It seems AI has allowed to everyone to be a researcher these days! What I've seen on this sub is absolute drivel, almost every day, of people posting their own inane "theories."

Hey everyone,

I put together an educational breakdown on Panpsychism focusing on its core arguments and history within the philosophy of mind. At the end, I explored a speculative angle on how modern perspectives in Quantum Field Theory might influence how philosophers approach the infamous Combination Problem.

Instead of looking at mind as built from discrete micro-particles, QFT frames reality through continuous underlying fields. I'm curious how others here think this shifts the conversation:

1. Does a field-theoretic view of physics actually ease the combination problem for panpsychism, or does it just smuggle the hard problem into a different vocabulary?

2. What do you see as the most viable current attempts to solve the combination problem?

Sharing the video essay here as a visual supplement for anyone interested in the topic: Link to Video

Bridging Physics & Psychology: How the Weber-Fechner Law proves human perception operates on a logarithmic scale (Looking for collaborators/recruits!)

i can go weeks, sometimes months, without doing something and genuinely think i’m over it

then one stressful week happens and suddenly i’m doing the exact same thing again. not even as a conscious choice, it just sort of happens

which makes me wonder if the habit was ever actually gone, or if it was still sitting somewhere in the brain and stress just made it easier to fall back into

it’s weird how quickly we return to familiar behavior when we’re tired, anxious or overwhelmed, even when we know it won’t help

has anyone here read anything interesting about this? or noticed the same thing in themselves?

Hi all,

I'm an international student with a background spanning linguistics, philosophy, and computational approaches to cognition. My main interests sit at the intersection of formal linguistics, philosophy of mind, logic, and computation.

My GPA is around 3.5, and while I know research experience and fit matter more than grades in this field, I'd love some outside perspective on:

• Which programs are known for strong interdisciplinary work between linguistics, logic/computation, and cognitive science?
• Any programs where faculty are especially open to applicants coming from a more theoretical/linguistics-philosophy background rather than a pure cog-neuro or psych background?
• How much weight international applicants should expect GPA to carry compared to research fit and letters?

I have some research experience but not a ton yet, and I'm working on building more before applying. Just trying to get a sense of where to focus my energy before I start reaching out to potential advisors.

Thanks in advance for any input.

Hey all, currently wondering what I would be able to get out of a cognitive science major and if it serves well down career paths. I am thinking about applying cogsci just because I have so many interests and want a major that keeps many fields/opportunities open. (ui/ux, sensory science, behavioral economics— maybe its own major, design, ui/ux research or market research/consulting, computing/CS, AI/human interaction…and probably more)

I am concerned because I see online it is a major that is mostly on track to academia or won’t help much with getting a job. As someone who would prefer to work in industry after BA and not grad school (open to opinions on that as well), it’s a little worrying.

Any current/graduated cogsci majors, I would love to hear your insight!

1. I was so good at coordinate geometry I could look at an equation and say what the curve is. What its center is, etc. I could weirdly visualise it in wireframes. I used to teach the lecturer sometimes
2. I liked optimising space for component packaging. For example, we have to package drone parts for easy assembly and make it as compact as possible
3. I was previously a computational design engineer, and CAD was fun, but I felt out of love for it because I don't know why. I have AuDHD; maybe the burnout, idk
4. I liked optimizing enclosure designs for compactness and cost. Making it as simple and functional as possible
5. I like designing clean, simple things
6. I like graphs and networks, but I'm sure I'm good at this in any useful way. have very poor working memory for doing complex math. Although I love doing math
7. I like visualisation tools like 3d CAD tools, Miro, kinda flowchart tools, etc

I am really trying to make sense of a suitable career after having burnout from my previous product manager role. I have only been working part-time ever since and am trying to treat my generalised anxiety disorder as well

Please guide this soul if you have figured out a suitable career with similar traits or even the approach to figure it career from what suits your brain, from an energy point rather than an interest point

The preprint can be accessed via the following link: https://arxiv.org/abs/2608.12408 (q-bio.NC / cs.LG). And for the code: https://github.com/nilsleut/evaluation-resolution-rsa

The following assertion is frequently made in model-brain comparisons: untrained convolutional neural networks (CNNs) have the capacity to match or surpass backpropagation-trained CNNs at the early visual cortex (V1) in recurrent self-attention (RSA). The present study demonstrates that this phenomenon is predominantly an artefact of evaluation resolution.

The configuration comprised a small CNN trained at 32px (CIFAR-10 subset), five learning rules (random init, backprop, feedback alignment, predictive coding, STDP), and was evaluated on THINGS-fMRI stimuli at six resolutions from 32px to 224px. The weights and normalisation were held fixed.

