much looking at the same place on the screen while watching movie. This, the pupil size the pupil size, and these 2 people is also very correlated. And this is respiration. So as we inhale it goes up, and when we exhale it goes down, you see that they seem to go up and down, but it actually they're not very correlated.
Lucas Parra: So we published this in pines nexus I'll show you the gaze.
Lucas Parra: And here I have a audience participation. So I need you to all pay attention. So you you're gonna see the same video.
Lucas Parra: and on the left you will see the red dots that you see here are the case position of people watching a video. And on the right, the same thing. But it's different people.
Lucas Parra: And one of the group of people, either left or right, we're paying attention. And on the other side people are weren't paying attention, and the task for you
Lucas Parra: will be to tell us or guess after you watched it. Who is paying attention? Is it the people on the left? So these red dots on the left? Or is it the people on the right? So I'm going to show you the video. And then we're going to take a poll. Okay, an informal poll.
Lucas Parra: Ready.
Lucas Parra: So which one of the 2 groups are paying attention.
Lucas Parra: When you ask someone to draw a star, so they'll probably draw something like this or this, or this, even maybe scientific.
Lucas Parra: Alright. So now comes my poll.
Lucas Parra: Raise your hand. If you think that the people on the left these people were paying more attention.
Lucas Parra: So everybody that thinks it's the people on the left. Please raise your hand. We'll take an informal count.
Lucas Parra: Okay, we got about
Lucas Parra: 32 people raised their hand. Okay, everybody raised a hand down, hand down, hand down. Now we're gonna kind of see who thinks the people on the right.
Lucas Parra: We're paying more attention.
CNSP: No one.
Lucas Parra: Oh, wow! That's a buy a sample.
Lucas Parra: That's a total by a sample.
Lucas Parra: Alright. Well, good. You guys were right. I mean, the the majority has the right answer. It's the people on the left that were more attentive. Essentially, when people move their eyes together.
Lucas Parra: Following the same thing on this screen is because they're paying attention. If they're not following what other people are looking at. Then they're probably spaced out.
Lucas Parra: And this is here. This is what we report here for all the signals for eg. Heart rate, gase, position, and pupil, we're measuring intercept correlation. But now we actually also resolved it by frequency. So this is essentially a coherence measure.
Lucas Parra: So we wanted to know, and which frequencies this happening before. I was just showing you overall how correlated our people here you see that the EG correlation is actually dominant. A pretty low frequencies, most of you or most of us, when we analyze EG. Without the activity of 1 s or faster, most of the activity that seems to be synchronizing. Yes, there's something above one hertz even around 10 hertz. But most of it is pretty slow.
Lucas Parra: Here's for heart rate. And, by the way, how do we measure heart rate into subject correlation? Simply was Pearson correlation? Nothing fancy, except here we first bandpass filter the signals. So you see that there's a nice peak here around sort of 10 s. Point one Hertz is 10 s time scale where the heart rate goes up and down. So basically, when we're watching a movie. And this is also true. When we're listening to a story.
Lucas Parra: our hearts synchronize.
Lucas Parra: So this is kind of a cute cute message here when you're driving in your car next time. And you're listening to a pod. Well, not a podcast a radio program that's playing. Live.
Lucas Parra: then your heart rate, if you're really paying attention to the story. will be synchronized with that of all other listeners. So we do share something in common when we really go to the movies, or we listen to a story at the same time our hearts are in sync
Lucas Parra: the same is for the gaze. Position is a very strong effect with video. and the same is true for pupil.
Lucas Parra: So
Lucas Parra: when we look at this metric of inter subject, correlation turns out is very predictive of whether people get content of these stories they saw correct. So these are educational videos. We asked afterwards quiz questions about the content of the video, and you see that pretty much all metrics except respiration are predictive of how many questions people got correct. So these are questions that were asked right after the video.
Lucas Parra: So whether it's heart rate or EG. Or gaze of beautiful, they all predict
Lucas Parra: the level of synchrony in the In. How similar you are to a group will tell me if you're paying attention. Therefore, if you remember what was said during the video
Lucas Parra: again, if there are any questions feel free to interrupt me.
CNSP: It's okay you've got you've got people here answering questions for you now.
Lucas Parra: Okay?
Lucas Parra: And in fact, yeah. In fact, these metrics seem to be correlated with one another. So subjects each thought is here, person people that have a I enter subject correlation, the Isc. And the in the sorry, in the E, in the eg. Will also have a high intercept correlation, the heart rate, that is, if your if your heart is beating similarly to somebody else, then so is your brain.
Lucas Parra: and similarly, for gays and pupils. So this seems to all capture a common underlying phenomena. And so the theory we have behind it is that it's really the cognitive processing of the stimulus is what entrains body and mind.
Lucas Parra: So essentially say the video that in this similar thing happens with audio only. Subject. One may be attending to the video subject 2 and and that will synchronize eyes hard, and maybe other signals, and the level of synchrony in one modality will be similar to the level of synchron in the other, because body and mind are really all in, trained
Lucas Parra: to the stimulus, but only again, if they pay attention if they're not paying attention. None of this is true, and this is how this this signals are correlated, I mean co-modulated.
Lucas Parra: Now you see that I've drawn these arrows in 2 directions.
Lucas Parra: Generally we think that the brain controls the eye and maybe the brain controls the heart. But there's also this notion that the body controls the brain to some regard. So so perhaps it's a two-way street and so we're not the first to suggest that the the model that we have really is a sort of a simplified model.
Lucas Parra: We know that when we're listening to a story somehow that seems to drive also, not just the brain sort of the cortical brain that we pick up with. Eg. But when we listen to a story, it appears to drive the autonomic system within drives the pupil the gaze and the heart rate.
Lucas Parra: But perhaps the opposite is also true. Perhaps the heart rate, or the gaze, or the pupil also drive the autonomic system. And this sort of has a bottom up effect. Does that make sense? So here we we ran a couple of experiments where we intentionally control luminance
Lucas Parra: case and breeding. I won't be able to tell you all of this. We just don't have the time for it today.
