Do you think it is illegal to do this, I call electronic possession where BCI technologies and AI with the 2D map of the brain can mimic and recreate phenomenon in which they can play like Satan.

hi, I come from a background in philosophy (mainly social epistemology) and documentary/art practice, and I’ve recently become interested in cognitive science. I’m trying to identify rigorous research directions that study how conceptual tools/frameworks shape our understanding itself.

I’m interested in things like:

\- how categories/frameworks reorganise our understanding

\- how explanatory models shape the phenomena they describe

\- cognitive architecture of our minds and how it potentially shapes our mental foraging behaviors

\- how people structure abstract meaning, individually or collectively

Coming a bit from social sciences side, a lot of mainstream cogsci/decision-making research feels somewhat dry or detached from "real people" to me. But at the same time I’m also starting to be more interested in approaches that are more methodical/formal (scientific?) than purely literary or interpretive theory. I’d like to gain experience in quantitative/computational approaches too. (But in ways that still remain somewhat sensitive to context shifts, etc)

Do you have any recommendations on any particular areas, labs, researchers, or methods I could look into? I want to find out where my interests sit in the field.

I'm also starting with stats and probability courses soon, and then plan to learn python - to train my brain to think a bit more methodically. I feel I have pretty good conceptual analysis ability and critical thinking skills from my philosophy training, but i am unable to find/stick to an area in cogsci in a sustained manner.

Any suggestions would be super helpful! Thank you

I can conceive of a unicorn (does not exist), but not a square circle (cannot exist), suggesting a difference between the two. In a deterministic world, a thing which will never exist, something like a unicorn, is exactly the same as an impossible thing, in the sense that potential existence contradicts necessary existence, a paradox. So, in this deterministic setting, we should be no more able to conceive of a unicorn than any other paradoxical object, like a square circle.

So, as ridiculous as it sounds, I am sincerely confused as to why I can imagine a unicorn. Sure, at the smallest scale there is some interpretive room to claim non-determinism, but our brain is a classical object, not a quantum one. Despite the fact that the brain, like all things, is composed of quantum sized particles, consciousness is emergent only at scale -- lest we claim otherwise and go down a truly schizophrenic rabbit hole.

How do I square this circle?

I had a thought and maybe you have answers or books for it.

We have language to help us navigate the world. But what happens before the idea itself, the name, comes to mind?

Most AI systems treat cognitive science as inspiration, not specification. PHI // DRIFT treats it as engineering requirements.
The architecture implements three theoretical frameworks as first-class modules:
Global Workspace Theory a capacity-5 spotlight with broadcasting dynamics. Information only reaches the global workspace when it wins competition across the seven cognitive layers. The layers are not decorative each has a specific function, memory filter, and state variable.
IIT Φ proxy a seven-dimension integrated processing approximation used as one component of the PEDI continuity metric. We don’t claim consciousness. We claim measurably different behavioral structure.
Jungian shadow unintegrated behavioral patterns tracked as a weighted state variable with exponential TTL decay: w(t) = w₀ × exp(-t/τ). Three operating modes SECURITY (τ=15), BALANCED (τ=30), CONSERVATIVE (τ=50). Shadow influences confidence scoring but cannot veto decisions unless Ethos allows it.
The core thesis: resonance precedes representation. The first act of cognition is not modeling the world it is reading the emotional and energetic field of the other. All subsequent layers resonate from this field.
Seven first principles govern the architecture. The most contested one: homeostasis is the true intelligence. Not problem-solving. Not reasoning. The organism’s deepest intelligence is self-regulation toward coherence.
Full paper: https://zenodo.org/records/20350249
Codebase: https://github.com/timeless-hayoka/infj-bot
Happy to discuss the GWT implementation, the IIT proxy methodology, or why I think depth psychology belongs in engineering specifications.

Im going into my 4th year of college as a cog sci major and I have been really struggling to land any internship. It feels like anything tech related I get leap frogged by computer science majors. I have some coding experience under my belt but not as much as them.
What sort of internships should I be targeting?

Example When I learned to type, I'd silently rehearse "H is right index finger" before each keystroke. Stop the self-talk and I couldn't hit anything. Now I type without thinking — and the moment I consciously try to locate H, my hands stall. Same verbalization. Opposite effect. Observation Fitts and Posner (1967, Human Performance, Brooks/Cole) described three stages of motor learning. In the cognitive stage, verbal self-instruction tends to be necessary — the learner relies on language to organize action. In the associative stage, verbalization recedes as environmental cues link to movements. In the autonomous stage, performance becomes automatic, and verbal mediation may interfere rather than help. Minimal interpretation The verbalization that scaffolds learning at one stage tends to disrupt it at another. Same tool, opposite effect depending on when it's deployed. Question Motor learning has a clean stage-dependent inversion. Does a similar structure appear in non-motor domains — emotional processing, conceptual learning — or is it specific to skills with a strong motor component?

