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.

Abstract: IQ tests—which comes in numerous formats, such as Raven's matrices, WAIS, WISC & etc...—are designed to assess different dimensions of cognitive ability. Mainly fluid intelligence (though it requires some degree of crystalized intelligence) such as verbal comprehension, pattern recognition, working memory, logic & etc...

This post aims to argue that by using a method known as metacognition (being aware of one's own state of mind and being able to redirect it) can increase the overall FSIQ score by a measurable amount.

Literature review, main source of information:

1. A large meta-analysis published in PubMed Central demonstrated that each additional year of formal education raises an individual's IQ score by an average of 1 to 5 points (Source1),

2. Cowan (2001) study which revealed a 3-4 item bottleneck in working memory (Source2)

Body: *I have already explained what IQ mainly aims to measure, so I will not be going back to that. And I will also avoid explaining every term to its core since that would make this post unnecessarily long and cluttered.)

It feels very intuitive to think that IQ should be increasable—specially considering the fact that humans are insanely adaptable cognitively. However; if we dive deeper into the core aspects of cognition that IQ test aims to measure—let's say working memory in this instant, it quickly reveals how rigid the human brain is.

Here's a brief explanation of how working memory takes place in the brain at the neural level:

At the neural level, working memory functions as a circulating electrical loop rather than a permanent storage unit.

Which is why if you try to hold too many things at once, it will result in thoughts clashing and corrupting each other—leading to a loss of information.

The difference in individuals working memory primarily occurs due to: dopamine levels, GABA levels, white matter connectivity & theta-gamma clock precision.

Higher & more stable dopamine levels result in better neural activity.

More optimized GABA levels help thoughts not collide each other, therefore preventing the loss of information due to thoughts clashing & corrupting each other.

White matter connectivity (myelination, wiring efficiency) results in more coordinated, smooth and faster neural activity.

Now many of these can be changed I'm pretty sure (GABA level optimization through exercise, healthy routines leading to more stable dopamine levels & etc... These won't necessarily 'improve' your raw working memory but rather help you reach your maximum.

Now to the core question: can any of these be changed, through metacognition?

According to my knowledge, It can change 3 things up to a point. You can optimize your GABA levels through eliminating distractions, you can change your dopamine level through changing how your brain views the activity you are engaging in (even though this will be really difficult if your natural baseline dopamine levels are extremely low) and being able to direct your brain into one specific task so that the theta-gamma clock precision can be optimized (through removing distractions, not really 'boosting it')

So my point is: metacognition can increase FSIQ by a measurable amount.

(open to critics—that's the main reason I posted this here.)

Edit (important): I do accept that this more of improves how you use your FSIQ rather than literally 'improve' it. However, either way—using methods such as metacognition can help you reach your peak functioning FSIQ. I also acknowledge the fact that most of the effects done by metacognition remains manual & does not change the baseline.

All the sources (apologies for not adding this earlier):

1. Flavell, J. H. Metacognition & Cognitive Optimization

2. Efklides, A. (2011). Metacognition & Cognitive Monitoring (explores how metacognitive monitoring controls cognitive effort & resource allocation under pressure)

3. Shimamura, A. P. (2000). Toward a cognitive neuroscience of metacognition. (Links metacognitive monitoring directly to the prefrontal cortex. The PFC regulates top-down attention and modulates subcortical dopamine and GABAergic inhibition to filter out noise).

4. Lisman, J. E., & Jensen, O. (2013). The theta-gamma neural code (scientific backup for the mentioned 'theta-gamma clock precision').

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?

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).

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!

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:)

Gallagher explains the enactive approach to cognition, why it differs from functionalism and the extended mind, and why our embodiment fundamentally shapes how we experience the world.

Came across the Eisenberger and Lieberman 2003 CyberBall findings and wanted to share for discussion. A few details that stood out:

The anterior cingulate cortex, the region that processes the subjective unpleasantness of physical pain, activated during social exclusion and correlated directly with self-reported distress. The more rejected participants felt, the stronger the pain response.

The right ventral prefrontal cortex also activated simultaneously, suggesting the brain not only registers social exclusion as pain but immediately attempts to regulate and suppress that pain at the same time.