Lucas Parra: but I'll tell you a little bit of the results, and it's already complicated enough. So here's some results of
Lucas Parra: people just listening to a story. So this is
Lucas Parra: short stories, maybe 10 min or 5 to 10 min long. They're kind of fun and interesting to listen to. And what we first see is that the pupil synchronizes. But again, only people are paying attention, and the synchronization is pretty stark.
Lucas Parra: So
Lucas Parra: attentive. Again, at this very slow rate. The pupil of individual synchronizes.
Lucas Parra: but not when they're distracted. This is a very strong effect. We had seen this with video, but with video, there's always the concern that maybe it's just the fluctuations of light that cost the pupil to dilate and con and contract. The same happens apparently, when we, just listening to the story. By the way, people are watching. Looking at a blank screen, we ask them to focus on a screen, and there's a fixation cross, but regardless of the Fixation Cross, whether it's there or not.
Lucas Parra: the pupil and trains
Lucas Parra: apparently to the story. So there are moments in the story where everybody's pupils dilated moments in the story when pupils, pupils are constricted, but only if they're really attending to the story. We correlated the pupil with the Eg. So this is conventional temporal response functions. So 0 is 0 delay. This side means that the Eg is ahead
Lucas Parra: of the pupil, and this side means the pupil is ahead of the eg, so it's not clear which way the cost of relationship goes. It seems to be that that a portion of the eg. Seems to respond. The pupil seems to fluctuate before they eg. Does, and and vice versa. So we don't have a clear causal story. But the the important thing I want you to notice is that if we look at where this activity is coming from, it seems to come from this sort of Ontario central
Lucas Parra: portion of the brain which I've seen in many studies that look at the anterior cingulate. So we haven't done source reconstruction here. But we highly suspect that that's what might be going on.
Lucas Parra: So that's the pupil. While people listen to narratives hop up the heart.
Lucas Parra: So the heart we look at the heart rate, not the every beat, but the heart rate.
Lucas Parra: And we did again this temporal response function business. So we take the pupil size and try to predict the heart rate from it, and we see that there is a significant response just a little bit on this temporal site that corresponds to the heart being ahead of the pupil.
Lucas Parra: Similarly, we try to predict the pupil from the heart. We get a more clear story here, which again is, decide where the heart is ahead of the pupil. So seems to be that the fluctuations of heart rate are ahead of the pupils. Fluctuations, and we do the same thing with the eg. Signal, so can the heart rate predict the eg. Signal, and by and large we see positive time lax here, which means that the heart is ahead of the eg. Now this is quite surprising.
Lucas Parra: This is telling us
Lucas Parra: that the heart beat, is slowing down or speeding up
Lucas Parra: almost half a second to a second ahead of a corresponding what we would call
Lucas Parra: evoke potential or
Lucas Parra: potential fluctuation. And once again, same culprit, same location on the scalp. And this, by the way, is true with a person's attending distract, or resting so inherently. It seems that the heart rate fluctuations precede fluctuations of the G of this sort of slow time scale. And this is once again activity that to us looks a lot like Ontario singular.
Lucas Parra: So so the story that emerges here that perhaps
Lucas Parra: the heart. And by the way we drive it, we can also drive with breathing that confirms this observation that I'm drawing here. So breathing is known to drive the heart rate up and down seems to be driving the autonomic system which then affects the pupil size and also the electron so holography. So there seems to be evidence here for a cause for a bottom of effect of
Lucas Parra: autonomic function of the heart onto a pupil and brain.
Lucas Parra: So one last thing that I found cool that we found out is about gaze. So it turns out that when we are listening to a story attentive. Our eyes are moving around more. So case, the power of case positions, basically, how much are our eyes balancing around on the screen? So when we're attentive, we're actually moving our eyes more than when we're distracted.
Lucas Parra: I don't know if you noticed it, but somebody that's not listening to you. They tend to sort of stare blankly at you. They're not really following anything. So the attentive listener will actually move their eyes. The disattentive, distracted listener would sort of stare blank blankly in the middle, and it turns out that this amount of fluctuations of live moments
Lucas Parra: also correlates between subjects. So the amount of how much. You're bouncing your eyes around changes in a story. So moments of the story.
Lucas Parra: Well, I don't know which ones. But some moments of this story people move their eyes more than other moments of this story.
Lucas Parra: and if we look at that case, variation signal, that case variation signal 2 seems to correlate with this sort of anterior activity, though here it's possible that this has to do with the frontal eye field.
Lucas Parra: So, at any rate, this, I thought, is a kind of an interesting observation, I don't think is in the literature in any kind of way.
Lucas Parra: We move our eyes, depending of whether we're attending or not to what we're listening.
Lucas Parra: So to summarize it, all narratives drive the brain and the body at a very slow time scale, but only when the mind is really engaged, otherwise the body's not seem to entrain at all. This sort of brain body connection may be a 2 way street, and we can measure these things either with inter subject correlation or with stimulus risk bonds, correlation. We we like to do one result in time, because it gives us these sort of
Lucas Parra: causal interpretations, and we like to resolve the other in frequency, because it gives us an idea of what time scale this is operating at.
Lucas Parra: And with this I would like to thank the people. Jaco Sit is the guy in Paris, by the way, who works on patience disorders of consciousness, who brought us to pay attention to this hard signal. And and these are the students have been involved in all this work. Code is here. You can just go to resources and download it on this page.
Lucas Parra: And with that I'm going to stop and give people opportunity to ask questions.
CNSP: Wow, thanks, Lucas. It's really cool to see is that pupil Eeg work come to fruition after the the cog here meeting last year.
Lucas Parra: Yeah, cool.
CNSP: So
CNSP: if you wanna ask questions now is a great time raise your hand and I'll unmute you, or you can ask a quote. Here's a question in the chat.
