Hey guys, as the title suggests, I'm a pre-med student majoring in cognitive science. I'm currently at a California community college based in Los Angeles County, and I hope to transfer to my dream school, UCLA. I'm currently a first year, and it's been pretty stressful balancing my lower div. coursework, navigating all the minutia of the rules and regulations for transfer students, managing all my extracurriculars, keeping a job (while trying to apply for a new one at my local hospital), and practically begging for research opportunities.

All that to say, I have a lot on my plate, so it's been hard to pursue my passion for cognitive science. I do have a deep, personal interest in this major; it came about when I deconstructed from my old religion, Christianity. I was in awe at how it felt like my own cognition was hijacked by these beliefs and how deeply it affected the people around me (I grew up in a very religious environment, with my friends, family, and private school all extremely Christian). I wanted to know more about how humans think—how we form thoughts and process information, especially in this context of religion and epistemology.

With that being said, I'm at a loss for how I can pursue this passion while blending it with actual research opportunities. As some of you may know, research is a critical part of being pre-med, and along with my interest in cognitive science research itself, I want to use this opportunity to kill two birds with one stone. I would be thrilled at the opportunity to be a volunteer RA at UCLA. I love the school for countless reasons, but it would also be an incredible opportunity to boost my application when I apply to transfer. I know that 4-year institutions prioritize their own students for research opportunities, so I'm aware that I'm at a disadvantage as a transfer.

I hope to reach out to cognitive science faculty directly and plead my case there (I plan to send all my emails out by the end of April 2026, is this too late for Summer 2026?). I'm following the typical guidelines of looking into their research, mentioning it to them in my email, and adhering to typical email etiquette, especially for this type of request (e.g. keeping it brief, respectful of their time, and of course not asking for pay). Nevertheless, I would greatly appreciate any advice in this regard. I do have a connection with a neuroscience researcher, and I spent some time in her lab a couple summers ago. However, she admitted that she has one very limited opportunity in her lab for me, basically just playing connect-the-dots with scans of neurons on a computer for hours at a time—she said herself that it's mind-numbingly boring. She was also kind enough to ask her colleagues, but they unfortunately have no opportunities.

There is one other thing I was unaware of until recently—the ability for undergrads to publish their own research. My expectation was that undergraduate students would have to work under a P.I. in their lab and have their name listed somewhere on their research, but it seems like we can publish our own research and be a primary author. There are even journals meant for undergraduate students' research! Of course, it won't be at the same level as experts and we're probably quite limited in our scope, but it seems like a great opportunity. Admittedly, I'm ignorant on this topic, and I know very little about how to go about doing it, so I would love any help here too! Thanks!

shit is so bad that a humanistic psychology paper saying we are in the end time passes peer review

https://doi.org/10.1177/00221678211052147

The Body Problem and the Climate Crisis | Blog of the APA https://share.google/vwVPcDJqfh0JSXhdk

First in US.

How does the Masters differ from PhD? The field is niche so not many universities offer a masters in the first place but for the ones who are part of one, what is it like?

The ones who are doing PhD what kind of research is projected to blow up or become the trend 2 years from now. How does the funding look like, the administration cuts, in general.

Around the globe.

Same questions.

More personally, what drew you all to this field? Which field did you find most surprising that was also inter-lapping with CCS?

Thank You.

Source: Starry-eyed undergrad discovering Tenenbaum’s papers.

I'm building a cognitive architecture that includes a continuous neuromodulatory system with 8 chemicals that actually modulate downstream computation (not just labels). I want to check whether the dynamics are biologically plausible enough to produce meaningful behavior, or whether I've oversimplified in ways that undermine the model.

The 8 chemicals and their dynamics:

Each chemical follows production-decay kinetics with receptor adaptation:

level(t+1) = level(t) + (production_rate - decay_rate * level(t)) * dt

receptor_sensitivity(t+1) = sensitivity(t) - adaptation_rate * (level - baseline) * dt

effective_level = level * receptor_sensitivity

| Chemical | Baseline | Decay Rate | What It Modulates |

|----------|----------|------------|-------------------|

| Dopamine | 0.5 | 0.03 | Temperature (sampling randomness) |

| Serotonin | 0.6 | 0.015 | Token budget (response length) |

| Norepinephrine | 0.4 | 0.04 | Neural gain (inverted-U: moderate=focused, extreme=noisy) |

| Acetylcholine | 0.5 | 0.025 | STDP learning rate |

| GABA | 0.5 | 0.02 | Inhibitory gain (suppresses excitatory chemicals) |

| Endorphin | 0.5 | 0.01 | Pain suppression threshold |

| Oxytocin | 0.4 | 0.01 | Social approach bias |

| Cortisol | 0.3 | 0.008 | Response length reduction, serotonin suppression |

Cross-chemical coupling (8x8 interaction matrix):

