Aging brains blend memories together instead of just forgetting them
studyfinds.com
studyfinds.com
It seems that memories must be stored as embeddings with single multi-neuron assemblies (cortical columns?) storing multiple embeddings as a kind of contents-addressable memory that is able to keep memories distinct due to the very high dimensional space (# neurons per assembly) being used. However, you'd expect that at some point if you store too many memories in a single assembly the recall accuracy is going to go down.
You'd expect that with big brains being so costly, evolution has only equipped us with brains big enough to store a lifetime of memories, so it would be odd if memory didn't suffer as we get old.
To make a computer analogy, it's a bit like a hash table getting too full. Say you had a hash table without any overflow mechanism... up to a point recall may still be pretty good, but as the table gets closer to full there will be more hash collisions and likelyhood of "false recall". Obviously the brain is not a computer, but the analogy may hold up reasonably well if you consider the hash table keys and values as embeddings and the store operation being an embedding merge rather than overwrite.
Engrams, that is, the physical trace of a memory, are not stable through life. They start out in the hippocampus, but as the stimulus recedes in time without reinforcement, it moves away.
No evidence exists though that the memory is encoded in one set of cells. This spatial segregation of memory is the worst hangover from the “brain is a computer” analogy. Even if it is, why in the world would it be like our digital devices which specifically have the Von Neumann bottleneck? In biology, memory and processing are not segregated.
There’s growing evidence the memory is much more distributed over the network, and is recomposed based on salience overlap with a new stimulus.
Another factor to keep in mind is circadian rhythms. There’s growing evidence for how much the memory system and timekeeping system overlap, at a molecular level. Every neuron (and other cell) has an intrinsic clock that ticks at roughly 24 hours, and continues to do so even in total darkness.
When you encode the memory has a lot to say, based on your chronotype, on how and how well you will remember it. Same with learning: there’s a time of day based variation.
Sleep, and dreaming, is when these memories seem to get replayed and critical features and connections are incorporated into the system and its regime, awaiting the right triggers to access a state similar to when the memory formed.
I’m stitching across a lot of different research, and I want to be clear many aspects of this system are not yet fully worked out.
But what we do know points to a system that works with different physical and algorithmic priors, and the dynamics are sharply distinct from current digital computers.
There are some hints that increased memory access times scale with the amount of information the brain has stored vs the more typical narrative that aging decreases the capabilities of the brain.
> Our results indicate that older adults'; performance on cognitive tests reflects the predictable consequences of learning on information-processing, and not cognitive decline. We consider the implications of this for our scientific and cultural understanding of aging.
Obviously not, which is why I didn't say it did!
However, if you want to identify where long-term memories are stored, then that is in the cortex, but it should go without saying that this doesn't make the cortex the storage component of a von-Neumann architecture!
> No evidence exists though that the memory is encoded in one set of cells
I'm not sure what you are trying to say.
Memories are presumably stored as embeddings - a distributed representation, and an episodic memory may well be stored as "chained together" episodic "scenes/chunks" where each chunk recalls the next.
However, a distributed representation isn't the same as a holographic one, and any redundancy may well still be localized within given cortical columns, so I think you may be wrong if you are saying that individual memories/chunks are not confined to one set of cells (some localized neural assembly such as a cortical column).
But you sneak it into your assumptions on what a memory must be.
> However, if you want to identify where long-term memories are stored
And why do you assume there is a specific “where” for the memory?
> then that is in the cortex
There’s good deal of evidence disproving this in the way you’re stating this. The cortex is involved in sensing, and yes, the sensory information associated with a memory will recruit appropriate cortical cells. This doesn’t mean the memory resides in the cortex. And detailed episodic recall keeps recruiting the hippocampus even for old memories, which is odd if the memory were somehow only in the cortex.
> I'm not sure what you are trying to say.
Let me restate what I’m saying then:
There are four claims bundled together in the way you were describing biological memory: that a specific ensemble is activated when a given memory forms; that it’s the same ensemble that gets activated over time when that memory is retrieved; that it’s spatially compact, like a column in region of the brain; and that this ensemble it’s dedicated to that memory, or similar memories .
The first is well supported. An engram, a network of neurons, is indeed activated when a memory first forms, and gets stabilized due to repeated stimulus. Re-activating these neurons in a different context can make the subject (a mouse) behave as it would if it had contextual signals to evoke said memory.
However: 1. This engram is not in one particular part of the brain. There’s a cortical part that overlaps the sensory regions that were involved. But plenty of other regions are part of the engram 2. There’s turnover, over the course of weeks, when the specific cells involved in the engram drift, while the behavior remains stable. 3. The same synapses participate in many memories.
> Memories are presumably stored as embeddings
No. Let’s consider songbirds, which are an excellent worked out example (in an animal without the complex columnar cortical architecture mammals show, by the way).
What’s learned is a temporal sequence, with neurons in a nucleus in their brains each firing one brief burst at a fixed point in the motif, so the content of the memory is its dynamics rather than any value. The circuit that evaluates the match against the tutor template is the same circuit generating the output being evaluated. Song degrades overnight during sleep replay and recovers the next day, and in seasonal species the song nuclei change size across the year with neurons added and lost while the song persists. There’s no read that leaves the item untouched, no persistent address, and no substrate holding still. “Stored as” imports all three.
And it goes below the neuron or synapse. Hearing a tutor song drives immediate early gene expression that habituates with familiarity, and singing drives large transcriptional changes in the song nuclei that differ by social context for the same motor output. Since transcription runs on minutes to hours and the proteins turn over, any persistent state has to be actively regenerated rather than deposited.
TLDR: the memory isn’t a static store. There’s no persistent “location” for it, distributed or otherwise, though specific locations can be in the chain that’s activated for retrieval/production. Instead, memory, over time, is driven by a dynamical regime that adjusts its dynamics to account for the temporal pattern in the salient stimulus.
Nothing, down to the epigenetic changes in the chromatin of these neurons, can be seen as “the” location of “a” memory, especially over time.
> if you are saying that individual memories/chunks are not confined to one set of cells (some localized neural assembly such as a cortical column).
That is indeed the case.
> and that this ensemble it’s dedicated to that memory, or similar memories
No - that's the exact opposite of what I said. My whole point was that a cortical column is NOT dedicated to a single memory (we'd run out of memory!), but rather acts as an embedding space containing many (sparse) embeddings.
Due to the size of the embedding space and sparsity of individual embeddings, there is little chance of much overlap between embeddings and therefore associative recall is reliable. When there are too many memories stored using the same set of neurons, then there will be non-trivial overlap between embeddings (this is the definition of "too many" / "full") and associative recall becomes unreliable.
Note incidentally that this explanation holds regardless of whether distributed embeddings are stored in a more localized area (or areas - visual, auditory, etc components) or more globally distributed. At the end of the day evolution has equipped us with a right-sized brain, and an individual that outlives the useful life it is adapted for can expect to experience memory failures.