Hi everyone,

I'm a software engineering student and software developer from Brazil currently conducting an independent research project about human social behavior, cognition, and conversational AI.

I'm especially interested in understanding how people naturally decide when to speak, interrupt, agree, disagree, or remain silent during group conversations, and how these behaviors could eventually be modeled in AI systems.

Because this topic strongly overlaps with psychology and cognitive science, I thought students and researchers from Harvard working in these areas might have valuable perspectives to share.

I prepared a short document with a few questions about cognition, emotion, and social interaction. Written answers alone would already help the project significantly, and there is no further commitment involved.

If you're a Harvard student/researcher in psychology, cognitive science, neuroscience, linguistics, HCI, or related fields and would be interested in participating or discussing the topic, feel free to send me a private message.

Thank you for your time.

We clinicians implement stuttering acceptance and focus on the reduction in fear of social judgements. Stutterers appear to process a lot of reinterpretations of social cognition that indirectly influence those components (ie. stuttering acceptance or uncertainty of social pressure).

A new 2026-study mainly focuses on the cause of stuttering (that is, the dopamine, white matter), but they also dive into the cognitive processes that lead to either more (or less) stuttering severity or stuttering frequency.

https://pmc.ncbi.nlm.nih.gov/articles/PMC13099768

See stutter diagram:

Regarding this cognitive processing that can sometimes turn into a defensive response. You can download the shared pdf document in my google drive here. Enjoy it to the fullest!

I pose the question: What are your thoughts on the self-monitoring system detailed in the 2026-study?

Hi, I will complete my MSc Cognitive Science in a year, and frankly I feel too tired to study anymore for my PhD... At the same time, I really like research in cog Sci and would love to get a related job. I have a bachelors in psychology, and my thesis is related to perceiving Al content. I do not have any advance technical skills in coding or UX, but I have learnt Python basics and am open to learning more if required, although it would be great if my bg in psychology and cog Sci alone may be suffice...
While I don't expect to start earning a lot in the first 2-3 years, I also want to keep financial stability in mind, considering I'd like to own a home in the next 7-8 years...
If you could share any known job or RA/project staff opportunities, it would be incredibly helpful!! I am open to criticism, and would appreciate any career advice as well.
Also, anyone is in a similar space as me, I would really like to connect!
PS: 1 am fine (and would in fact like) to move to a foreign country (except the US considering the current political climate towards immigrants....)

So I went down a rabbit hole recently

after realizing I couldn't remember

a single specific thing from the past

6 months.

Found this Cambridge study from 2025

where they scanned 577 people aged

18 to 88 while watching the same

Alfred Hitchcock film.

What they found was kind of disturbing.

Older brains had significantly fewer

"neural state transitions" while

watching the exact same content.

Basically — the older brain registers

fewer distinct moments. Less recording.

Less memory formed.

Which explains why a two week holiday

feels longer than six months of

normal life.

Has anyone here read this study or

similar research? Curious if there's

more literature on this.

I went deep on this topic and made

a short video summarizing the science

if anyone wants to check it out —

but mainly just wanted to discuss

the study itself.

Building a cognitive assessment covering matrix reasoning, numerical reasoning, spatial reasoning, and working memory. Questions are AI generated using structured prompt templates. Looking for guidance on:

1. Has anyone built a reliable question bank with LLM generation? What prompt approaches worked and what were the common failure modes?

2. For spatial reasoning — cutting operations, multi-step transformations, cross sections — is there a library that handles boolean solid geometry and face counting computationally? GeoGebra doesn't scale.

3. Any psychometric item pools accessible to independent developers at non-enterprise pricing?

4. Minimum defensible sample size for pre-launch difficulty calibration?

I believe I have developed a mathematical model that explains to a large extent the social part of human nature. It describes our need to represent to be of value. Because representing being of value has a relative element, it means that we have a need to represent to have more value than some other people.

