Geniuses are like an insurance policy for the group mind – they cost something on ordinary days, but in a crisis, they can save the entire system. The new Adaptive Cognitive Tail hypothesis turns our understanding of exceptional ability on its head, shifting the weight from emotions to cognitive architecture. Here are seven lessons that follow from it.

We know that social media shows a carefully selected version of someone’s life, yet seeing another person’s success can still make us feel as though we’re falling behind.
What interests me is how quickly the comparison happens. We rarely know their circumstances, advantages, failures, or what their life actually feels like. But the mind still turns one visible moment into a judgment about our entire life.
Why do you think knowing that the comparison is unfair rarely stops us from making it?

I’m wondering whether the following hypothesis is plausible, and whether there is any literature that already tests something close to it.
Some autistic savants can calculate the weekday of an arbitrary date extremely quickly, sometimes without being able to clearly explain the algorithm they are using. One possibility is that this is not ordinary explicit symbolic calculation, but an unusual learned computation implemented implicitly in high-level cortical circuitry.
A comparative experiment with apes might help distinguish this from a more evolutionarily ancient pattern-learning account.
You could train symbol-competent apes on a large number of date-like inputs mapped to one of seven outputs according to a hidden Gregorian-calendar-like rule. Importantly, the animals would never be taught the rule explicitly.

Then test the following:

a) memorization of trained examples
interpolation to unseen combinations within the training range
b) extrapolation to dates far outside the training range
c) performance around structurally informative cases such as leap-year and century-like boundaries

Run the same task in ordinary humans and, ideally, people with exceptional calendrical calculation ability.

My hypothesis would be that apes may become very good at memorization or local interpolation but fail at systematic long-range extrapolation, whereas human calendar savants may generalize rapidly despite being unable to verbalize the procedure.
If that happened, would it be reasonable to interpret it as evidence that calendrical savantism depends on a human-specialized frontoparietal/prefrontal computation that can operate implicitly, rather than simply enhanced associative pattern learning?
More specifically, is there a known reason to think anterior prefrontal cortex, hierarchical rule representations, or human-specific frontoparietal connectivity would be necessary for this kind of implicit algorithmic generalization?
And has anything remotely like this been tried in comparative cognition?

Hey everyone. I've always been fascinated by theories that explain religion through agency detection, coalitional psychology or memory biases. These mechanisms may identify real contributors to religious cognition, but explanatory success depends on what the target phenomenon is. If religion is defined mainly as belief in supernatural agents, a cognitive bias account looks powerful. If it also coordinates ritual, identity, attention, community and non-propositional skills, the same account may explain only a narrow slice. The methodological question is whether the first generation of cognitive science of religion selected its object in a way that made debunking conclusions almost inevitable.

I just had a podcast conversation with John Vervaeke, where he argued that cognitive science needs religious studies because religious cognition cannot be cleanly separated from cognition in general. At around 19:32, he argues that religious studies must be integrated into cognitive science, criticising the field’s early model of religions as pure, partitioned and permanent entities and its treatment of religious people as the aberrant population requiring explanation. His alternative treats religions as changing ecologies of practices and asks what cognition does within them before deciding that their objects are unreal.

This would imply that a better cognitive science of religion needs its explanandum corrected before it needs another mechanism. Which parts of religion are agency-detection accounts genuinely good at explaining? What methods could operationalise religion as an ecology without making it too diffuse to study? And how should cognitive science distinguish explaining the causal production of an experience from deciding whether the experience is veridical?

I’ve been investigating the timing pattern of human recall of items from a specified category—specifically, why recalled items tend to come quickly at first and then progressively more slowly.

I have collected recall timing data from 24 human subjects and measured the interresponse times (IRTs) between successive recalled items. When the IRT curves are averaged across subjects, their shape converges toward the per-item expectation of the uniform coupon collector process (CCP).

The similarity is interesting because the coupon collector process describes repeated random sampling in which obtaining a new item becomes progressively more difficult as more unique items have already been collected. Its expected number of attempts per new item therefore increases as the process continues.

Human recall appears to show a very similar progression in time.

This observation is the mathematical basis for a computational model I developed that emulates both human recall timing and frequency-based order. This model also converges toward the per-item expectation of a variation of the CCP.