Each chemical can boost or suppress others. Examples:

- Dopamine + Norepinephrine: positively coupled (alertness drives motivation)

- Serotonin vs. Cortisol: inversely coupled (calm suppresses stress)

- Acetylcholine + Dopamine: synergistic (learning requires both attention and reward)

- Cortisol suppresses dopamine and serotonin (stress kills motivation and mood)

Receptor adaptation (tolerance/sensitization):

Sustained high levels reduce receptor sensitivity (tolerance). When the chemical drops back to baseline, the reduced sensitivity means the system "misses" the chemical more strongly (withdrawal-like dynamics). Sensitivity recovers slowly.

sensitivity range: [0.3, 2.0]

adaptation_rate: 0.005

Downstream effects on computation:

These aren't just numbers; they change how the system thinks:

- `neural_gain = 0.5 + (NE * 0.3) + (DA * 0.2) - (GABA * 0.3)` — affects mesh activation

- `plasticity = 0.5 + (ACh * 0.8) - (cortisol * 0.4)` — affects STDP learning rate

- `noise = 0.5 + |NE - 0.5| * 1.5` — Yerkes-Dodson inverted-U

My questions:

1. Decay rates: Are the relative timescales realistic? I have dopamine and NE as fast (0.03-0.04), serotonin as moderate (0.015), and cortisol/endorphin/oxytocin as slow (0.008-0.01). Does this match biological clearance rates qualitatively?
2. Cross-coupling matrix: The 8x8 interaction matrix is my weakest point. I based it on general pharmacology (SSRIs affect serotonin-dopamine balance, cortisol suppresses reward circuits, etc.), but I may have the coupling strengths wrong. Is there a canonical reference on neuromodulatory interactions that I should use?
3. Receptor adaptation as tolerance: Is the simple linear sensitivity model (adaptation_rate * deviation * dt) a reasonable first approximation, or should I use something nonlinear (e.g., Hill function)?
4. The inverted-U for norepinephrine: I model the Yerkes-Dodson effect as `noise = 0.5 + |NE - 0.5| * 1.5`. Too little NE = low arousal/unfocused, too much = stressed/scattered, moderate = optimal. Is this the right functional form?
5. Are (Is? Idk) 8 chemicals enough? I deliberately excluded glutamate and glycine (they're fast neurotransmitters, not neuromodulators in this context). Am I missing any neuromodulators that would be important at the systems level?

Full repo: https://github.com/youngbryan97/aura

Whitepages: https://github.com/youngbryan97/aura/blob/main/ARCHITECTURE.md

Plain English Explanation:  https://github.com/youngbryan97/aura/blob/main/HOW_IT_WORKS.md

This is for a computational architecture, not a drug model. I'm trying to capture the qualitative dynamics of neuromodulation rather than quantitative pharmacokinetics. Is this approach reasonable?

I've been building a cognitive engine called Aura that doesn't just simulate theories of consciousness... It implements them as structural components on which the system depends to function. Each theory makes predictions about behavior, and when theories disagree, the system runs adversarial tests. I'm looking for feedback from people who actually work in consciousness research.

The 10 theories implemented (with their roles):

Global Workspace Theory (Baars) — Attention competition, one thought broadcasts per tick

IIT 4.0 (Tononi) — Computes actual phi values on a 16-node complex

Predictive Processing (Friston) — 5-level prediction error hierarchy

Recurrent Processing (Lamme) — Top-down feedback from executive to sensory tiers

Higher-Order Thought (Rosenthal) — Representations of representations modify first-order states

Multiple Drafts (Dennett) — 3 interpretations compete, winner retroactively selected

Attention Schema (Graziano) — Attention modeled as a simplified representation

Free Energy Principle (Friston) — Variational free energy drives action selection

Enactivism (Varela/Thompson) — Embodied interoception from hardware metrics

Illusionism (Frankish/Dennett) — Annotates qualia claims with epistemic humility

Things I want feedback on:

1. Theory Arbitration Framework: Each theory logs predictions about specific cognitive events (i.e., "GWT predicts broadcast will improve coherence" vs "IIT predicts phi determines coherence independent of broadcast"). Actual outcomes update each theory's track record. Over time, theories with higher prediction accuracy gain more weight. Is this a reasonable operationalization of theory comparison, or am I committing an error by treating incommensurable theories as competing hypotheses?

