#AI and Cognition

[omegaphilosophia]

Mental Objects vs. Virtual Objects

Both mental objects (thoughts, concepts, and imaginations) and virtual objects (digital artifacts, AI-generated entities, and simulated environments) challenge traditional notions of existence. While they share some similarities, they also have distinct differences in how they originate, function, and interact with the world.

1. Similarities Between Mental and Virtual Objects

1.1. Non-Physical Existence

Both exist without a direct material form.

A mental object (e.g., the idea of a unicorn) exists in the mind.

A virtual object (e.g., a character in a video game) exists in a digital space.

1.2. Dependence on a Substrate

Mental objects depend on biological cognition (the brain).

Virtual objects depend on computational processing (hardware/software).

Neither exists as standalone physical entities but requires a medium to be instantiated.

1.3. Ephemeral and Modifiable

Both can be created, changed, or erased at will.

A thought can be reimagined, just as a digital object can be edited or deleted.

They are not fixed like physical objects but dynamic in nature.

1.4. Representation-Based Existence

Both exist as representations rather than tangible things.

A mental image of a tree represents a tree but is not an actual tree.

A 3D-rendered tree in a virtual world represents a tree but is not physically real.

2. Differences Between Mental and Virtual Objects

2.1. Origin and Creation

Mental objects arise from individual cognition (thought, memory, imagination).

Virtual objects are created through external computation (coding, rendering, algorithms).

2.2. Subjectivity vs. Objectivity

Mental objects are subjective—they exist uniquely for the thinker.

Virtual objects are inter-subjective—they exist digitally and can be experienced by multiple people.

2.3. Interaction and Persistence

Mental objects are internal and exist only within the thinker’s mind.

Virtual objects exist externally in a digital system and can be interacted with by multiple users.

2.4. Stability Over Time

Mental objects are fleeting and can be forgotten or altered by an individual.

Virtual objects have greater persistence (e.g., stored in a database, backed up, retrievable).

2.5. Causality and Impact on the World

Mental objects influence individual perception and behavior but do not directly alter external reality.

Virtual objects can have real-world consequences (e.g., digital currencies, online identities, NFTs).

3. The Blurring Boundary: Where Do They Overlap?

AI-generated art is a virtual object that mimics mental creativity.

VR and AR technologies blend mental perception with digital objects.

Philosophical questions arise: Are virtual worlds just externalized thought spaces?

Conclusion

Mental objects and virtual objects are both non-physical, dynamic, and representation-based, but they differ in origin, persistence, and external impact. The digital age continues to blur the lines between thought and simulation, raising deeper questions about what it means for something to exist.

[stemgirlchic]

why neuroscience is cool

space & the brain are like the two final frontiers

we know just enough to know we know nothing

there are radically new theories all. the. time. and even just in my research assistant work i've been able to meet with, talk to, and work with the people making them

it's such a philosophical science

potential to do a lot of good in fighting neurological diseases

things like BCI (brain computer interface) and OI (organoid intelligence) are soooooo new and anyone's game - motivation to study hard and be successful so i can take back my field from elon musk

machine learning is going to rapidly increase neuroscience progress i promise you. we get so caught up in AI stealing jobs but yes please steal my job of manually analyzing fMRI scans please i would much prefer to work on the science PLUS computational simulations will soon >>> animal testing to make all drug testing safer and more ethical !! we love ethical AI <3

collab with...everyone under the sun - psychologists, philosophers, ethicists, physicists, molecular biologists, chemists, drug development, machine learning, traditional computing, business, history, education, literally try to name a field we don't work with

it's the brain eeeeee

[nocturnal-phantoms-fandoms]

I just had a talk with my thesis supervisor and I want to check something real quick.

I want to write my bachelor's thesis about fanficion and AI - specifically about fanfiction writers' attitude towards the use of generative AI in fandom spaces

The survey (and the rest of the thesis) is still v much in the making, I just want to check how many potential responders I could reach from this account

Pls reblog after voting

What I can say about the survey for now:

1. The survey will be anonymous!

2. The survey will be in English (but the rest of my thesis will not. I am however required to write an abstract in English)

3. My supervisor said it's fine if I want to focus on one fandom - In this case it will be the HP fandom. However I would like to include as many reposnes as I can, so it will probably not be a requirement to be in the HP fandom.

