So Michael is a brand new professor.
Assistant professor, let me get on to my.
Slide show there. It's brand new assistant professor of cognitive AI at the University of Amsterdam and is interested in how brains and artificial systems understand language and make sense in the world.
You're strained at the Donders Institute and the Max Planck Institute for Psycho Linguistics under the supervision of 2 highly regarded scientists forced along.
And Peter Haggart and wrote a provocatively titled thesis. At least I think maybe not so much provocative but Interesting.
She's here. I like the idea of the bi-directional window. Into things.
So I really wanted to put that up on the screen. Michael also published one of my lab's favorite Early Stads Labs favorite papers last year.
Not just because they really analyze some of our labs data. Which happens to be available on the CNSD website.
But in this paper you analyze word level predictions from a large language model. And showed, multiple hierarchical predictions.
So maybe getting at some of those questions that we're asking. Then there. That were dissociated in neural activity.
Which we just thought was really exciting that he could pull that out of our data and some other data.
So I'm really excited. To introduce our early career keynote. To be given by Mike. So join us.
We're welcoming him.
Alright, so. If everything should be. Working you should be able to see my slides now. Is that correct?
See you slides in here.
Yes.
Perfect. Thank you, so much. But thanks for the very kind introduction and for the invitation, of course.
It's an honor and it's a lot of fun to be here. It's also really nice and cool speaking just after the, because our work is very similar in some ways, at least this is a sum of what I will be presenting.
But I'll be coming from a pretty different theoretical background, but the methods are in many aspects quite similar.
So I guess that's also very nice and pedagogical for all the attendees. They can see very similar ways of working.
And drawing quite different conclusions or learning different aspects of the same types of analyses. Right.
So today what I want to talk about is a sort of more high level framework for studying the brain and how we can use both the types of naturalistic data analyses that we've been that we are discussing in these 2 days and modern AI developments.
And I can combine them. So now I hope that my clicker will work. Alright, cool. So the theoretical backdrop here is the idea of the predictive brain.
And the predictive brain is this sort of very high level term. For a family of models and theories really.
That all revolve around the idea that the brand neural processing in the brain relies on continuous prediction.
And mechanistically, this is formalized in most detail in vision. In particular around. Mechanistic ideas like predictive coding.
And here it's typically formalized as bidirectional processing cascade. Where the feed-forward component works something akin to a con of that in the sense that it extracts increasingly abstract features and this is then complement by top-down component, which conveys predictions from a generative model.
And so these predictions are constantly compared to what is coming in. And by making this comparison the brain computes a prediction error which is used both for inference at short time skills to try to make sense what is out there and longer timescales to learn to perform self-supervised learning.
And importantly, this is postulated to be economical cortical computation, which is I guess a fancy way of saying that it's something really quite general.
And something fundamental and something that is found all over the brand. So this should be a fundamental principle that applies not just to vision but also the hearing and the language and what have you.
Now, there is quite some evidence. That is in line with this idea. And that has made the framework pretty influential over the past years.
It's a way this is in the domain of visual perception. Very influential set of paradigms.
It's for instance statistical learning. So participants would then be exposed over and over to one stimulus predicting another stimulus such as here a jukebox predicting a clock.
And what is observed is that as participants are exposed to these regularities, a jukebox clock, hookbox clock, 2 box clock over and over.
As once stimulus becomes predictable, say the clock. You see that the brain responds differently. Much more weekly.
Than if it's unpredictable. And you see this in many domains. So for instance in language, in Malaysia, if you' to the N 400.
So when a sentence and expectedly, so I drink my coffee with cream and sugar. And the brain responds very differently and much more weekly than if the sentence ends unexpectedly.
Like I drink my coffee with cream and dog. Right, it's a time and again in all these domains, but all these types of evidence seem to point to is this predictive brain idea but the brain is seemingly constantly comparing what is going in.
To its own expectations.
Now, oh, this foundational work using, let's say, classical Gog Neural Methods.
