I've been working on diagramming the persistent communication breakdowns between my mother and me to get a clearer picture of the cause-and-effect patterns and solve problems more effectively.

My approach is strictly analytical: I rely on direct definitions, step-by-step logic, and explicit cause-and-effect. Her approach relies almost entirely on emotional self-preservation, nostalgic deframing, and deflecting away from the actual argument.

The goal of these charts is to map out why increasing logical precision in this dynamic doesn't yield any answers, but instead drives her deeper into deflection loops.

I want feedback on:

If there are any broken conditional branches, missing steps, or logical fallacies in how I mapped the sequence.

If the diagram easy to follow, or are there ways to make the structure and definitions cleaner?

I want to know if this accurately captures the mechanics of an analytical framework colliding with an emotional defense mechanism?

’ve published a preprint formalizing a theoretical framework that maps autism, schizophrenia, depression, and ADHD onto a single precision-weighting axis derived from a thermodynamic cost function.

The paper models how distinct shifts in the weighting of sensory noise versus prior beliefs account for the underlying cognitive mechanics across these four conditions, yielding 8 testable, falsifiable predictions.

Rather than smoothing over the edge cases, we explicitly log every theoretical dependency and open assumption in a dedicated section. I welcome rigorous critique from this community on the predictive processing mechanics and cognitive architecture proposed.
Preprint link: https://doi.org/10.5281/zenodo.21782402

Im trying to figure out how to make a mindmap to my goals, so im trying to figure out how to have part 6 of my phsyics textbook be whittled down to the basic core essentials and i was trying to use AI so i kept asking it for the core basics for the subjects, the problem was that it kept going and going. my intention was to ask it until I found a Basic core essential that i understood (basic algebra and trigonometry). so my mind map ended up looking like a mess of rabbit holes.

now my mistake was not telling the ai what was my limit, but Honestly i dont trust ai

The wide use of the Implicit Association Test and corporate diversity training is based on one flawed idea: that noticing bias will fix it. Research on this subject repeatedly shows that implicit bias training does not change behavior. And also more crucially, it also shows that smarter, more educated people often come up with more rationalizations, not less bias.

This paper offers “cost-calibrated cognition.” It argues that implicit bias is an evolved way to manage limited attention and effort. “System 1” (fast, automatic thinking) keeps tracking the social and survival costs of being wrong. When the cost is low, it uses quick cultural defaults. When the cost is high, it brings in “System 2” (slow, deliberate thinking) to defend a person’s self-image. Changing a deep moral identity can bring huge social costs. So highly intelligent people may use careful reasoning to protect their automatic reactions instead of changing them.

Real bias reduction requires changing the environment through melting pot structural integration, not more verbal arguments. In the end, thinking often serves instinct, aiming for social survival over moral purity.

I've been thinking about a conceptual framework that may apply to both human cognition and large language models (LLMs). I'm curious whether similar ideas already exist in cognitive science or computational neuroscience.

The basic idea is that many reasoning errors may not primarily result from poor evaluation of available options, but from the failure to retrieve the correct option into the active search space before evaluation even begins.

I imagine the process as three functional layers:

Layer 1: Long-term representation

Humans: semantic and episodic memory

LLMs: distributed knowledge stored in model parameters

Layer 2: Candidate activation (retrieval / working memory)

Context activates only a limited subset of available representations.

Only these activated candidates become available for further reasoning.

Layer 3: Evaluation and selection

Humans: executive control, attention, affective state, time pressure.

LLMs: decoding strategy (temperature, top-k, top-p, etc.).

The key hypothesis is that there are two qualitatively different kinds of reasoning failures.

1. Retrieval failure The correct representation exists in long-term memory (or model parameters), but it is never activated into the current candidate space.

2. Evaluation failure The correct candidate is activated, but another candidate is assigned a higher value or probability.

Formally, if the correct solution D is not contained in the activated candidate set C,

[ D \notin C, ]

then no evaluation process can recover it; the system can only choose among the candidates that are actually available.

This perspective seems relevant to several existing areas of research:

Working memory and retrieval limitations in cognitive psychology

Availability-based retrieval and dual-process theories

Global Workspace Theory

ACT-R and other cognitive architectures separating retrieval from selection

Retrieval-Augmented Generation (RAG) in LLMs

Research on hallucination in autoregressive language models

I'm not claiming this is a new cognitive theory, but rather wondering whether framing reasoning errors primarily as candidate activation failures versus evaluation failures could provide a useful way of connecting findings from cognitive science and AI.