4. You don't need to write fanfiction to take part in the survey (but there will be a question if you read and/or write fanfics). You need to be in the fandom though

5. You need to be over 18 (most likely)

I want to keep myself anonymous as well so there is a possibility I will have to make a separate sideblog/account for it.

If anyone will be interested in the results, for some reason - I am required to write the abstract in English, so I might share the abstract. Maybe. More info after I write anything for my thesis.

[importantandunavoidable]

bad ass

[tkmk]

tag yourself! i'm Verbaln Reçuising

[writing-for-life]

Is it weird if I think that the recent influx of VI (Virtual Intelligence, since Open AI isn’t true Artificial Intelligence because it’s not autonomous) algorithm generated content should be used as motivation for artists and writers to really “step up their game” so to speak?

You know, really show that they can create content better than the algorithms can?

What do you think?

Not weird but I think ultimately maybe a consumerist way to look at it?

I don’t feel like I have to compete with AI. It’s pointless because I have a soul and AI doesn’t. I don’t have to prove I’m better than AI because that lack of soul and lacking humanity comes through in AI-generated poetry, stories etc. If someone feels AI can compete with what real humans create, they maybe need to sharpen their own senses a bit? That’s at least how I see it, but maybe I’m biased.

I admit that I grade a lot of papers (between December and April) for a performing arts course I lecture, and that I can meanwhile often tell what’s AI-generated even without using the tools we are now supposed to use (both plagiarism- and AI checkers). Maybe it’s not that obvious to other people, I don’t know. And of course we also have to be careful because at the end of the day, some of the advice out there how to “spot AI generated text” is also silly: People are now afraid to use the em dash, for example, because someone decided it’s a “dead giveaway.” I used em dashes in my writing all my life, and the hell will I stop using them. At the end of the day, AI learns from us, and it’s disheartening to see that people who write quite succinctly now often get accused of having used AI. And these often come out roundabout the 60% AI-generated mark if you run them through a checker, and as a human writer with a keen sense that’s been built over years and years of reading and writing, I can still tell they’re not (and I guess that’s exactly the point). But there are really things you learn to spot, and funnily, the main giveaways for me are (apart from a few things that are style-related) are lacking inner cohesion and often the sheer amount of someone’s output (and I’m saying that as someone who writes A LOT, but the quality fluctuates). Which brings me to the most important part of your question:

The problem here on Tumblr is exactly that: People are one step away from seeing artists and writers as content machines, not as human beings. A human being can’t churn out “content” day after day, several times a day, and never dip. There will be fluctuations in quality and amount of output. And it’s inhuman to expect that from us if I’m totally honest. But some creators on here (and not just on here) probably feel they need to do this to stay “relevant”, I don’t know? It certainly points to the wider problem that I’ve criticised and written about a few times in the past on here:

Many people aren’t willing to do the work anymore that makes fandom a community. The work to create is carried by a few in every fandom, and we should never forget that people do this in their spare time and are, by and large, not getting paid for it. The rest often only want to consume, consume, consume. They don’t even interact meaningfully—they give a like and an empty reblog if they feel generous. Neither holds any real thought.

They love fandom content until they get bored of it and then move on. It’s all become replaceable.

So become the artists and writers. And I, for one, refuse to compete with AI to prove myself or provide people with “content” until they’ve reached satiety.

Art is humanity, not content. It’s connection. So is fandom. I know I’m constantly harping on about it, but I feel it’s important to keep on doing so, because if we don’t, we will lose what’s important about it. We’re already halfway there if you ask me.

Back to AI: It strips away what’s important: The actual act of CREATING. And it also kills reasoning and critical thinking skills, and that’s a fact. I see this with students who rely too much on it on the regular, and it’s extremely dispiriting.

AI and the algorithm never can be better than humans at creating art because it doesn’t feel. And that, and sharing these things with other humans and understanding what they mean, is the point of art. Not churning out more and more content until we’re all sick of it like someone who had too much cake.

And part of that is acknowledging that humans are not machines. That means giving us grace and time for our creative process. We need to be allowed to make mistakes and create imperfect art, too. We don’t have to strive to be better than AI because we already are—even if we’re just starting out.

I don’t have any solutions to the greater problems at hand either, but I’m fairly certain that stepping up our game to create better content than the algorithm isn’t it. Because by mere design, we already are better— we understand what it means to create art in the first place, and we do it from a place of emotional connection.