I'm not sure why you bring up bird brains, and specifically bird songs(!), but FWIW it seems that their short term memory likely works similarly to our own in as much as it is based on the hippocampus, with a very strong correlation between bird hippocampus size and memory capacity (ability to memorize 10's of thousands of hidden seed locations in some species). Some birds such as crows certainly have long term memory where I'd guess those may have migrated to their pallium, but we're discussing human memory here (or at least I thought we were).
Yes, like basic reading without digging into details. You must have heard about HM, since you’re saying all this. But here’s the facts:
When H.M.’s remote memories were probed carefully, they turned out to be gist-like and semanticized, not vivid re-experiencings of specific events. Here’s the paper:
https://pubmed.ncbi.nlm.nih.gov/15716139/
Only semantic memory of the episodes can be said to be “cortical” (though please note, lack of hippocampus doesn’t mean lack of other brain regions…). Rich recall absolutely does require the hippocampus.
Once again, please try not to “spherical cow” the complexity of the brain to try and fit it into your analogy to digital computing. You will get an underdermined model that will miss the subtleties, and lead you to claims that are poor fits for the reality.
As for why I brought up bird brains… there part of the same evolutionary web. Is there some reason you want them excluded? They’re a well studied model for a fairly complex memory task, using substantially smaller neurons more densely packed in a different architecture than mammals.
In cognitive science, as in computer science I’d imagine, it’s useful to look at the full picture before making strong claims.
The bird case is interesting because the region of interest is a nucleus, rather than cortical columns, and actually has well documented structural variations in size, as well as gene expression, over the seasons, while the memories are forming.
If your model is correct, it needs to account for those facts.
Let me make it simple for you.
We have a finite number of neurons in our brain, as do birds, and our brain is attempting to store an ever growing number of memories in those. And, no, this is not a digital computer (is your reading comprehension really so bad?).
Nobody, including you, knows exactly where different types of memory are stored, and for my argument it makes no difference. What does make a difference is how they are represented, which I am suggesting is sparse embeddings.
So far, you've been ignoring my actual argument and instead responding to various strawmen of your own making, so it's not clear if you even understand what a sparse embedding is.
If you do understand, then it should be obvious that it makes no difference whether the neurons comprising this embedding space are in the hippocampus, cortex/pallium or anywhere else. Clearly you do NOT understand, since you bring up bird brains (pallium vs cortex) and want to argue about location of storage (hippocampus vs elsewhere) as if it made a difference to MY argument.
My argument (if you care to respond to it, which so far you have not) is that when sparse embeddings have little to no overlap, then associative recall by a similar pattern will work reliably, but when multiple embeddings have too much in common then recall will suffer as multiple embeddings will match.
Hint: if you think this has anything to do with digital computers then you have misunderstood and need to go back and re-read more carefully, or google for any terms you do not understand.
Let’s say I’m a complete moron and don’t know what a sparse embedding is.
Pretty please, can you define it for me and then tell me, in detail, where in whatever region of the brain you think this is going on… how is it going on?
Explain how “memories must be stored as embeddings with single multi-neuron assemblies (cortical columns?) storing multiple embeddings as a kind of contents-addressable memory”
You have moved past the cortical column. But still seem to be insisting it’s a bunch of neurons, somewhere… or has that also conveniently changed? Whatever your current position is, please go ahead and explain what components of what cells or otherwise are involved in this process you’re describing.
An entity embedding doesn't need to use all the dimensions of the space it is positioned in - some dimensions may be unused (sometimes represented as a coorrdinate of 0 in that dimension). These are called "sparse" embeddings. For example, an LLM's tokens are represented as embeddings in what is typcially an approximately ~1000 dimensional space, but start out as sparse embeddings just representing a short letter sequence (but then go on to be transformed/augmented with additional information and so become less sparse).
As an example, let's say an embedding space has 10 dimensions, then a couple of sparse embedding examples could be:
[0 0 1 0 0 1 1 0 0 0]
[1 1 0 0 0 0 0 1 0 0]
Embeddings are used in many types of artificial neural networks, not just LLMs, for example face recognition networks, where they are trained such that similar faces (multiple photos of the same person) are close together in the embedding space, and post-training you can then "look up" any arbitrary photo (in the training set or not) by embedding it and seeing what is nearby in the embedding space, which will be similar looking faces.
Presumably real neural networks used embeddings in a similar way, since, for example, it obviously requires many neurons to represent the many differences between different faces, and there is going to be overlap between the neurons used to represent multiple faces (this is not a computer with one storage location for face #1, and a different location for face #2).
A neural network, real or artificial, uses groups of neurons (e.g. a cortical column) to represent an embedding space, with each neuron corresponding to a dimension. A single group of neurons (column) can store multiple embeddings (e.g. faces) represented as different activity patterns (which neurons are firing), and if these are sparse embeddings then the firing patterns corresponding to different memories stored in the same column will have little in common.
Now, I don't know how you believe associative recall is implemented in the brain - how does someone's voice, or half obscured face, recall their entire face, so feel free to imagine it as implemented however you will, but I'd suggest that in an assembly such as a cortical column that when a set of synaptic inputs are triggered the assembly as a whole will learn to reactivate the entire pattern when only part of the original set of synaptic inputs are triggered, and this is the basis of associative recall. There are papers that suggest exactly how this may work given the cortical column microcircuit.
So, with all that said, the suggestion I was making for why (or at least one reason why) memory degrades with age, with memories blending together, is that with a finite quantity of "storage" (cortical columns) you will eventually be storing so many memories (absent a deliberate forgetting mechanism) that there will inevitably be overlap between the sparse embedddings, and this associative recall will therefore not cleanly recall individual memories but rather recall blended memories according to what they have in common.
Obviously some types of memory are at least initially stored in the hippocampus, so no reason to focus on cortical columns, but I expect the use of embeddings is universal.
https://authors.library.caltech.edu/records/znzhp-4j547
Faces live in a ~50-dimensional continuous space (25 shape axes, 25 appearance axes). They measured about 205 neurons across 2 macaques (human studies have substantiated much of this, some from the same lab), and the key thing is: every neuron participates in every face.
The paper shows faces are embedded, but as points in a dense linear space where neurons are axes, not as sparse activity patterns where neurons are on/off slots.
The mapping between the neuronal activity and the facial structures is invertible. Record these same cells, and their firing pattern can be used to reconstruct the face. Or, if you generate a novel face, you can predict the firing rates of these neurons for it. As far as I understand, this doesn’t work for sparse embeddings.
Some cells carry the shape coordinates and others carry the appearance coordinates, in a heirarchy.
There’s an embedding space, yes. But that space isn’t defined by a network of “on” and “off” neurons. The embedding space is instead constructed by the activity of neurons, and the differences in activity distinguish the faces, using the same set of neurons.
And distance in the ensemble activity of these neurons tracks the distance in face space.
If faces use sparse embeddings, you wouldn’t expect similar faces to evoke similar activity would you? Yet that is exactly what this paper shows, and the same has been shown in the human brain for faces.
There are places where it’s sparse activity of a subset of neurons that maps to specific memories. What you’re describing is what you’d see if you look at how the dentate gyrus (part of the hippocampus) handles your memories in the same location.
