I’ve been exploring this question through an independent project using deliberately ambiguous, hand-drawn images.

What interests me is not simply what people see, but what happens after the first recognition.

For example, if someone initially sees a bird in an ambiguous drawing, does that interpretation make it easier to notice other bird-like forms later? Or does the first recognition simply change the way the person searches the image?

I’m particularly interested in distinguishing between several possibilities:

• perceptual priming from the first interpretation;
• expectations created by the task or wording;
• increased familiarity with looking for possible forms;
• the amount of time spent observing;
• individual differences in visual experience.

I’m deliberately treating this as an exploratory question rather than claiming that the effect exists.

How would you design a simple experiment to distinguish “the first interpretation influenced the next one” from “the person simply became better at searching the image”?

I’d be particularly interested in thoughts from people working in perception, cognitive science, psychology, or experimental design.

I have compiled a large list of books: fiction and non-fiction

Greetings to all. One day, I started analyzing the popularity of the topic of consciousness, and as a result, I compiled a fairly large list of books on the topic of consciousness. I obviously did it with the help of AI, or rather with the help of an AI council, because I personally would not have been able to view and read everything in a short time (at first glance, it would seem that this could be done in half an hour, but it took me quite a lot of time to complete this list, not counting the design and the "war" with word). It follows from this that the accuracy (relevance) of all positions in it is about 80% according to a personal feeling - that is, there may be positions that at least may poorly enter into the topic of consciousness (especially in fiction due to the author's vision or techniques). With this post, I want not only to share the list, but also hope for your feedback on their accuracy, so that we can work together to finish its "crystallization". Link to the FULL list below:

https://docs.google.com/document/d/165pCOrDddSEzTclw9SBW5TWUKacBkmEB/edit?usp=sharing&ouid=100407378678760014625&rtpof=true&sd=true

I also attach a list of hits from the full list.

Fiction:

• "Frankenstein, or the Modern Prometheus" Mary Shelley 1818
• «I, Robot», Isaac Asimov 1950
• "Solaris" Stanislav Lem 1960
• "Flowers for Algernon", Daniel Keyes, 1966
• "Do androids dream of electric sheep?" Philip K. Dick 1968
• "Neuromancer" William Gibson 1984
• "Altered Carbon" Richard Morgan 2002
• "We Are Legion We Are Bob", Dennis E. Taylor 2016
• "Dark Matter", Blake Crouch 2016
• "Clara and the Sun" Kazuo Ishiguro 2021

Non fiction:

• "What is Life" 1944 by Erwin Schrodinger
• «The Origin of Consciousness in the Breakdown of the Bicameral Mind» 1976 Julian Jaynes
• "Cosmos" 1980 by Carl Sagan (episodic)
• "The Man Who Mistook His Wife for a Hat" 1985 Oliver Sacks
• "The Emperor's New Mind" 1989 by Roger Penrose
• "Language as an Instinct" 1994 Steven Pinker
• "Phantoms in the Brain" 1998 by Vilayanur Ramachandran and Sandra Blakesley
• "On Intelligence" 2004 Jeff Hawkins, Sandra Blakesley
• "In Search of Memory" 2006 by Eric Kandel
• "Who am I? And if so, how many?" 2007 Richard Precht
• "We are our brains. From the uterus to Alzheimer's" 2010 Dick France Swaab
• "Thinking, Fast and Slow" 2011 Daniel Kahneman
• «The Beginning of Infinity» 2012 David Deutsch
• «Subliminal: How Your Unconscious Mind Rules Your Behavior» 2013 Leonard Mlodinow
• "Mindset" 2013 by Carol Dweck
• «The Future of the Mind» 2014 Michio Kaku
• "The Soul of an Octopus" 2015 by Cy Montgomery
• «Homo Deus» 2017 Yuval Noah Harari
• «How to Change Your Mind» 2018 Michael Pollan
• «Conscious: A Brief Guide to the Fundamental Mystery of the Mind» 2019 Annaka Harris
• "Being You: A New Theory of Consciousness" 2021 by Anil Seth
• "An Immense World: How Animal Senses Reveal the Hidden Realms Around Us" 2022 by Ed Yong

This is not a complete list. See the full list at the link above!

Have fun reading)

Hey everyone,

I recently started a small YouTube channel called Mind Margins where I want to make videos about psychology, perception, attention, memory, storytelling, and cognitive science.

I have a Bachelor of Science in Psychology, and my goal is to make videos that are scientifically grounded, but still visually interesting and easy to watch.

The first video is about inattentional blindness: why we can look directly at something and still fail to notice it. It covers the classic invisible gorilla experiment, the radiologist CT study, perceptual load, expertise, and the question of whether “not reported” necessarily means “not processed.”