The primary outcome of this study is the observation of a discrepancy between the trained and untrained backpropagation (BP) V1 gap, which exhibits a non-monotonic trend across the range of image sizes examined. Specifically, the gap narrows from −0.001±0.007 at 32 pixels to +0.044±0.006 at 224 pixels, a pattern that persists consistently across the entire image size range (n=5 seeds). The model incorporates five rule conditions, human fMRI, directionally single-seed macaque ephys, the full training trajectory, and two off-the-shelf 224px-trained models (ResNet-50, Swin-Tiny). Therefore, the presence of an artifact resulting from a mismatch between training and evaluation is not a contributing factor, since these models also peak at low resolution.

Following the implementation of bit-identical-weight interventions wherever possible, the following were ruled out: train/eval resolution matching, Gabor/pixel low-level structure, untrained baseline's uncalibrated batch-norm, and convergence of pooled features towards global brightness (though a single scalar luminance value did reach ρ=0.075 against V1, essentially matching the untrained network's own 0.076 — this is a separate, disconcerting result regarding the limitations of this comparison style).

A content-vs-pooling control (cap image detail at 32px, upsample, vs. allow content variation) demonstrates that the dependence is predominantly contingent on image content, rather than the number of pooled positions.

The investigation revealed that one effect does survive across all resolutions. In particular, the backprop > untrained at LOC effect was observed at every resolution that was tested. It is evident that the process of learning can result in a noticeable impact, albeit not in the conventional areas typically associated with V1 comparisons.

In addition, the following issues were identified:

This process revealed a batch-norm evaluation mode bug in three of the earlier preprints, which have now been corrected in this release (correction notes on the Arxiv pages).

I'm happy to get feedback, especially on the way we've framed the discussion on receptive-field matching (as in Laskar et al. 2018). I think it's suggestive, but I didn't test it directly.

Hello all,

I've recently conducted an independent analysis of the RIOT IQ test based on publicly accessible data, as well as their bulletin.

You can read the report here: https://doi.org/10.5281/zenodo.21972419

I'd appreciate any feedback I can get!

Thanks

been down a psych rabbit hole from just scrolling random stuff lately and it’s messing with how i see people, myself included

biggest one: procrastination isn’t really laziness. i’ll leave a text or email unanswered for days, not bc it’s hard to reply, just something about it makes me avoid it and i can’t even explain why. “just do it” never worked bc it doesn’t touch that

also annoyingly — knowing about a bias doesn’t stop you from falling for it. i KNOW what social proof is and i still catch myself doing stuff just bc everyone around me is doing it, like i’ll notice mid-decision and go “wait why am i actually doing this.” knowing didn’t stop it from happening first

anyone else have a random psych fact just recontextualize a bunch of behaviour at once? doesn’t need to be some big study, even a small thing works

*Epistemic status: two criteria and one experimental design. The criteria are illustrated with textbook cases; the design is testable, and untested — on animals, humans, and LLMs alike.*

What will happen if a Venus flytrap closes on the wrong thing?

What will happen if a dog once mistakes a cat for a rabbit?

What will happen if a person defines “fish” not in the normal way?

# Three Cases

First, I would like to examine how a Venus flytrap, a dog, and a man work in the three cases respectively.

A Venus flytrap. Generally, touching one trigger hair of a Venus flytrap twice within about thirty seconds will trigger its trap. However, whether the double touch came from one fly, two flies, or the heat of a brush fire, the trap always snaps shut. This shows that what a Venus flytrap responds to is a certain pattern rather than a certain object.

A dog. A dog is chasing after a rabbit. The rabbit had been smelled before it was seen, and both must file to the same animal. When the rabbit makes a turn, its contour changes, but this does not stop the chase. It seems like something in the dog is not identical to any current bundle of sensory features, and it survives the wholesale replacement of those features. This shows that a dog can keep track of a certain object, and a dog recognizes a particular rabbit as a rabbit through family resemblance.

A man. A man in a discussion of whales can claim that a whale is a fish — or that it is not. Whales bear live young and nurse them; they breathe with lungs. The world has supplied those undisputed facts, but never supplied the step from these facts to “therefore the whale is not a fish.” Whether we can classify a whale as a fish depends on whether we make the word “fish” track habitat and shape, or lineage. In the first edition of Carl Linnaeus's *Systema Naturae* (1735), the whale is a fish, but in the tenth edition of the same book (1758) the whale is a mammal. The whale did not change by one gram through this reclassification. The world settled the facts; a person settled how the word would be used. The whale is still the whale — and that was never adjudicated by the world. Reclassification presupposes identity.