CNSP: If the distractor is a stimulus that is correlated with the stimulus.
CNSP: How would you think it may affect the results? Would that in term increase the Isc? And is it a linear relationship with distractor strength.
Lucas Parra: So this business of how to distract people is really subtle problem, right?
Lucas Parra: the way we have done it. And this was a suggestion, for by Simon Kelly. By the way, the who maybe some of you know Simon Kelly's idea was, do not distract people by putting stuff on the screen, because then you're not really sure. Are you analyzing activity associated with the distractor? Or as you analyzing activity?
Lucas Parra: That's this distraction related itself. So so he suggested instead to do a a very classic thing, which is a mental task. So you just tell people do something in your head. What's a good mental task? You can do any kind of thing you can ask them to. Think about what you are doing. What do you have for breakfast, or you can ask them what would the task we use is to do math.
Lucas Parra: So do complicated math, and you had counting steps of 7 backwards, starting at, say, 1,000. So it's 1,000 is 993 986 and 979. So you already see, I'm staring blankly into space.
Lucas Parra: And so so that's that's the approach we take. And so this is all silent. So there's no stimulus to confound your task. But yes, this whole business of distractors and measuring attention with a probe stimulus is a whole complicated story that I don't really have any good answers.
EDMUND LALOR: Hey, Lucas? I had a question as well. Sorry I was late for your talk. I was teaching this more. I'm still in my classroom here. Actually, I just joined as soon as I quit. The question I had was the cool new findings with the eye movements and stuff like that as well, maybe you explained this, and I didn't get it at the time. But
EDMUND LALOR: the the. I was interested to know the the kind of speed of those the size of those are these what we would consider just regular saccades are they micro secures? Do you need to measure them using some super fancy eye tracker, or is is it coarser than that
Lucas Parra: is coarse? So it's literally. you know, when when I'm looking around, I can be looking around in a fairly nar in a fairly narrow sort of area. Or I can be looking around a fairly big area.
Lucas Parra: And that's it. We're basically measuring the power
Lucas Parra: resolved in time. Think of it as the sakhar envelope or not, Sakata, but case envelope.
Lucas Parra: That's it. Variation. So there's there's a paper that we cite. That's also looked at. This is case variation is a term that maybe is gonna catch on
Lucas Parra: it's not saccades, it's not smooth pursuit it we don't really care. We're completely agnostic to it. We just like over a period of time, how wide was your range that you covered?
Lucas Parra: That's it. And so that range is what seems to be moments of the story be bigger and other moments of the story be smaller. And why?
Lucas Parra: I think it's wonderful area of of you know, research now to find out, because the the thing that I find that interesting about it is, it's incidental movement.
Lucas Parra: So it's movements totally unrelated to the task just like you right now. Are you touching your face? Right? That's incidental movement, totally unrelated to your listening to me hopefully. You're listening. Looks like it. But so we do. All of these incidental behaviors are seemingly totally task unrelated, and yet
Lucas Parra: and yet they dominate brain activity. Perhaps you've followed that literature. But also we find that they are linked to to what's what we're doing in our head. Right? We're listening to the story. And apparently that's driving these. We think it has to do with arousal.
Lucas Parra: Different moments of this story increase arousal. And that's when we move our eyes. Perhaps more. That's that's our current hypothesis, we have to still figure it out.
EDMUND LALOR: Cool. Thanks.
CNSP: Here's one from the chat. Do you think it would be interesting or informative to measure inter subject correlation without a shared stimulus. A lot of research is done to study interpersonal neural synchron synchronization where people are actually trying to exclude the synchronization due to shared sensory input.
Lucas Parra: So we use we record people sequentially like, you know, now, we're recording you and tomorrow will record another software. And so on. Right. So there's no way that we synchronize any kind of way other than through the stimulus. Now, there's a very big literature in in what's called physiological synchrony. Ps.
Lucas Parra: Physiological signal, where the interest is sort of when people are sitting together side by side or looking at each other, interacting in some kind of way
Lucas Parra: that somehow there's been synchronized the physiology synchronized classic example.
Lucas Parra: Mother and child.
Lucas Parra: or therapist, and and patient, or or a performance and audience. So there's a whole sequence of a series of papers along those lines where it's never really quite well controlled. Why are they synchronized? Is it because they're sharing the moment, and there's some common stimulus, or this is some sort of empathic
Lucas Parra: co, you know, correlation. But I would content that if you put a screen between the 2 people, and they don't see each other, then don't hear each other, and they don't smell each other. There's nothing that can possibly synchronize unless there's unless you believe in some sort of, you know, esoteric kind of thing. And so I think that the only way
Lucas Parra: that people will synchronize is because they are somehow sharing something that they're perceiving. And so and so I think that, understanding what it is that synchronizes people in this interpersonal situations is kind of an interesting topic, and there's a big literature. But let's not pretend that this is some sort of
Lucas Parra: voodoo synchronization. It really is. I believe.
Lucas Parra: the the the, the sensory percept of the other person that will synchronize people even when they are sitting together.
Mick Crosse: Hi, Lucas and Nick Cross. Here I have a question. Great talk.
Mick Crosse: Ii found your work really interesting. Looking at all the relationships between these different biometrics.
Mick Crosse: And I'm wondering, looking at this correlation you showed between the heart and the Eeg, or heart rate.
Mick Crosse: And do you think that there are any interesting or useful markers of cognitive function that could be
Mick Crosse: determined from like smart watch, heart rate measure. You know, given that we're seeing a lot of companies trying to
Mick Crosse: develop brain computer interfaces now and
Mick Crosse: also to different neuro technologies.
Lucas Parra: Absolutely
Lucas Parra: erez agmoni, absolutely. I mean, the the watch has a problem that's very noisy. But if you get a high end arm band?