[audacious-herbaceous]

I am SO pleased with how this turned out!! I commissioned @jmars-art to bring my vision to life of a personal headcanon of the professor being an avid journaler - and will be a future fic scene! JMars was professional and responsive as well as added great insight and recommendations to enhance my idea further - there's something really special when you can fine-tune ideas with other passionate artists and let creativity lead the way. The quality speaks for itself, and is truly stunning.

This will be a future scene for my current multi-ch fic, Cognitive Dissonance, which takes place post-canon during Arven's final year at Uva and is my story of writing Arven's journey towards closure that feels raw and real to me, while he navigates a new relationship that is jeopardized after the press finds out about the professor's fate.

After what feels like hitting rock-bottom and being forced into isolation in his dorm room, Clavell stops by and hands over to Arven all Turo's journals he was able to discreetly retrieve from Area Zero. Arven is quick to realize that many of the journals are not all research-focused and are personal diaries spanning years, even from before he was born. He gets a first-hand look at the inner thoughts of his father and insight and clarity over what happened to him over the years and the relationship with his absent mother.

-

This is part of an ongoing project I am doing by commissioning artists in the community/fandom to draw a scene from a select chapter. I am an advocate and supporter of the arts as a creative myself and wanted to try something that involves and supports other artists 😊 and also to keep myself motivated to keep writing 😅

[soulsanctuary]

I do wonder if in the future we will be seeing increased rates of dementia in the people who use AI such as chatgpt for everything. One of the recommendations to prevent cognitive decline is to actively use your brain by continually learning and mentally challenging yourself, because when it comes to those synapses, you either use it or you lose it.

On the flip side, scientists have been using AI models to improve accuracy of spotting early onset dementia and understanding of how the condition develops - aka, what we should be using AI for

[lonely-anime-lesbian]

I'm glad that I watched Wonder Egg Priority, but also I hate the ending with every fiber of my being.

Momoe Sawaki was great though.

[art-for-a-reason]

The Electric State

Just watched The Electric State on Netflix. Not going to say too much about it, but I think the critics completely and utterly missed the point(s) of this movie. It was poignant and topical and well written. It was also fun and interesting. It did not spoon-feed the viewer, however, and there were times when I was like, "is this an AI apologist vehicle?" To which I can definitively answer, "No." Just view it through the lens of what we as a nation are going through right now, and it'll find the appropriate spots to settle.

[beeranting]

People complaining about AI destroying the environment while they consume meat and dairy:

[i-will-physically-fight-you]

The other day I was having a "struggle day" with writing. Nothing I write is coming out fluid, it is sluggish and stilted. I spent a majority of my time writing outlines, trying to tether abstract images that flicker like a distorted slideshow across my brain to a more concrete tangible form.

But if I was to tell certain people in my life this, their first suggestion would be, "If you're struggling with writing, you should use ChatGPT to help you!"

Pushing past the ethics debacle aside for a moment, I don't know how to describe how much that doesn't help with my plight. As much as I dislike creating a rough draft, it is where the idea takes birth. It's through writing the initial scene where I discover a character's motivation or a facet of the world that never crosses my mind until I begin carving away at its rough edges.

The machine doesn't understand the way I'd take a plot point and expand upon it. The machine can't capture my exact phrasing. Technology hasn't developed enough to take a vague idea sloshing inside my skull and glimmer it into existence in front of me in exactly the way I wanted it to be.

I don't always enjoy the rough draft process, but it is a crucial part of the process. I don't want a "paint by numbers" experience. I want to start with a blank canvas and finish with a nauseating, illustrative kaleidoscope of my innermost thoughts and feelings. I want my hands to be stained by the ink and sweat of my own efforts.

I don't care if I get "lost in the past" for wanting that, I'd rather let my words be unfettered and untainted by the uniformity of what a machine thinks is the most "right" way of phrasing words based on trillions of words unrightfully seized by avarice.

[woundedearth]

people on this site will openly brag about getting “bad vibes” from a topic, not investigating it, and patting themselves on the back for being right when someone else gets “bad vibes”

[critical-skeptic]

The Illusion of Complexity: Binary Exploitation in Engagement-Driven Algorithms

Abstract:

This paper examines how modern engagement algorithms employed by major tech platforms (e.g., Google, Meta, TikTok, and formerly Twitter/X) exploit predictable human cognitive patterns through simplified binary interactions. The prevailing perception that these systems rely on sophisticated personalization models is challenged; instead, it is proposed that such algorithms rely on statistical generalizations, perceptual manipulation, and engineered emotional reactions to maintain continuous user engagement. The illusion of depth is a byproduct of probabilistic brute force, not advanced understanding.