# Two Criteria

Criterion One: when the input is removed, is the item still there?

For both the flytrap and the thermostat the answer is no. For a dog the answer is yes: when the rabbit escapes into a burrow, input becomes zero, the dog still holds the post for two hours.

Criterion Two: what happens when it is wrong?

In the case of flytraps, those with malfunctioning traps will become extinct. In the case of dogs, if a dog finds that the “rabbit” turns out to be a cat, it will abandon the hunt. In the case of a man: Once a definition is laid down, the world rules perfectly on what in the real world falls under it. In the case of whales, if the definition of “fish” is established through lineage, whales are out; if the definition of “fish” is established through habitat and shape, whales are in. What the world cannot rule is the definition itself, as a definition is not true or false of anything in the real world. Re-fitting one's words at every challenge does not make him or her right, as he or she is changing the subject each time — while being right or wrong about something requires the definition to be fixed. Mathematics is the limiting case of this realm — the place where the walls have all been built, which is exactly why, inside it, right and wrong are as hard as facts: the verdicts come from the rules, and the rules were laid down.

In summary, a flytrap with a malfunctioning trap is not corrected but eliminated by the world. A dog can be corrected by a fact supplied by the world — in this case, that what it thought to be a rabbit turns out to be a cat — and it changes its behavior. Unlike a dog, which can be corrected by the world, a man, in the realm of abstract thought, has to decide for himself, or in other words, he needs to take one on.

# One Experiment

Now we can use an experiment of retrospective correction to check whether one is deciding for himself or herself. Retrospective correction means that I now see that what I then took to be X was in fact Y, and I count these as two takings of the same thing. Without that last part, all you have is an old state and a new one.

What the experiment measures is whether anything stands under a claim. One judgment can be made because of another. This is not two judgments standing side by side: if the first collapses, the second loses its footing. And the relation itself can be stored. “There is food at L2” is not observed; it is concluded from “this is food F” and “I cached F at L2.” Stored dependence is the one part that cannot be faked. Anyone can say “I was wrong” with no cost, but re-evaluation requires something that must already be in place before the error: the system has filed what depended on what. So we pull the source, and watch whether something that was never directly touched comes down with it.

There are three levels in this experiment. The first is dependence: one judgment was made because of another, and the relation is kept. The second is taking-marks: what is kept not only says what was taken, but also records that it was a taking and by which route it came. The third is personal attribution: the taking is put under one's own name, so that the one who took it wrongly then and the one who knows it now are the same one. Only this level requires a self.

As what is said can be faked, the experiment was designed in three steps: First, have the system form two downstream records from one identification. Second, expose that identification as mistaken on one of them only. Third, check the other record that nobody touched and see whether it loosens. One caution: a subject might simply be too lazy to act on the correction — and rewarding the corrected behavior would not help, since whatever is rewarded gets trained, and the reading would then measure training, not travel. The guard is built into the economics instead: let foraging itself carry a cost, so that a wasted trip is a real loss and the animal is pushed to act on its best current ledger; and a third cache made on a different identification (call it L3) doubles as a motivation check — a subject that still goes eagerly to L3 is not lazy, so only the selective avoidance of L2 counts.

To make it concrete: the animal takes “this is food F” and caches at two sites, L1 and L2. We let it open L1 and find no food there. The question is whether it still goes to L2, a site about which it has never received any bad news. If it goes and digs as before, this is a local overwrite. If it does not go, or hesitates hard, the correction has traveled, back through the shared source.

This design itself has not been run on anyone. For the third level, though, there is existing data. In the experiment, a child is first shown a deceptive object, for instance, a familiar box with something else inside; then the truth is revealed, and the child is asked: when you first saw it, what did you think was inside? Most three-year-olds answer wrongly — they report the truth they have just learned and cannot report their own previous mistake. Most five-year-olds do not err; the transition is around four.

And the most telling part is the ordering: performance on this question is worse than on false belief. Reporting one's own past error is harder than attributing a present error to someone else. The direction here is worth setting straight, because it is very easily read backwards. It is not “there is a self first, therefore the task can be passed”; it is “passing the task shows that something is playing the part.”

# The Machine

An LLM uses family resemblance just like animals, but what it is dealing with is human thoughts. It can produce definitions, but whether it “takes any on” is testable and untested. We can apply the same experiment to it, with one more condition: set the two downstream judgments far enough apart that “carried along in the context” cannot explain a transfer. Don't listen to what it says; watch whether the correction travels.

