I can't even imagine (no pun intended) what would be like having aphantasia.
It would be weird to me not being able to draw from memory a map of my own home.
Or not being able to answer a question like "if you enter your home, walk straight ahead until you reach a wall, and then turn 90° counterclockwise, what would you be looking at?".
Unless I'm wildly misunderstanding what "having a mental image" means.
Sorry for the delayed reply, but if you happen to come back... In my case, I can definitely do the two things you mention there (funnily enough most people with aphantasia actually have better than average spatial awareness), but there is no mental image of any of it - I can't see anything in my head, but have an entirely different "sense" of it all. The best description I've managed to come up with for my own attempts to mentally see something is that I can hold the essence of the object in my head. Ask me to imagine an apple and it's kinda like my brain has stored the underlying code that when run would render an apple, but the renderer is broken. I know what an apple is, I know what it looks like, I could describe one with great detail, but I can't picture one in my head.
Thanks for the response, that's more or less where I was leading with my message.
I don't think I have aphantasia myself, but what you say makes sense to me and describes my experience.
We might never know the answer, but for all we know, what you describe here might be what others call a "mental image", and maybe aphantasia is just a huge misunderstanding because the language we use to describe it makes it sound like it should be something more vivid that it really is.
Or maybe I also have aphantasia myself and I'm just finding out now.
There's a test you can do here [0] which is quite interesting in showing the variation in people's internal experience. I did it with my wife and daughter who are both very visual thinkers - I was astonished at their answers and how different their mind's eyes appear to be to my own, they were similarly astonished at mine.
I don't have much faith that the stated goal is realizable, I don't think fMRI data holds enough information for faithful reconstruction. What it will end up being, like all projects like it before, is a massive network that contains:
- An implicit multi-class classifier that returns 1 out of n previously seen images
- Gigantic model on top to hallucinate a reconstruction close to that previous image
In previous projects of this type the reconstruction was blurry because it was essentially the mean of the previously seen images close to the label predicted by the implicit classifier, in this project I imagine they will be photo-realistic since it's a stability.ai project but no closer to the content the subject is thinking about.
I don’t know. Now that you explain how it works, it’s starting to worry me. Imagine being hooked up to an ML model designed to classify whether you’re thinking about porn or not, and emits a beep whenever it detects it. The implications of that are astonishing, if it’s accurate.
It hinges on accuracy. If it can detect a topic, the image reconstruction is almost moot, because you can start interrogating people about what they know.
I wish I knew more about biology to know whether this is at all feasible, or if it’s just smoke and mirrors. (A distressing number of projects like this are to secure further funding for a lab, not really to advance science.)
Since you seem to know about this, do you have any details about the classification process? I assume it requires the cooperation of the user, but it’s easy to imagine that a “primal brainwave” pattern might be common to all humans.
lol yes, the social implications are horrible for this kind of work but I'd say more so for dissidents, journalists, and so on rather than porn.
Projects that are at the intersection of life sciences and machine learning are rarely interesting along both axes, it's always novel on one side and the other side is just there to make it seem cool.
For example you'll find a lot of ML scientists investigating medical issues but lacking the basic medical knowledge to actually make an impact, similarly you'll see doctors using neural networks where a linear regression would do. In both cases the paper seems novel to the people in the field it's being published in.
The classifier I was talking about is implicit, if you look at their proposed diagram they want to use fMRI embeddings, they serve as a continuous equivalent. You put a sufficiently large model on top of any embeddings and it will learn to reconstruct their modes.
The embeddings are trained across subjects so it's not specific to a single patient but they hope to fine-tune it per patient using latent space alignment.
If you look at the papers in their recommended reading list, it's apparent that only low-frequency signals are learned, for example for face reconstruction the model can learn hair color, sex and beard/no-beard, but the actual face doesn't look anything like that of the person being looked at. Similarly for other benchmarks, the model can tell it's looking at a plane, but not much beyond that. This is cool from a technological standpoint but I recall papers doing the same thing at least 7 years ago. The novelty of their approach will be the architecture that comes after the embeddings, which can be interesting but won't add any more information to the fMRI scans.