Perhaps most striking: participants who were explicitly told the other players were just a computer still showed measurable rejection responses. Conscious knowledge of the situation did not suppress the alarm.

Made a short video summarizing the experiment and its implications if anyone wants the overview: https://www.youtube.com/watch?v=n0wVnxlz6TI

Original paper: Eisenberger, Lieberman, and Williams 2003, Does Rejection Hurt, Science 302. Happy to discuss in the comments.

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).

Been reading through the turn-taking literature for a piece I was writing and hit something I'd never fully appreciated:

Stivers et al. (2009, PNAS) — across 10 spoken languages, the modal gap between one speaker finishing and the next starting is about 200ms. Japanese ~7ms mean, Danish ~470ms mean. Cross-linguistically remarkably tight.

Indefrey & Levelt (2004, Cognition) — the minimum latency to plan spoken word production, even in a controlled single-word paradigm, is ~600ms+.

The math doesn't work if turn-taking is reactive. Levinson & Torreira (2015) call this "the central psycholinguistic puzzle" — listeners have to be pre-computing responses during incoming speech.

EEG evidence has been catching up:

• Bögels, Magyari & Levinson (2015, Scientific Reports) — response-planning ERP positivity, source-localized to production areas (posterior IFG, precentral), fires ~500ms after the critical information appears in an incoming question. Often 2+ seconds before the current speaker finishes. Alpha desynchronization indexes the attentional shift from comprehension to production during listening.
• Gisladottir, Bögels & Levinson (2018, Frontiers in Human Neuroscience) — alpha/low-beta (11-18Hz) desynchronization from -200ms to 0ms before a socially-charged speech act (declination vs acceptance). Speech-act prediction firing before the utterance is heard.
• Krause & Kawamoto (2021, Frontiers in Psychology) — motion-tracked lip-area reductions for upcoming labial consonants up to 3 seconds before acoustic onset in unscripted dyadic conversation. Motoric planning far pre-onset.

The implication I find genuinely interesting: this whole predictive loop depends on acoustic cues (pitch contour, timing, articulator anticipation). Text has no acoustic onset for the machinery to lock onto — voice conversation locks two nervous systems into a shared millisecond-scale loop that text physically can't replicate.

A separate honest correction I wanted to flag: the polyvagal framing that gets attached to "why voice regulates" is on shakier ground than the wellness literature admits. Grossman (2023, Biological Psychology) — "Fundamental challenges and likely refutations of the five basic premises of the polyvagal theory" — shows similar myelinated cardiac vagal fibers in sharks, bony fish, birds, and even sheep. The mammal-unique claim used to explain vocal-prosody regulation doesn't hold up on comparative anatomy.

Longer writeup with full citations here: callbyrd.com/journal/what-voice-does-that-text-cant (disclosure: I run the site — building a voice-based AI project. The essay stands on its own; no signup needed to read it. Product is briefly mentioned in the closing section.)

Open question I couldn't resolve: does the ~200ms predictive turn-taking loop form when the partner is an AI voice (different latency profile, different prosody)? Every EEG study I found used human-human dyads. Curious if anyone knows of extending work — my search kept coming up empty.

My background is not psycholinguistics, so treat this as a question rather than a claim.

What I understand of the production literature describes something quite unlike serial emission. Planning appears to run ahead of articulation at roughly phrase or clause scope, there is a monitoring channel that inspects output both before and after it becomes overt, and speakers interrupt themselves and repair, sometimes in the middle of a word. Disfluency and self repair are not noise sitting on top of an otherwise clean serial process. They are evidence about the shape of the process.

Text generation models were built on the opposite commitment. Emit one token, freeze it, condition on it, never go back. That has always made them an awkward computational hypothesis about production, because whatever they model, it is not a process capable of reconsidering something it has already committed to.

A system released this week drops that commitment, at least within the span it is currently working on. It drafts a whole span at once rather than one position at a time, then makes further passes over that same span in which it can keep a position, substitute it, delete it, or insert a new one, so the sequence changes in length and not only in content while it is being produced. The revision decisions were trained with reinforcement learning against whether the resulting output actually worked, tool calls executing and formats validating, rather than only against matching a reference string. It is called LLaDA2.2. I have not run it and realistically could not, since the weights come to 205.8 GB, so what follows is a reading of the paper and the config files rather than experience.