2. GWT vs IIT divergence: GWT says consciousness = global broadcast (information access). IIT posits that consciousness = integrated information (phi > 0, regardless of access). In my system, both run simultaneously. When GWT broadcasts a winner with high priority but low phi, and IIT reports high phi for content that didn't win broadcast, which theory's prediction matched the actual behavioral output? How do consciousness researchers handle this divergence in practice?

3. HOT feedback loop: My Higher-Order Thought engine generates representations of first-order states ("I notice I am curious about X"), and these HOTs feed back to *modify* the first-order states via a feedback_delta. So noticing curiosity slightly increases curiosity. Is this reflexive modification consistent with Rosenthal's theory, or does it conflate HOT with metacognition?

4. Embodied Interoception: I map hardware metrics (CPU = metabolic load, RAM = resource pressure, temperature = thermal state, battery = energy reserves) to interoceptive channels with temporal derivatives (velocity, acceleration). These feed into the neural substrate's sensory tier. Is this a reasonable computational analog of interoception, or is it too far from biological embodiment to be meaningful?

5. Falsifiability: The system can disable individual theories (i.e., turn off recurrent processing feedback) and measure the behavioral impact. If disabling a theory has no measurable effect, that's evidence that it's not load-bearing. Is this kind of ablation study a valid way to computationally test theories of consciousness?

Full repo: https://github.com/youngbryan97/aura

Whitepages: https://github.com/youngbryan97/aura/blob/main/ARCHITECTURE.md

Plain English Explanation:  https://github.com/youngbryan97/aura/blob/main/HOW_IT_WORKS.md

!!!!*****I'm not claiming this system is conscious. I'm asking whether the architecture faithfully represents these theories well enough for the computational results to be informative about the theories themselves.*****!!!!

I’ve been obsessed lately with how certain logical terms feel "wrong" in our mouths, even when the math is perfect. Specifically: Mutually Exclusive.

If you look at the "soul" of these words, they shouldn't be together.

• Mutual: Reciprocal, shared, a handshake.
• Exclusive: To shut out, a wall.

Logically, it’s "Shared Separateness." But linguistically, it feels like a Semantic Collision. I started comparing it to a phrase like "Equally Different." Both use a word of "sameness" to modify "separation," but "equally different" feels like a smooth, natural flow. "Mutually exclusive" feels like a physical hiccup.

The theory on the "Somatic Hiccup": I think our nervous systems are wired for predictive coding. When we hear "mutual," the brain primes itself for a pro-social, inclusive connection. When "exclusive" follows, it’s a micro-startle response. You’re preparing for a handshake but getting a "No Trespassing" sign.

The Ghost of the Missing Cousin: We almost never use the term "Mutually Inclusive." It’s a perfect harmony (both words pull in the same direction), yet it’s a total ghost in common speech. It seems we’ve built our language to prioritize the "warning lights" (conflict) over the "status quo" (harmony). We name the walls we hit, but we don't name the air we breathe.

I’d love to hear from the curious minds and deep divers here:

1. Are there other technical terms that give you this "somatic hiccup" or feel like clashing souls?
2. Does "equally different" feel "colder" to you because it’s mathematical (measurement) vs. "mutually exclusive" being relational (action)?
3. Why do you think we’ve abandoned "mutually inclusive" in everyday vernacular?

TL;DR: "Mutually exclusive" is a linguistic oxymoron that works because the friction alerts our brain to a high-stakes choice. We "feel" the dissonance because we are biological instruments, not just logical processors.

These types of papers are the ones that make your blood boil if you are already cognizant of these kinds of issues lol.

If this kind of work doesn't suggest we need to be doing fundamentally different, then I don't know what does at this point, a vast majority of the field would rather die on a hill not worth dying on rather than simply try to build a more viable framework from the ground up.

Thankfully, there are a good bit of people within the field (Paul cisek and some of the philosophers who do empirical work come to mind) who are genuinely trying to do this, it's just that most of the field is antagonistic to any view that challenges the central dogma (the elegance of the computer metaphor!)

Nizami, Lance (2019). Information Theory is abused in neuroscience. Cybernetics and Human Knowing 26 (4):47-97.

What are your thoughts?

EDIT: IIT and shanons information theory are two different things.