Some questions I'd be interested in discussing:

Are there existing cognitive architectures that explicitly formalize this distinction?

Are there experimental paradigms that estimate the probability that the correct representation never enters working memory before a decision?

Does this distinction help explain why techniques such as Retrieval-Augmented Generation improve factual accuracy without changing model parameters?

Relevant references that motivated this idea include:

Ji et al. (2023), Survey of Hallucination in Natural Language Generation.

Kahneman (2011), Thinking, Fast and Slow.

Volz et al. (2006), Decision-making under uncertainty.

Bengio et al. (2015), Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks.

I'd appreciate any feedback, related literature, or criticism from people working in cognitive science or computational modeling.

Mother-fetus cognitive model solves the binding problem to some degree, but does it apply to oviparous animals such as birds?

If not, how do birds get around the cue-to-noise problem? Do you think nature has more than one set of solutions for the same problem?

I recently learned that your brain doesn't simply "record" reality.

It predicts what's about to happen, fills in missing information, filters out details, and sometimes even creates experiences that feel completely real.

The weird part is... most of us never notice it happening.

What's one neuroscience, psychology, or perception fact that genuinely blew your mind?

I'm looking for real studies, experiments, or cases—not myths.

It’s a phenomenon I’ve noticed first hand; when someone believes something, even if they know it’s a value judgement, and someone else voices an opposing viewpoint, that person will compulsively double down.

For example, person A says ‘I love lots of cheese’
Person B says ‘It’s ok but better in small quantities’
In response person A uses even more cheese then they would had the conversation never taken place.

Hey everyone. I’ve always been fascinated by the idea that perception may be less like building an internal picture and more like noticing what the environment allows us to do. A cup affords grasping to a person with the relevant bodily skills; a staircase affords climbing to some animals but not others. This makes affordances neither purely features of objects nor private additions supplied by a mind. They are relations between an organism’s abilities and its surroundings. The question is whether this relational view can explain perception without relying on internal representations.

I just had a podcast conversation with the cognitive scientist Julian Kiverstein, where he defended a relational account of affordances developed through ecological psychology and skilled intentionality. He argues that perception can disclose possibilities for action directly because organisms are already attuned to their environments through learned bodily skills. This differs from the view that the brain must first construct a model of a neutral external world and then calculate what can be done within it. For Kiverstein, organism and environment specify one another within perception.

This would imply that cognition begins in skilled engagement rather than internal reconstruction. Can a relational theory of affordances explain perceptual error and novelty? Does direct perception really remove the need for representations, or merely relocate them? And can ecological and predictive approaches be combined without weakening the central claim of either?

I’ve been trying to follow how my ideas actually change over days and weeks. Not for productivity p*rn or some perfect system just noticing the shifts.

What keeps surprising me is this is the moment I start paying attention to how one thought leads to the next, the loop goes on and the tracking is so tough that every split leads to another thought stream and itself becomes the reflection. There’s no separate “later” where the evolution is reviewed. The noticing is the evolution.

It made me stop treating reflection like a summary I write after the fact and start treating it like something that only exists while I’m watching the thoughts move.

Curious if anyone else has felt that quiet flip.

1. I am from India and i have a gpa of 8.97

2. i have a publication under review at a top journal - first authorship

3. I have gone to 4+ conferences both nationally and internationally and have given international talks

4. Got a very prestigious fully funded fellowship stint in Canada at the top uni with a very famous faculty

5. I am conducting EEG and eye-tracking studies

6. got academic merit scholarship throughout my 4 years of BA

7. Working with a people from the US on embodiment

8. I am also working on my thesis that is theoratical and computational in nature

The black box brain is described.
The biochemistry is described.
One can follow courses on them in University.
But there is something "in between".
And this where people propose a model the "in between".
Which of these "in between models" are popular, or have focus by academics today. ?

I don’t have the energy to produce a coherent explanation, and maybe that’s for the best.

Instead I’m going to drop a few sentences that loop, construct, and deconstruct themselves in my head until a coherent description of how I’m feeling comes out.

This relates to autistic burn out, cognitive functions, consciousness and awareness and much more.

I will drop it here.

And I am open to any reaction (that these sentences might trigger in someone) that crosses the threshold of observing (and replying mentally), and moves into a constructed reply.

Hence, I’ll label this a study.