But even there, the sheer number of cells makes this combinatorially such a vastly overdetermined system for a lifetime that there’s no capacity limit of the kind you’re describing. Even 1% of these cells lighting up for a specific memory leaves you with so many possible combinations that you’d have to live for a few million years to be in the right scale to at least being to talk about capacity issues.
The brain just isn’t capacity limited by the number of neurons the way your intuition is pointing you.
If you say this has nothing to do with the Von Neumann bottleneck or computational functionalism, fine, but how do you square that with the statement below, which you made further down responding to another post?
> but it's hard to imagine that all of the classical chemistry, let alone quantum, details are important. It's necessarily built out of chemistry, but selection is happening at the level of behavior - presumably depending only on a much higher level set of abstract capabilities (ability to learn, etc), not the exact details of chemistry. The success of LLMs, a crude prediction mechanism built atop a crude ANN, does tend to support the idea that low level details don't matter. Timing will matter if we want to go beyond LLMs to AI that can learn time-based things and not just sequence order, but how much else will matter remains to be seen!
It’s really odd to see these two paragraphs, because the second actually tells you why your first is wrong.
Simply put, the biochemistry is timed. I urge you to study how temperature compensation of circadian rhythms is achieved. That anticipatory function goes all the way down to the molecular level.
It might go down to the quantum level too. In birds, magnetoception depends on a protein called cryptochrome IV, which uses a singlet born, entangled radical pair of electrons to sense the very weak magnetic field of earth.
Now cryptochrome 4 is bird specific and mammals don’t have it. Other cryptochromes are critical clock molecules. And the whole shebang of these evolved initially to be sensitive to blue light and repair DNA.
Try as you might, you can’t separate out the deep linkages from the molecular to the behavioral in biology.
Trying is perfectly fine for stuff like language models. But if you’re going to build models with internal time, best of luck if you ignore the molecular and the energetic considerations. Time emerges from the ground up, in biology, as in physics. Doubt we’ll get a free ride with computers.
Well, my analogy was comparing computer hash tables collisions to sparse embedding collisions, so what you are discussing now is my suggestion itself (pertaining to embeddings and recall), not the analogy, which is fine!
The study we're discussing was nominally about associative recall, not faces per-se, and specifically about the hippocampus not the cortex (that Macaque face study).
> Memory accuracy for pairing faces with objects and scenes dropped sharply
https://studyfinds.com/aging-brains-blend-memories-together-...
That said, I wouldn't be so sure that face embeddings are fully dense, even if they are not particularly sparse either, given that not all faces have the same set of features, such as facial hair, glasses, blemishes, etc. OTOH, it's possible, perhaps likely, that similar faces are stored together, in which case they may be more dense.
> If faces use sparse embeddings, you wouldn’t expect similar faces to evoke similar activity would you? Yet that is exactly what this paper shows, and the same has been shown in the human brain for faces.
With embeddings in general, sparse or not, you'd expect individual dimensions/neurons to represent different axis of variability, so you would expect individual neurons to be active for multiple different faces that are similar along that same axis (e.g. eye color). Note that the study you are citing used individual neuron recordings as well as fMRI, but of course we don't currently have the ability to simultaneously record from the hundreds of neurons that are likely being used to embed faces, so I don't think this study has much to say about the degree of sparsity of these embeddings. Obviously IF faces both with and without glasses are stored in the same embedding space (same set of neurons), then one would expect the "glasses neuron" not to be firing for a face without glasses, which would confirm some degree of sparsity.
> The brain just isn’t capacity limited by the number of neurons the way your intuition is pointing you.
It's highly unlikely that our brains are wasteful and have unused capacity - this recalls daft pop-sci articles saying that we only use 10% of our brain ... We know that brains and memory do degrade with age, and the only question is how - maybe the encoding mechanism itself is failing resulting in embeddings that have more overlap than they should (or one could hypothesize a dozen other possble failure modes). Do you have any theory that explains the "aging brains blend memories" study that we're discussing, at the level of detail of the hippocampal patterns they are seeing?
> It’s really odd to see these two paragraphs, because the second actually tells you why your first is wrong.
> Try as you might, you can’t separate out the deep linkages from the molecular to the behavioral in biology.
Of course the linkages are there since our brain is built from chemistry, yet selection pressure is happening at a much higher functional level. The part of my response you are referring to is addressing the question of how much of this molecular level detail needs to be retained in an ARTIFICIAL neuron model sufficient for it support the same phenotype-level functional behavior, and the answer is we just don't know, because nobody has yet tried to do it.
Prior to LLMs a lot of speculation about what is necessary in the brain to learn language, e.g. Chompysky-ian language-organ nonsense, might have sounded logical and compelling, but now we have proof-by-existence that "prediction is all you need". We're going to need to wait until we have built an artificial brain, capable of learning time-based phenomena, and everything else our brain is capable of, to similarly be able to point at something (a future elaboration of an artificial neuron model), and then be able to say that this is the most that is needed.
As far as this specific point - how much of the detail of a real neuron is functionally necessary vs how much of it is just a reflection of how it is built, you could also compare the massive complexity of something like a digital circuit transistor or logic component if you get down in the weeds and look at the specific gate architecture, and how it operates via quantum tunneling etc, or you could instead look at the functional behavior as a circuit component, and realize that none of it actually matters, and that transistors are interchangeable as long as they are functionally equivalent.
> A neural network, real or artificial, uses groups of neurons (e.g. a cortical column) to represent an embedding space, with each neuron corresponding to a dimension.
You:
> The paper shows faces are embedded, but as points in a dense linear space where neurons are axes, not as sparse activity patterns where neurons are on/off slots.
So you are saying that neurons are axes (aka dimensions), exactly as I just said!
> If faces use sparse embeddings, you wouldn’t expect similar faces to evoke similar activity would you?
Yes, of course you would, because that is precisely how embeddings work, and how you recognize someone even though their head is turned or they are wearing a baseball cap or whatever.
This is the ENTIRE point of emebeddings and why evolution has discovered them as a way of representing things and a way to recall them. You may have seen someone a million times, and yet the sensory patterns your visual cortex is fed are likely different every single time because they are not in the exact same orientation, making the exact same facial expression, with the exact same haircut, etc, etc, etc.
To your brain these are merely similar inputs, similar faces, but there is only so much facial variation between individuals, and if the input is similar along dozens or hundreds of axes of variability (i.e. close in embeddign space) then it is alomst certainly the same individual.
Note that "recall keys" (embeddings) are typically sparse even any stored embedding is not, since the face you are looking at may indeed be turned left or half obscured, and this partial/sparse pattern needs to recall the full one.
How can you be a neuroscientist, or even self-identify as one, if you are not already familiar with things like embeddings, and are making such basic 100% wrong assumptions as "you wouldn’t expect similar faces to evoke similar activity" ?!!!
Would delay line memory be a fair analogy? Information stored in a delay line memory never stays in one place. I think you are saying biological information is "stored" in the amplitude and phase of oscillations of our cells. Is that correct?
I'm still inclined to believe there is some form of static store, which would be required for inherited memories. Things like our (and many other mammals) ability to recognize emotions in others. Somehow our gametes encode what a happy, sad, or scared face looks like. I instincts as inherited memories.