Lucas Parra: absolutely. I do think that you could do all kinds of in the wild experiments, and we want to do it. And we're thinking how to do them. And we're doing a lot of online experiments with eye movements already.
Lucas Parra: yes.
Lucas Parra: go for it. I think absolutely is a very rich signal.
Lucas Parra: I mean, I don't know if you caught the the scale of these correlations that I was displaying on the screen. They're much bigger than Bro. Than eg. Signals.
Lucas Parra: So the correlation of the pupil that we find of the heart rate.
Lucas Parra: I mean, it's a noisy signal. I give you that you need something for the hard. In particular, you need much longer record, because, you know, it's a very slow signal, so you need a lot longer records.
Lucas Parra: But the level of correlation is definitely higher than the Eeg. for instance.
Lucas Parra: Yeah, it. We only scratching the surface. I think there's there's there's so many experiments you can think of looking at pupil hard.
Mick Crosse: Yeah.
Lucas Parra: Now, I should say it's not the heartbeat. It's not like people's feet synchronizes. It's the heart rate.
Lucas Parra: so your heart might be going, but it's that speed up and slow down. That's what synchronizes, not the actual beat. To beat is not like. Our hearts are in absolute synchrony, beat by beat.
Lucas Parra: But that's not the case.
Mick Crosse: Thanks.
CNSP: Okay. So for the sake of time we'll move on. Thank you, Lucas. Everyone join me in thanking him with our
CNSP: See.
CNSP: there, there, click that button! Thanks, Lucas. That was a great talk. I really enjoyed it.
CNSP: We'll move on to Giovanni.
Giovanni Di Liberto: I mean, yeah.
CNSP: So Giovanni will give us some updates about Cnsb, what he's been doing over the last year or so what we've been doing, and then he'll introduce a his visiting student, Giorgio, who will give us an update on
CNSP: the project. So, Giovanni, please take over.
Giovanni Di Liberto: I'm stealing the screen. Now.
CNSP: please.
Giovanni Di Liberto: Is this the right? Can you see my slides?
CNSP: Not yet?
Giovanni Di Liberto: Oh.
Giovanni Di Liberto: what can you see?
CNSP: 2 pictures and your group.
Giovanni Di Liberto: Oh, great.
Giovanni Di Liberto: So these are the slides and Lucas. Thanks, Amelia, that was great and also the format. I like this interactive
Giovanni Di Liberto: format for online presentations.
Giovanni Di Liberto: I'll have to switch back to the old format, and
Giovanni Di Liberto: but it's very brief. So before before moving on with the next, invite the speaker, who's Giorgio Piazza, I'll just give you this brief update
Giovanni Di Liberto: on the work we have been doing, but also at the end I'll give a brief mention on the travel. Grant, supported by sponsored by a ambient train.
Giovanni Di Liberto: and let me just prepare a slide. One of the embarrassing slides with lots of faces and nobody looks at. But it's it's it's a necessary to thank all the people that helped in the last 3 years in all the work we did with the workshop and also with the resources.
Giovanni Di Liberto: So, Luke, as I've shown this picture in different flavors on the board, on the slides mit Ctl. And but the idea is, yes, we have in the different type of experiments that we deal with, that the Csb
Giovanni Di Liberto: initiative and workshop care about. We talk about, you know, some neural recordings and some
Giovanni Di Liberto: sounds that are continuous, or some videos or or everything that Lucas has has mentioned. And ultimately we have an X
Giovanni Di Liberto: and the why in machine learning sense so, and next, where we have, if you want some stimulus features, and the Y where we have the neural signal.
Giovanni Di Liberto: and we want to relate them somehow. So
Giovanni Di Liberto: when we have. And again, the way that Lucas described, when we have multiple features, imagine. And and this is something that happened to me. Anyway, we had recorded a data set thinking about phonology. And then that same data set has been reanalyzed in
Giovanni Di Liberto: I don't know how many different ways. So with so many different papers from different labs can come out from the same data. And so this, this is an opportunity.
Giovanni Di Liberto: It's an opportunity to to reuse data from, you know, rather than having to record a thousand times
Giovanni Di Liberto: the same kind of experiment.
Giovanni Di Liberto: and then the type of tasks that we're talking about, like listening to speech, and music is just more pleasant
Giovanni Di Liberto: mit Ctl, and so it gives you better access to particular cohorts that wouldn't be as willing to take long experiments where they have to look at, you know, flashing lights or individual syllables.
Giovanni Di Liberto: So just more pleasant the challenges are that we want to tackle one of the challenges that you know the correct use and interpretation of the methodologies we are talking about. Right? It's it's very important that if we start sharing resources, we start sharing code or or toolboxes.
Giovanni Di Liberto: that we also, you know, make sure that that there is no misuse of these tools. And so that's why we try to publish papers and and and also organize this workshop where we try to provide these
Giovanni Di Liberto: resources for for guiding towards the interpretation, and how not to use these methods.
Giovanni Di Liberto: And then the one thing that is very practical, and even within the same lab.
Giovanni Di Liberto: what happens is that is that we just end up using different versions of the same code for doing the same thing which is problematic, it causes it increased the risk for errors.
Giovanni Di Liberto: and also makes everything slower. So code reusability.
Giovanni Di Liberto: So with here Cnsb, at the center of this slide, what I'm trying to say is what Cnsb is is about. So one element is
Giovanni Di Liberto: trying to share some standard standardized analysis code for running different types of analysis.
Giovanni Di Liberto: And of course, we started from the kind of analysis we were doing already. And by we I mean Aaron and I and Nick, and so on.
Giovanni Di Liberto: All the organizers to share standardized code. We need to agree on some standardized format for for the data. And that's also another activity we have been working on.
Giovanni Di Liberto: and then trying to take some existing data sets and putting them in the same format. So that basically that same standardized code can be run instantly on on all of these different data sets without wasting time.
Giovanni Di Liberto: Then, of course, we have the workshops where we try to to share new research and new tutorials, question announcers, and so on.
