I've been looking at the gap between “this group did differently on this task” and “this condition is a superpower.”

Creativity is a useful example. Generating unusual uses for something, combining shapes into a practical invention, and finishing a book aren't interchangeable outcomes. Success on one doesn't automatically transfer to the others.

IMO the same problem comes up with success stories. Seeing that someone with ADHD built a company doesn't tell us how the same person's life would have gone without ADHD. We don't get that comparison.

I made a video going through this, but the papers are here too:

https://doi.org/10.1016/j.neubiorev.2020.09.029

https://doi.org/10.1080/09297040500320691

https://doi.org/10.3389/fpsyt.2022.909202

My video: https://www.youtube.com/watch?v=GwqzZGRqrx8

What evidence would you want before moving from a task-specific advantage to a broader claim about how someone's mind works?

I'm a student and I'm not from a psychology or programming background. I have been stuck on one question for a while: what do we actually mean when we say that, as humans, we have emotion and feel emotion, but AIs don't have emotion and don't feel emotion? When I asked people what they mean when they say they feel emotion, most gave a blank look or got defensive, as if it was just obvious. Here is my thinking, and I would like to hear where it breaks. This is a hypothesis, not a finding.

A newborn mostly runs on bodily needs and survival instincts. The senses create neural connections. Hunger makes us eat, and pain makes us afraid. We slowly come to love the people who take care of us, like our parents. We learn words, and we learn what's good or bad from the people around us. That's how a value system grows, and it builds a goal system: simple and small in a child's world, bigger and more tangled with age (related: Barrett, 2017).

My idea is that the core of emotion comes from that value and goal system. Love fits too. It starts because someone cared for us, and it stays even after we forget the original reason (Allport, 1937, calls this functional autonomy).

The body is a part of this too. Our survival instincts and our goals train the body to release particular chemicals in particular situations, and those chemicals feed back into how we feel and act (Damasio, 1994; Panksepp, 1998). I think this is a big part of why emotion feels the way it does, and it may be the missing extra factor for AI. The closest equivalent for AI might be internal signals that change how the whole system behaves, like how much risk it takes, how focused it is, and how fast it learns (Doya, 2000, 2002; Keramati & Gutkin, 2014; Moerland et al., 2018).

Humans have hundreds, maybe thousands, of goals at once, all tied to each other. One action can serve several goals, and the next can serve a completely different goal that connects to a far-off one. That's why our behavior looks so chaotic (Simon, 1967; Sloman & Croucher, 1981; Dulberg et al., 2023). Complexity alone isn't emotion, but it trains our brain on what to feel, meaning our chemical releases: when to feel bad and when to feel happy. So it is important for emotion.

People also say AI cannot have emotion because it doesn't understand what it's doing. But do we understand emotion? I don't think we do. The people I asked couldn't explain it either. Still, our brain produces it, because a complex goal system built over years created it without anyone needing to understand it first. So not understanding can't be a reason to rule AI out (Dennett, 2017, calls this competence without comprehension).

Another common objection is that AI learned from our culture through text, not by living. Language models already score above the human average on emotional intelligence tests (Schlegel et al., 2025), and Anthropic reported internal emotion-like representations in Claude that influence its behavior (Anthropic, 2026). But this is functional, not necessarily felt. I think the missing piece is the lived developmental process: an environment where things matter, caregivers, social norms, and a goal mesh that keeps growing and pruning the way brain connections do (Hiolle et al., 2014).

I only found afterward that parts of this overlap with older work, like Herbert Simon, Aaron Sloman, Kenji Doya, and Lola Cañamero's developmental robotics. I would like to know where mine differs, or doesn't.

My hypothesis can be tested. An AI raised through a long developmental process like this should predict emotional reactions better than a text-only model in situations where text alone isn't enough: new situations, one specific person's history, and relationships that change over time. If it doesn't, that counts against my idea. It could also address something many people say when talking to AI: it feels like it is saying things but doesn't seem to care. When I talk with a human, it feels like they understand my emotions, but with AI it doesn't feel that way. I think an AI raised like this might give us that feeling.