I’m struggling to find a reference, but I’ve already seen this used in the reverse during interrogations.
You use something like an EEG and show someone a series of images to establish a baseline of “familiar” and “unfamiliar.” Then you show them photographs of the crime scene that haven’t been made public to test for familiarity.
I question its accuracy but your scenario already seems possible.
Edit: for siblings comments about journalists - “identifying” a source could probably be “accomplished” using the above technique already, no need for AI. Show photos of random people mixed in with the photo of the suspected leak in your gov. department.
> classify whether you’re thinking about porn or not, and emits a beep whenever it detects it
Already gone down this road with lower tech. In the 50s - 70s it was in vogue to monitor blood flow to the genitals while subjecting the subject to erotic imagery of various kinds to see what turned them on. [1]
It was then realized that arousal is also correlated with pupil dilation, which meant that a much less invasive test was possible. Which of course led to the Canadian government (and probably other governments) using it in the 1960s to detect possible homosexuals in the public service. [2]
The scientific flaws and human rights violations of such a test are too many to even begin to list in a short post like this. Still, the inconvenient and disturbing thing is: it does work. Sort of. Kind of. Not good enough to be confident about the result with an individual, but a properly run protocol has better than chance odds at determining if a man is gay or straight, in terms of aligning with their self-reports about what arouses them.
Whether someone is actually erotically aroused by something based on a physical response is obviously controversial. For example, a repeatable finding is that self-reported-heterosexual men with intensely homophobic views are more aroused by homosexual male imagery than the average heterosexual male. Commonly reported in the news and media as: homophobic men are secretly gay. And I guess that's one possible interpretation. But someone about to to fly into a fit of rage is also aroused. Sometimes literally -- sexual arousal during flight-or-fight and disgust responses is a thing. So it's tricky to interpret what that result means, if anything.
No doubt we will fall into similar traps again, with the brain scanning version of it.
I'm actually struggling to find the usefulness of this. The person needs to be in an fMRI machine, so we're not talking about some sort of unobtrusive scanner in a public area. On top of that, in cases like what you're describing which basically just classify thoughts to a binary level, then it's pretty much useless because if someone knows that they're being scanned to see if they're actively thinking about pink bunny slippers, then they're going to think about pink bunny slippers.
I generally agree with the assessment that the limiting factor in quality is the signal from the MRI. Just one slight correction to the description of previous projects. We first built models that predict the voxel responses from images using ML. Then recorded the fMRI response on a new image. Then we ran many many images (not just those previously seen that have an fMRI response) through the voxel models and picked the top N images with predicted responses close to the actual response. Basically we used the big list of images as an approximate "natural image prior". Then we'd often show the average of that top N images so you get one picture (which destroys interesting multi-model stuff, but we didn't have good stable diffusion models of images back then that could do a gradient search on or something).
There's already been preprints released showing fMRI reconstructions that appear to do better than an implicit multi-class classifier [1] [2]. But also, even if the result is an implicit multi-class classifier, if the n is sufficiently high then that would still be quite impressive!
I see no evidence that they do better than multi-class classification, in fact they both work as I described. They learn embeddings of fMRI which perform an implicit classification of the data (which can be recovered just by quantizing the embedding space to get its modes) and put very large generation models on top.
The only reason the reconstructions are much better than before is because they use the latest generation models. Those models have internal models of the classes which allow them to fill in the high-frequency details in the reconstruction. The only information they get from the fMRI is the same low-frequency signal that previous papers already had, and indeed the only things the reconstructions get right are low-frequency: class of object/scene, broad position of object, broad shape of object.
fMRI scans are aggregates of brain information, they act like low-pass filters over the brain state. You can put as big a model on top as you want it won't make it more truthful a reconstruction.