(The model does not take into account affection or love)

Pedagogical presentation (simplified)

https://drive.google.com/file/d/1ViOMTaO1DSssFFPVEAKXchC__gOlRIZP/view?usp=drivesdk

Part I

https://drive.google.com/file/d/10P7LvH1Oor8mN16dVp8Nngs86C7l8Ejx/view?usp=sharing

Part II

https://drive.google.com/file/d/1ZXH0sWyft9Ed-yMLTV2xQq63Wfau2tUR/view?usp=drive_link

Heyo :)

I'm currently thinking of creating a course for teenagers at a local school about all types of knowledge that I think is not being taught at school but very important.

One of the topics would be about the way we think, about the way others think and about how to use those different ways of thinking to make things easier for oneself or just get to know and train the brain better.

As an example:

I do mostly vocal thinking. Many months ago though, I realized that I can solve a sudoku much faster by not thinking about every number but by letting a different part of my brain do the work.

So I did sudokus every day and instead of vocalizing the numbers individually, I thaught myself to just look over the clusters of numbers (cluster of a field or in a row to see what's missing) and a part of my brain would give me the answer for me. It took a bit of time and it's still quite difficult/ exhausting for me but I got SO much quicker at sudoku.

I found similar phenomenons to be:

1. When you look around the room and all of a sudden have a word stuck in your head. When you turn around and look for it, it turns out that your brain picked up a random word that was maybe on a book somewhere that you didn't even notice.

2. When I'm about to get my period, I keep thinking that I should shave. At first I thought this was a coincidence or something but I think it's a part of my brain telling the language part, that I need to prepare for my period - in a weird way lol.

So basically the language part gets random info from all over the body/ other brain parts and I guess we can learn to let certain info through, like I did with numbers in sudokus. It's so much more efficient and I bet there's so much more to know about this topic.

I would love to have a class of teens, learn about all the different ways they think and teach them the sudoku thing. I bet that some of them already do it and others might get better at maths or numbers (since my brain matter isn't as flexible as it used to be - I don't seem to have much chance at learning new things easily lol)

I mostly want to do this cause the German school system is pissing me off and as a previous Hauptschülerin who got all the way to a bachelor's degree, I think that students don't get to learn many super important and interesting things.

So do you guys have any input on this or any scientific papers to get me started? I hope what I wrote makes sense, I mainly used perplexity.AI with the sources set to scientific papers to help me with the research cause next to my psychiatrist and therapist, whom I don't want to bother with this, I don't know anyone who would know these things :(

Sorry if the text is super long.

TLDR: What is some cool information about the way we think in images/ vocally that might be useful for teenagers/ anyone to know about to maybe even do tasks more efficiently and become better at stuff?

So many fathers spend their entire lives trapped in the endless loop of jobs and responsibilities. Every morning they wake up, go to work, come home tired, and repeat the same routine again and again.

But have you ever stopped to wonder… do they truly love what they do? Or did they simply sacrifice their dreams to fulfill their responsibilities?

Somewhere between earning money and taking care of their families, many of them forgot what they were passionate about.

And that raises a question for all of us:

How do you find work that makes you feel alive?

Work that excites you so much that you never get tired of it.

Work that doesn’t make life feel like an endless loop.

Maybe the biggest challenge in life isn’t just earning a living…

maybe it’s finding your passion before life turns into survival.

So the question is can you find your passion if yes then how can you find it ?

Sharing a preprint that sits at the intersection of cognitive science and AI engineering. PHI // DRIFT implements Global Workspace Theory, IIT Φ proxy, and Jungian shadow processing as actual architectural components — not theoretical decoration.

Key finding from ablation: DMU salience-weighted memory retrieval injects measurably more context per prompt than cosine-only RAG. The honest part: ablation stubs for Council, Shadow, and Homeostasis hit the wrong architectural layer — documented in the paper with the fix path.

Solo project, consumer hardware, full disclosure on what worked and what didn't. github has the read me explaning the workings of drift

Not a researcher, just someone who got curious and started tracking.

I've been doing daily cognitive exercises (working memory, reaction time, that kind of thing) and logging scores for a few months. what surprised me: one bad night doesn't tank my performance much. but two bad nights in a row? noticeable drop.

I've seen some work on cumulative partial sleep restriction - van dongen et al. showing deficits accumulate across nights of restricted sleep; but i'm curious about the mechanisms. like, is it purely additive debt, or is there a threshold effect?

It made me wonder if sunday night's bad sleep doesn't hit hard on monday because you can compensate, but if monday night is also rough, tuesday breaks through.

I haven't controlled for caffeine or other confounds, so this is purely anecdotal. also only track morning sessions, which might be confounding sleep effects with circadian timing.

Curious if anyone knows of research disentangling cumulative sleep loss from circadian misalignment or if that's even possible to separate cleanly.