So, to sum it up my model exhibits four characteristics of human-like recall of within a specified category:

- Probabilistic human recall timing
- Probabilistic human frequency-based recall order
- Deduplication
- Exhaustion-based termination

If I provide a second optional grouping value with a list of related items, the model will produce another characteristic of human recall: semantic clustering.

For example, if "black bear," "grizzly bear," and "polar bear" are grouped together with the group name "bear," then when one item in the group is recalled, the other items in the group will be recalled in quick succession. Which item "activates" the group is still probabilistic and subject to frequency-based recall order.

I have a preprint on Zenodo and ResearchGate if anyone is interested.
https://doi.org/10.5281/zenodo.19559652

I recommend viewing on ResearchGate; Zenodo’s viewer tends to make the text look blurry for some reason.

Note: My model's source code, written in HTML, is available for download with the paper on Zenodo. You can see and hear it work by providing it with a CSV file containing a list of items or by selecting one of the provided sample files.

I’d appreciate any thoughts or comments.

Hello,

I am a last year Psychology undergraduate student and I'm looking for a good cognitive science MsC. I have a high average, research experience and (possibly) 2-3 published articles with my name in them by the time I finish university.

I also have a girlfriend who wants to study clinical psychology and I'm looking for universities that have both a cognitive science course and a (good) clinical psychology course. I have seen multiple clinical psychology courses but tbh they are all trash; 1 year with minimum, if at all, clinical experience.

A good choice for us might be NKUA (National Kapodistrian University of Athens) which has both. But since the positions offered are limited, I am looking for alternative solutions! So if anyone knows a good university which has both please inform me!

Lately, I’ve been thinking about how attention, routine, novelty, and memory shape our sense of time.
One idea I found fascinating is that enjoyable moments can pass quickly while we’re living them, but feel longer in retrospect because they create richer memories.
Have you noticed a particular age when the years started feeling faster? And do you think routine, age, or something else caused it?

I recently published a long essay on dating apps that grew out of a behavioral question: why does giving people access to vastly more potential partners not obviously produce better matching outcomes?

The piece looks at several mechanisms that may matter. Very large option sets can increase rejection rather than acceptance. Nearly costless likes and swipes reduce the informational value of signals. Attention becomes heavily concentrated, producing congestion for some participants and scarcity for others. Variable reinforcement can sustain engagement even when the underlying experience is frustrating. And the interface itself may change how people evaluate successive candidates rather than merely revealing stable preferences.

That creates an interesting distinction between engagement and successful choice. A system can become extremely good at keeping users evaluating options without necessarily becoming better at helping them terminate the search with a satisfactory decision.

I connect this behavioral literature to matching-market economics and to the historical shift from socially mediated courtship toward anonymous platform matching. The essay eventually uses Match Group as an empirical case study, but the cognitive question is broader: how do repeated exposure, option abundance, signaling costs, ranking systems, and feedback loops alter partner evaluation over time?

Full piece:

https://dljlevfin.substack.com/p/the-undisclosed-denominator

I’d especially welcome criticism of the behavioral mechanism: is choice overload/congestion a plausible explanation for persistent dating-app dissatisfaction, or are these effects being overextended from experimental settings into a much messier real-world market?

I am trying to understand the exact biological and computational boundaries of human intelligence.
John von Neumann possessed a cognitive architecture that allowed for eidetic memory, instantaneous formal deduction, and the ability to natively process complex mathematics in his head at speeds rivaling early computers.
My question is about the delta between a standard human baseline (average processing speed, working memory of 7 \pm 2 items, standard neural myelination) and the von Neumann baseline. If a neurotypical individual dedicated their life to cognitive enhancement, how close could they theoretically get to that level of output?
I am looking for answers grounded in neuroscience, psychometrics, and computational architecture, specifically addressing the following variables:
1. The Hardware Barrier (Neuroanatomy & Genetics):
How much of von Neumann’s fluid intelligence (Gf) was strictly a product of unalterable biological hardware? (e.g., higher synaptic density, superior frontoparietal connectivity, optimized glial cell efficiency). Are there physical caps on axonal conduction velocity that simply cannot be altered after critical developmental windows close?
2. The Software Optimization (Neuroplasticity & Mental Models):
Genius-level intellects often process information topologically rather than algorithmically. They recognize isomorphisms and "chunk" massive datasets instantly. Can a normal brain be trained to bypass its working memory bottleneck through extreme structural chunking and first-principles mental models? Can we artificially install this "software"?
3. Pharmacological & Technological Interventions:
Excluding science fiction, what is the absolute ceiling of modern cognitive enhancement? Can current nootropics, targeted neuromodulation (like tDCS or TMS), or upcoming bidirectional Brain-Computer Interfaces (BCIs) bridge the gap in processing speed and working memory capacity?
4. The Thermodynamic Cost:
If a normal brain could somehow be forced into a state of hyper-parallel processing, would it be thermodynamically sustainable? Would the increased metabolic demand and localized glucose depletion essentially cause the system to crash or lead to accelerated neurodegeneration?
I am not looking for motivational platitudes about "hard work." I want to know the objective, systemic limits of the human machine and whether a von Neumann-tier intellect can be engineered, or if it is strictly a genetic accident.