I think, as you say, that detecting as many classes as possible is already a pretty good goal, developing new embeddings and techniques to see how much juice we can squeeze out of the scans. I like the arxiv preprint you posted in particular since it does just that and evaluates accuracy (although the way it does it is flawed since it uses an image classifier on the reconstruction which presents the same problems). What I don't like is the misrepresentation of what's going on when people put those large generative models on top of this kind of data.
For the love of God stop using Discord to organize your community and hold all your discussions, especially scientific endeavors! you are bound to lose everything and have immense difficulty searching for all the past information in the future
For off the record discussions you can host a Matrix server. For recorded meeting you can use Jitsi. For recorded discussions and knowledge you can set up a wiki or a forum
fMRI will never have the resolution necessary for something like this. But Neuralink or other precision implants might, if they can put enough probes in enough of the right places.
This technology one can imagine will be used in the future in books about a digital dictatorship , where the author has computers monitor the workers brains in society who are operating airport scanners and other security/important control jobs .... who might be tempted to not follow the dictatorships increasingly crazy rules or for example allow someone/something through in a critical situation that could lead to an attack by the societies people in the name of freedom.
"The culmination of all the above projects. We will consolidate our findings to ultimately reconstruct images from brain activations in real-time (i.e., reconstruct a seen image in a few seconds following perception, while the patient is still being scanned)."
"I was told that if I wanted to, I could vote with my feet and choose a job that didn't use B2I. The only problem was, they didn't exist anymore. What good is a choice when only one option exists?"
26 comments
[ 3.0 ms ] story [ 76.5 ms ] threadIt would be weird to me not being able to draw from memory a map of my own home.
Or not being able to answer a question like "if you enter your home, walk straight ahead until you reach a wall, and then turn 90° counterclockwise, what would you be looking at?".
Unless I'm wildly misunderstanding what "having a mental image" means.
I don't think I have aphantasia myself, but what you say makes sense to me and describes my experience.
We might never know the answer, but for all we know, what you describe here might be what others call a "mental image", and maybe aphantasia is just a huge misunderstanding because the language we use to describe it makes it sound like it should be something more vivid that it really is.
Or maybe I also have aphantasia myself and I'm just finding out now.
These mind topics are always fascinating.
[0] https://aphantasia.com/vviq/
I don't have much faith that the stated goal is realizable, I don't think fMRI data holds enough information for faithful reconstruction. What it will end up being, like all projects like it before, is a massive network that contains:
- An implicit multi-class classifier that returns 1 out of n previously seen images
- Gigantic model on top to hallucinate a reconstruction close to that previous image
In previous projects of this type the reconstruction was blurry because it was essentially the mean of the previously seen images close to the label predicted by the implicit classifier, in this project I imagine they will be photo-realistic since it's a stability.ai project but no closer to the content the subject is thinking about.
It hinges on accuracy. If it can detect a topic, the image reconstruction is almost moot, because you can start interrogating people about what they know.
I wish I knew more about biology to know whether this is at all feasible, or if it’s just smoke and mirrors. (A distressing number of projects like this are to secure further funding for a lab, not really to advance science.)
Since you seem to know about this, do you have any details about the classification process? I assume it requires the cooperation of the user, but it’s easy to imagine that a “primal brainwave” pattern might be common to all humans.
Projects that are at the intersection of life sciences and machine learning are rarely interesting along both axes, it's always novel on one side and the other side is just there to make it seem cool. For example you'll find a lot of ML scientists investigating medical issues but lacking the basic medical knowledge to actually make an impact, similarly you'll see doctors using neural networks where a linear regression would do. In both cases the paper seems novel to the people in the field it's being published in.
The classifier I was talking about is implicit, if you look at their proposed diagram they want to use fMRI embeddings, they serve as a continuous equivalent. You put a sufficiently large model on top of any embeddings and it will learn to reconstruct their modes. The embeddings are trained across subjects so it's not specific to a single patient but they hope to fine-tune it per patient using latent space alignment.