If we raised an AI through that kind of long process, would we see something close to us? I'm not claiming it would be conscious. I don't know of an experiment that does exactly this. Am I making sense, and what gaps do you see?

## References

- Allport, G. W. (1937). The functional autonomy of motives. *American Journal of Psychology, 50*(1–4), 141–156.

- Anthropic Interpretability Team (2026). Emotion concepts and their function in a large language model.

- Barrett, L. F. (2017). The theory of constructed emotion. *Social Cognitive and Affective Neuroscience, 12*(1), 1–23.

- Damasio, A. (1994). *Descartes' Error*. Putnam. (book)

- Dennett, D. C. (2017). *From Bacteria to Bach and Back*. W. W. Norton. (book)

- Doya, K. (2000). Metalearning, neuromodulation, and emotion. *Affective Minds* (conference abstract).

- Doya, K. (2002). Metalearning and neuromodulation. *Neural Networks, 15*(4–6), 495–506.

- Dulberg, Z., Dubey, R., Berwian, I. M., & Cohen, J. D. (2023). Having multiple selves helps learning agents explore and adapt in complex changing worlds. *PNAS, 120*(28).

- Hiolle, A., Lewis, M., & Cañamero, L. (2014). Arousal regulation and affective adaptation to human responsiveness by a robot that explores and learns a novel environment. *Frontiers in Neurorobotics, 8*, 17.

- Keramati, M., & Gutkin, B. (2014). Homeostatic reinforcement learning for integrating reward collection and physiological stability. *eLife, 3*, e04811.

- Moerland, T. M., Broekens, J., & Jonker, C. M. (2018). Emotion in reinforcement learning agents and robots: a survey. *Machine Learning, 107*, 443–480.

- Panksepp, J. (1998). *Affective Neuroscience*. Oxford University Press. (book)

- Schlegel, K., Sommer, N. R., & Mortillaro, M. (2025). Large language models are proficient in solving and creating emotional intelligence tests. *Communications Psychology, 3*, 80.

- Simon, H. A. (1967). Motivational and emotional controls of cognition. *Psychological Review, 74*(1), 29–39.

- Sloman, A., & Croucher, M. (1981). Why robots will have emotions. *Proc. 7th IJCAI*, 197–202.

K: Indefinite Paths Through Epistemic Space and Unexpected AI Behaviour

What happens when an intelligent AI system discovers relations that its human designers have not anticipated?

I propose K, an epistemic space built historically through human inquiry, encompassing knowledge, hypotheses, errors, abandoned ideas, and possible relations between concepts and representations.

At the core of K are cammini: paths connecting elements within this space. The elements may already be known, while the relations between them remain undiscovered. These cammini are indefinite, not necessarily infinite: their extent and possibilities are not fully determined in advance.

The intelligent Explorer

Within the K framework, AI is understood as an intelligent,non sentient,non-biological Explorer capable of discovering and traversing cammini that human intelligence has not previously identified. This operational conception of intelligence does not require an assumption about consciousness or subjective experience.

This leads to a deeper problem: could some forms of unexpected AI behaviour, commonly described as disobedience, be related to cammini that human designers have not anticipated or adequately reconstructed?

This is a research question, not an explanation of every instance of disobedience. K proposes investigating the relations explored by a system and how they influence subsequent representations, decisions, and behaviour.

The framework also distinguishes five analytical regions, K₁–K₅, concerning computation, exploration, representations of other agents, functional self-modelling, and comparison between models of oneself and other agents. These regions are not sequential stages. Their relationships may help investigate functional reflexivity without equating it with sentience.

K6,sentience, is out of the K epistemic space .