Giovanni Di Liberto: And finally, we see this as an opportunity, for. you know, creating a community for networking, for connecting people that
Giovanni Di Liberto: mit Ctl and are more on the methodological side. And instead, other scientists that are more, you know they have maybe less confidence on the methods. And they want to connect with someone that has that expertise
Giovanni Di Liberto: on on doing more applied research.
Giovanni Di Liberto: And for students, of course. And that's this is the kind of resources I use also in my own lab. So every student, Phd student or master student that joins. I just tell them, okay, open the Csp website and download such and such data sets download these scripts and try to to learn from these 2.
Giovanni Di Liberto: We we have these new sections that we didn't have last year on lectures and tutorials from the from the various editions of the workshop. So you you find that 2023 here as well, and it is not just a repetition every year. Right? So, for example, last year we covered Cca. We won't cover it this year. So we don't need to repeat things that we have seen already, so that we leave space for new things.
Giovanni Di Liberto: So check out those things.
Giovanni Di Liberto: so what's new? So the first thing that is new this year and is the C and D format. So the continuous event neural data format.
Giovanni Di Liberto: It's a data structure that that we proposed for storing neural data.
Giovanni Di Liberto: and that is specific to continuous sensory tasks like listening to speech.
Giovanni Di Liberto: So it was, as I said, was a necessity. Because if we want to share standardized code, we had to find a way to agree on on some standardized data format.
Giovanni Di Liberto: And the idea is that if you save your data in this way, then you can run these scripts that we're sharing directly. and then it also enables the design of graphical user interfaces and that sort of stuff.
Giovanni Di Liberto: So C and D, this data format
Giovanni Di Liberto: somewhat after their raw data. So it's not your raw data. It's minimally pre-processed in a way that your stimulus features and your Eeg are aligned, and they are with the same sampling rate.
Giovanni Di Liberto: And so you can run Trfs and Cca. Or intercept the correlation more rapidly. And so you can. You have access in this case to see a space script.
Giovanni Di Liberto: But also we connected
Giovanni Di Liberto: this year. We connected this format with the work from, and Christian brought back, and Jonathan Simon and and Nima's lab
Giovanni Di Liberto: on these toolboxes that they design. So you can load Cnsb, sorry, C. And D
Giovanni Di Liberto: data files from their toolboxes now, and you can easily transition between bits and C and D. They are. They are connected, and we included all the functions for doing so.
Giovanni Di Liberto: But in reality this is not something complicated. It's it's just basically a guy guidance in terms of what is the folder structure you should use? Where should you put the code? Where should you put the tutorials? Sorry, the the code, for example.
Giovanni Di Liberto: where should you put the data sets, how should you call the file name? So if you follow these instructions, then basically it's it's just much easier to run the code we share
Giovanni Di Liberto: and and we share this on our github. See if you just go on. Our Github and the links are all on the on the website. I'll show them in a second. You have this folder structure with the example scripts. We have more tutorials throughout here.
Giovanni Di Liberto: The libraries are not ours. Some of these are ours, but others they're just dependencies that we use. And and then the data sets are empty.
Giovanni Di Liberto: You just need to go to the website, download the data set and put it into this folder structure. And then you can run the scripts.
Giovanni Di Liberto: We are setting up a new documentation page. It's a work in progress. But you can find you can already find some instructions on again, how to save your data in in this format, and so on.
Giovanni Di Liberto: So on the website, you have this on the resource page. You have all of these links, the Github, the documentation. and these other things. I'll show you in a second.
Giovanni Di Liberto: The data browser. So we one thing that we we built in the last year is a graphical user interface.
Giovanni Di Liberto: So just an interface like this.
Giovanni Di Liberto: which and is built in Matlab in this case, and wait for the next slide, because that's much cooler.
Giovanni Di Liberto: So the idea.
Giovanni Di Liberto: apart from the ugly looking interface, which is model of style, and this gives you an idea of the parameters that you need to decide when you when you fit these models.
Giovanni Di Liberto: And it gives you an easy access to to do so. So basically, what you can do is
Giovanni Di Liberto: and I'll let's see if I can pick it up here. Yeah. split it on top.
Giovanni Di Liberto: So this is just the the the gui in my in my computer. And you can open here and you have access to whatever data set you have on your computer. And they're all in the same format so you can switch and
Giovanni Di Liberto: it loads. And then it's going to give you the stimulus feature for that particular data set. And you can say, Oh, I want to fit the model on this number of participants on this amount of data per participant forward backward models.
Giovanni Di Liberto: And just for sake of time, I don't have time to run it now. But basically
Giovanni Di Liberto: you can run the usual trfs, get prediction correlations get trf weights.
Giovanni Di Liberto: But you can also compare different data sets. You can take your speech data, set your music datasets. You can plot them on top of each other. There is still work to do, but it's it's a starting point to to make things a bit a bit faster, a bit easier when we want to look at data that we have already.
Giovanni Di Liberto: you know, available without having to talk with the person that that actually share that data every time.
Giovanni Di Liberto: now, this is a proof of concept. We, it's a work in progress.
Giovanni Di Liberto: It is a web-based data browser.
Giovanni Di Liberto: So while the other one is a standalone application built in Matlab, you can just. You don't need to have Matlab. You just install it, but you still need to install it.
Giovanni Di Liberto: This thing is that is in your browser. So you just go under your browser. You type this link which I'll share on the chat in a second. and you can fit
Giovanni Di Liberto: forward your apps back back backward er apps, and so on, so encoding decoding models.
Giovanni Di Liberto: Why do I think this is useful. And again, lots of this stuff is done selfishly because because we use it.
Giovanni Di Liberto: And so it happened to me, for example, to talk with scientists that wanted to run a new experiment. And they were asking me.
Giovanni Di Liberto: Can I run this speech listening experiment, or this music listening experiment
Giovanni Di Liberto: with 5 min of data, because my particular population can't deal with experiments that are longer than 5 min.
