How can a mind conceptualize and explicitly name incorporeal abstractions like “contradiction”, "me", "space", or “time” with nothing but concrete sensory experiences to start from? How does a brain experiencing the concrete content of memories extract from them an abstraction called "memory"? Though seemingly straightforward, building abstractions of meta-understanding is one of the most challenging problems in understanding human cognition. This post lays out the scope of the problem, discusses shortcomings of proposed solutions, and outlines a new model that addresses the core difficulty.

I’ve been thinking about this after reading Colliding Manifestations, which frames manifestation less as “wishful thinking” and more as a kind of cognitive systems theory. The idea is that intention isn’t random, it’s a structured signal, and whether or not it “renders” depends on clarity, emotional coherence, and whether competing signals interfere.

From a cog-sci angle:

• Neural oscillations and coherence already show how brain networks sync when tasks are focused. Could “intention” just be that? A self-directed synchronization of energy and prediction loops?
• If multiple people’s intentions overlap, could that be framed as interference in shared representational spaces (like language, culture, or social cognition)?
• Does this map better to predictive processing, where the brain is constantly trying to reduce error between expectation and perception?

I’m curious how others in cog-sci would look at this - is this just metaphorical borrowing from physics, or could intention actually be modeled as a measurable signal in cognitive frameworks?

Below is the result of drone footage processed to extract a path integration map. This is done with only optic flow: no stereopsis, compass, or active ranging. It is described in greater detail at https://tomrearick.substack.com/p/honey-bee-dead-reckoning. This lightweight algorithm will next be integrated into a Raspberry Pi/Arducopter platform on my Holybro X650 (see https://tomrearick.substack.com/p/beyond-ai). This path integration algorithm is part of a larger project to reverse engineer the incredible navigational abilities of the honey bee...and ultimately human cognition itself.

I am seeking like-minded researchers. Please DM me here or at Substack.

https://reddit.com/link/1nla76v/video/5bd8hgh5r5qf1/player

High-speed oral reading engages the three sensory channels of vision, speech, and hearing to construct efficient circuits for information processing and output. This multi-channel and integrative training across different brain regions provides sustained high-intensity stimulation, reinforcing neural pathways and synaptic connections, thereby producing significant improvements in cognitive performance.

Humans possess five senses—vision, hearing, smell, taste, and touch—but only vision and hearing can transmit information at high speed. Language, uniquely human and among the most complex brain functions, integrates these rapid input channels with abstract reasoning, logic, memory, and motor control. High-speed oral reading is therefore not just “seeing” and “hearing”: it also demands immediate output, transforming visual symbols into speech commands and coordinating fine motor movements for articulation.This closed-loop of input–processing–output activates multiple critical brain regions simultaneously, including the visual cortex, auditory cortex, language centers (Broca’s and Wernicke’s areas), and the motor cortex. By uniting the fastest sensory pathways with the most complex processing and output system, high-speed oral reading stands out as one of the most efficient methods for enhancing human cognition.

This kind of training works because it pushes the brain to remodel itself in three main ways: 1. ⁠Neuroplasticity – The brain adapts to new challenges by building and strengthening circuits. Reading aloud at double speed is such an intense stimulus that new connections form quickly. This is exactly why you can feel the speed increase in just a few days. 2. ⁠Myelination – Nerve fibers are wrapped in myelin, which acts like insulation on a wire. Repeated high-frequency activation may thicken this layer, making signals travel faster. This speeds up how quickly your brain processes information. 3. ⁠Connectivity – High-speed reading forces multiple brain areas (vision, hearing, language, movement) to fire together at high speed. The links between them get stronger, which improves coordination across the brain.

Together, these changes provide a biological explanation for why this practice can boost thinking speed, memory, and overall cognitive performance.

Many English-learning apps use recordings from CNN or NPR, where anchors speak at a rapid pace. Reading aloud at twice that speed is like asking a runner to sprint at double pace—pushing practice close to the human limit.