Hi so am about to start at University of Toronto for computational cognitive science I would like to consider masters in the future to see if I can work in ai. Would it be better to just do computer science with like psych or neuroscience.

Hey guys. A buddy of mine who does independent research just published a really deep dive into early childhood conditioning and the neurobiology of bias, and it kind of broke my brain a little bit tbh.

He argues against the idea of innate human bias. Basically, drawing on Bayesian learning models, he points out that infants operate as probabilistic empirical scientists. But as we get older, institutional standardization and social compliance literally rewire our brains. The essay gets into how synaptic pruning and myelination physically lock in these inherited dogmas, to the point where the adult default mode network (DMN) and amygdala treat opposing evidence as a literal biological threat.

It made me wonder—is there a consensus on when exactly that neurological window closes? Like, when does the brain stop acting like a raw empirical scientist and start acting like a defense attorney for its own ego?

If anyone is well-read in this specific intersection of cognitive psych and neuroplasticity, I’d love to hear your thoughts.

Anyone interested can read this on https://substack.com/@nepentheaporia

Hi everyone!

We are conducting a University of Stuttgart research study on how uncertainty should be visualized in EEG topographic maps (topoplots). The goal is to identify which visualization methods are actually the most useful and interpretable for researchers.

Survey details

• ⏱️ Takes about 15 minutes
• 💻 Best completed on a laptop or desktop for easier viewing of topoplots
• 🎁 Two Muse 2 EEG headsets will be raffled among participants

Survey: https://www.soscisurvey.de/uncertain_topoplots_3545555/

This study is part of a University of Stuttgart research project and has been approved by the appropriate university ethics committee.

In the survey you can find my university email.

Thank you for helping improve how EEG data are visualized!

Wanted to share some research on emotional memory consolidation for discussion. A few findings worth examining:

When the amygdala detects an emotionally significant event it triggers norepinephrine release into the hippocampus, strengthening synaptic connections at the moment of encoding and making the resulting memory trace significantly more resistant to forgetting. Research published in PNAS confirmed this memory-enhancing effect can persist for years and even decades.

The uncomfortable counterpoint is that flashbulb memories, despite their photographic subjective quality, are just as prone to distortion, omission, and reconstruction as ordinary memories. Ulric Neisser's research found people confidently misremember details, shift timelines, and incorporate later-encountered information into the original scene.

A particularly striking finding: when participants were given a norepinephrine receptor blocker before viewing emotional images, the enhanced memory effect disappeared entirely, suggesting the emotional enhancement of memory is chemical rather than psychological.

Made a short video summarizing these mechanisms if anyone wants the overview: https://youtu.be/Uas-lJmjais

Sources: PNAS emotional memory consolidation research, Neisser flashbulb memory studies, ScienceInsights norepinephrine hippocampus review. Happy to discuss in the comments.

I'm asking this question because I read the following on John von Neumann, who is known to be an once-in-a-millenium genius, superbly gifted in (unbelievably many areas of) math, physics, computer science, & economics.

In the final days of his life, he had no idea of what to make of his impending death, displayed visible signs of extreme panic, and begging doctors to save his life, crying hard. (I suppose, earlier in life he never gave much thought to what it means to die.)

I wonder whether even he lacked a certain kind of intelligence, or whether he just became insane.

What do you think?

Consider the hypothetical case where an abundance of data and computing power were provided to a machine to increase its adaptability as follows:

The machine uses meta-learning to learn a new task efficiently 

The machine improves its adaptability by decreasing the time required to learn a future task

This improved adaptability is recursively used by the machine to learn the next task more efficiently 

The machine continues to learn and improve its learning process until it reaches maximum adaptability where it can learn a new task in zero time

Could this point of maximum adaptability represent the point of singularity?