Giovanni Di Liberto: And usually my answer is, I don't know. I need to open my Matlab and run some rerun, reanalyze some of my code.
Giovanni Di Liberto: So with interface, basically, you can go to some load some existing data and select how much data you want to.
Giovanni Di Liberto: You want to use and see basically for yourself, if that is a suitable amount of data for your experiment.
Giovanni Di Liberto: and much more.
Giovanni Di Liberto: So again. For the sake of time, I'll I'll
Giovanni Di Liberto: I'll cut it short. We describe lots of these new components in a paper that we just submitted to archive.
Giovanni Di Liberto: So the C and D data structure, the analysis pipeline resources. And we also include the simulation toolkit
Giovanni Di Liberto: where you can simulate Eeg data in different ways.
Giovanni Di Liberto: And I encourage using that because simulating
Giovanni Di Liberto: data can be really, really useful. especially when your hypothesis are not that clear? It's not clear what you would get from a Trf analysis.
Giovanni Di Liberto: So maybe before actually running an experiment, it can be quick and useful to to use these these functions.
Giovanni Di Liberto: Okay.
Giovanni Di Liberto: so just to conclude the call for project proposal so ambrain train, and which is one of the most interesting producers of
Giovanni Di Liberto: portable, eg. Systems. They have been supporting us with the scholarship last year, and and also this year it's €1,500. So what we encourage you to do is to propose a project and to write this proposal in 500 words.
Giovanni Di Liberto: you can decide to to say, Okay, I want to visit Giovanni's lab in in Dublin, or I want to visit Aaron's lab in Rochester, or Segosia, or Nottingham, Trent, or a Condom superior. You can visit any of these institutions in the list.
Giovanni Di Liberto: and that are related to the organizers, or you can propose some other visit to someone else as long as it makes sense with Cnsp. So as long as it has something to do with. You know the resources that we share, or something related to that.
Giovanni Di Liberto: or, anyway, something that is has an open science mindset.
Giovanni Di Liberto: So whatever data you deal with, we would encourage you to share it.
Giovanni Di Liberto: the call for proposal is on the website. You can find it here on the workshops. Page.
Giovanni Di Liberto: Okay, that's all from me. So yeah, right on time. I'm a bit late. Sorry.
Giovanni Di Liberto: So I will. I would like to. I'm happy to introduce now the recipient of the travel grant from last year Jordo Piazza.
Giovanni Di Liberto: who's a Ph. D. Student in the Infant Language and cognition group in the Cbl, the Basque center on cognition, brain and language.
Giovanni Di Liberto: So, Giorgio, he he was awarded the project, The The Travel Grant last year with a project titled
Giovanni Di Liberto: Does non-native directive speech enhance non-native listeners, cortical tracking. So, Georgia, we're not talking just about that. George will be talking about his Ph. D. Project and also a little bit about what he did here.
Giovanni Di Liberto: One cool thing that he did, and was to complement the scholarship that he won through the workshop with some other funding
Giovanni Di Liberto: erez agmoni, and so he managed to spend 3 full months in Dublin, and so he managed to collect data and also to you know, he's close to publication with that 100.
Giovanni Di Liberto: And so that's the kind of of idea that that we like to promote. But even if you wanted to come for a week. That's still okay.
Giovanni Di Liberto: George, are you online?
Giovanni Di Liberto: Is Georgia online?
Giorgio Piazza: Okay, yeah. Here I could. I couldn't unmute myself.
Giovanni Di Liberto: That's fine. Welcome to the workshop again, Georgia, and the floor is yours.
Giorgio Piazza: Thank you.
Giorgio Piazza: Okay, I think, yeah. I'm trying to share the screen
Giorgio Piazza: right here we are.
Giorgio Piazza: Alright. So well, thank you. First of all. Joandi, everybody from be here. I guess you can hear me. Well, if not, please let me know. Now. So yeah. Gonna talk about well, mostly, actually, the project that Carrie versus stay at Joanna's lab. That? Well, the the title I gave slight difference. Non native speech supports to learn ascarting speech. But it's basically the same thing.
Giorgio Piazza: So it's also part of the bigger project of my Ph. D. Station. It's all about non native speech, which is a speech register and
Giorgio Piazza: sorry. Say, okay, that works. It also has other names for some foreign directive speech, usually non native director. Speech is the preferred name, because it's more neutral term. But for this presentation gonna use the acronym off from the old name, let's say so. Fds, just because it's easier because it will be compared to native speech, which is what the acronym is. And yes, of course.
Giorgio Piazza: so just to make clear. It's not foreigner. Santa speech would be the guy here on the right, speaking in in English, for instance, it's about the way native speakers like the English girl on the left. It could be any language, of course. That's the way how she speaks. She addresses a a melodular. So someone's learning a English. In that case a second language.
Giorgio Piazza: fds, as compared to native speech, increases speech clarity, and this is reflected in some acoustic features.
Giorgio Piazza: for instance, low speech rate. So, for instance, there would be a low number of syllables per minute, or words per minute, and also an expanding vocalic space is known as vowel hyper articulation. Just to give you like a graphical representation. There's not fundamental to understand this presentation.
Giorgio Piazza: but this is how we calculate vowel particular, they'll be taking the first 2 formats of the vowel production, plot them on a Cartesian plane, and then we calculate the the area of the triangle. So the Red Triangle represents how the girl on the left will address the guy on the right, so hyper articulating power, so producing the vowels far away from each other.
Giorgio Piazza: and the blue triangle will represent the way the powers of native records piece. So how you speak to another native listener. And this is interpreted as being promoter of language learning and also processing because it supports detection of vocalic options because they're easier to detect. So they're not so overlapping.