I guess my point is the brain not being "von Neumann" in architecture or digital is not proof that isn't a "computer" of some sort.
Capacity in what sense? Are we saying it’s X MB of data the brain can store? That claim is steeped in assumptions.
On the other hand, no one is claiming the brain has infinite memory or anything. And it’s certainly not a very accurate memory system. I’m arguing against “capacity” being understood as “these specific physical components located here and here we can ID store memories, and can get crowded with too many memories” sense.
This most particularly fails because not all memory is even identical in the brain, whether we mean the physical changes associated, the topology of the information, or how it’s activated.
> There is no reason to believe distributed memory doesn't suffer from the capacity component of the bottleneck.
I didn’t know there was a capacity component to the bottleneck, only a bandwidth one.
All I’m trying to say is that analogy to current typical memory storage systems to explain the brains memory processes is not helpful.
> I guess my point is the brain not being "von Neumann" in architecture or digital is not proof that isn't a "computer" of some sort.
Indeed, since the word computer was first used for humans. But what kind of computer matters enormously. Ising machine? Quantum+classical stack? Reservoir computer? All those frameworks have processes in the brain they can point to as homology.
Which points to a possibility: maybe the brain is multiple types of computers interacting. And the physical realization of these computing architectures aren’t spatially separated but thread through each other in the biochemistry and physical dynamics of cells.
"All I’m trying to say is that analogy to current typical memory storage systems to explain the brains memory processes is not helpful." Again, my impression is that the original author's idea was about capacity in general, then he gave an analogy. The analogy was wrong, but your reply went way beyond his specific analogy to the extreme of discarding memory itself as useful concept. To be clear, I agree that it is distributed and lossy and time-dependent. I agree it is not just a simple read-off of a static chunk with a fixed address.
"Which points to a possibility: maybe the brain is multiple types of computers interacting. And the physical realization of these computing architectures aren’t spatially separated but thread through each other in the biochemistry and physical dynamics of cells."
I would say that is both non-falsifiable and well-accepted.
https://mitpress.mit.edu/9780262041997/theoretical-neuroscie... might interest you and on the other end bialek's spikes shows quite a bit actual does happen near the single neuron limit. bialek also does/did a lot of other stuff on capacities and processing you might be interested in. https://mitpress.mit.edu/9780262181747/spikes/
The problem with the hopfield model is it simplifies the brain too much. The base unit is “the neuron”. Ok… but what about the Astrocyte? Mathematically you can write it as a different kind of neuron. Or ignore it. But why, as a biologist, must I buy this model which ignores the third partner of every synapse, which has an entirely distinct physical tiling architecture compared to neurons, and which are at a temporal offset from neurons?
Those facts about the brain are missing from the model from 1982. Which isn’t shocking since we didn’t know all this then.
Are you saying the brain is a Hopfield network, and that’s it? Because later you indicate otherwise. Kinda confused what I’m to make of it.
> Again, my impression is that the original author's idea was about capacity in general, then he gave an analogy. The analogy was wrong, but your reply went way beyond his specific analogy to the extreme of discarding memory itself as useful concept. To be clear, I agree that it is distributed and lossy and time-dependent. I agree it is not just a simple read-off of a static chunk with a fixed address.
Ok, but my argument wasn’t with the OP mentioning capacity, but with their analogy. Am I not allowed to break down that analogy with evidence?
> I would say that is both non-falsifiable and well-accepted.
Why’s it non-falsifiable? If you do find a single computational paradigm that explains all brain dynamics we can measure, then you have falsified the hypothesis that it’s an integration of multiple computational types.
That actual evidence already gives the notion credence doesn’t make it unfalsifiable in principle.
> https://mitpress.mit.edu/9780262041997/theoretical-neuroscie... might interest you
Went through the description. Doubt it’ll interest me. As a rule I’ve stopped giving too much time to models that predate the last decades actual mechanistic facts. They’re fun curiosities, but hard to take seriously anymore. Here especially, the absence of astrocytes in the picture makes it hard to buy they have anything real to say about the mechanics at play. Half the cells of the brain not in the explanatory picture is just too likely to fail.
(note: I’m certain astrocytes are mentioned as support cells, or maybe regulators… but we just know a lot more now due to new techniques that makes downgrading them like that questionable science to me)
I intentionally gave older books and papers. I very clearly understand as does everyone in the field that they are not current or complete. But that awareness is precisely my point.
"Are you saying the brain is a Hopfield network, and that’s it? Because later you indicate otherwise. Kinda confused what I’m to make of it." You being confused by that is kind of my point, it shows the projection you're applying to the entire field. I understand models are wrong and iterative. I also understand the parts that survive, survive in the literature and the field at large. For example, the PNAS paper that showed "hey we think memory is distributed, right? Here's a simple model showing how memory might roughly be stored and recalled via these very simple dynamics over this very simple model. That's cool. Let's build on it." Did I make it clear enough yet that it was simple and wrong? Do you thus believe it wasn't valuable? I guess that would match your confident opinion that the whole field is dummies and of course those dummies cited an incorrect model so much.
If you stopped with saying "we know memory isn't a memory stick and here's the well-established evidence", then I'd fully agree with "Ok, but my argument wasn’t with the OP mentioning capacity, but with their analogy." and would've said nothing. Instead your posture was "I know some specifics based on recent findings that aren't obviously incorporated as of now into the current understanding -- which I refuse to read because it doesn't address precisely this thing only I understand(1) -- , so here is my theory that is certainly novel don't dare try to point me to preexisting literature that might help me refine my theory."
To be clear, I want everyone to explore their ideas and think it's great that you doubt things. But I don't like someone claiming superiority over a field they refuse to understand. And if your doubt something maybe you should check first if there's anyone else doubting it. (Hint: not only did everyone doubt it, they knew it was wrong and they're all actively working on using that doubt to improve it.) Of course a theory that predates a discovery doesn't cover that discover. Of course a simple model of memory isn't a complete model of memory, that was in fact the point! The notion of distributed memory and how it could feasibly work according to dynamics is made more precise than ever before to that time in that paper (among others). And of course it's outdated. But it contains a more precise statement and falsifiable statement of "memory is complicated dynamics and those dummies don't know and also that book is stupid because it doesn't cover X(2,3)" your theory espouses. My issue is with the arrogance and certainty you know best while refusing to actual understand what others think. And thus give no credit to. Science is iterative. Someone has an idea, builds a simple model as proof of concept. Someone else builds on that model precisely where it has holes (whose precise shape only present itself after new experiments motivated in part by the obviously wrong and simplified model and other attempts to break the model). Repeat. Built on the shoulders.. etc.
"Why’s it non-falsifiable? If you do find a single computational paradigm that explains all brain dynamics we can measure, then you have falsified the hypothesis that it’s an integration of multiple computational types.". I said non-falsifiable AND well-accepted. Non-falsifiable was the wrong word. What if I instead said trivially falsified and also adds nothing not known except to the layman who posits an analogy they clearly aren't sure of? Its non-falsifiable relative to the current state of the field, which I now know you're not interested in, because it says nothing not already stated more precisely, as of like 40 years ago.