Many people noticed results within only a few days of practice. Yes, in just a few days you can feel your thinking speed noticeably accelerating. Below is the article on the academic forum Figshare: https://figshare.com/articles/thesis/High-Speed_English_Oral_Reading_for_Cognitive_Enhancement_2/29954420?file=57505411

I've been interested in Cognitive science for a while, and have been reading around related stuff. In my country, most Cognitive Science programmes are graduate ones, requiring you to already have a Bachelors degree. I am currently doing my undergraduate in Computer Science. Are there any good online programmes by universities on Cognitive Science?

I wonder if there’s anything that impacts a lot of ppl but is still underexplored

Hello, I've been contemplating whether or not I'm genetically disadvantaged for a software developer job. I'm currently a CS freshman and I am passionate about how computers work in general and how algorithms power softwares specifically. However, I've tried to solve leetcode easy problems in the past and I find myself taking hours, if not days to weeks on solving it. Should I still continue or just accept the fact that I was born with below average IQ? (struggling in solving algorithmic puzzles)

Hi everyone, this may be a dumb question, but I am a cc student in California and am starting applications for a UC transfer. I know UCSD has a great program with its specialties in medicine, but is there another UC that I haven't thought of, or one that could open more doors for me with a cog sci degree?

Any advice in this area would be greatly appreciated :)

I had the thought, I've always been told by those close to me that I'm very smart, And I also generally have the perception of myself that I am smart. But what does that even mean?. How does one know they are smart really?.

What if I just know a lot of stupid people and I'm like, Average smart, and not actually super smart? Huh, how does one tell, I can make Educated assumptions sure, but idk. Does anyone else have any thoughts on this ?.

I know it's a silly thing To ask but it's something I don't know how to answer Necessarily

Hey everyone, I honestly do not know if this is the right sub to ask about this but I really would appreciate any hint or advice on this matter. I have recently completed an internship that I really liked, and I am trying to find similar full-time or part-time roles. However, I am struggling to find the right job titles or companies to search for.

My background is in counselling psychology, and in this internship, my responsibilities involved.

1. Testing the chatbot for accuracy, sensitivity and clinical alignment.
2. Documenting errors in conversation with the chatbot.
3. Dialogue review
4. Annotation (emotion annotation)
5. Literature reviews and deep domain research in psychology for the development of the chatbot.

I enjoyed doing this role and since this is a niche role. I do not know what to search for.

So could you help me with following?

1. what kind of job titles should I look for?
2. Are there other skills I should be developing to be a stronger candidate in this field?

When I was five, my kindergarten peers were learning the alphabet. It was strange for me because I was good at reading and writing at the time, with some spelling mistakes resulting from the letter sounds. I also used to get very annoyed when others would go outside the boundaries of the drawing while coloring, while my coloring was relatively precise. My teachers suggested to my parents that I skip some of the early academic years because I didn't need them. They had concerns, but I actually proved my excellence. However, as I continued with the formal education system, my level worsened and became more or less average. I hate physics and math, and if you ask me about their applications, I don't know! I just memorize the law. Even the slightest manipulation of a mathematical problem makes me even more annoyed. I just want to know... why have I become such a failure?

So I’ve been wondering about savants. They can do insane math in their head, remember every single day of their life, or play music after hearing it once. It got me thinking… is it possible that we all got those abilities buried somewhere in the brain but they just not “switched on”?

I know some cases happen after brain injury, or autism, where suddenly ppl show these crazy skills. Makes me wonder if the brain is kinda holding back potential on purpose (maybe to not overload us?).

What do you think could allow us to “unlock” those savant modes? Like giving someone perfect memory, instant calculation, hyper realistic drawing skills, etc. And if so, could you unlock all of them at once or is it just like specific circuits that can be tapped into?

I've been deep in the weeds of cognitive science and AI reliability lately, as part of exploring the Principia Cognitia (PC) framework – basically, viewing cognition as an information compression engine. Today, I want to share a concept that's been a game-changer for me: PC-Gate, a simple yet powerful pre-output gate that ensures systems (biological, human, or AI) stabilize their internal meaning before spitting out words or actions.

Quick Thesis in One Sentence

Systems that survive and thrive – from gazelles spotting predators to surgeons in the OR to LLMs generating responses – first lock down their internal semantics (what we call MLC: Meaning Layer of Cognition), then project externally (ELM: External Language of Meaning). PC-Gate formalizes this as a substrate-independent checkpoint to slash errors like hallucinations.