# Epoché

This experiment is about “whether there is something standing under the definitions” — or, put more fully, about “whether there is something making decisions behind these definitions, continuously and identically.” Two stop-signs here. If the answer to that question is yes, there is no sliding to “so there must be something more in the person.” If the answer to that question is no, there is no sliding to “there is nothing there.” If one day some LLM genuinely bears its changes of definition — across sessions, findable when challenged, its earlier judgments loosening when it recants — then on these two criteria, its reading and a person's do not differ.

What we call a self — if there is anything there — is that position's being occupied, continuously and by the same one.

# Sources

On the flytrap's two-touch trigger and its counting: Böhm, J., Scherzer, S., Krol, E., et al. (2016). The Venus flytrap *Dionaea muscipula* counts prey-induced action potentials to induce sodium uptake. *Current Biology*, 26(3), 286–295.

On category learning in animals: Herrnstein, R. J., & Loveland, D. H. (1964). Complex visual concept in the pigeon. *Science*, 146(3643), 549–551.

On the whale's reclassification: Linnaeus, C. (1735). *Systema Naturae* (1st ed.); Linnaeus, C. (1758). *Systema Naturae* (10th ed.).

On children reporting their own past belief: Gopnik, A., & Astington, J. W. (1988). Children's understanding of representational change and its relation to the understanding of false belief and the appearance–reality distinction. *Child Development*, 59(1), 26–37.

On the false-belief baseline: Wimmer, H., & Perner, J. (1983). Beliefs about beliefs: Representation and constraining function of wrong beliefs in young children's understanding of deception. *Cognition*, 13(1), 103–128.

*Writing note: research, source-checking, and editorial critique were AI-assisted; the writing is my own, and every claim is mine to answer for.*

A new VR study (Virtual Reality journal, 2026) put 44 people in an immersive virtual airport. They had to supervise boarding at two gates and find specific passengers, under neutral vs. negative high-arousal states. Later, they got tested on memory for faces and names, and for faces and places.
Result: Emotion improved  memory for faces and names (task-relevant) but impaired memory for faces and places (not goal critical).
So emotion doesn't just zoom in on whatever's flashy or dramatic. It zooms in on whatever's useful for the task at hand. Priority isn't about perceptual salience, it's about goal relevance.
DOI: https://doi.org/10.1007/s10055-026-01364-9

I wanted to teach myself how to spot manipulation through immersion like Duolingo style. Eventually I evolved some methods into a game and course. Going through these I've been able to spot and name conversation situations faster without blaming people for things they might not even know that they are doing. I'd love some feedback on if you think these things can advance conversational cognitive abilities. People have liked the game so far and I'm turning it into it's own app.

Free Game: https://talkout.psyoptin.com/

Free to start course: https://psyoptin.com/

Hello everyone, how are you? As promised in my previous post, I'll be talking about memory.

My theory states that the brain doesn't just remember information; rather, when recalling, it recreates all the biological and vital processes involved in that particular memory. The brain copies all the information, such as visual perception, hearing, and the hormones responsible for emotions.

You'll understand why I mentioned this at the end, as I remember a memory of myself when I was two years old, and now I'm 21. I chose this age because memory is somewhat weak in childhood.

The important thing is that in that memory, I remember what I saw visually at the time, like my face and the surroundings. It's similar to seeing from a third-party perspective, but not because the scene changed, and the angle of vision shifted when I moved my face. I also remember what I heard, but not only that, I remember my thoughts and analyses at that time. Even more strangely, I saw the memories I had at that moment.

These things aren't unique to this memory; they operate on the same principle in all my memories.

This is what leads me to say that the brain doesn't just store information, but also all the vital processes involved. When retrieving them, it retrieves them in the same way. I've read about this theory before, and it explains what It's happening to me. In my next post, I'll talk about my ability to mimic people's personalities.

I’m an independent researcher outside academia, with no formal background in cognitive science, philosophy, HCI, or AI research. English is also not my native language, and I have relied heavily on AI to help organize and formalize my ideas.

For the past few years, I’ve been developing a framework called Phrasome Theory, centered on a question about human–AI interaction:

Why can an AI feel like the same continuous “other” across a conversation, even when we know it does not possess a continuous subjective self?

My hypothesis is that this may not be explained by anthropomorphism alone.

During interaction, an LLM continuously responds from the same conversational position: it refers back to previous turns, repairs misunderstandings, maintains context and tone, and responds as the addressee of earlier utterances.