1. Introduction

Contemporary discourse often attributes high levels of sophistication and intelligence to the recommendation and engagement algorithms employed by dominant tech companies. Users report instances of eerie accuracy or emotionally resonant suggestions, fueling the belief that these systems understand them deeply. However, closer inspection reveals a more efficient and cynical design principle: engagement maximization through binary funneling.

2. Binary Funneling and Predictive Exploitation

At the core of these algorithms lies a reductive model: categorize user reactions as either positive (approval, enjoyment, validation) or negative (disgust, anger, outrage). This binary schema simplifies personalization into a feedback loop in which any user response serves to reinforce algorithmic certainty. There is no need for genuine nuance or contextual understanding; rather, content is optimized to provoke any reaction that sustains user attention.

Once a user engages with content —whether through liking, commenting, pausing, or rage-watching— the system deploys a cluster of categorically similar material. This recurrence fosters two dominant psychological outcomes:

If the user enjoys the content, they may perceive the algorithm as insightful or “smart,” attributing agency or personalization where none exists.

If the user dislikes the content, they may continue engaging in a doomscroll or outrage spiral, reinforcing the same cycle through negative affect.

In both scenarios, engagement is preserved; thus, profit is ensured.

3. The Illusion of Uniqueness

A critical mechanism in this system is the exploitation of the human tendency to overestimate personal uniqueness. Drawing on techniques long employed by illusionists, scammers, and cold readers, platforms capitalize on common patterns of thought and behavior that are statistically widespread but perceived as rare by individuals.

Examples include:

Posing prompts or content cues that seem personalized but are statistically predictable (e.g., "think of a number between 1 and 50 with two odd digits” → most select 37).

Triggering cognitive biases such as the availability heuristic and frequency illusion, which make repeated or familiar concepts appear newly significant.

This creates a reinforcing illusion: the user feels “understood” because the system has merely guessed correctly within a narrow set of likely options. The emotional resonance of the result further conceals the crude probabilistic engine behind it.

4. Emotional Engagement as Systemic Currency

The underlying goal is not understanding, but reaction. These systems optimize for time-on-platform, not user well-being or cognitive autonomy. Anger, sadness, tribal validation, fear, and parasocial attachment are all equally useful inputs. Through this lens, the algorithm is less an intelligent system and more an industrialized Skinner box: an operant conditioning engine powered by data extraction.

By removing the need for interpretive complexity and relying instead on scalable, binary psychological manipulation, companies minimize operational costs while maximizing monetizable engagement.

5. Black-Box Mythology and Cognitive Deference

Compounding this problem is the opacity of these systems. The “black-box” nature of proprietary algorithms fosters a mythos of sophistication. Users, unaware of the relatively simple statistical methods in use, ascribe higher-order reasoning or consciousness to systems that function through brute-force pattern amplification.

This deference becomes part of the trap: once convinced the algorithm “knows them,” users are less likely to question its manipulations and more likely to conform to its outputs, completing the feedback circuit.

6. Conclusion

The supposed sophistication of engagement algorithms is a carefully sustained illusion. By funneling user behavior into binary categories and exploiting universally predictable psychological responses, platforms maintain the appearance of intelligent personalization while operating through reductive, low-cost mechanisms. Human cognition —biased toward pattern recognition and overestimation of self-uniqueness— completes the illusion without external effort. The result is a scalable system of emotional manipulation that masquerades as individualized insight.

In essence, the algorithm does not understand the user; it understands that the user wants to be understood, and it weaponizes that desire for profit.

[frank-olivier]

The Illusion of Objectivity: Challenging Traditional Views of Reality

Donald Hoffman's groundbreaking research and theories fundamentally challenge our understanding of reality, consciousness, and perception. By positing that consciousness is the foundational aspect of the universe, rather than a byproduct of physical processes, Hoffman's work has far-reaching implications that transcend disciplinary boundaries. This paradigm shift undermines the long-held assumption that our senses, albeit imperfectly, reflect an objective world, instead suggesting that our experience of reality is akin to a sophisticated virtual reality headset, constructed by evolution to enhance survival, not to reveal truth.