If you look at the papers in their recommended reading list, it's apparent that only low-frequency signals are learned, for example for face reconstruction the model can learn hair color, sex and beard/no-beard, but the actual face doesn't look anything like that of the person being looked at. Similarly for other benchmarks, the model can tell it's looking at a plane, but not much beyond that. This is cool from a technological standpoint but I recall papers doing the same thing at least 7 years ago. The novelty of their approach will be the architecture that comes after the embeddings, which can be interesting but won't add any more information to the fMRI scans.
You use something like an EEG and show someone a series of images to establish a baseline of “familiar” and “unfamiliar.” Then you show them photographs of the crime scene that haven’t been made public to test for familiarity.
I question its accuracy but your scenario already seems possible.
Edit: for siblings comments about journalists - “identifying” a source could probably be “accomplished” using the above technique already, no need for AI. Show photos of random people mixed in with the photo of the suspected leak in your gov. department.
Already gone down this road with lower tech. In the 50s - 70s it was in vogue to monitor blood flow to the genitals while subjecting the subject to erotic imagery of various kinds to see what turned them on. [1]
It was then realized that arousal is also correlated with pupil dilation, which meant that a much less invasive test was possible. Which of course led to the Canadian government (and probably other governments) using it in the 1960s to detect possible homosexuals in the public service. [2]
The scientific flaws and human rights violations of such a test are too many to even begin to list in a short post like this. Still, the inconvenient and disturbing thing is: it does work. Sort of. Kind of. Not good enough to be confident about the result with an individual, but a properly run protocol has better than chance odds at determining if a man is gay or straight, in terms of aligning with their self-reports about what arouses them.
Whether someone is actually erotically aroused by something based on a physical response is obviously controversial. For example, a repeatable finding is that self-reported-heterosexual men with intensely homophobic views are more aroused by homosexual male imagery than the average heterosexual male. Commonly reported in the news and media as: homophobic men are secretly gay. And I guess that's one possible interpretation. But someone about to to fly into a fit of rage is also aroused. Sometimes literally -- sexual arousal during flight-or-fight and disgust responses is a thing. So it's tricky to interpret what that result means, if anything.
No doubt we will fall into similar traps again, with the brain scanning version of it.
[1] https://en.wikipedia.org/wiki/Penile_plethysmography
[2] https://en.wikipedia.org/wiki/Fruit_machine_(homosexuality_t...
Or whether you're thinking thoughts the government doesn't like.
Not any time soon: https://duckduckgo.com/?q=fMRI+device&ia=images&iax=images
If you wanna see what the reconstructions look like without fancy image priors (to get a better idea of how little signal there is) check out this figure from one of the early papers: https://pubmed.ncbi.nlm.nih.gov/19778517/#&gid=article-figur...
[1] https://openreview.net/pdf?id=pHdiaqgh_nf
[2] https://arxiv.org/pdf/2211.06956.pdf
The only reason the reconstructions are much better than before is because they use the latest generation models. Those models have internal models of the classes which allow them to fill in the high-frequency details in the reconstruction. The only information they get from the fMRI is the same low-frequency signal that previous papers already had, and indeed the only things the reconstructions get right are low-frequency: class of object/scene, broad position of object, broad shape of object.
fMRI scans are aggregates of brain information, they act like low-pass filters over the brain state. You can put as big a model on top as you want it won't make it more truthful a reconstruction.
I think, as you say, that detecting as many classes as possible is already a pretty good goal, developing new embeddings and techniques to see how much juice we can squeeze out of the scans. I like the arxiv preprint you posted in particular since it does just that and evaluates accuracy (although the way it does it is flawed since it uses an image classifier on the reconstruction which presents the same problems). What I don't like is the misrepresentation of what's going on when people put those large generative models on top of this kind of data.
[1] https://www.youtube.com/watch?v=nsjDnYxJ0bo
"The culmination of all the above projects. We will consolidate our findings to ultimately reconstruct images from brain activations in real-time (i.e., reconstruct a seen image in a few seconds following perception, while the patient is still being scanned)."