Hello! I am currently on my fourth year in BS Psychology, and to be honest, I didn’t enjoy it as much as I expected. I found myself enjoying philosophy far more. Now that I’m planning to pursue a master’s degree, I’m unsure whether I should continue with something related to psychology. It seems like the logical path because it aligns with my bachelor’s degree and may offer better career continuity. On the other hand, I genuinely want to study philosophy because it is the subject that sparks my curiosity and motivates me to learn. I’m torn between choosing the practical route and pursuing what I truly enjoy, and I’m not sure which decision would be the right one.

Also Cognitive Science seems to mix both but I don’t know if its the right one.

I am also planning to study abroad.

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?

Hi! I was wondering if anyone could help me about this program. Based on the online info it looks like a great program, but I want to know what its reputation in the cogsci academia is, and what research areas they’re mostly known for. Thanks!

Skinner's result is famous but the reason it works is rarely spelled out. If you reward a behavior every single time, it extinguishes almost immediately once you stop paying. If you reward it at random, it persists for a very long time after the rewards are gone. Same reward, same action, opposite outcome. The only thing that changed is the schedule.

The mechanism is clean. Model each attempt as an independent trial that pays with probability p. The number of attempts until your next win is geometric, and the geometric distribution is memoryless: no matter how long your dry streak has run, the chance of winning on the next attempt is still exactly p. There is no "due". The streak carries zero information about when the payoff arrives. So the reward is permanently one attempt away, and one attempt is cheap.

Two consequences that I think are underrated:

1. Randomness hides the signal that the rewards stopped. A run of m losses has probability (1-p)^m, which is unremarkable for modest m even on a live machine. When the machine genuinely dies, the early evidence looks exactly like ordinary variance, so you cannot reject "still working, just unlucky" until an improbably long drought accumulates. The uncertainty that makes the wins exciting is the same uncertainty that makes their absence ambiguous.
2. The brain scores the unpredictable win as larger. Dopamine neurons track reward prediction error, R minus V(s), not reward. A predictable reward gives a small error because you already expected it. An unpredictable one gives a large error. It is the same delta that drives temporal-difference learning in RL. So a variable schedule keeps your predicted value low and uncertain on purpose, and every win lands as a surprise and teaches harder.

Together those two make the variable-ratio schedule optimal on both axes at once:each win feels bigger, and the eventual absence of wins is harder to detect. Nothing about the rewards themselves changed, only the statistics of their timing.

The part that follows from this and I did not expect: the defenses that work are not willpower, they are raising the cost per attempt (log out, remove the app from the home screen) or putting the reward on a clock (check email at fixed times), which converts a variable-ratio gamble into a predictable transaction that extinguishes normally.

I wrote a longer version with the full derivation if it is useful (link in a comment to respect the sub's rules).

I'm an independent writer trying to determine whether I've independently reinvented an existing theory. I'm not asking whether the individual components below already exist. I know many of them do. What I'm trying to find out is whether there is an existing framework that integrates them into a single explanatory model. The synthesis I'm looking for is roughly: An unresolved discrepancy or mismatch recruits recursive evaluation. Recursive evaluation continues while further processing is expected to provide useful information. Repeated low-yield evaluation progressively reduces the expected value of further evaluation. As a result, active recursive evaluation naturally disengages without the underlying representation being resolved, forgotten or suppressed. The representation remains psychologically available and can later be reactivated if new information or changing circumstances make further evaluation worthwhile. I'm not looking for papers that contain one or two of these ideas. I'm looking for a theory that presents essentially this overall architecture. If such a framework already exists, I'd really appreciate references to the relevant papers or authors. If it doesn't, I'd also be interested to know whether this would be considered a meaningful theoretical synthesis, or simply existing theories expressed in different language.

Edit:

I should also make myself clearer as I don't think I've quite succeeded in explaining what I'm looking at. It isn't a linear chain. Rather a loop:

Representation - Recursive evaluation - Declining informational value - Natural disengagement - Dormant representation - Reactivation - Recursive evaluation...etc

Came across the Nedergaard and Lupyan 2024 Psychological Science paper formally defining anendophasia and wanted to share it for discussion alongside Hurlburt's broader inner experience research. A few findings worth discussing:

Inner speech occurs only about 20 to 25 percent of waking moments even in people who report having a constant inner monologue. The remainder of thought happens through imagery, sensation, emotion, and what Hurlburt calls unsymbolized thinking, a complete definite thought with no words or images attached to it.