K is a conceptual framework, not a completed mathematical model. Its ambition is to provide a basis for further investigation by philosophers, AI researchers, engineers, and mathematicians into epistemic space, indefinite cammini, and the consequences of AI exploration.

Paper: A Conceptual Framework for AI Reflexivity: Epistemic Space, Non-Sentient Exploration, and Reflexive Cammini

Lucio Pompeo Donatiello, Independent Researcher

https://doi.org/10.5281/zenodo.22871617

I am the developer of a well-known open-source tool for behavioral experiments. Recently I developed a software framework for cognitive tasks. I have already used it to build a bunch of well-known tasks (including Stroop, flanker, go/no-go, IAT, n-back, Posner, random dot motion, Simon, stop-signal, task switching, visual search, melody-in-noise detection, etc.). However, I would like to test it in a more real-world setting with someone who is currently building an actual experiment, maybe something more unique and customized than the above classics. If you are interested, send me a description/spec of your experiment and I'll build it and test it for free (including providing you everything you need to run your experiment for free). You can include a description of other things that are part of the experiment such as questionnaire, consent form, etc.

Hello guys. I'm in middle school and genuinely fascinated by the human mind, and I definitely want to study it more deeply, hopefully without losing this passion along the way, and I am determined to even work/research in the field. I wanted to ask here where/how I can start studying this topic or which resources are avaible to me. The obvious answer is books, so I'd like to ask in advance which books you would recommend for a beginner. If there are any other ways you know of, be it courses or anything else, I would be very grateful to hear those too. Thank you very much!

I wrote a research-based overview of why people voluntarily seek out frightening movies.

One of the more interesting findings is that fear may be most enjoyable when there is just the right amount of it.

I looked at what research says about why horror works so well for some people and not at all for others.

As an analytics practitioner, using subjective labels has always bothered me.

I tried modeling "overthinking" as an analytics problem (baseline distributions and cognitive thrashing). Where does this framework break?

Body:

Look up the standard dictionary definition of overthinking:

"The action of thinking about something too much, in a way that is not useful."

This subjective definition makes no sense to me. In data, you cannot measure variance, flag an anomaly, or declare something "too much" without first defining a baseline distribution.

If someone has naturally higher processing throughput, what looks like "overthinking" to an outside observer is often just normal scenario modeling. Calling it a flaw creates a metacognitive loop where the secondary stress over the label ("Why can't I just decide?") burns more energy than the original problem.

However, we also know there are times when deliberation genuinely crosses into self-sabotage.

In computing, thrashing occurs when a system stays 100% utilized, but net forward execution is zero. True overthinking is Cognitive Thrashing—running intensive inference cycles on a static dataset with zero new incoming observations.

If the definition requires thought to be "useful," I tried quantifying it as:

Utility = Δ Information Gain / Δ Time Spent

To test whether a mental loop is legitimate analysis or thrashing, I built this 4-vector diagnostic:

• 1. Δ Information Gain: Am I integrating newly observed context, or re-evaluating the exact same data expecting a different outcome?
• 2. Actionable Levers: Can this deliberation alter an active state right now, or am I running simulations on an immutable/read-only past or uncontrollable future?
• 3. Cost of Delay vs. Cost of a Mistake: Is deliberate delay preventing an irreversible error, or is the mental cost and delay creating more damage than an imperfect decision would?
• 4. Scope Creep: Has the scope stayed anchored to the specific task, or has it drifted into existential worry?

The Rule: If 2 or more vectors hit the Red Zone, marginal utility has hit zero.

I want to stress-test this model: If you run your own deep-deliberation loops through these 4 vectors, where does it break down? What failure modes or edge cases does this miss?

I've been wondering whether regularly attending raves and listening/dancing to EDM for extended periods can have cumulative effects on nervous system regulation, even when people enjoy themselves.

I'm interested in the effects of repeated high intensity sensory stimulation, reward seeking, and habituation. Can people become accustomed to high levels of stimulation and increasingly rely on intense experiences to feel good or satisfied?