Is of course very important and it has taught us a lot and it's the reason that I got interested in the predictive brain in the first place.
But there are some limitations of this. Sort of classical approach. And the first is generalization. So, but we now know very well that the brain indeed starts predicting in these specific paradigms, like 2 box clock to a box clock.
But it's not so clear is whether the findings in those twobox clock scenarios would also generalize the perception more broadly.
And this is actually very important for the predictive brain because for the predictive brain, the idea is not that the brain can predict.
But that it always does.
Right, so does it also apply to a more naturalistic scenario like when you're listening to music or you're watching movies or perhaps you just see complex images.
The second question is what is meant by a prediction? Because this predictive brain theory is a very general theory.
So according to the predictive brain, a prediction can be anything. So if you're predicting a clock for instance, you might predict some high-level idea or some visual prototype or perhaps even the very specific low level features of that exact clock.
No, of course, nearly these difference options, these different scenarios would imply a very different mechanism.
But it's often not so clear in these sort of classical methods what types of predictions are manipulated and that for what is going on.
No, there are 2 reasons for the developments in neuroscience and AI machine learning. That allow addressing both these issues.
And the first is the rise of generative AI. So for a long time, this notion of a generative model that predicted brain theory is talked about was quite elusive and people could write down the equations and it was formulated in a Bayesian brain framework, but no one could actually implement the types of predictions that we thought the brand would be making.
And this is not changing since generative models are actually working. So what for instance in language one sort of very striking example is in GPT 4.
And other sort of more sophisticated generative models. So these are models that can predict stimuli and and then they can generate text for instance.
It's the same is true for videos so they predict video frames and they could generate a video.
Now the second development is sort of the rise of neural data science and open science more broadly. So we now have these large data sets of very high quality, naturalistic images of naturalistic stimuli that is both sort of in the mouse or rodents like invasive electrophysiology and also the types of data set that we've been talking about today.
So what I've been doing is sort of combining these 2 trends. And then I call it the generative AI framework.
For studying the predictive. So how does that work? So instead of, so the setup in general is quite simple.
You start with stimulus, often a naturalistic stimulus and not a dupex block stimulus, but for instance in language could be an audio book or natural music.
Or simply, natural images. And these are simply shown to the brain and the brain activity is recorded.
At the same time, the same naturalistic image is analysed with a neural network and then specifically a generative model.
So a model that can predict for instance an expert or it can predict the next note in music.
And then these predictions can be compared to the actual stimuli to compute the predictability. And then we, inform the regression to test if indeed the brain response is different.
When this the stimulus is protectable for this unpredictable. And this regression can be depending on the the situation.
So if it's a time continuous naturalistic stimulus you would need decomposition approaches like implemented in the MTRF toolbox.
It can also be a NON deconvolution if there's no temper overlap. But the important aspect is that it's a multiple regression approach.
So we can include all the baseline variables like Marty's already talked about such as acoustics or in vision can be local contrast.
And on top of that, we add the expectedness estimates to test if the brand responds differently. To unexpectedness.
For low level conference. Not this has 2 sort of main advantages and the first is generalization, right?
So we can move beyond the due box clock scenario and test if even when you're simply watching a movie if the brand responds differently when stimulus.
It's, it's predictable. And the second, perhaps more interesting aspect, is that because we model the prediction instead of manipulating them, You can make the concept of prediction as precise as you want.
So you can friends this, compute the. Overall predictability of a word, but you can also separately model the predictability at different levels of abstraction, like the predictability of the meaning.
Or the predictability of the exact sound. And so on. So here you're only limited by your own modeling skills and imagination and you can make this concept of production arbitrarily precise.
So you can have as I like to go. As I like to go, you can have an unlimited conceptual resolution.
Sorry, for using really powerful and beautiful, open data sets, we have very nice temporal resolution, very nice spatial resolution and Only limited by your own modeling you can have very strong conceptual resolution.