(1) "As a rule I’ve stopped giving too much time to models that predate the last decades actual mechanistic facts. They’re fun curiosities, but hard to take seriously anymore. Here especially, the absence of astrocytes in the picture makes it hard to buy they have anything real to say about the mechanics at play. " In response to a book that has a lot to say about your new and novel dynamical theory.
(2) A hole it couldn't cover because the experiments and techniques didn't exist at that time. But they knew their theory wasn't complete. They were sharing what was learned up to that point especially the parts that seemed to generalize and survive specifics. That is, an incomplete model they knew was incomplete. But which I promise would help advance your journey.
(3) "Instead, memory, over time, is driven by a dynamical regime that adjusts its dynamics to account for the temporal pattern in the salient stimulus." Which is true and everyone in the field agrees with. And everyone agrees current models don't fully account for everything. Hence their continuing efforts to improve them by accounting for new findings.
If someone makes an analogy between aspects of A and B, then there is an implicit assumed shared understanding that A & B are DIFFERENT, and what is being pointed out is that they nonetheless may be considered as having something, typically fairly abstract, in common.
Calling a horse's reins as analogous to a cars steering wheel doesn't mean that the person making the analogy can't tell a horse from a car - that would be a very dumb take. A reasonable criticism might be "it's an imperfect analogy since the reins also act as the brake".
If you disagree with an analogy, then you need to address the analogy, not do the dumb take of "a horse is not a car" ("a brain is not a computer").
So, yes, you're "allowed" to "break down" (criticize) the analogy, but please don't just do the dumb take. So, hash key collisions are not analogous to associative recall and sparse embedding overlaps in what way?
When a person says “I crashed the car because my steering wheel tore, like reigns tear”, they are overfitting their analogy, and it’s perfectly fine to point out the structural and physical differences that make the analogy useless for the question at hand.
You have dismissed the biology that shows the problems with your analogy as immaterial. And continue to insist it’s the right one for the question at hand. This is a pretty pickle, because no facts can shake you from your certainty that you're right.
You seem in love with your analogy no matter how incorrect it is. And I have no problem with that. But when you put half baked biological claims to support it, I’ll point it out. If that’s too much for you to bear, maybe come up with better analogies?
Where the hell did you get that from?! I mentioned the hash table / embedding space analogy precisely ONCE, in my initial post, and, just to try to avoid people like yourself being triggered by it, I even included "obviously the brain is not a computer".
But you still got triggered by it, still did the dumb take of "a brain is not a computer" (no shit - I just said that), then spent your entire energy on fighting your own strawmen and not once even responding to my actual suggestion.
Now, finally, you are asking "what is a sparse embedding"! Maybe next time don't bother responding to something if you don't even understand what is being talked about.
[1] https://www.pnas.org/doi/pdf/10.1073/pnas.79.8.2554 a paper he confidentally rejected as meaningless, unaware and not interested in the overwhelmingly likely possibility that his current brand new ideas trace back to it.
[2] "I’m stitching across a lot of different research, and I want to be clear many aspects of this system are not yet fully worked out.
But what we do know points to a system that works with different physical and algorithmic priors, and the dynamics are sharply distinct from current digital computers." Hmm, I wonder if "we" figured that stuff out from individual and especially collaborative efforts combining abstractions and more theoretical approaches with expertise in biology, not to mention EE, biophysics, physics, and whatever else other fields that don't know about his favorite molecule and thus can be ignored if not shit on?
It's more about computational complexity it seems but maybe it cites stuff that might interest you.
It's using computational neural networks that no one has ever believed represent the biology of the brain, but I think I must still disclose the following to save the precious time of the genius solving the problem all by himself:
It doesn't fully account for every biological detail ever documented, so it's probably a meaningless "curiosity".
Oh it probably also doesn't account for every detail discovered since publication so even if had value at time of publication it is not worth reading now.
I wonder to what extent evolution selects for longevity (& graceful degradation) past a certain point? I would think that once you are past breeding age it's generally more beneficial for the species if you die off and make way for the next generation, other than having some residual benefit as babysitters for the grandkids and leading the herd to the water source in the next drought.
I’ve been consuming a lot of talks / writing recently about “enactive” pictures of how our brains function. From what I gather, recent studies have called into question the entire idea of real world concepts being “represented” by an area of the brain at all. While it’s true that atandard fMRI-style snapshots of brain activity are semi-stable over the course of a short experiment, it’s not true over longer timeframes. The response to the same stimulus will change over time. They refer to this as “representational drift” in the literature, and some people are using this to bolster theories of mind that they consider non-representational. They instead emphasize the brain as a kind of dynamical system that learns to “resonate” with the world to pull itself back into homeostasis. The focus shifts away from facts and memories as data, and sees neuronal plasticity more as a mechanism for tuning the brain’s resonant frequencies. This obviously places high importance on the spiking, recurrent nature of actual neurons, as opposed to the neurons-as-functions / back-propagation / ML approach.
My mind’s not made up on how interesting and revolutionary this approach is / isn’t. The distinction seems to be about whether learning is more like “writing to disk” or “tuning a PID controller” - but in either case, the world is leaving a stateful imprint on your brain that will impact how it processes future data. Is that important to understanding how brains work, or is it just semantics?
It's akin to how the saying goes, that when we argue, we need to reach the same wavelength where the other one is to reach an understanding.
In communication studies or sociology or linguistics or one of those fields, there's the idea that communication is about making pacts about meanings, and finding the common ground to understand and delimit the message.
In a sense, from that basing, one could argue that understanding can be seen as an act of harmonizing. I think there's something universal in it.
The universe (and its intrinsic functions) holds no special value for people or trees or life in general. The best we can say is that it seems to have a slight preference for matter over antimatter for some reason.
All our moral codes and social norms we live by are purely human invention, derived from things that happen to work to produce a somewhat functioning society.
I'd say much of it is selected for by evolution. e.g. Most animals don't fight or kill more than they need to since this is not optimal - risk of personal injury and depleting a food resource (there are exceptions of course such as the fox in the henhouse), and this is so universal it seems it has to have a genetic basis.
Religious people would disagree, and the evidences are all to see, hear and ponder from their sacred holy books. But the main questions are that which holy books are truly sacred, meaning that purely and utterly God words. Otherwise it's human innovation/exaggeration or something in between (i.e corrupted God words).
Based on these holy books, religious people adhere and follow their prophets teachings and actions to the best of their ability since the prophets are their fellow human beings and not angels send from the heavens.
Yes, I fall more towards the camp that a lot of our cognitive models, built from times when we didn’t have the resolution of understanding we have of the capacity of even a single neuron, and before we knew how astrocytes played a role, suffer from being an abstraction describing an abstraction. They are not tethered in the dynamics of the molecules and cells that give rise to the behavior, but rather from an interpretation of observed behavior.
This paper from the field of chronobiogy is one I’d recommend that helpfully contrasts this:
https://www.sciencedirect.com/science/article/abs/pii/S00393...