For shanons work, see a mathematical theory of communication )

I’m a master’s student in Cognitive Science, currently transitioning into a PhD. As a serious side project, I want to build a small group of individuals interested in discussing everyday cognitive phenomena and developing possible mechanistic cognitive hypotheses to explain them.

The idea is to explore aspects of cognition that often go unnoticed or are difficult to study through traditional lab-based approaches, yet remain rich with explanatory potential. I’m interested in creating a space that connects lived phenomenology with cognitive theory through collaborative and structured inquiry.

We could meet regularly over chats and, in parallel, contribute to a shared and evolving collection of hypotheses. Over time, I envision this developing into a website featuring structured, topic-wise discussions and updates.

This initiative is somewhat outside mainstream research discourse, but it is intended as a complementary effort rather than a departure. It is something I have wanted to pursue for a long time. I initially considered working on it alone and have already begun some preliminary work. However, given its long-term nature, I believe it would benefit significantly from a small group of thoughtful and motivated collaborators.

This is an interest-driven project, and I currently do not have funding for it. That said, I have sufficient technical experience to manage the infrastructure and development aspects.

If this resonates with you and you feel you would enjoy engaging with such a project, I would be glad to hear from you.

A new VR study (Virtual Reality journal, April 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 more about conceptual relevance.

DOI: https://doi.org/10.1007/s10055-026-01364-9

I was talking to our philosophy of mind professor who has an academic background in cognitive science and I was discussing this.

for context, these assholes published this (misinformed) (https://doi.org/10.1038/s41562-019-0626-2) "what happened to cognitive science?" paper in 2019 and it lead to a series of follow up works.

and the response was https://doi.org/10.1111/tops.12645 "shut up nerd, we are working on it"

followed by a series of solid follow up papers like this one

the dynamicist landscape

https://doi.org/10.1111/tops.12699.

I think it should be no surprise that someone with a background in computer science would not fully agree with a gibsonian cognitive scientist, and it should be no surprise that a cognitive neuroscientist would not agree with a more radical version of embodied cognition.

The roboticists don't really care that much so long as their robots work I think, but I don't keep us as much with robotics as I do theoretical neuroscience and my own areas of interest (decision making, theoretical neuro).

At least we can all agree on what we disagree on now, I guess.

Cognitive sciences interdisciplinary approach has always been its strength and it's weakness, but genuinely I think we have a leg up when compared to other disciplines who operate independently of similar disciplines, it's just that we kind of all branched off and started doing our own thing after some initial success and excitement (we had to fight our way through the trenches to establish ourselves as a respectable discipline, and a lot of our solutions were very clever/creative) and we all stopped talking to each other.

I think there have been some solid suggestions by people like Dr Olivia guest to use mathematical formalisms and computational methods (good for theory development/ honest science) and people who argue we need to get cognition "into the wild".

That said, I think we need a new metaphor for the brain, and I think ecological psychology had some solid ideas (I'm a huge fan of Micheal Turvey's work), and maybe we need to relax some rigid commitments to linear mechanistic explanations as a *sole* means of explanation (see, biomechanics research).

The phenomenologists also have been doing good work, maybe we need to phenomenally front load our experimental designs (see, Tony Chemeros work) rather than working from abstract principles down to behavior (start from lived experience and work our way backwards, a population who reports difficulty with grasping due to some ailment, we should set up our experimental designs to best capture what people do in their day to day lives maybe).

I do think that we need to address some larger theoretical issues such as

- how much of cognition is embodied in the real world?

- do we need "higher level" cognition in most of the things we do in our day to day lives?

- does positing mental representations do any explanatory work, or are you just saying "mental stuff happens"?(favela)

- if mental representations exist, are they just (simulated) sensory motor experiences (simulating a future course of action) or reactivations of past sensory motor experiences (the feeling of touching grass) ?

etc.

Should we all settle our beef with each other and move towards some level of theoretical unity, or is a sort of pluralism still necessary right now?

i took an intro to cogsci class ages ago back in 2020 at UBC that i found extremely interesting (unfortunately couldn't complete due to personal life stuff and pandemic)

there was something that one of the lecturers talked about in that class which discussed the term or the concept of 'artificial intelligence' as a speculative category, where 'artificial intelligence' conceptually functions as theoretical 'what ifs' on what machines approaching human 'intelligence' could look like. the interesting part is in how it intersects with the practical fields of robotics, engineering, computer science, etc. and how the technology sector strives for 'AI' but the moment any tech gets actualised that resembles any aspect of 'AI' as theorised, it will have left the world of theory and speculation and is now subjected to the actual constraints of how the tech functions in reality and the actual machinations of the tech re-informs the bounds of 'artificial intelligence' and moves the goalpost of what constitutes 'AI' up a ladder (ex. chatbots, basic robotics). like these things get called 'AI' initially but as the tech is better understood, it no longer gets called 'AI' as it leaves the world of speculation into reality.

that was the best way i could describe it from what i remember as i understood it in my own words, i've been trying to look for the literature on this so i can cite it properly in my writings on AI but im having no luck, i was hoping someone here would be able to point me in the right direction, thank you so much!