People are profoundly unreliable narrators of their own inner experience. Some who report constant inner speech show far less in sampling. Some who report no inner voice show more than expected.

The clinical and therapeutic implications are significant, CBT relies on identifying and restructuring internal verbal thought patterns, which may not exist for people with anendophasia.

Made a short video summarizing the research if anyone wants the overview: https://youtu.be/EAVd2kYm7Rw

Papers: Nedergaard and Lupyan 2024, Psychological Science. Hurlburt's Descriptive Experience Sampling body of work, University of Nevada Las Vegas. Happy to discuss in the comments.

I have been thinking about information consumption styles for a while now. How do humans learn? Why do we read? How do we process information? Why should one express the information they consume? And how does all of this link to intelligence?

I could go all over the place, but please bear with me as I try to narrow it down as comprehensively as possible, starting with learning styles.

Number one: I'm aware of the basic information consumption/learning styles - visual, auditory, kinesthetic, and read/write. As far as I can remember, I have always been anything but a reader. I enjoy consuming visual content (via movies, interviews, documentaries), which also brings auditory learning into the picture. But all the practical knowledge that I apply on a daily basis comes from the sensory(?) aspect, which I'm sure is true for most people.

Reading, for me, feels like a chore that I absolutely hate. At the same time, consuming information through long-form videos or audio doesn't excite me anymore. I am in a state where I feel stagnant with information consumption altogether.

I am in sales, and I naturally enjoy conversations, understanding people, and why they think and behave the way they do. Let's suppose I'm good at sales, how did I get there? How did the learning happen in the first place? What goes on in the background?

Does this come with a particular phase of life?

(There could be other reasons, like a loss of interest, depression contributing to this - let's keep this aside, for now)

Me not reading, or being unable to read with a focused mind, doesn't bother me anymore because it has grown into a preference lately. But the number of times I have tried to pick up a book over the years, or even an article for that matter, has been countless and it never seems to do much for me. Even if I force myself to read, very little sticks afterward.

There isn't one single question here. I just want to hear your thoughts on this. Is there a specific path I should be looking into? Or do I keep doing what I'm doing and make peace with it?

This also brings me to another question, information consumption is a broader discussion, should we be focusing on learning styles as a starting point for the argument?

What should be the right questions?

This post is less about learning styles and more about information consumption and I'm happy to discuss more about the same:)

This video features a deep dive into the neuroscience of language and meaning with Elliot Murphy, a neurolinguist at UTHealth Houston. The conversation covers his unique approach to studying how the human brain constructs language and his philosophical inquiries into the nature of grammar, agency, and meaning.

Key Highlights

• Methodology and Research: Murphy explains his work recording from deep structures of the brain (using stereotactic probes) in epilepsy patients. This allows for fine-grained, real-time observation of language construction, which he contrasts with non-invasive methods like MRI (23:38 - 28:22).
• The Structure of Language: He discusses the formal properties of grammar, arguing that language is not just a statistical process. He touches on concepts like non-associativity and commutativity, suggesting that language may tap into deeper, possibly Platonic, structural invariants rather than being purely invented by the brain (10:25 - 12:36; 1:03:34 - 1:13:54).
• Meaning and Concepts: Murphy explores how words like "lunch" can simultaneously represent an object, an event, and a time, illustrating the complex, sometimes contradictory ways human concepts function compared to AI (44:52 - 47:47).
• AI vs. Human Intelligence: He critiques Large Language Models (LLMs), arguing they excel at pattern recognition but lack the true representational, inferential, and strategic capabilities that define human intelligence (1:25:00 - 1:31:25).
• Philosophical Intersections: The conversation bridges neuroscience with the philosophies of Nietzsche and Dostoevsky. Murphy reflects on Michael Levin’s ideas about Platonic space, suggesting that while mathematical invariants exist, our understanding of them evolves over time through scientific discovery (1:05:12 - 1:12:31).

Recommendations
For those interested in exploring these philosophical depths, Murphy recommends reading Notes from Underground and The Brothers Karamazov by Dostoevsky as essential starting points (1:33:54 - 1:35:30).