>In circadian research, the models are not proposals regarding the basic architecture of circadian mechanisms; rather, they are used to better understand the functioning of a mechanism whose parts, operations, and organization already have been independently determined. In particular, circadian modelers probe how the mechanism’s organized parts and operations are orchestrated in real time to produce dynamic phenomena—what we have called dynamic mechanistic explanation.
And what you’re describing, the enactivist description of cognition, (and 4E cognition more broadly as a framework), is one way the neuroscience community is trying to move past these issues.
Few things that give me confidence these are the right track:
1. Circadian rhythms are evolutionarily ancient. Bacteria have em. Plants have em. But different molecular tools shape very different clocks, though the same 24 hour cycle is being tracked. 2. The way these rhythms are generated is not through some central system that broadcasts the information to other regions. Instead, it’s instantiated in every cell in the body, and the behavioral rhythm is due to the synchrony between cells. Resonance absolutely plays a role, and has been well documented. The brains role, via the suprachiasmatic nucleus or SCN, is to orchestrate this synchrony, but it is not the source of the rhythms. 3. This slow rhythm definitely regulates cognition (time of day effects in learning, memory formation, recall etc are well documented), but turns out, the molecular mechanisms by which the clock responds to light hugely overlap with the molecular mechanisms of learning in the synapse, and even more recent work has shown clock proteins are actually in the synapses and synaptic activity affects the clock.
All this points to nested oscillators with cross frequency coupling, and even better, because this is all grounded in actual molecular dynamics, there’s plenty of falsifiability. The phase amplitude links are best established for the faster rhythms, the famous “brain waves”. Highly recommend György Buzsáki‘s work on this:
https://www.jneurosci.org/content/32/2/423.short
What’s missing is going down into lower frequency rhythms, and testing how exactly they all couple. We have a lot of the pieces, but no single experimental paradigm that has looked at the full sweep over different times in the same organism. It’s not easy to do, but we’ll get there.
Clock disruption, depending on how you do it, has huge impacts on time perception, cognition, memory, aging AND consciousness. As that data and evidence gets more and more saturated, I hope we see more studies account for chronotype and the internal dynamical state of their test subjects when assessing outcomes.
Obviously I’m biased (also did chronobiology in school), but hopefully I’ve left you curious. Happy to answer more questions all this may have set off.
I’d say that I feel there’s homology in language. What reservoir computing says about the efficiency benefits of having a fixed but tunable dynamics to use as an underlying reservoir feels very adjacent to how I intuitively think of brain function.
The key thing from the circadian field you’ll appreciate:
The biological clock is a limit cycle oscillator. You have a bunch of chemical reactions that have negative feedback and some feedforward arms, and together they create a dynamical 24-regime. About 40-60% of the transcriptome of any given cell shows circadian dynamics.
Now this gives you phase, and an internal temporal reference for all your functions. In chronobiology, you call this the organisms subjective time. The system is chemically partitioned not just physically but over time, and behavior results from the dynamical interactions underneath which are concerned with anticipating solar and lunar periodicities in the environment, since those are so very common and determinative to fitness in many niches.
Note the fact that it is subjective time but has an objective description. However, external measurement without the background of the chronotype accounted for will thing of a lot of variance as “noise”.
My own philosophical conclusion has been that this is the source of our confusion with consciousness. We don’t account for the internal causal order of events, which are timed, and with cross frequency coupling and phase-amplitude linkages begging to be worked out with real world data.
It's a computer, it's just not a digital computer
But even digital computers can have stuff like processing-in-memory. "Computers" doesn't need to mean whatever architecture usually run as of 2026
[1] https://en.wikipedia.org/wiki/Solomon_Shereshevsky
"His memory was so powerful that he could still recall decades-old events and experiences in the smallest details. After he discovered his own abilities, he performed as a mnemonist; but this created confusion in his mind. He went as far as writing things down on paper and burning it, so that he could see the words in cinders, in a desperate attempt to forget them. Some later mnemonists have speculated that this was a mentalist's technique for writing things down to later commit to long-term memory. Reportedly, in his late years, he realized that he could forget facts with just a conscious desire to remove them from his memory, although Luria did not test this directly."
I believe that one need to have superhuman memorization abilities to have definite confusion due to too much remembered. More trivial explanation of this effect in normal ageing persons is age-related brain shrinkage.
> Obviously the brain is not a computer,
[2] https://www.nature.com/articles/s41598-024-62539-5
[3] https://pmc.ncbi.nlm.nih.gov/articles/PMC11655932/
[4] https://pmc.ncbi.nlm.nih.gov/articles/PMC12230014/
[5] https://pubmed.ncbi.nlm.nih.gov/6204761/
Generally claims of eidetic memories are overstated, doubly for older claims, but Nigel Richards memorized a French dictionary in 9 weeks, over 6k words a day, x2 including the alphagram.
I consider myself to have a decent memory, but that is 200x what I'd think myself capable of, assuming I want to retain it all at the end of the 9 weeks.
By that logic, a Blackwell GPU contains 208 billion quantum computers.
Each transistor is a quantum mechanical system and takes hundreds of model parameters to describe. So apparently a GPU is a 208-billion-node quantum supercomputer.
"A computer" in the first statement is IMO obviously intended in the common usage sense of the term, i.e. the brain is not a desktop computer or smartphone, or Turing machine, or etc, and thinking of it like these things will cause more error than insights. Also, billions of interlinked mini bio quantum computers arguably produce something with emergent properties and behaviour much, much more complex than "a computer". I am with GP, the analogy to "a computer" is not super helpful here unless you highly restrict the meaning.
But yeah, the Shereshevsky case is a super interesting one, thanks for linking!
No one has ever thought that a brain is a desktop computer, and that is not the common usage sense of the term in this context.
Multiple other responses also point out the definition given leads to absurdities / triviality (the liver is a computer in this context), so the "correction" with citations is both inept and inapt.
Well, sure, it's a computer of sorts, although in the abstract sense of being able to perform computations so is our liver, so perhaps not a very useful concept.
What I meant (as I assume you realize) was "not a von Neumann architecture computer", but I'd also fairly confidently assert that it's not a quantum computer either.
Our ANN model of a neuron is obviously too simple (especially being a synchronous model, not a real-time asynchronous one), but it's hard to imagine that all of the classical chemistry, let alone quantum, details are important. It's necessarily built out of chemistry, but selection is happening at the level of behavior - presumably depending only on a much higher level set of abstract capabilities (ability to learn, etc), not the exact details of chemistry.
The success of LLMs, a crude prediction mechanism built atop a crude ANN, does tend to support the idea that low level details don't matter. Timing will matter if we want to go beyond LLMs to AI that can learn time-based things and not just sequence order, but how much else will matter remains to be seen!
Mostly analog, with some threshold-triggered functions and lots of weighted adders and maybe multipliers.
> but how much else will matter remains to be seen!
Indeed, and the similarities between the brains we were born with and the brains we build is tantalizing. I'm really curious about the next chapters of this all.
In any case, fuzzy associative recall is really the "design spec" for a brain, so semi-graceful degradation and fuzzy recall comes for free.