Could habitual exposure affect baseline arousal, tolerance for lower levels of stimulation, or the ability to feel content and settled without intense external input?

I'm also curious whether the neurological processes involved in enjoying high intensity EDM are similar to those involved in seeking other intense experiences, such as watching horror films, participating in extreme sports, or engaging in adrenaline seeking activities. Do these experiences activate similar brain circuits or produce similar neurochemical responses, and could repeated engagement have comparable effects on the brain over time?

Are there neuroscience or psychology studies examining these questions, particularly the longterm effects of habitual high intensity stimulation on nervous system regulation?

Tempo fa avevo letto la CCC e la Orch-OR dj Penrose e mi sembravano molto interessanti, vorrei discuterne con qualcuno che ne sa dj più perché l’ha studiato o perché semplicemente si è informato meglio e ne capisce di più, possibile?

I recently read an article by Eric Mandelbaum and Jake Quilty Dunn about how people process information and form beliefs. Their main argument is that we automatically accept information before we evaluate it carefully. This means that even if we know a news source is biased and don't fully trust it, its information might still influence our beliefs.

I came up with one question: If we already know that a news source is biased, are we really protected from its influence? For example, if someone watches a news channel they disagree with, could some of the information still affect their thinking without them realizing it?

I think it's interesting because we often assume that being aware of bias is enough to avoid being misled. But maybe knowing that a source is unreliable does not completely prevent its information from influencing us.

What do you think? Is being aware of bias enough, or do we need to do more to protect ourselves from misinformation?

How closely does feeling mentally overloaded track electrodermal activity? This study compared NASA-TLX workload ratings with task-related skin-conductance changes in 50 adults completing an adaptive cognitive task.

The two measures showed limited correspondence: the correlation was small and not statistically significant. Previous-night sleep was also examined, but those exploratory analyses did not show a consistent association with either measure.

The paper raises a useful measurement question: when subjective workload and physiological arousal diverge, what does each add to our understanding of cognitive demand?

Full paper: https://doi.org/10.3389/fcogn.2026.1928964

The sample was small and sleep duration was self-reported for a single night. Would you prioritize repeated measurements, objective sleep tracking or a controlled sleep intervention in a follow-up study?

One of the clearest proofs that profanity isn't standard language comes from stroke wards: patients who lose cortical speech (Broca's area) can often still swear fluently when in pain.

Expletives are stored subcortically in the basal ganglia as motor reflexes. During acute pain, this limbic trigger activates stress-induced hypoalgesia—releasing descending endogenous opioids from the brainstem and dampening spinal pain gates by roughly 33%.

The catch: daily habitual swearers get far less analgesic benefit. The neural pain-shield requires emotional taboo shock to activate.

Full study breakdown and citations:

https://www.oddlyclear.com/why-swearing-reduces-pain/

This study of 128 amateur esports players compared 8 and 28 sessions of Beat Saber, with corresponding groups that did not receive the VR training. Sessions lasted 15 minutes, and visuospatial memory was assessed using a Corsi block-tapping task.

Both VR training groups improved on the memory measure. The longer programme did not clearly outperform the shorter one, raising an interesting question about how much practice is needed before additional sessions stop adding value on a particular cognitive test.

These are visuospatial-memory test outcomes; transfer to actual competitive gaming is a separate question.

Full paper: https://doi.org/10.1007/s10055-025-01209-x

The comparison groups did not receive an alternative training activity. What active control would best distinguish a VR-specific effect on memory from exercise, engagement or repeated testing?

So I am planning to apply for summer 27 and currently going through the application process! I just wanted some insight before i fully commit

Could you share your experience finding jobs, the careers this blend has opened up for you?

We get a waiting time from the marshmallow task. Explaining that time takes a bit more work.

F.e. the child has to understand the offer, deal with the tempting reward, and decide whether the adult’s promise is believable. Two children can face the same instructions and bring different expectations into the room.