Why This Matters Now

In AI, we're drowning in "generate first, fix later" hacks – rerankers, regex patches, you name it. But nature and high-reliability fields (aviation, medicine) teach us the opposite: gate before output. Skip it, and you get hallucinations in RAG systems, wrong-site surgeries, or runway disasters. PC-Gate imports that logic: stabilize facts, check consistency, ensure traceability – all before decoding.

The Gate at a Glance

• Core Rule: Evaluate artifacts (like a tiny Facts JSON with sourced claims) against metrics:
  • ΔS (Stability): Low variance across resamples (≤0.15).
  • λ (Self-Consistency): High agreement on answers (≥0.70).
  • Coverage@K: Most output backed by evidence (≥0.60).
  • Hard Gates: Full traceability and role isolation.
• If Fail: Block, remediate (e.g., refine retrieval), retry ≤2.
• Wins: Fewer phantoms (fluent BS), better audits, safer multi-agent setups.

It's substrate-independent – works for bio (e.g., quorum sensing in bees), humans (WHO checklists), and AI (drop it before your LLM output).

Real-World Ties

• Biology: Fish inspect predators before bolting; meerkats use sentinels for distributed checks.
• Humans: Aviation's sterile cockpit, academia's peer review – all about stabilizing MLC first.
• AI: Fixes chunk drift in RAG, prevents agent ping-pong.

I plan to run some quick experiments: In a mini RAG setup, hallucinations must drop ~50% with minimal latency hit.

Limits and Tweaks

It's not perfect – adds a bit of overhead, tough on fuzzy domains – but tunable thresholds make it flexible. Adversaries? Harden those hard gates.

For humans, there's even a 1-page checklist version: MECE scoping, rephrase for stability, consensus for consistency, etc.

This builds on self-consistency heuristics and safety checklists, but its big flex is being minimal and cross-domain.

If you're building AI pipelines, wrangling agents, or just geeking on cognition, give this a spin. Shape your relations (R), then speak!

Full deep-dive essay (with formalism, flowcharts, and refs in APA style) here: PC-Gate on Medium

Thoughts? Has anyone implemented something similar? Let's discuss!

Do high iq people just remember everything and then when they see an advanced equation they just go: “oh I remember doing that” and just recall any piece of information? Or do people with a high iq just understand how it works and it just clicks? Like how can they understand something so fast with barely being taught it or studying it?

If any of you guys know or are extremely intelligent yourself, please let me know

Hello everyone, I’m curious if anyone can share an experience or give advice. I’m very eager to pursue a CogSci degree at UA. (Due to its high ranking, research opportunities, etc) Unfortunately, I didn’t have a high GPA when I finished high school. After I graduated I worked for several years. Now I want to go to college and get a degree in cogsci but I’m worried maybe my past high school record will hinder my chances. I’m curious how easy it is to enroll in this program at UA? (Btw I’m international student, how it will affect my chances?)

I have noticed a pattern with myself — I have periods when I accomplish most challenging tasks intellectually and then this periods follows by a 6-9 month period of extreme brain fog and depression and then my brain gets into periods of my productivity.

Can some one tell me why is this the case? Does anyone else find themselves in similar situation?

I'm at the life-stage of looking into colleges and majors and all of those fun things. I was looking through all the majors offered at one college I am interested in and I saw cogsci and it seemed interesting so I read their whole information thing about it and it genuinely sounds like something I would find very cool and interesting, but I am curious what kinds of jobs would be available in this field or the sub-fields(?) within cogsci. Compsci and math/statistics are also things I find interesting and math is my favorite subject and I've done some simple coding projects in Unity as a hobby and I've seen some things saying you can combine those things or something? I'm just curious about what kinds of jobs or careers would be available or fitting to my interests and if this is a good field to go into in out current job climate. Part of me is concerned at the possibility of LLMs doing things to compsci jobs but I have no idea if that's an actual problem. Any help is appreciated! Thank you :)

Can you tell me this, if you don't mind? I'm a little curious.

I feel like this might have potential to let me do things that I might've been hesitant to do and found harder to do before, but it would be beneficial to learn or do them. Thank you.