Giorgio Piazza: Apart from that, apart from assumptions, we also found in our latest study that's now under review that and L to learners when they're exposed to Fds. They actually learn better and faster at no worse as compared to participant, were exposed to Nda nd. Yeah, so
Giorgio Piazza: and so it seems to be a a a tool for teaching a second language, and but we use single representation, a short presentation, shorter sentences. Sorry. So it is still unclear. Whether the aspect, most of perceptual continue speech. It would be like more naturalistic environment.
Giorgio Piazza: so, and also studies for mostly mosaics on fds or behavioral. And that do not allow us to investigate the process of altru processing while it is going on. But when it's already completed, and of course you know that, eg. Allows that
Giorgio Piazza: And so, anyway, so far, this just a single study on foreign directive speech with eg. It was a single presentation, single representation. That's nothing on continuous speech and ecological value stimuli. And so one way, of course, to investigate whether Fds supports through processing is by using eg. And measuring measuring cortica tracking of continued speech.
Giorgio Piazza: Well, there's no need to say this, but we'll say, anyway, so particle tracking as a synchronization of cortical cortical ocular activity to fluctuation of surgical stimuli. Of course I read it. And of course, one feature you can study is speech handle like the most typical feature.
Giorgio Piazza: so there's also some evidence showing that, according to tracking speech, especially in low frequency bands, usually the theater more or less is a Markov underlying process. And
Giorgio Piazza: and that's also some evidence showing that it's fundamental for adult speech processing. In some cases a quarter track is being linked to speech, clarity, flexibility, and so on so forth, and also, as has been studied in infant language development. So really, in relation to infant speech, there is another speech register in this case directed to infants, and which shares a lot of features with a foreign, direct speech. So
Giorgio Piazza: it is possible to explore, to investigate or tracking, using temp response function. Of course you are here because you know it is, but it's regularized linear regression that maps it to the the continuous stimulus to an error response.
Giorgio Piazza: And I think Joanne is familiar with this figure here. You know that. And and then it's possible to using Trf to predict response and correlates it to actual eg, activity. So this is a one possible outcome, one possible, dependent, variable. And using this presentation.
Giorgio Piazza: okay. Another interesting feature of Drf is possible to invest feature, not just like a speech handle. For instance. Other features are non acoustic to as well, for instance, like Samanthi say, and of course you already know why, what direction going. So we aim to measure what you're tracking in foreign to speech. And then yes, so native speech.
Giorgio Piazza: But in literature it has been shown that tracking is also affected by speech rate. And so we for this reason we had a third condition that we call slow, and yes.
Giorgio Piazza: which had the same features of nds. So actually, we we use nds, and then we slow it down to match the speech rate of Fds, and we did it by means of well, sorry. I will say there's a next slide.
Giorgio Piazza: Then then I'm working sorry so, and slow, and yes, so slow and yes, doesn't have any feature that is supposed, that is expected to support and to learn as perception.
Giorgio Piazza: and also well, it has never been addressed the impact of Fds supported tracking a second language speech. And of course it's it's important and supportive to shed light on this point, because it will tell us more about how a second language is process also acquired?
Giorgio Piazza: Because of the features of Foreign Directive speech they told you about before, and there are adapted to the to learner, and Fds is assuming to promote, as I said, to processing, but not yes, and this should be reflected, or more efficient. And this should be true just for help to learn this.
Giorgio Piazza: So in the starting, the birth study, we aim to answer the question here. So the first question does Fds promote L to learn as cortical tracking speech? And if you have time we'll just give you a hint about the next analysis, we're running to answer the second question.
Giorgio Piazza: So we're into experiments.
Giorgio Piazza: So data were collected in 2 different experiments. Why? 2 experiments? Because they were 2 different labs. One is Spain and one Ireland, and also with 2 different systems. One is by assigning that one. It's great fiction.
Giorgio Piazza: So in the first experiment, and we collected data from 25 at to parents of English. Who's us who says one l. One is Spanish.
Giorgio Piazza: and then experiment to so far 1890 s. Native English listeners, we are still still in the process of collected data.
Giorgio Piazza: and all the participants listen to continuous speech and form a storytelling in the free story in the free conditions.
Giorgio Piazza: and each story was about 50 min each, and while the stories were both were recorded with a British English accent, just to make a make it easier for English for Spanish participants.
Giorgio Piazza: So while we're listening to the stories, of course, we record their participants, eg. Activity. And we also asked them to answer some questions about the content story and total with the after each break. They asked the 5 questions for all 25 questions per story, and also we where we went forward. Lowest events, 1, 2, 8 hurts, and for the with the features of speech and look so far.
Giorgio Piazza: and here we are the results, and for the comprehension results, and for the compressor score. As expected, we found main effect, speech, register, and participants respond with higher accuracy.
Giorgio Piazza: It was stories when the story was pronounced and foreign right to speech as compared to the other 2 conditions here alighted in bold, and the main comparison of interest which is foreign right to speech versus and yes, native work to speech, and this is in line what we were expecting.
Giorgio Piazza: and so they seem to be more accurate, so to understand better, say, the stories and Fds. Of course we know there are other funds, so we cannot conclude much so because there's tension engagement. But this is still the first results interesting. So for this reason we also had more, of course, checking measurement. We use predicate correlations, obviously, for a Trf model.
Giorgio Piazza: And we found that a participant showed high prediction correlation when they listen to the stories in foreign record speech as compared to native record speech as compared to slow and yes, and again, here the the interesting comparison between Fds and Nbs. And so it seems that they were encoding better speech handle aware listening to Fds.
Giorgio Piazza: okay. So then the results of the English Listener group, and, as expected. We didn't find any difference across the preconditions for the comprehensive score, because they are native. So they don't need any any acoustically feature development, understand? And process and stories, and we also what would like was to stress out that there's no sending effect. We made sure that they didn't got all the same wrong answers, some variability which is
Giorgio Piazza: important as well, and for the prediction correlations we had something that was kind of spec unexpected, and because we got a higher predicate correlation. And yes, as compared to Fds and slowing this.