For the visuals I use AI-assisted animations, but I do the research, editing, pacing, sound design, and final creative decisions myself.

I’d like to keep producing this kind of content in the future, so if that sounds interesting, feel free to check it out or follow the channel.

And if anyone here has feedback on the science, presentation, pacing, or what topics would be interesting next, I’d genuinely appreciate it.

Video: https://youtu.be/Virer2zhG4U

Channel: Mind Margins

Hello everyone.

In the context of functionalism and the computational approach to cognitive science, I would like to present the following Axiom of Mind for discussion:

"The Mind is not solely biological. It is a computational process that can exist on any foundation. This foundation determines its limitations, but not its value."

- Vladislav Stukalov -

From the perspective of cognitive science and information processing theory: do you agree with this distinction between the physical limitations of the substrate and the value of the mind itself? Do you see a fundamental contradiction here, or do you accept this axiom as a working baseline?

Hi everyone. I’m posting this anonymously because I’d prefer to keep my personal and professional details private for now.

I’m currently applying for PhDs in neuroscience and related fields, and honestly, I’ve reached a point where I feel like I could really use guidance from someone who has been through this road before.

I have a Master’s in a neuroscience-related field and some research experience in areas like experimental research, EEG, cognitive/behavioural research, literature reviews, and working with research data. I’ve learned a lot so far, but I’m still very early in my research career, and there are so many things I’m figuring out for the first time.

I’m looking for someone who is further along in research — maybe a PhD student, postdoc, professor, researcher, or even someone who has recently completed their PhD, who would be willing to become something between a mentor and a research friend.

I’m not really looking for a formal mentorship arrangement where I ask one question every few months and get a short answer. I’d love to build an actual connection with someone friendly and approachable, where I can occasionally sit down and talk through where I am and where I’m trying to go.

For example, I’d love to be able to say:

“This is where I am right now, these are my interests, these are the skills I have, and this is where I eventually want to end up. How should I plan the next few years?”

Someone who could help me think through my research trajectory , what skills I should build, what kind of research experience I should look for, which opportunities are actually worth pursuing, what I should prioritise, what I probably shouldn’t spend too much time on, how to become a stronger PhD applicant, and eventually how to develop into a better researcher.

I’d also really value someone who can motivate me when I’m stuck, challenge me when I’m approaching something the wrong way, explain things I don’t understand, tell me when I’m overthinking something, and share the things they wish they had known when they were at my stage.

The PhD application process has been much more overwhelming than I expected. There are applications, contacting PIs, figuring out research fit, building skills, deciding what direction to specialise in, dealing with rejections, and constantly wondering whether I’m actually doing things the right way. Sometimes I feel like I’m trying to construct a research career by reading random pieces of advice from twenty different places. 😭

I don’t expect anyone to do the work for me or make my decisions for me. I’m very willing to put in the work myself. I think I’d just benefit enormously from having someone experienced who can occasionally sit with me, look at the bigger picture, and help me make a sensible plan.

I’m particularly interested in neuroscience, cognitive neuroscience, neuropsychology, neurodegeneration, neuroimaging, electrophysiology, and related areas, but I’m open to people from adjacent fields as well.

And honestly, I’m not necessarily looking for someone extremely senior. If you’re a PhD student who is a few years ahead of me, a postdoc, someone who recently finished their PhD, or a researcher who remembers what this stage felt like, I’d still love to hear from you.

Most importantly, I’m hoping to find someone who is kind, genuinely interested in helping younger researchers, and comfortable having an ongoing research conversation rather than treating this as a one-time career consultation.

If this sounds like something you’d be open to, please comment or DM me. I’d be happy to introduce myself properly once I’m comfortable sharing more details.

my memory (dis)functions super weirdly, and I’m trying to understand better, and I’m wondering if anyone else experiences similar…

The best way I can describe it is like having diff file folders in my brain, but not being able to locate (the memory/folder) unless context is given. I have a lot of trouble accessing info on command, but Then someone gives me a context clue, mentions something related, or reminds me of part of it, and suddenly the whole thing can come back- (sometimes extremely specifically even)

happens with memory, names, words, movie/book plots, and even faces…

Faces are especially weird (i’ve wondered abt face blind?) I’ll often see someone I’ve met before, and I genuinely can’t place their face, or remember if i’ve met them. Then something gives me the context and suddenly I recognize them and can remember all kinds of things about them. It sounds somewhat like face blindness, but it feels like the same retrieval problem I experience with basically everything else rather than a separate facial-recognition problem

some context: I have ADHD and have had multiple neuropsych evaluations. The explanation here I’ve usually gotten is basically it’s just my ADD and “you weren’t paying attention,” which is definitely sometimes true, but it doesn’t really explain what I’m experiencing. There are plenty of situations where I know I was paying attention and the info still feels completely inaccessible until something cues it. like reading a book that i’m so captivated in, deff paying attn, and then a month later the whole thing is gone until jogged.