Alright, so I, so that's still very high level. Now the first sort of study where I was approaching this framework and I worked out in the deal.
Was a study from a PhD, which was on language. And here we analyze. People listening to audio books and one of the datasets was actually The data set recorded by, I think, to Jeff funny and it was, yeah, graciously shared by the Lor Lab.
On another day at the set to say EEG, the MEG data set that we regarded ourselves at the D.
And to model the predictions. We use GPT 2 which is this large generative model which can predict upcoming word.
Not from GP to do what you get on the high level of predictability of work. So for instance, this is one of the sentence for one of the audio books.
It made the boy sad too. And then we can get from the model is a probability distribution over every potential next word.
And what we, so what we came up with in this, in this paper was a method to then disentangle this overall prediction into different levels of prediction.
So for instance, how does that work? You can see here that it made the by start to think the seat and note the hair to say to be to analyze all these predictions.
You see, even though GPT 2 is not exactly sure about the identity of the next word, All these are verbs.
So it's pretty damn sure that the next word will be a verb. You can do the same for semantics and for speed sound.
So you can compute not just how predictable or is overall. But also at what level, syntactically, lexically or phonologically.
So the striking thing of this paper was that we found that the brain responds not just to where the unexpectedness but it responds differently to each type of unexpectedness.
So here you can see the TRF. And you see that for instance, the syntactic predictability.
Leads to a very early Frontal effect whereas semantic predictability. Results in much later and especially much more distributed effect.
Whereas, phenomenological, phoning predictability, it's this early effect. And here I'm showing the TRF, but we also did.
A analysis like Malise described where you look at if the model if each of these variables explains unique cross-falated variants over and above the the baseline variables.
And so we found Independent, dissociable, and sort of unique contributions of predictions that eat like each level of abstraction.
So given that it seems that when you simply listen to speech, the brain predicts that multiple levels of instruction.
The next question we asked was How do different levels interact? And we already touched on this a bit during Q&A with Malise.
One way to look at that. Is to look at different ways of doing phone in prediction. So normally how this works, phoning predictions are sort of formalized by the idea of incremental word recognition.
So as phonemes come in, The cohort of potential lexical candidates that are consistent with the funding so far shrinks.
Until the word is incrementally recognized. You can cost this as a process of probabilistic phony in prediction.
By grouping each of the candidates next words. By their identifying next phony. So of fact, family fat will be at false for fortunate would be all figure if Finish fishermen would be, etc.
And if you then attach a weight to each of these candidates. You can do the math. And derive a probability distribution over phone names for each farm.
Now there are 2 ways of assigning the weights. And one way is to use the simple, the lexical frequency or the uni.
So here you would take the probability of an overall word overall in English. So for instance, the word 4 is very frequent, so we would have very high a priority likelihood.
And the overall, probability for the next phoneme is they're not highly biased by these high frequency words.
Now alternative possibility is that it's the way it's not determined by the overall word, word probability or at frequency, but by the contextual probability that we take from GPT 2.
And here the distribution would be quite different because if we look at this context, they say his father was it.
The word for even though it's very high frequency. It's not very likely because it wouldn't be even chromatical.
They say his father wasn't a 4. But as fisherman, even though overall it's a very unlikely word.
In this context, it's very likely because this is from the old man in the sea. I don't know if you've read it, but it's not about fishing and fishermen.
So GPT 2 understood that and knew that it was indeed very likely. So the overall probability distribution looks very different.
Importantly, while the this frequency model is a single level model in the sense that It uses short sequences of context, so short within words, it uses a phone, as a context plus fixed prior, namely the overall probability of each word in the English language.
The contextual model is hierarchical. In the sense that you use not just shorts intra word frequencies of prior phonemes But the prediction is also based on the longer sequences of prior words.
So it's hierarchical in this way. The concept of I brought graphical models in Bayesian inference.
For instance, you use 2 levels of context. And so what we did is we compared the, sort of cross validated prediction performance of both models and compared it to the baseline model.