I'm not sure if old-age memory unreliability is totally random - I would not expect it to be. I'd expect these associative recall failures to happen when there are too many memories partially matching the same "key" (activation pattern), which presumably happens mostly when they do have something in common.
I’m a lot better at assessing things from a high level, knowing where to find the information I need, and knowing how to assess it for accuracy - but I can’t remember details anymore. Books become summaries. Trips become a few key snapshots representing the trip.
I see this kind of "interconnecting ideas" of my current years as somewhat connected with "fuzzy memory". It's like, when I retrieve data, it's not as precise, and sometimes I get neighbouring data, but that can actually of use.
It hasn’t, and rightfully so. I don’t think there’s any evolutionary advantage in remembering the first steps you did or the first time you drank milk, for example, and indeed, nobody remembers those.
A brain that remembers where the berries grew last year, where that cosy cave is, or where that angry tribe lives has evolutionary advantages. Most of the rest such as remembering specifics about events we visited, I think, is collateral non-damage, plus, oftentimes, reinforcement learning (many childhood memories get reinforced by your (grand)parents telling you repeatedly what they remember happened)
The brain, before your born, in GW25 (gestational week 25) has finished growing all the neurons you're basically [we can talk about this later perhaps] going to have for the rest of your life. At GW25 current estimates say you have ~86 billion neurons. Now the timing for this next part is a bit unsure but your body doesn't need anywhere close to 86 billion neurons, so at some point as early as early adulthood you start to lose 85,000 neurons per day pretty steadily until you die. Now neurons are not the end all be all because connections are potentially what really matter but that is to say that the neurons that you have after GW25 are the neurons you basically have. Now going onto synaptic connections where things start to matter more. Now synapses form and then get pruned all the time its natural. But the rate of formation and the rate of pruning is not the same at all times of life. Now from a raw number of synapse scale we see a tipping point at around 16-26 years old (debated hence the big range) where the number of synapses start to go down, indicating that the rate of pruning is now outpacing the rate of formation. [It does seem however that the dysfunction of the rate of pruning i.e. not enough ends up with consequences like schizo or asd {autism}]. There is another factoid that the rate of decline seems to stay relatively stable until you hit ~60 and then synapse related decline becomes much more noticeable and we start to think of synapse loss as exponential.
Now about new neurons after GW25 is a whole topic in of itself... heavily debated but I won't get into that history, the most accepted viewpoint is that it does exist in the hippocampus [other regions as well] (really cool work with c14 carbon dating from atomic bomb {2010} and perhaps less cool new sequencing methods give evidence {2025/2026}). Note estimates of how many are born are comparatively low-ish, 500-1000 neurons per day.
That begs another question however which is why and what do they do? Final interesting part is that when you stop the mouse hippocampus neurons from dividing, [unethical to do in humans :( ] distinct representations of experiences start to look similar and overlap. I also know if you ablate neurogenesis in mouse nasal cortex I think mice lose the ability to form new sensory sensations all together.
Disclaimer, the evidence for function of new born neurons is actually pretty low only a few studies have tried this so its no where close to accepted and thus far far from textbook standard so take it as you will.
Existing memories may be degrading as a result of ongoing neuron death and synapse pruning, but at the same time our "storage capacity" (neurons+synapses) is essentially fixed or decreasing, and new memories are going to increasingly be competing with existing ones.
Presumably there is at least some connection between our neuron + synapse counts and species longevity. If we had evolved to live longer then one might naively expect us to have more neurons/synapses to support life-long learning and memories, although you could also argue that, for example, episodic memories too far back are not useful to remember, so maybe there is a cap to how much episodic memory capacity we need, and the complexity of the evolutionary niche we inhabit also limits the amount of declarative knowledge we need to store.
It's been noted in birds that hippocampus size seems highly correlated with the species memory capacity - but specifically for memory of seasonal seed hiding locations, where the bird only needs memory capacity for the number of seeds (can be 10's of thousands) hidden in a single season - they presumably reuse this hippocampal memory capacity each season, and forget the last season.
Have there been any studies relating brain size / memory capacity to the need of a species for lifelong memory?
And I've more than once found that an anecdote I've been telling has been incorrect, precisely in a "melded memories" kind of way. It happened at this sort of gathering which were usually held for that reason so X was involved... and then finally look at the old pictures and there's no sign of X. It is literally the brain invoking "lossy compression" to make it all fit, just like certain web services that let you upload pictures without limit might reduce old ones, that hardly anyone ever looks at any more, to lower quality to limit storage bloat.
Of course any married man can confirm that this only applies to men. Women's memories are flawless. If they claim that they remember a conversation exactly, word for word, 20 years later, who's to prove them wrong?
Came across screen captures of the original Everquest (SO COOL) and other games, photos of anime/cosplay events, city shots, family, friends, etc. Really fun for them, and fun for people who like me who still enjoy surfing the web.
edit: Screencap of an Everquest screencap for funsies: https://imgur.com/a/HUqc6p5
But she's gone now and so is that habit.
This is one thing for all of the pairs of genuine friends doing this, but I also have two friends who I introduced to each other exactly once, and who have never spoken since, who have a 6 year snap streak together, and both of whom have streaks like that with dozens of other people, many of whom are virtually strangers.
For the two friends I'm thinking of, my impression is that they don't know each other any better through snapchat than they do from me knowing both of them and mentioning them to each other (they live 1000 miles apart and only met once). I guess the best I can say is that I would be comfortable seating them at the same table at my hypothetical wedding. Same vibe as my cousin I have on Facebook but have only really met twice in the last decade.
Why did you have to destroy my memories? :)
It's like looking at the first DivX encoded movie that was soooooo good vs. VideoCD/LaserDisc. And then you look at it today and it's so damn blocky and also in that tiny resolution. Ugh!
My first read of this headline was to observe that people seem to get more comfortable stereotyping people different from them as they age. Thanks for the datum.
For example, having no photographic memory, I can remember the general "feel" of the innards of an antique shortwave receiver I used to have as a kid. But almost all of that is context. I know what canned octal base vacuum tubes and big tuning capacitors look like, what sort of components were in the big band selector "sled" (for geeks: This was an NC2-40D) and so on. So I can "zoom in" on the coarse memory by just filling in details that are generic, not specific. And these details may be from different sources than the original memory too.
And so the brain needs to remember - if one is not faceblind, which in fact I am - just the overall geometry of someone's face, and all the other stuff can be filled in as needed (how they look when they smile or are anxious etc.) Compression, compression. We have a lot to learn from nature. Note that a faceblind person does this too, of course - just not with a geometrically exact visualization of that person's face, just one with the correct "feel".
And that's also why synthetic memories can be formed so easily. You really don't have to insert much. The brain infers the rest as needed.
Except, my wife thought it was the other surgery. And insisted. I was incredulous and angry. I literally nearly died during the complications. I think I would remember the surgery it was from. And it definitely wasn’t from my vasectomy. I decided to make a stand.
I found the oxygen sensor we purchased for the event. Found to manufacture date. It was after her event. So it was physically impossible have the o2 sensor before her event. Wasn’t good enough. Maybe the date was wrong.