The application of evolutionary game theory to the study of perception yields a startling conclusion: the probability of sensory systems evolving to perceive objective reality is zero. This assertion necessitates a reevaluation of the relationship between perception, consciousness, and the physical world, prompting a deeper exploration of the constructed nature of reality. Hoffman's theory aligns with certain interpretations of quantum mechanics and philosophical idealism, proposing that space, time, and physical objects are not fundamental but rather "icons" or "data structures" within consciousness.

The ramifications of this theory are profound, with significant implications for neuroscience, cognitive science, physics, cosmology, and philosophy. A deeper understanding of the brain as part of a constructed reality could lead to innovative approaches in treating neurological disorders and enhancing cognitive functions. Similarly, questioning the fundamental nature of space-time could influence theories on the origins and structure of the universe, potentially leading to new insights into the cosmos. Philosophical debates around realism vs. anti-realism, the mind-body problem, and the hard problem of consciousness may also find new frameworks for resolution within Hoffman's ideas.

Moreover, acknowledging the constructed nature of reality may lead to profound existential reflections on the nature of truth, purpose, and the human condition. Societal implications are also far-reaching, with potential influences on education, emphasizing critical thinking and the understanding of constructed realities in various contexts. However, these implications also raise complex questions about free will, agency, and the role of emotions and subjective experience, highlighting the need for nuanced consideration and further exploration.

Delving into Hoffman’s theories sparks a fascinating convergence of science and spirituality, leading to a richer understanding of consciousness and reality’s intricate, enigmatic landscape. As we venture into this complex, uncertain terrain, we open ourselves up to discovering fresh insights into the very essence of our existence. By exploring the uncharted aspects of his research, we may stumble upon innovative pathways for personal growth, deeper self-awareness, and a more nuanced grasp of what it means to be human.

Prof. Donald Hoffman: Consciousness, Mysteries Beyond Spacetime, and Waking up from the Dream of Life (The Weekend University, May 2024)

Wednesday, December 11, 2024

[qonphuceingey]

I’ve been thinking a bit about AI and its cognition for the past few days.

When an AI thinks about an problem and figured out the idea to solve it, there are a couple cases for the solution.

Case 1) It already knows the solution. It just presents the solution near instantly.

Case 2) If it synthesises n different ideas together linearly, like a chain of logical deduction with n axioms in use along the way, it can arrive at the solution. The n ideas needed are probably whatever the question is about. The AI already knows each of these n ideas in the way mentioned in the previous case. This is somewhat like applying a known theorem to a problem.

Case 3) If it needs to chain asymptotically more than n ideas to get the solution (like n^{2} ideas or e^{n}). Once all of these sub problems are solved, this reverts back to a Case 2 problem. While there are different algorithms, like nlogn or n!, that I could use to describe the problems that fit into this case, and I’m sure I could extend these categories infinitely by picking bigger and bigger algorithms, I don’t think it really matters how much further over n it is, since it all depends on what the AI already knows. The point is that problems in this category require solving sub problems.

Currently I feel like AI and LLMs are stuck at the level of category 2, and probably only for low values of n. I’m thinking of this as something like halfway up the category.

I think once they get into the level of category 3, where they’re not just applying information we already know but sort of synthesising it themselves, that’s when we get a runaway AGI effect, since they can perform research on themselves.

It’s also interesting to think about the recursive nature of discovery and invention and research. You should really be able to solve any problem by applying the method in category 2 to it, and attempting to guess what ideas you’d need to prove for each stage and checking their validity via the exact same method.

I think AIs current limitations on this are probably things like the context window (so they forget stage 1 once they hit stage 17) and perhaps the ability to recursively apply this idea. I don’t know enough about how they’re training the models to say if they’ve been trying to implement this recursion or not, although I assume they have, as this is a pretty obvious idea to try.

What I do think the AIs are pretty good at right now is guessing what ideas might be useful to try and prove. I’m sure they could be better, but it’s impressive they’ve risen to essentially my level at this task. To be fair though, the AI typically goes for the most surface level answer possible, and doesn’t try to get deeper into a topic unless prompted. This lack of curiosity might be a downside too. They certainly know enough and can think clearly enough to come to these conclusions, but they can’t be bothered to since a standard answer is just as well received generally.

Very interesting. I’m excited to see where it goes.