I hope we found both in the EEG and in the imaging participants. That the models that include word phonium unexpectedness were better than the baseline but the models that include hierarchical unexpectedness work better still.
And this is indeed very much in line with the sort of concurrent paper from Kirsten brought back.
Where they actually found they asked a different question and they found that Sing the single level and the hierarchical model.
Seems to work in parallel. So we did a very. Rough is one better than the other analysis.
And it seems that the brand indeed performs a Rococo computation but they may it may also perform a single level of computation.
So the story may be more subtle there. But overall what we found in language is that when people listen to natural language, the brain constantly predicts, it protects multiple levels of abstraction and we find this very suggestive evidence that at least in the domain of phonemes, higher level context can inform lower-level context, which is exactly what you would expect from this predicted brain idea.
And with a master student, we performed a similar study in music. So the setup was quite similar.
We again did one MVG study at the Dunders and graciously, very thankfully used the data set it provided by Giovanni just EG.
And the setup was again similar so people listened to MIDI which is converted to audio. And then at the same time, we have a transformer very similar to GPD 2.
It analyzes the media and then we ask if the brand responses to the music or notes or not words but hear musical modes are different as a function of the unexpectedness of the music or not.
And again, we found as well as in the MEG, ED as in the ME, that we find these.
Characteristic TRFs and we find that the brain response is indeed modulated by the unexpectedness of time.
Also similar to Matiovanni and others found. The twist of this study was not the TRF itself but was what type of information is used in the prediction.
So here we use not just one neural network, but we use it actually many neural networks, which each use a different amount of context.
And what we found first is that the neural network that is the most context. Could predict the music best.
And this is not surprising, but it was an important sanity check. So the more information we gave to the transformer, the better it became at predicting the next night.
But when we did the same analysis for the the delta. It's for the for the not the ability to predict the music, but the ability to predict the brain.
Using the unexpectedness of the music, we find a very different curve. So here we found that the models that best predict the musical surprising response was actually not the model that used the most context but that used one of the fewest contexts.
So context window maybe only 5 to 10 words. So this is a very short Just a few seconds, just one or 2 musical motives.
So contrary to language, what we found is that the musical surprise response actually seems to be reflect very low level in the sense of short-range acoustic predictions.
And yeah, this study was by the way very much. Spearheaded by peers who is now starting a PhD and I wanted to mention him specifically.
Alright, so in the last study I wanted to highlight. I wanna take this idea to the visual domain.
And here it's just the visual perception of naturalistic images. And here's the situation is quite different.
Because when you simply see a natural scene, a complex image like this, there's no temporal prediction.
I have a one form of prediction that might be there and that is very important in sort of classical predictive coding theories is spatial prediction or pattern completion.
No, pattern completion is pretty easy to think about in very extreme cases like a checkerboard where if you perceive only a small part of the stimulus you can perfectly pat and complete and predict the best.
But how can you probe this in more naturalistic images? Now here we took an idea. Inspired by a paper from last year from Neuron, which is very nice paper.
And the key idea is that it relies on receptive fields. So just to make sure that everyone understands what I mean by that, the visual cortex is sensitive to only these small apertures, more parts of the visual world.
And if you have a data set where you know the receptive field for each voxel over each neuron, like in the Allen Institute visual coding.
Data set of the mouse visual cortex. What you can do is that you can take out, you can mask out the receptive field from the perceived image.
Take a generative model that is trained for filling in for in-panting, for reconstructing, to reconstruct and to predict the content of the receptive field.
And then compared to predicted receptive field to the actual receptive field to compute the visual predictability.
And then you regress that again on the brand response. We're taking into account another level of visual information like.
The acoustic, the visual contrast. And importantly, like before, you can compute not just the overall predictability, but you can compute this predictability at multiple levels of abstraction.
So if you take your actual receptive field batch and your predicted receptive field batch, what you can do is extract the hierarchy of features.
Here we did that with a convolutional neural network. You could theoretically also use something else.