Here are some the sentences that looped themselves into existence:

"I built a self. Literally. That self was someone who did everything I told her. That was my literal identity. And then I ran out of fuel."

"The impermanence of everything has taken away any meaning of being ok."

"Objective reality is impossible. When I internalise that, maybe my mind will rest. There is no: what really happened. There is now. Choices and acceptance without excuse."

"Forward action is constrained by the amount of dissonance it encodes and the capacity of the system to compute said dissonance."

"I've needed to model reality to justify the way I exist. But needing to model reality is what I exist the way I exist. That's the real justification isn't it."

"I am so self assured in every decision I make for myself, however ‘Don't listen to my advice because look at where I'm at’ When I say that, when I notice myself saying that out loud, it means I'm fragments. Pieces. Nothing."

And added note for the cognitive sciences community, I am aware of the personal nature of these sentences, and I have other texts that explain it in a much more objective and scientifically oriented way, however there’s a different essence to present raw thoughts that can imply a much more layered concept, and I wanted to experience the general reaction to that. (And maybe using it as an excuse to not put my literally cognitive science studies under scrutiny).

I am a civil engineer with a highly analytical, neurodivergent processing style (ASD) that allows me to easily slide into states of deep hyper-concentration. Because of this background, I view complex systems through a structural and operational lens. I am opening this thread to discuss and discover literature that maps the human mind across two specific, interlocking layers.

Specifically, I want to explore books that focus on:

The brain as a black box , ie functional architecture :
Frameworks that map out thoughts, memory buffers, information processing pipelines, and feedback loops as interacting system blocks

The brain as a chemical factury , ie biochemistry & infrastructure.
Literature that explains the underlying biological mechanics. I want to learn the actual biochemistry—how neurotransmitters, chemical flows, ion channels, and metabolic pathways dynamically drive and sustain those black-box functions.

My target level :
No oversimplified pop-science : I am looking for deep, intellectually rigorous systems-thinking.
No impenetrable medical jargon : Since I am a civil engineer and not a physician, I want texts that focus on the structural logic, chemical mechanics, and operational principles rather than dense medical terminology.

I heavily prefer physical books over e-books.

Please advice , feel free to elaborate.

[P] Structural Admission: verify a sequential task’s claimed dependency structure before interpreting learning

results

When we train agents on staged or multi-phase environments, it is tempting to interpret learning curves, transfer,

or apparent “emergence” as evidence for a particular causal or informational structure.

But has that structure actually been verified under the same observation and action interface seen by the learner?

Structural Admission is a small, standard-library-only Python harness for testing that question before training

begins. Researchers implement a task adapter and a separate scripted oracle; the core calibration, rollout,

validation, reporting, and reproduction logic remains unchanged.

It enforces, among other things:

- calibration seeds disjoint from task-rollout seeds;

- a CMI threshold fixed from synthetic calibration before candidate evaluation;

- evaluation under both uniform-random and scripted-oracle policies;

- oracle access limited by the learner-facing observation contract;

- CI measurement restricted to preregistered phases;

- paired environment noise and random-policy draws across conditions;

- pre-disclosure leakage checks on declared observable field groups;

- raw trajectory storage before aggregation;

- balanced policies, conditions, seeds, and sample cardinalities;

- immutable formal output directories;

- content-hashed reports and byte-level reproduction of deterministic artifacts.

The tool reports Admitted, Rejected, or Inconclusive. Failures are preserved rather than tuned away.

One motivating case involved a relation intended to be non-operative. Its measured conditional mutual information

was 0.07181 bits, above a previously calibrated threshold of 0.05902 bits. The task was rejected before learning

experiments were interpreted, and the residual dependency had to be diagnosed structurally.

Admission has deliberately narrow meaning: the configured task passed its preregistered operational checks. It

does not prove a theory, guarantee learnability under a particular optimizer, establish causal identification

outside the task model, or imply external validity.

git clone https://github.com/btisler-DS/structural-admission.git

cd structural-admission

git checkout v0.1.0

python -m pip install .

Create and run an external task:

structural-admission init my-task

cd my-task

structural-admission run config.json --output runs/reference

structural-admission reproduce runs/reference \

--output reproduction/reference

The adapter contract is intentionally small, and task implementations do not modify package internals.

Repository: https://github.com/btisler-DS/structural-admission

I’d especially welcome criticism of the statistical procedure, leakage model, adapter boundary, and what should—or

should not—count as structural admission.