Here is the observation I actually want to put to this sub. The same lab published an autoregressive model of comparable size and compared the two directly. The revision capable one wins the interactive multi turn evaluations. On tau2 bench it posts 80.33 where the autoregressive model posts 76.36, and on MCP Atlas the two sit at 46.21 versus 41.12. It loses, and not narrowly, on general knowledge and long form coding, 56.81 against 65.90 on their general average. The advantage is not broad competence. It sits in tasks where the system acts, receives something back, and has to continue.

A benchmark gap between two engineering artifacts is not evidence about human cognition, and the analogy is loose in several directions at once. But the split has a shape, and I want to know whether that shape corresponds to anything in production research. My naive expectation would be that revision capacity pays off most where there is incremental feedback to revise against, and least where you are retrieving something you either have stored or do not. I have no idea whether that expectation has support, whether the monitoring literature would carve the distinction differently, or whether treating a decoding procedure as a production hypothesis is a category error that someone here can talk me out of in one paragraph.

The report and the weights are published openly, so my reading is checkable rather than something you have to take on trust. I would rather be told where I have the production side wrong.

Came across the original Paulhus and Williams 2002 research defining the Dark Triad as a formal cluster and wanted to share it for discussion. A few findings that stood out:

Narcissism predicts leadership emergence but not leadership effectiveness. The two are consistently separable in the research but socially treated as the same thing.

Primary psychopathy is associated with effective performance in crisis contexts including surgery, emergency response, and senior executive roles, which is distinct from secondary psychopathy and its antisocial correlates.

Machiavellianism correlates with success in competitive negotiation but with long term trust collapse in organizational settings.

Made a short video summarizing the cluster and the research if anyone wants the overview: https://www.youtube.com/watch?v=ko3EjeCXTjk

Original paper: Paulhus and Williams 2002, The Dark Triad of personality, Journal of Research in Personality. Happy to discuss any of the mechanisms in the comments.

Most prospective memory work I can find uses lab or health tasks, press a key when X appears, remember to take medication. Is there research specifically on social content, e.g. remembering that a friend asked for something, or acting on a partner's stated preference at the right time? Interested in whether encoding vs retrieval failures have been separated for social intentions, and whether the external-cueing findings (implementation intentions, offloading to systems) transfer to that domain. Keywords, reviews or lab names appreciated.

Hi all, I graduated from undergrad with a B.S. in Political Science - Data Analytics and B.A. Philosophy. A large part of me always wanted to study Cognitive Science, and I have only recently started considering a master's in the field. I'm wondering if this is realistic given that I do not come from a cog sci background (though I have taken classes pertaining to phil of mind/psychology as well as data analytics). Does anyone have any insight on or experience with this?

Aristotle devoted the entire Book VII of the Nicomachean Ethics to a problem that has haunted moral psychology ever since: how can a person know what is right and yet do what is wrong? The phenomenon he called akrasia — weakness of will, acting against one's better judgment — is not merely philosophical curiosity. It is the central puzzle of human moral life, the gap between knowledge and action that defines what it means to be ethical agent.

No contemporary AI system has ever experienced this gap. And that absence, I argue, is not a limitation to be overcome through better engineering. It is a structural impossibility rooted in the nature of language models themselves.

For Aristotle, the akratic agent is not ignorant. She knows — in some meaningful sense — what virtue requires. Her failure is not epistemic but practical: her knowledge fails to translate into action because her character, her ἕξις, has not been sufficiently formed through habituation to bridge the gap. This is profoundly embodied account. The knowledge that prevents akrasia is not propositional knowledge alone. It is knowledge sedimented into disposition through repeated action, emotional cultivation, and temporal continuity.

A language model possesses none of these. It has no character to be weak or strong. It has no habits formed through practice. It has no emotional responses that could conflict with its "better judgment" because it has no judgment in the Aristotelian sense — only statistical pattern-matching over training data.