And in CNN, the early layers extract simple patterns like edges and lines, whereas later layers extract things like textures and object parts, some more high level features.
If you do the comparison at each level, you can compute not just how unexpected or how unpredictable the visual information is overall, but also at what level of abstraction.
Like at the level of simple edges or textures or shapes. etc. And so just to make sure they understand what's going on, the data set here is the data of mice viewing the large full field images all for 250 ms at the same time.
And each image is repeated 50 times. In total, it's like 6,000 trials and you have thousands of neurons.
Alright, so if you look at V one, the average response look like this, it's the blue line.
We see that when a natural image comes online, the The fibering rate starts to peek.
And so the red line here is sort of analogous to the TRF, although we didn't use a TRF method, but just linear regression.
But it's the effect of the average response to a natural image plus one as D of the unpredictability.
And just for comparison, the gray line here is the effect of the average response plus that of local contrast in the preferred a spatial orientation and special frequency.
Sounds quite interesting as you can see that unpredictability modulates the response. But it modulates the response only later in line with the more recurrent process, whereas a low level contrast modulates the immediate feed-forward sweep and it has also stronger effect.
Actually do that for each convolutional layer, so for each level of abstraction, you see that Overall, into layer seems sort of the unexpectedness of each convolutional layer.
Modulates the brand response. But especially the highest convolutional layers. So if we now summarize over time and look at the effects, the level of abstraction.
In terms of their effect size. What we find is that indeed. And the later layers of Alex N have the largest effect.
If you're a vision scientist, you may not be a bit puzzled because this is in the visual early, this is V one.
So we see that the most abstract feature on predictability has the largest effect. We know from V one that V one itself is sensitive to low level features.
So indeed if you do the classical sort of decarlo encoding approach, where you so we're not thinking about unpredictability now, but just the sensitivity to the visual features.
We see, we see exactly the opposite curve. So V one neuron neural responses are best predicted by low level features.
But high level unpredictability. So you see exactly the opposite. Which is kind of interesting.
Oh yeah, and then sort of as a last. Tidbit all these work by the way is still very much in progress.
So it may still change a bit. As we do some additional and analyses. But one thing that I didn't wanna Ignored today is the laminar analysis.
So in this data set, the mice have these neural pixels probes in their visual cortex and they measure not just at one, at one cortical location, but they measure all throughout the cortex.
So what you can do is look up for every unit that you record where they are in cortex and then sort of.
Very roughly, you subdivided them into superficial or deep cortical layers. If you do that, you find that the effect is much stronger in the neurons that are classified as superficial.
Then does in deep. This is only for the future unpredictability. We don't find this for contrast.
So it seems that this unpredictability effect is not just tuned inversely to what you might expect from V one, but it's also strongest.
For the superficial layers compared to the deep, which is exactly in line with many of these. Theories about the predictive brain.
Alright, so if you come back to that question, so generalization. Do we find that indeed the brain always predicts?
So what did these studies so far show, including others, that in language, yes, in music, yes, and vision, yes, we find predictability effects and continuous prediction in all these.
If you then ask the second question. What is actually in this prediction? We see that in language we find this hierarchy of predictions.
But the brain is constantly predicting at multiple levels of abstraction and higher levels seem to be able to inform lower level predictions.
Where's in music we find strikingly low level in the sense of short range predictions. And I find it in vision, we find these higher level predictions that are then fed back to V one.
But I have to say that, yeah, this visual, this vision project is still work in progress. The exact conclusion is may still change.
Alright, so summing up. The rise of generative I, AI, enables stronger and more precise deaths of the predicted brain hypothesis.
And then so far, we've ubiquitous prediction in many domains and across levels of representation.
And to me at least this really showcases the power of AI to change the way we do cognitive neuroscience.
And yeah, the last point is just. It's my own personal view, but I think this is really the future for the study of the predicted brick.
So with that I just wanted to thank you of course for your attention and all my co authors and especially the people who provided all the open source software and data.