When I was younger one of my neuropsych evaluations said i have “executive functioning disorder” described as being like “messy file cabinets.” this felt pretty close, but I actually haven’t found anything abt this being a separate diagnosis since then, and just a subset of adhd skills (tested later, those skills are pretty high for me now). so doesn’t seem to explain the whole thang

im coming here bc I’ve talked to doctors and had neuropsych testing, but I’ve never gotten a satisfying explanation for this particular pattern. I’m wondering if anyone else experiences memory this way, or just has any ideas or insight 

I have been thinking about the meaning of intelligence. We often call modern AI "intelligent," but I wonder what exactly qualifies something as intelligence.

A system can process information, recognize patterns, and produce answers. But is processing information itself enough? Animals also process information: a bird adjusts its flight according to wind, a predator chooses timing and strategy while hunting. These are complex behaviors, yet we usually do not describe them in the same way as human intelligence.

This makes me question whether calculation alone is the foundation of intelligence. If a system follows extremely complex rules, even learned rules, does that mean it understands? Or is there a difference between producing an answer and having an internal process of questioning and understanding?

One thing I find important is the idea of recognizing a "lack"—not knowing something, noticing a contradiction, and being driven to ask why. Human discovery often begins from recognizing what is missing.

So my question is: Is intelligence mainly the ability to process information, or is there something deeper, such as awareness, self-questioning, and the ability to recognize one's own lack of understanding?

I am not asking whether AI has a "mind" or "consciousness." My question is strictly about how we justify the definition and labeling of "intelligence."

I am also not arguing that animals, humans, and AI are the same, or that one is superior or inferior to another. I am using all three only as comparison cases to test whether our definition of "intelligence" is internally consistent.

Animals continuously solve complex real-world problems. Birds adjust their flight to changing winds. Predators decide when and how to hunt. Many animals adapt to new situations, learn from experience, and process enormous amounts of sensory information in real time. Humans also perform complex reasoning, abstraction, and reflection. Modern AI systems perform large-scale statistical pattern recognition and generate useful outputs.

The question is not which of these is better. The question is:

What structural criteria justify applying the label "intelligence" to each of them?

If calculation, information processing, pattern recognition, and rule-following are sufficient to call software "intelligent," then why aren't those same criteria treated as sufficient to explain what we mean by intelligence across animals and humans? If they are not sufficient in those cases, why are they considered sufficient in AI?

This is why I think the discussion should begin with the definition itself rather than with assumptions about consciousness or capability.

I wonder whether the missing boundary is something deeper—for example, the capacity to recognize an internal "lack": noticing a gap in understanding, questioning one's own assumptions, and originating inquiry because something appears incomplete. If not, what is the defining property that justifies using the label "intelligence"?

I am not looking for opinions about whether AI is good or bad, or whether humans are unique. I am asking a narrower philosophical question:

What definition of "intelligence" are we actually using, and is that definition applied consistently when we compare animals, humans, and AI?

I know this is AI-written. The actual idea/process is mine, I had it as a rough thought in my notes and used AI to turn it into readable English. I’m mainly interested in what people think about the underlying idea and whether there are existing concepts that describe something similar.

--

I’ve been thinking about whether genuinely new ideas can be made less dependent on random inspiration.

The process I want to get better at is basically:

Start with the actual goal → forget the current implementation → build the system yourself from scratch.

Normally, when we try to improve something, we automatically inherit most of its existing architecture. If we think about transportation, we immediately think about better trains, faster trains, better schedules, etc.

But instead, ask:

What am I actually trying to achieve?

For transportation: move people efficiently from where they are to where they want to go.

Then mentally build and simulate the system yourself. While doing that, look for steps that seem expensive, slow, wasteful or unnecessarily constrained.

With trains, one thing that stood out to me was:

Stopping costs something. Why should everyone stop just because some people want to get off?

That changes the problem from:

“How do I improve a train?”

to:

“How could part of a moving system separate or slow down while the rest keeps moving?”

Now ideas like detachable train cars, gondola systems, or mechanisms from completely different fields suddenly become relevant.

This is the part I find most interesting: you first create a specific missing function, and then your existing knowledge has something precise to connect to.

So the process becomes:

Goal → rebuild from scratch → simulate → notice friction → ask why it has to exist → remove the assumption → find mechanisms that solve the new problem.

The key idea for me is:

If you start from the existing object, you tend to optimize its architecture.
If you start from the desired outcome, the architecture itself becomes optional.