I finally went to the hospital and got the records. I was right. My wife simply could not or would not acknowledge it.
I spent the majority of my marriage being “the forgetful one”. And here was a single case where I was right and she had memory issues. And she could not simply say “oops, I got it wrong”. I began to doubt her memory and thought maybe I was being gaslit sometimes.
It was a very influential event in the course of our marriage. I lost a lot of respect for her and began to wonder what our future would be like.
We were divorced within a year.
Not only this, but other things too. But this was a big part of it.
Life is too short to be miserable, and we don't celebrate divorce enough. I'm happy for you both, and hope you are too. It's just as much of a milestone as any other big life event.
I've never been married, but I have noticed some couples get sort of locked into the level of emotional maturity they were at when they got married. I've always assumed it was a lack of new experiences stunting personal growth.
Obviously, similar could be said about me since I've never been married. That too is a lack of experience. Though it is worth pointing out that it's stories like this and my own past relationships that have scared me away. Being emotionally stable and reasonably humble is underrated.
Eventually after 19 years I wised up.
Resentment started building up and up and up.
Our model of the world, and this includes "memories" is very highly compressed, and extremely lossy. We reconstruct everything from what neurons activate when we recall something, so it can also be like a corrupted zip, where we don't access everything required in the moment. This can be affected by sleep, stress, hormones, etc.
What is quite interesting is when we try and recall something and can't, but the subconscious processing continues and you recall it later.
I don't think the ageist comment in the headline is appropriate anywhere.
It is well known that memories and perceptions at every age are largely constructed more as "what makes sense" ("no, a gorilla did not run across the basketball court") than "as recorded" recordings.
EDIT: https://en.wikipedia.org/wiki/Inattentional_blindness#Invisi...
Then again, i'm 45 and I've been losing my keys and my IDs since I was 20
The entire article is AI-generated.I wish such sources would include the prompting used as well as the model so a (human) reader can better evaluate the document.
There is a responsible signer.
If you think their method can be improved, write at https://studyfinds.com/contact/
They disclose an "AI policy" - https://studyfinds.com/ai-policy/
> This process includes multiple checks using different LLM platforms, human editorial verification, and cross-referencing with original sources to ensure accuracy and eliminate false information, misleading content, fabricated content, bias, and unreasonable speculations or editorialization
However, when you experience the same thing repetitively -- having breakfast; getting ready for school/work/etc. -- it doesn't make sense for your brain to remember each of these events as distinct events but to mush them together into a single or combined memory. This may be due to how the brain indexes/references memories and that memories with the same references get combined together.
This is also partly why time appears to go faster when you get older -- the same day-to-day events blend together but the key milestones (holidays, etc.) stick out.
It kind of makes sense in that we do not retain the raw information, it's always interpreted, and it needs to be updated to be in line with other things we know.
https://www.youtube.com/watch?v=PqtggjVAi8M
> But memories are not static like photos. Like dioramas made from wax, each time they’re under the spotlight of your attention they can melt and change a tiny bit.
I can remember the neighbors standing slightly in our yard and then explaining to my parents the comet and pointing it out to us. I can remember at some points that followed being aware of the parallax effect as it seemed to appear on either side of my other neighbors’ house as I walked up and down the driveway.
But just a year or so later, I knew that the picture in my memory was more like an illustration of a comet. The accurate image was completely gone even though the memories surrounding it remained, and in the place of that image was something that I had imagined.
There's also the issue of the more we remember something the more we change it. Which could contribute to the blended memories.
Also that skill is probably excellent for interviews which is the ultimate memory test. I.e. Tell me a time when you turned water to wine, plus 5 deeper questions, any hesitation will be chalked ip as suspicious AI use.
For me I either do or I remember which makes performance management and interviews hard for me (as someone vouching for someone as evidence is rare now).
My friend recently told me about seeing that an old coworker of hers from 20 years ago described her role as HR manager rather than HR assistant on her LinkedIn. I can easily see how someone with a fallible memory can retroactively exaggerate their title or responsibilities, especially when it's been 20 years.
And the actual study: https://academic.oup.com/cercor/article/36/7/bhag114/8758582
But, she's come around on that, and recently been upset about the affair he's been having (they divorced in 1980), and is irate over him having another kid with his new wife (1983), but isn't surprised that I haven't spoken to him in 12 years. She's never surprised at how I've aged, but doesn't remember her grandkids even exist and insists on meeting them soon (but remembers them when she sees them). Two weeks ago, she called to yell at me for things I did in high school (1990). At this point, I seem to be the anchor for everything recent, but when I'm not around physically, it's scattered everywhere else.
My mom & aunt took yearly turns keeping her in their homes. If you kept her away from the stove, she still seemed quite happy helping fix her own breakfast, taking her meds, etc. Give her a warm washrag after, and she'd wipe down the table and then proceed to putter all around the kitchen, wiping and straightening. She preferred cartoons on TV (easier to see, I guess), and was always glad to fold clean towels or clothes, if she had the chance.
Once, she actually recognized me, and remembered my name. It was a very precious 5 minute chat, but then the memories were gone. Still, I'll always remember her that way, because it was just like how I remembered her, when I was a child.
Mine is showing signs that several of us believe to be dementia, but she refuses to be seen and screened. It has gotten worse over time - repeated conversations, sometimes inside the same phone call, and a shift from "can't remember things if she doesn't write them down" to "needs to ask on multiple occasions because she hasn't remembered to write it down" - but not to the point where she seems to be a danger to herself.
I love my mother, but she is difficult. She also has grown with age. While she is difficult now, she used to be abusive and manipulative. My father would never speak ill of her, but from some things she's said I believe she was worse before she was raising us. If my mother regresses to an earlier state of mind, we will both need professional help. She will need professional care, and I will need help to not internalize the way she treats me.
I recently had a very curious experience - my in-dream me recalled a memory from a past dream. It was a really strange feeling that definitely added to the "realism" of the dream.
Like I remember a good friend picking me up from my childhood home, yet I didn’t even know that person until I’d long moved out (many years) from that place.
My grandmother has dementia, and if you know her you can see exactly which events/memories she's erroneously combining.
Instead, they are selective representations of parts of a subjective experience. It also means that memories aren’t static, but shift over time and can even be imagined.
We know that with age the amplitude of these rhythms can decrease, as can the synchrony between cells.
This kind of mid-management seems to have at least some circadian component, and it would have been great if they looked to see if the effects were equally bad at all times of day, and knew the chronotype of the participants to use as a reference. I’d love to see if every test participant was tested at their cognitive peak, too.
So that's an interesting parallel. Says something about memory.
In contrast, when we forget something we are not summarizing anything, we are just losing the ability to recall something. We might remember what we have forgotten later on, showing that it is still somewhere in the mind, and that the reason we have forgotten it is not necessarily because it has been discarded.
Even then, what this article is talking about is not even normal forgetting as a function of the brain. It's talking about a degenerative and dysfunctional form of memory loss where different memories are confused with each other.
Supposing it is the submitted (under Antonaros), see https://news.ycombinator.com/item?id=49538906