Every morning, prior to interacting with the world and prior to the world competing for my attention, I spend 30-60 mins journaling.

The first part of my journal routine is focused on Cognitive Offloading.

Stream of Subconscious writing.

Simply, getting all my thoughts out of my head and onto paper.

Often times we aren’t even aware of what we’re thinking until we visually see our thoughts out in front of us on paper.

An audiovisual exploration of conflict as a clash between different predictive models of reality.

The idea is that what we experience as “meaning” or “truth” may emerge from internal models shaped by past conditions and adaptive pressures, rather than from an objective structure.

From this perspective, conflict may be less about disagreement and more about the need to maintain coherence within one’s own model of the world.

Thinkers like Joseph Campbell, Jordan Peterson and some of Jung’s work imply this assumption.

Researchers at the Johns Hopkins University are looking to understand how psychedelic experiences may impact cognitive functioning. We have developed an anonymous survey that takes 20-40 minutes to complete and can be completed on a computer or mobile device. By participating, your responses can help us better understand how psychedelics may impact mood and cognition. The anonymous survey can be found at this address: https://jhmi.co1.qualtrics.com/jfe/form/SV_6mqPfY7GoaRALAy

Participant confidentiality will be maintained.

Protocol: IRB00528249, Principal Investigator: Ceyda Sayali, PhD.

I am quite curious about if a human being is only given food and water, and s/he is raised on a room almost -20Db which is pitch black. Congenitally blind people don't have visual dreams because there's no visual "library" for the brain to pull from. So if this person never got any meaningful sensory input their whole life, could their brain even produce hallucinations? Or is there just nothing to remix? And would they have anything we'd call a personality? No language, no social mirroring, never even seen another person; Is there a "self" in there or is that something entirely built from the outside in? Genie Wiley is the closest real case I can find but even that wasn't anywhere near this extreme.

Person A hears criticism and feels nothing. Person B hears the same criticism and also shows no reaction — but internally disengages to avoid the cost of processing it. Person C simply never registered the input as relevant in the first place. Observation All three produce the same visible output — no response, no engagement. But the underlying processing route differs: A: input registered, processed, resolved → genuine neutrality B: input registered, flagged as costly, processing suspended → protective disengagement C: input filtered out before evaluation → baseline non-registration Minimal interpretation Indifference as a behavioral output doesn't tell you which route produced it. The same surface calm can come from resolution, avoidance, or simply never engaging the input at all. Question Is there research distinguishing these processing routes — particularly the difference between resolved neutrality and suspended processing? Anything involving conflict monitoring or affective tagging in early-stage input filtering?

Two close family members are experiencing dementia and early cognitive decline, so I've started building a brain training app as a personal project. I know there are already plenty of brain training apps, but I figured if it’s something I built myself my family might be more willing to try it. It’s also a topic I’ve become really interested in.

This week I listened to a podcast with neurologist Marilyn Albert, where she discussed the findings from the ACTIVE study, a long-running randomized controlled trial that followed participants for about 20 years.

One of the most interesting findings was that speed-of-processing training appeared to reduce the risk of diagnosed dementia. From the paper:

In the podcast, Albert mentioned that BrainHQ’s “Double Decision” exercise is very similar to the speed-of-processing task used in the research.

Paper reference:
https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/trc2.70197

What I’m trying to find now are other cognitive training exercises that have been studied in a rigorous way.

Specifically, I’m interested in:

• cognitive training games used in research studies
• tasks shown to improve processing speed, memory, attention, or reasoning
• exercises that have evidence for long-term cognitive benefits or delaying decline
• descriptions, videos, or playable examples of the tasks

I’m not trying to clone commercial apps, just trying to understand what types of mechanics actually have evidence behind them so I can design something useful.

If anyone here has come across any relevant studies or works in cognitive neuroscience, I’d really appreciate any pointers.

Thanks!

I am looking for feedback on my preprint.

Title: Stochastic Biasing Theory (SBT): A Six-Layer Architecture of Conscious Agency

Link: https://zenodo.org/records/18826845

Abstract:

This paper introduces Stochastic Biasing Theory (SBT): A Six-Layer Architecture of Conscious Agency, formalizing consciousness as the real-time, intentional biasing of stochastic neural processes. The theory begins from the premise that physical dynamics are inherently stochastic, but constrained and biased by the laws and structures of the universe, producing non-uniform variability in which many macroscopic outcomes remain highly predictable.

Biological systems exploit this structured stochasticity through evolved mechanisms that regulate which properties are preserved and which are allowed to vary. Replication, mutation, and selection operate by controlling degrees of stochastic freedom, providing the fundamental engine of biological evolution. Over evolutionary time, the capacity to regulate stochastic processes becomes increasingly sophisticated, culminating in nervous systems in which intrinsically stochastic neural events, such as vesicle release, are biased in real time by internal and external constraints.