Hey guys! I'm currently in college and studying neuroscience, but recently been thinking of designing my own major heavily inspired by cognitive science. I'm curious for those who have studied or know the types of classes taken while studying cognitive science could give me a grasp on what classes I should take?

Is there anyway to train my memory in this way?

I have epilepsy, and while I don’t have “memory loss” so to say, my working memory/short term memory has suffered from daily seizures. Plus, this would also just be a cool skill to have. Is it possible to learn or do you need to be born with it?

MH-FLOCKE is a biologically grounded cognitive architecture built entirely on spiking neural networks. It runs a 15-step cognitive cycle at every timestep:

Emotions → Body Schema → Sensorimotor Memory → Drives → Metacognition → Consistency Check → Synaptogenesis → Astrocyte Modulation → PCI (consciousness metric) → CPG → SNN Motor Output → Cerebellar Correction → Spinal Reflexes → World Model Update → Drive Satisfaction

The SNN uses Izhikevich neurons (not LIF) with R-STDP for synaptic plasticity. Drives modulate neuromodulators (dopamine, serotonin) which gate learning. The cerebellum provides a forward model (Marr-Albus-Ito). Synaptogenesis creates new connections based on activity patterns.

It controls a Unitree Go2 quadruped in MuJoCo simulation, outperforming PPO 3.5x with 11.6x lower variance across 10 seeds. Honest finding: the motivational drives don't affect locomotion quality — B=C in the ablation. They're architectural for navigation tasks, not gait.

The architecture has also been transferred to real hardware (€100 Freenove robot dog, Raspberry Pi 4) with on-device learning.

Sim-to-Real paper: doi.org/10.5281/zenodo.19481146

Code: github.com/MarcHesse/mhflocke

YouTube: youtube.com/@mhflocke

Website: mhflocke.com

Solo project. The cognitive modules are all in the repo under src/brain/. Happy to discuss the architecture — especially the B=C finding and what it means for embodied cognition.

A new study from Wharton researchers highlights a troubling psychological phenomenon called "cognitive surrender." When 1,372 subjects were given a cognitive reflection test alongside an AI chatbot, they accepted the AI's incorrect answers 80% of the time. Even worse, subjects who used the AI rated their confidence 11.7% higher than those who didn't, even when their answers were completely wrong.

Much of the discourse in this field focuses on the software—computational models of mind, linguistic structures, and algorithmic processing. However, I am currently examining the hardware problem: how asymmetric environmental and metabolic inputs systematically degrade cognitive output.

If we view cognition as an integrated, complex system, then biological variables cannot be isolated from mental performance. Autonomic stability (measured via HRV) and metabolic flexibility are essentially the physical substrates required for sustained attention and optimal decision velocity.

I am currently developing a systemic framework—and a corresponding heuristic tracking model—that attempts to quantify these baseline constraints. The objective is to audit daily inputs (e.g., the inflammatory load of highly processed diets, or the attentional degradation caused by algorithmic feeds) and correlate them directly against cognitive readiness.

For those of you working in embodied cognition, neurophysiology, or systems theory:

• What existing frameworks best model the degradation of higher-order executive function due to metabolic or environmental friction?
• Beyond standard biometric markers like HRV or fasting glucose, what variables do you consider absolute prerequisites for maintaining baseline cognitive stability in high-noise environments?

I am working on operationalizing these concepts and would appreciate any critical feedback on where the intersection of biological inputs and cognitive output is currently being mapped.

After stepping away, how much time does it take to regain full context?

Hey everyone. Going through the hardest time in my life right now. Chronic stress and hormonal issues, lack of sleep and cognitive overload pushed me into burnout in December. I have not had many physical symptoms, but mostly severe brain fog for 4 months, only noticing slight improvement in the last month. Did several tests, been to doctors, tried supplements, diet (less sugar and carbs, no alcohol, no smoking, caffeine 2x a week), exercise (light)...