With the special mention here, Giovanni and the lower lap more generally. Because we like to give it would have been possible what I've been doing over the past years.
So with that, thank you very much.
Well, we'll talk. Really liked it. I'll anyone have any questions?
Should I stop sharing?
We have I have a question. Oh, you, you can keep it up in case you have slides that could possibly answer questions.
That's true. Yeah.
So we have one for one of the attendees. I'll, we'll let attendees ask questions first and then, you know, I have a question. Giovanni has a question.
So if there's no questions, we'll. We'll ask some questions, but there's an attendee who has a question.
Feel free to unmute if you'd like.
Yeah.
So, I have a question about the language model. So you talked about how, I mean, I know you used it as an example, but in the old man in the sea.
Example, it sort of used the context. I'm assuming I don't know so much about the GPT models, but I'm assuming it uses context that it finds online to sort of make the predictions.
Is that a correct assumption?
Yeah, definitely.
So would then understanding the human brain, like wouldn't that be a big jump because the human brain doesn't have all of the information that like the model would have.
So like context clues would be more in terms of like the meaning of the sentence. But then when you look at that, that include all of the literature, you know, ever written.
Yeah. Yeah.
Wouldn't that be like a not equal comparison?
Yeah, yeah. No, I'm very happy you ask this question. Because it's yeah, I first had a slide on sort of clarifications and common objections but I removed it for the sake of time but one of these things but it was actually really happy to ask because it's a Question it comes up a lot.
So in this approach where you use a generative model to quantify the statistics that you hope that you assume that the brand is tracking.
It doesn't require assuming that the exact statistics computed by GPD are exactly what is dragged by the brain.
And what I often, Do is make a comparison to something like, how we compute word frequency, for instance.
And so, for, frequency, we often have these algorithms that, that we count the word occurrences in a huge corporate.
And they are often much larger than what you read. In your entire lifetime. But so this is not meant to be an exact statistic of what you exactly have in mind.
But what people tend to find is that you exactly have in mind. But what people tend to find is that the better you objectively approximate this statistic, On average, the better it that you track.
The specific. Predictions in the brain. And that's also what you find in language. So, Marise mentioned before that she couldn't really find significant effects.
Differences between different models like an RNN and an NG, but in reading time in the reading time literature people have done this a lot and what people always find is that the better the language model becomes.
So going from an NG, via an RNN, files, simple transformers up to like GPT.
There's this monotonic, almost linear relationship between the quality of the language model and the quantity of the fit.
And that's probably not because the human brain is exactly making that prediction. Probably the human brain is doing something less sophisticated.
It has also read read less and learned less. But the more it matches the objective statistics. The more on average it will be closer to the idiosyncratic statistics on individual participants.
So that was a long winded explanation, but I hope it makes it clear.
Thank you.
Yeah, it makes more sense. Thank you.
Hey, go ahead.
Hi Mike, this is, Ed Laura here. I had a question on, If I just try to understand the you had a kind of an X shape for these and predictions versus low-level contrast that the V one was sensitive to.
No.
Is that right, So I'm maybe you could just talk us through that again because I was trying to understand how what V one might care about in terms of low level features.
Yeah.
No, you know, if I think about predictive coding theory, surely it should be getting predictions.
Yeah.
That are of predicted low level features. So My misunderstanding. I don't maybe you could talk me through this curve again or just
No, no, no, you didn't misunderstand it at all and it's also surprising and it's also what I expect that even though this particular neuron paper already pointed a bit in this direction.
It was also what I expected. So I was I was expecting to find at early, early visual cortex is sensitive to predictability of lower level features whereas later visual cortex would be sensitive to predictability, high level features.
And that would indeed be in line with. And very strict or sort of cartoonist view of predictive coding, but we find is the opposite.
So I think you understood it perfectly. We find this inverted. Tuning functions where in terms of the log level features v one is indeed sensitive to the lowest levels.