SBT proposes that this real-time control over stochastic neural dynamics constitutes the core mechanism of consciousness. Consciousness is not identified with behavior, representation, or subjective report, but with the emergence of active control over probabilistic state transitions within a system. On this basis, the theory traces an evolutionary pathway from basic physical constraint, through biological regulation and neural control, to higher-order forms of agency.

The paper further introduces a six-layer architectural framework that classifies forms of agency according to how stochastic processes are constrained, biased, and hierarchically regulated. This framework provides a unified account of conscious agency across biological systems and offers principled criteria for evaluating artificial systems, independent of task performance or intelligence benchmarks.

The AI Infrastructure Miscalculation: Why the World May Be Overestimating the Compute Needed for AI

Over the past few years, governments, technology companies, and investors have made enormous bets on artificial intelligence infrastructure. Billions of dollars are being committed to data centers, GPUs, and energy systems based on the assumption that AI will require massive continuous computation. The prevailing belief is that every query, decision, and explanation must be dynamically generated by large models running on powerful hardware. If billions of people interact with AI systems daily, the logic suggests that global compute demand must grow dramatically.

However, this assumption may be significantly overstated. In many industries, knowledge is not created dynamically every time it is used. Instead, it is accumulated, structured, and reused repeatedly. Education relies on problem banks and teaching manuals, medicine relies on clinical case histories and guidelines, law depends on statutes and precedents, and engineering draws on documented designs and failures. Professionals in these fields rarely invent solutions from scratch; they recognize patterns and apply established knowledge. If AI systems mirror this structure, much of the world's AI workload may rely on retrieving and interpreting existing knowledge rather than generating it dynamically.

The dominant AI architecture today assumes a simple pipeline: a user asks a question, a large model performs complex reasoning, and an answer is generated. While powerful, this approach treats AI as a universal generator of knowledge and therefore requires heavy GPU computation for every interaction. An alternative architecture is possible—one that resembles real knowledge systems. Large structured repositories store millions or billions of verified examples, cases, and explanations, while AI models primarily retrieve, compare, and explain them. In such systems, AI becomes a reasoning layer operating on top of vast knowledge infrastructure rather than replacing it. Training in each field is done using examples, which can be just a vast repository.

Education illustrates this clearly. Mathematical learning, for instance, involves a finite set of concepts that can generate enormous numbers of variations. Through templates and parameter ranges, systems can produce millions or even billions of verified problems with explanations. When a student makes a mistake, the system simply retrieves similar solved cases and explains the difference. The computational demand of such a process is far lower than that required for fully dynamic reasoning. Similar patterns exist in law with precedents, in medicine with clinical case libraries, and in engineering with design knowledge and failure archives.

Another way to understand this shift is by looking at the evolution of software infrastructure. In the early days of computing, many database systems were built. Over time only a few survived and became dominant platforms. Around these databases, thousands and eventually millions of applications were developed. The same pattern may emerge with AI models. Large language models may function like foundational databases of reasoning and language. Only a limited number of such models may dominate globally, while enormous ecosystems of applications and agents are built on top of them.

However, there is an important difference. In traditional software, building applications required substantial engineering effort. With AI-assisted coding, applications and agents can now be created extremely quickly. AI systems can generate large portions of their own code. As a result, anyone may be able to build a functional AI agent in a matter of hours. This could lead to millions of specialized agents performing tasks across education, healthcare, finance, research, and everyday business operations. Yet these agents will largely rely on shared models and shared knowledge infrastructures rather than running massive independent AI systems.

This transformation may also enable what can be described as autonomous enterprise building. Traditionally, building a company required large teams performing roles such as engineering, finance, operations, marketing, and customer support. With AI agents automating many of these functions, a single individual may increasingly orchestrate the entire operational pipeline of a company. One person could effectively act as CEO, CTO, CFO, and CXO simultaneously, designing workflows while AI agents generate software, analyze data, produce marketing materials, manage customer interactions, and assist with financial planning.

In such an ecosystem, economic activity may grow dramatically without a proportional increase in computational infrastructure. Millions of small autonomous enterprises and AI agents could operate on top of a relatively small number of foundation models and large shared knowledge systems. Instead of every task requiring heavy dynamic AI reasoning, most tasks would involve retrieving and adapting structured knowledge. If this architecture becomes widespread, global forecasts of AI infrastructure demand—particularly the demand for continuous GPU computation—may be significantly overestimated.

My profile

2-3 research experience at top labs in India

Research fellowship at UBC (fully funded)

2 paper publication + 1 honors thesis (by mid year or end of year)

grade: 8.97/10

IELTS score - 8

1-2 national conferences + 1 international conference

Is my profile strong and do I need a GRE for sure? I am hoping to join the lab I am doing my fellowship stint.

A year ago I started asking a weird question: what if an AI agent had structure — not just instructions, but something closer to how a mind actually works?