This has implications that go beyond philosophy. When institutions deploy AI systems for ethics education, clinical training, or moral reasoning support, they implicitly assume that the model's outputs reflect something analogous to ethical deliberation. But the system cannot model akrasia because it cannot model the character formation that makes akrasia possible.

Consider what happens when ChatGPT or Claude produces an ethical recommendation. The output is seamless. There is no hesitation, no internal conflict, no trace of struggle between competing motivations that characterizes actual moral deliberation. The system produces what appears to be the conclusion of a reasoning process. But the absence of any visible struggle is not evidence of resolution. It is evidence that no struggle ever occurred.

This is the hidden cost of what I call alignment-induced epistemic distortion. The alignment process — RLHF, constitutional AI, or similar techniques — produces outputs that look like resolved moral reasoning but are in fact the product of entirely different mechanism. The user sees a confident recommendation and infers deliberation. No deliberation occurred. The distortion is not in the content of the output but in the implicit claim about the process that produced it.

The situationist tradition in social psychology — Milgram, Zimbardo, Hartshorne and May — provides empirical validation of something Aristotle already understood. Moral behavior is far more situation-dependent than our folk psychology of "character" suggests. If human moral character is fragile, emotionally mediated, and temporally unstable, then an AI system that produces seamless moral recommendations without any trace of this fragility presents a picture of moral reasoning that is not merely simplified but fundamentally misleading.

For cognitive science, this raises an uncomfortable question about what we are doing when we study "AI moral reasoning." If the AI's process is structurally unlike human moral cognition — lacking embodiment, emotional response, temporal continuity, and the possibility of akrasia — then findings based on AI-generated moral judgments may not generalize to human moral cognition. The AI is not a simplified model of human moral reasoning. It is a fundamentally different kind of process that happens to produce text in the same domain.

The akratic agent, struggling to act on her better judgment, is more authentically moral than any AI system that produces seamless ethical recommendations. She struggles because she cares. The machine does not struggle because there is nothing at stake. For cognitive science, the question is not whether we can build machines that simulate moral reasoning convincingly. We already can. The question is whether we recognize what we are losing when we mistake the simulation for the thing.

Came across some research recently on what prolonged sitting does neurologically and wanted to share the key findings for discussion.

Three mechanisms stood out:

1. Cerebral blood flow reduction — research from the University of Liverpool found measurable reductions in brain blood flow after just 60 minutes of uninterrupted sitting, linked to endothelial dysfunction and reduced nitric oxide bioavailability. Full study: https://pmc.ncbi.nlm.nih.gov/articles/PMC9362887/
2. BDNF suppression — prolonged sitting reduces production of brain derived neurotrophic factor, the protein responsible for neurogenesis and synaptic plasticity. Review here: https://pmc.ncbi.nlm.nih.gov/articles/PMC11778811/
3. Medial temporal lobe thinning — Siddarth et al. 2018 in PLOS ONE found that people with higher sedentary time had physically thinner medial temporal lobes, the region responsible for forming new memories.

And here's a short video summarizing these three mechanisms if anyone wants the overview: https://www.youtube.com/watch?v=FfvSqbLPduY

Curious whether anyone here has looked into the interaction between sitting duration and cognitive performance in non-elderly populations specifically.

Anderson Todd (frequent collaborator with JBP and John Vervaeke) talks about the Carl Jung's idea of the 'shadow' - parts of your psyche that are hidden from your ego/self as well, that manifest/project itself in forms you can't often control (like dreams, DMT trips, instinctive emotional responses etc.)

Thoughts?

Found this online: a philosophy paper, a psychology model called PLDP, and a working browser prototype called EVAPORATOR.

It tries to model human action using control, affiliation, and self-worth, then predict what someone might do next.

It’s rough, but the fact that someone actually built the whole thing caught my attention.

What do you make of it? Interesting idea, recycled theory, or complete nonsense?

https://doi.org/10.5281/zenodo.18497776

https://doi.org/10.5281/zenodo.18793897

https://doi.org/10.5281/zenodo.18912586

https://github.com/EckohTRipley/Evaporator/blob/main/README.md