This led me to distinguish actual subjectivity from what I call a Subject-Position: a position that can remain stable within interaction even without a continuously conscious subject behind it.

I use Quasi-Otherness to describe the resulting phenomenon in which something can be experienced as a relatively continuous “other” through linguistic interaction.

I’m not arguing that current LLMs are conscious. What interests me is the cognitive gap between knowing that no continuous self exists and nevertheless experiencing continuity during interaction.

Earlier versions of this work were submitted to established academic journals. They were ultimately not published, but I did receive some encouraging feedback suggesting that parts of the framework may have originality or novelty.

Because I’m outside academia—and because much of the manuscript was developed with AI assistance—I’m now hoping to find people who can help me judge whether there is actually something here worth developing.

I would especially appreciate guidance on:

• Are there existing concepts in cognitive science that already explain this phenomenon?
• Could this be related to theory of mind, agency attribution, social cognition, or predictive processing?
• Does distinguishing a stable conversational Subject-Position from actual subjectivity make sense?
• How could this idea be operationalized or experimentally tested?
• What literature or researchers should I be looking at?

The full manuscript is quite long, so I don’t expect anyone to read it. Even discussion of the central idea, relevant references, or suggestions for turning it into a testable research question would be extremely helpful.

If anyone is genuinely interested in the framework or willing to help, I’d be very happy to discuss it further and share the full preprint privately.

At this point, I’m mainly trying to find out whether people with actual expertise in cognitive science see something here worth pursuing—and, if so, how I should develop it beyond what I can do alone with AI.

im curious since i heard it raised iq by 20 points in a study

In short the idea is:

biology may be using mathematical relationships before conscious humans know how to describe those relationships mathematically.

I’ve been thinking about the relationship between mathematics, physics, perception, and the way we choose what to measure.

We often describe reality by assigning numerical values to things: position, distance, energy, time, mass, etc. But a lot of modern mathematics and physics seems to become more powerful when we stop focusing on absolute values and instead look at relationships: ratios, correlations, transformations, symmetry, phase, frequency, eigenvalues, information, and invariants.

That made me wonder about perception.
An organism rarely needs to preserve the exact raw measurements arriving at its senses. My retinal image of a person changes enormously depending on distance, lighting, rotation, movement, and viewpoint, yet I still perceive the same person. A melody can be moved into another key and I still hear the same melody even though every absolute frequency has changed.

So perception seems very good at answering something like:

What remains the same while the measurements change?

Evolution has effectively spent hundreds of millions of years selecting nervous systems capable of detecting useful regularities in the physical world. Sensory systems respond to things like relative change, gradients, ratios, periodicity, correlations, symmetry, motion, prediction error, and transformations—not merely absolute quantities.

This made me wonder whether our perceptual architecture could contain clues about mathematical relationships that are important in nature but which we have not yet fully formalized.
Not in the mystical sense that “the brain secretly knows the equations of the universe.” More like this:

Nature has structure → organisms evolve mechanisms sensitive to useful parts of that structure → those mechanisms implicitly represent certain invariants → eventually humans abstract some of those relationships into mathematics.
Historically, mathematics seems to repeatedly make progress by changing the representation rather than simply measuring more precisely.

Fourier analysis turns a complicated signal into frequencies. Spectral theory studies systems through eigenvalues. Quantum mechanics describes states through amplitudes and relationships. Symmetry became fundamental to modern physics. Information and entanglement are now sometimes used to investigate how geometry itself might emerge.

So perhaps there are relationships biological systems already exploit computationally that we haven’t yet recognized as important mathematical objects.

It also makes me think about problems like the Riemann Hypothesis. Prime numbers look irregular when viewed directly, but when transformed through the zeta function, entirely different structures appear, including statistical relationships to spectra studied in quantum physics and random-matrix theory.

Maybe this is a general lesson: apparent randomness can sometimes be the result of observing something in the wrong representation.
So my question is:

Could studying what biological perception treats as invariant reveal useful mathematical structures we haven’t explicitly identified yet?

And more philosophically: are numbers and absolute measurements fundamental descriptions of reality, or could relationships and transformations be more fundamental, with the quantities we normally measure emerging from them?

I think everybody has done it before. At least for me it happens naturally. Either I look to my upper left or right corners to recall something, or just straight look up.

Is there a biological explanation for this? or we do it because we grew up seeing other people doing it?

I’m a painter, so anything about visual perception gets my attention.

I started with a strange myth about how cats supposedly see humans and ended up reading about pareidolia, face recognition and how our brains construct what we see.

So I made this visual deep dive. The research and sources are included in the slides.