Of, here you quantify its file, the encoding forms of Alex. In terms of unpredictability, it's exactly the other way around.
And, yeah. I can. Speculate why that may be. But in terms of if you've understood it correctly, I think you understood it perfectly.
Okay, yeah, thanks.
No, yeah. If there's not another question, I can also speculate a bit why this may be the case.
One thing that I find myself especially interesting is that there are now results in in machine learning. Where people are impressed by sort of the performance of GPT for instance and they think okay if we can learn everything there is about language from predicting words can we also learn everything there is to learn about.
A vision just from predicting pixels. So they train these large models to predict pixels based on their occurrence.
And what they find is actually that models that become really good at predicting pixels are really not so useful for high level visual tasks.
And the only methods that work really well in terms of this self-supervised free training, our methods that explicitly in some way or another discourage the production of low level features and encourage the prediction of eye-level features.
So what you see from machine learning is that the thing that's really useful in self-supervised breed training is prediction of high-level features.
And then what we see in the brain is that the thing that really drives early visual cortex is the predictability of high level visual features.
So that's to me at least is striking parallel. And to me, what that may suggest is that this may, this may reflect a learning signal, which we know from machine learning to work really well.
But that's of course speculation. You could also come up with other explanations, but it's my Beth theory for why we may find this opposite tune.
That's very interesting. Thank you.
Jodie, do you want to ask a question or should I?
Yeah, have a have a quick one.
Go for it.
Yeah.
Music. Hierarchy. Have you tried to look into, you know, your second point in the conclusions was that, yeah, there is the hierarchy in speech.
But in music, you find this slow. Small, you know, short context to be the optimal, but have you tried to combine short long context?
No, no, I said the, you don't know. So yeah. The answer is no.
I also really wouldn't, know how you can do that actually. This is why I like working with language so much because we have this large inventory and we know that sort of words exist, at least if you believe words exist in the mind, we have syllables and phonemes and we can Really?
Dissect stimuli at these multiple levels and then explicitly look at how one level informs the other.
I wouldn't really know, so I know what a note is, but I wouldn't, I don't have the conceptual apparatus to know what a word would be in in music.
So we could, I wasn't smart enough. Let's do it like that, to perform the same types of analysis.
But what do you think? How can you test this in music? What is the rights level of.
Yeah, I mean, I'm just thinking, I mean, from a purely methodological point of view, you could have multiple features fit with different context lines.
That's the boring thing. The other thing is inspired from what I said to me years ago on classical music that the first time you listen to it, maybe you would just focus on the on on patterns that are short-term and then you start getting the you know different levels of of context and so maybe maybe modulating that on the participants.
Got in multiple repetitions of complex patterns and see whether that curve that you have there changes, you know, having the same identical method you have there, but with different conditions.
Or different. Yeah.
Okay.
Right. Yeah, no, that's true. It would require, yeah, and we wouldn't be able to use your download, your data and use such an open end today.
It is about indeed. Yeah, I agree with sort of very clever experimental design. I think you can go there.
And what did you mean with the first boring one? You meant like a decoding analysis. And then sort of comparing the decoding performance on a time result basis and relating that to expectedness or something or
Sorry, I don't this is not as quick as I thought. And, and it's like what you do in language, where you include multiple features.
Multiple surprise vectors that were fits using different context lengths. You know what I mean?
Right.
If they're independent of each other.
You could you could either simply use that model and have different context lengths. Okay. Sorry, and I mean, they could be both.
Right.
And it just, maybe that's not, maybe not with that model, I don't know, maybe the other models.
Yeah. I don't know. I do like this effect because initially, Piers came up with the idea and I was like, are you sure of?
Because I think the better the model is, the better it will fit the brain. Do you think we can have an interesting effect?
And then peace lies like, well, we don't know, blah, blah, blah.
And then we did it and we find it's really strike and dissociation and I never thought we would have.
So, yeah.
Good. Thanks.
Thanks, Lance.