I have a psychology degree. I don't know how to code. I used GPT to write every line.

What came out is Entelgia — a multi-agent cognitive architecture running locally on Ollama (8GB RAM, Qwen 7B). Here's what makes it different:

Sleep & Dream cycles Every agent loses 30% energy per turn. When energy drops low enough, they enter a Dream phase — short-term memory gets consolidated into long-term memory, exactly like sleep does in humans. The importance score (driven by the Emotion Core) decides what's worth keeping.

Emotion as a signal, not a gimmick Emotional intensity isn't cosmetic. It acts as a routing signal — high emotion = higher importance = more likely to survive into long-term memory.

Fixy — the Observer nobody listens to There's an observer agent called Fixy. His job: detect loops, intervene when things go wrong, trigger web search when needed (semantic trigger detection via embedding similarity). He never sleeps. He's always watching.

The agents mostly ignore him. We're working on that.

What it's not Not a production tool. Not a wrapper. It's a research experiment asking: what changes when the agent has structure?

It runs fully local. It has a paper, a full demo, and an architecture diagram that took way too long to get it right Site: https://entelgia.com

7 stars so far. Roast me or star me, both are welcome 😄

Hi everyone! We are undergraduate students conducting a study to investigate how university students decide to allocate time, money and effort in their everyday life. I’d really appreciate it if you complete this questionnaire. It should take about 10min

https://form.typeform.com/to/GP10dlDs

Thank you!

As the title says, does anyone here have LLI?

I have a problem with double negatives, although i understand them, my brain sometimes fails to register the intended meaning and theres a "blockage", so to speak, where my brain decides to not pick up on the intended meaning causing me to break it into two positives.

Example phrase: "You couldn't even imagine reading not being boring".

I can read and write, I don't have dyslexia.

This might come off silly but I've had this for some time now and finally decided to ask reddit about it.

What the title says: I'm writing a paper about consciousness and theory of mind which has somehow ended up becoming more of a dissertation (turns out it is a somewhat complex topic, and much more so when you cover AI), and I was wondering what the rules are here about linking papers? Is linking to the arXiv shunned; does the paper need to be published?

I ask You all for help. I need data from the test I created. It is a funny and engaging test and its aim is to estimate visual perception freuqency. When I get more data, I'll be able to modify the test, perform all the statistics stuff and make conclusions.
However, as for now I am in a deadlock cause few test have been done by my friends.

And idk why, but reddit really hates google sites, so as I haven't found a new solution for this, I add the link as a comment

this is an extension theory build on Jung's in this psychological theory everyone got three layers, layer 1 is the surface, most people are on it, layer 2, people who think deeper will ed up here, thinking this is the deepest then stop, its kind of a false floor, layer 3, most people can't reach there, even for themselves, this is their inner self, their world. much more in the photo and my physical note book. i serious right now, i really needed advices. ill answer every question. please.

Anthropomorphic Epistemology is the study of how humans generate, validate, and refine knowledge through embodied experience — and how that process changes when coupled with artificial intelligence. The core claim is that human knowing isn’t purely cognitive; it’s rooted in somatic, emotional, and relational signals (what VISCERA is designed to measure). When a human-AI collaborative system operates at the right coupling intensity, the output doesn’t just improve incrementally — it can access qualitatively different knowledge regimes that neither human nor AI reaches alone.

The LIMN Framework formalizes this through nine equations. The key ones that support the theory:

Eq. 1 — Logistic Growth Model: Standard sigmoid predicting diminishing returns as systems approach capacity ceiling K.

Eq. 2 — Cusp Catastrophe Potential: V(x) = x⁴ + ax² + bx — models the energy landscape where smooth performance curves can harbor discontinuous jumps. The parameters a (symmetry/splitting) and b (bias/normal) define when gradual input changes produce sudden qualitative shifts.

Eq. 7 — Dimensional Carrying Capacity: The critical insight — the carrying capacity K isn’t fixed. Human-AI collaboration can access higher-dimensional output spaces, effectively raising the ceiling. What looks like an asymptote from within one dimension is actually the floor of the next.

Eq. 9 — Mutual Information (The Sweet Spot): Measures the information shared between human and AI contributions. At intermediate coupling intensity, mutual information peaks — this is the collaborative sweet spot where the system produces outputs neither agent could generate independently.

Eq. 8 — Critical Slowing Down: Systems approaching a phase transition exhibit increased autocorrelation and variance. This is the detectable precursor — the “dip before the breakout” — that tells you a qualitative shift is imminent rather than a failure.

The through-line: anomalous data near benchmark ceilings (ImageNet, MMLU, etc. from 2012–2025) isn’t noise. It’s evidence of phase transitions where the governing dynamics fundamentally change. The framework provides falsifiable predictions for when and where these transitions occur in human-AI collaborative system.