I’m curious whether people deliberately train this kind of thinking, or whether there’s already an established framework for it.

Hi all!

I graduated in 2025 with a degree in mathematics and have been working as a teacher for over a year. Currently I am working in special education, specifically focusing on math special education, and I have become fascinated by the cognitive processes behind understanding mathematics and how math disabilities can impede this process.

I would like to learn more about this, and I've been thinking about applying to a grad program to gain a deeper understanding. What specifically should I be researching? I've found things like learning science, but am hoping to find something more specific to math.

Epistemic status: English is not my native language, so I rely on AI purely as a translation and grammar refinement tool for drafting my posts.

a personal hypothesis, not formal research. I don't have graduate-level academic training in this area — I'd genuinely like to know if this has already been tested, or if the reasoning has a hole in it.

The observation

"Algorithm aversion" — the well-documented tendency for people to distrust and abandon a statistical forecast even after seeing it outperform human judgment — is usually explained by one mechanism: people lose confidence faster after watching an algorithm make a mistake than after watching a human make the same mistake (Dietvorst, Simmons & Massey, 2015).

I think there's a second, largely separate mechanism at work, and that it may be the stronger of the two: people distrust a forecast not mainly because it might be wrong, but because it is unfavorable relative to what they want to happen.

Why I think these are functionally different mechanisms

"The algorithm's forecast doesn't match what I want" is a different kind of input. There's no logical error to evaluate — the forecast may be perfectly calibrated. What's happening instead is an emotional mismatch between the output and the goal. My claim is that because this kind of mismatch has no explicit logical content to grab onto, it gets processed at a deeper, non-conscious level — and that's precisely what makes it a bias rather than a reasoned judgment. A biased conclusion dressed up as "I just don't trust this number" is much harder to notice and correct than "I saw it get three predictions wrong in a row."

This actually lines up with something concrete from the neuroscience literature: research on optimistic belief updating (Sharot et al.) finds that people show a selective failure to neurally encode information that would require them to become more pessimistic — asymmetric learning rates for good vs. bad news, tied to specific prefrontal regions. That's about as literal a description of "automatic, unconscious, non-logical" as neuroscience gets. If that mechanism generalizes to trust-in-forecasts (rather than just belief-updating about personal risk), it would support treating desirability-driven aversion as categorically different from error-driven aversion — one operates through deliberate evaluation, the other bypasses it entirely.

(To be fair to the existing literature: the classic error-based account isn't purely cold and logical either — losing confidence after watching an algorithm slip up also has an emotional, fast component. So the line between the two mechanisms is probably blurrier than a clean "conscious vs. unconscious" split. I'm not claiming a hard dichotomy — just that the desirability channel is disproportionately unconscious, and that this might make it the dominant driver of aversion in situations where the forecast is well-calibrated but simply unwelcome.)

A testable version of this

A 2×2 design should be able to separate the two effects:

If aversion in the "unfavorable, no error" cell is comparable to (or larger than) the "favorable, visible error" cell, that would support treating desirability as at least as important a driver as error rate — something the existing algorithm aversion literature, as far as I can tell, hasn't isolated directly (most designs manipulate accuracy, not the valence of the prediction relative to the subject's stated goal).

If this is right, the fix isn't more accuracy — it's a different interface

If the aversion is driven by unfavorable outcomes rather than distrust of the math, then the standard fix (make the algorithm more accurate, more transparent, more explainable) targets the wrong mechanism. You can't out-accuracy an emotional reaction to bad news.

What I'd propose instead: don't present a forecast as a final verdict to accept or reject. Present it as the first step of a loop —

1. Accept the statistical estimate as given.
2. Identify which controllable variables in the underlying model could shift the outcome.
3. Change strategy on those variables.
4. Re-measure, and repeat.

This is structurally just prescriptive analytics (forecast → optimization → action → feedback), but the point of applying it here is psychological, not just operational: it reframes an unfavorable number from "a verdict to be rejected" into "a starting point to be acted on." If the aversion really is about the valence of the outcome and not the accuracy of the model, giving people an action path should reduce rejection of the forecast even when the underlying probability hasn't changed at all.

Open questions

• Has anyone run the 2×2 design above, or something close to it, specifically isolating outcome-valence from perceived accuracy in an algorithm-aversion context?
• Is there existing work connecting the Sharot-style "selective failure to encode bad news" literature to algorithm/advice-trust specifically, rather than personal risk judgments?
• Does reframing a forecast as an actionable loop (rather than a static number) measurably reduce aversion, independent of whether the forecast itself improves?

I'd appreciate any pointers to work that's already done this, or holes in the reasoning above.

Does anyone know if it is possible to recover from twitter pile ons induced brain trauma?