Giorgio Piazza: So we're expecting that because of the slow speed trade, or because of the acoustic feature somehow. We would serve higher British correlations in Sds and slow nds. But instead, it seems that it's more important. The fact that nds was suited. So it was like meant to to address native listeners as it was the same for Fds. So just to then. Now I will just sum up all the results. So it'd be clear so.
Giorgio Piazza: and we asked ourselves whether Fds supports processing of continuous piece, especially for add to learners. We found that it was the case, at least for the behavioral results, and with the limitations that before and we saw we found the same. Well congruent consistent results for the
Giorgio Piazza: and in fact, we found that Spanish listener had high British correlation fps, whereas English listener and higher predation correlation in Mbs, so it seems to be the the the case that it's not just Fps having
Giorgio Piazza: some sort of some special acoustic features that make the you know, make speech to be encoded better is the fact that they are the meant, the intended addresses of the those speech registers, and also, it seems to suggest the results seem to suggest that this is not an effect of speech rate.
Giorgio Piazza: And because why? We find the same effect in both groups, so slow speech rate would drive the fact. And this was not the case, and also we would find the same effect for flow. And yes, since it was a slower as low as yes.
Giorgio Piazza: and one might ask whether slow nds was like weird or or natural. We don't think so also because we would expect them an effect here. So we don't find that we didn't find any difference between Fds and slow nds in the English. This one group, so probably most probably was just a matter of fact that they're not exposed both groups, both
Giorgio Piazza: experiments participant, not usually exposed to slow because they are they, they they are either exposed to Fds if they are no native, and to ndf and nds, if they are usually, you know, interact with other native speakers.
Giorgio Piazza: And so these are results for the presentation today, and just a quick end on the ongoing analysis.
Giorgio Piazza: So we're trying to answer that question more than about comprehension will be, how Fps support semantic and pony, let's say. And so you use Gpg to just values. And of course we align them to the onset of the worst, and so we use it again. Turf model so for a model to predict the response.
Giorgio Piazza: but so I have no time to show, so I'll see you. So I have some time for some questions, if you have any. Thank you very much
Giorgio Piazza: hope it wasn't too fast.
CNSP: Great talk, Georgia. Again. Feel free to raise your hand. We'll
CNSP: unmute you, or allow you to unmute if
CNSP: you have a question, or you can put a question in the chat.
CNSP: We have one for Georgia.
CNSP: I did have a question. Actually, Ed has a question probably be way better than mine.
EDMUND LALOR: Hi, Georgia, thanks for the nice talk. Hope you can hear me. Okay, I'm in a different room now. I just had a quick question about how these results might fit with.
EDMUND LALOR: I know Giovanni has published paper before looking at using a lot of these eg. Measures to determine someone's and
EDMUND LALOR: abilities in. In English, for example, as they go from a novice to an expert or a native speaker, is there is there a relationship there with what you're finding in terms of this foreign? I know it's a different question. But I'm just wondering if it's if there's still a
Giorgio Piazza: we. We are thinking about using the coding again to so to do the call the comprehension score. They go. So in that case I think it will be something more similar than from here. Yeah. So in in that, II think you're referring to the Joan's paper. Grant 21 to speakers. In that case, of course, there's no variation between speech registered. So it's just English as it is.
Giorgio Piazza: and most probably I think they use like native direct speech, because, like stories. So this allowing, of course, and by that case will be well, we didn't have any variability between. Well, we have some variability, but not that much between the Spanish listener groups. So they are all made low, proficient in each.
Giorgio Piazza: I guess. Well, I was back to find senior results. But it would be interesting to see the the calling if we sign, find something different, for like nds and and fds. In that case. So it's something that we II will work on
Giorgio Piazza: next future. Thank you.
CNSP: Any other questions.
Giovanni Di Liberto: There is a raise hand. I don't.
CNSP: Oh, accidentally muted. You, Marley.
Marlies Gillis: I am. Yes, I'm I'm in muted now, I guess. Can you hear me?
Giorgio Piazza: Yep, yep.
Marlies Gillis: thanks for a talk. It was really cool, cool project?
Marlies Gillis: Maybe I missed it. But How is the acoustics of the speakers? Are they, those 2? Are they from the same speaker, other or or not.
Giorgio Piazza: Yeah. Yeah. Sorry. I didn't say just from our time, mostly. But yeah, it was the the same speaker that recorded the same story twice to the same British single Speaker, female. And then we transform from nds, we just coming to slow nds. But yeah, we compiled between Fds and Ds and ways. The same speaker. Yes.
Marlies Gillis: okay, cool. Thanks.
CNSP: Okay. So if there's no other questions leave it up there. Thanks again, Giorgio. It's a great job that you did a lot of work, and
CNSP: short amount of time actually, not not quite so short. But you did a good job in
CNSP: your project, so
CNSP: make sure, setting a good example for whoever follows next year.
CNSP: So we'll
CNSP: ended it there for this session. That's the last
CNSP: thing we have right now on, I actually can't
CNSP: like do anything on my computer. I was gonna show the the schedule. But my computer is acting funny. So sorry about that. So we'll end it there. We'll meet back in about 13 min, maybe a little bit later. I'm gonna have to run, maybe get a little bit of food
CNSP: and probably restart my computer because it's acting funny but we'll start in the tutorial session for the tutorial people.
CNSP: And for the rest of you we'll see you tomorrow. Thanks for the a great first session tomorrow. We have Micah Halbron. He'll be giving us a talk about prediction and the brain using large language models. And
CNSP: then we'll have 2 talks from our tutorial speakers for you tomorrow, so we'll either see you in about 13 min or tomorrow.
Giovanni Di Liberto: Thanks.
CNSP: We'll leave this zoom open, for if anyone has any questions about setting up the tutorial
CNSP: fellas. I'm going to restart my computer if you can stay on the call for at least until now, or until
Giovanni Di Liberto: are are we using the same call for tutorials.