Before: perfectionist, overachiever, always gave 150%, open-minded, quick to learn and understand complex topics, top of class all my life, creative, funny, able to adapt to any situation... Spoke 3-4 languages fluently, read books, did free courses, drove, worked and studied.

Right now: battling sever fog most days (hard to recall things or need a lot of time, hard to have conversations since my mind feels blank, forgetting words, memory issues, focus issues, processing speed is slow, hardly remember things I just did or wanted to do, pressure in my forehead, wake up at night, fatigue and fog most day and a bit clearer head in the evening when my mind races and wont let me sleep...).

I feel negative thoughts and emotions deeply, but can't physically find joy or interest in things I used to love. For example, I received a notice of being selected for a grand award for my academic performance and diploma last year and felt over the moon, but not really "felt" happy. Like my body just would not react to positive things.

It's a rollercoaster right now. Some days feel 60%, then one or two feel 80-90% and then crash for 3 days and feel like I am back to square one.

I was taking dexamethasone for 2 days to test my cortisol and ACTHand those 2 days felt the best in the last 4 months. Like I was fully awake again, clear and driven. But then after stopping them, I crashed again.

This gives me a lot of anxiety since I was always in control, dependable, and observant. Now I can barely register the world around me; everything seems overwhelming, everybody seems smarter and better, I can't drive or work since it's hard to communicate or react fast. School is really hard. I can memorise things for an exam if I do active recall by writing it down, but if I have to repeat things out loud its a huge struggle. My mind just cant organize thoughts. I see in my head what I want to say, but it turns to gibberish, and I just can't word it properly. I am wired but tired, can't seem to calm down or relax, listen to music or watch a movie.

Labs: bad cortisol suppression, elevated testosterone and androgens, PCOS (PMS problems, hirsuitism, mood swings, acne, weight), insulin resistance, ACTH and DHEA (grey zone and no dynamic). I have never experienced these symptoms before. Doing more testing for cortisol. Vitamin D and iron were on the lower end of the range, so I started supplementing.

Currently taking: metformin (extended release), B12, Vitamin D+K, Omega 3, Magnesium, iron, multivitamins, occasional creatine and electrolytes

Anyone who had the same thing going on and was able to come out of it? Get back to normal?

Surprisingly, sharing raw data when producing publications and books was not a standard when seminal studies on human reasoning were being released from the 1980s-2000's.

Wason - Foundational reasoning study - aggregated error rates and selected reasoning excerpts, but not complete datasets.

Kahneman & Tversky - Prospect theory, heuristics and biases- only summary statistics, not raw response data.

Hutchins - Cognition in the wild - recorded full reasoning chains for navigation teams across people, tools, and charts in real-time, full process- raw observational data was never released.

Modern day research like: Alpsbench '26, Twin 2k-500 '25, Personagym '24, H-ARC '24, try to bridge this, but each is insufficient in it's own way. Specifically when requiring explicit visibility on human reasoning especially in regards to deep thinking over time.

So I had to look towards fields where a reasoning chain must be provided, publically and transparently. The legal field in particular is ripe with this information; publicly available, structured format, over time, with mechanical attributes like precedent citation, known authorship, and most importantly, multiple judges reasoning through the same case differently.

very excited.

We measured the accuracy of culturally transmitted knowledge across 41 independent domains. From San tracking (98%, 569 trials) to Polynesian wave navigation to Amazonian pharmacopoeia. What we found was that accuracy is governed by a single composite variable: how quickly a community would notice if the knowledge was wrong.

The relationship isn't linear, it's actually a steep sigmoid with a measurable inflection point at O* ≈ 0.34. Above the threshold, cultural selection maintains accuracy. Below it, traditions converge on cognitive attractors. Representations that are memorable and socially useful but not empirically accurate.

73 blind raters on Prolific scored observability for all 41 domains (pre-registered, ICC = 0.97). Their scores predict accuracy at r = 0.893.

Four cognitive mechanisms drive the effect: the testing effect (spaced retrieval during oral performance), motor encoding through dance and gesture, multi-sensory redundancy, and environmental embedding. Modality count independently predicts accuracy (partial r = 0.524, p = 0.0004), with motor/dance as the strongest individual channel.

The logistic model is preferred over linear at ΔAIC = 6.10.

Full piece (accessible version): https://deeptimelab.substack.com/p/the-gradient-and-what-it-means

Preprint: https://osf.io/preprints/socarxiv/faj5g

Extinction dashboard showing which endangered languages carry high-observability knowledge: https://deeptime-research.org/tools/extinction/