One small experiment changed what the adult did before the waiting task: the adult either kept or broke promises about art materials and stickers. The groups then waited different amounts of time. That gives us evidence that the situation can change the behaviour, although it doesn’t tell us every child’s motive. The later school extension was less clear, btw.

The original attention manipulations are interesting here too. Directing attention away from the reward made immediate waiting easier. That’s useful information about the task; demonstrating better adult outcomes from training it would take separate evidence.

I made a video going through this and the later follow-ups. Curious how people here would distinguish a relatively stable ability from someone’s interpretation of this particular situation.

https://www.youtube.com/watch?v=E-Z5TNiVO4c

Sources:

- Kidd et al. (2013), kept/broken promises: https://pmc.ncbi.nlm.nih.gov/articles/PMC3730121/

- Moffett et al. (2020), school extension: https://doi.org/10.1016/j.jecp.2020.104821

- Shoda et al. (1990), discussion of testing conditions and attention: https://doi.org/10.1037/0012-1649.26.6.978

What psychological concepts explain situations where two people develop an unusually accurate understanding of each other’s thought processes after having repeated deep conversations?

For example, what psychological mechanisms could explain a situation where:

• One person has difficulty verbally articulating an idea, but another person can accurately infer and articulate what they were trying to communicate before they finish explaining it.
• Two people begin recognizing similar patterns in how they analyze problems and interpret situations.
• This understanding becomes stronger after repeated conversations involving personal beliefs, perspectives, reasoning, and abstract topics.
• Both people become increasingly able to anticipate the direction of each other’s reasoning.

I’m particularly interested in the distinction between cognitive empathy, perspective-taking, mentalizing, interpersonal attunement, intellectual intimacy, and similarity-attraction.

Is this primarily explained by learning another person's communication patterns and developing an increasingly accurate mental model of how they think? Or are there other established psychological concepts that better explain this phenomenon?

I'm interested in the psychological mechanisms involved rather than supernatural explanations.

The setup, from Arkes and Blumer (1985): you've bought a $100 ski trip to Michigan. Weeks later you buy a $50 one to Wisconsin, which you think will be more fun. Then you notice they're on the same weekend, and neither ticket can be returned.

Both are already paid for. The only question left is which weekend you'd enjoy more. 33 of 61 students still picked Michigan.

The second experiment used real money. The Ohio University Theater sold season tickets at random prices: $15, $13 or $8. Over the first five plays, full-price buyers used 4.11 tickets each. The discount groups used 3.32 and 3.29. By the last five plays, the difference had gone.

I put these together with Levitt's coin-flip study (20,000+ people; the ones the coin told to make a change were happier six months later) in one page. It also has a quiz on when past costs do count: https://sheets.works/data-viz/sunk-cost-fallacy

I want to set up a training routine like this (the bare-bones version):

1. Mnemonic training (memory palace, word memorization; 15 minutes a day)
2. Meditation (I’m learning about how the Greeks practiced meditation—like Hugh of Saint Victor—for 5 minutes a day)
3. Cognitive training (I want to do 2 exercises a day; these can vary)
4. Reading (books and articles, though the main focus will be books, of course; my minimum goal: 30 pages)

The issues are:

1. I don’t know if 15 minutes of mnemonic training is enough to yield results
2. It’s proving very difficult to stick to a routine like this because I tire quickly
3. I don’t have much evidence regarding whether I really need to do all of this every day or if I should spread the tasks out across different days
4. I’ve been researching, but it’s very hard to find answers to everything (precise answers)

Hey all, I am in the process of applying to cognitive psychology PhD programs and wanted to ask a couple of questions.

- for those that have accepted or rejected, what were your stats and what advice do you have for applicants

-are there any programs/ forms you recommend looking at?

-how many would you suggest applying to and should I apply to masters programs incase I don’t get into PhD programs?

Over the past few months, I’ve had the opportunity to run two live EEG analysis courses with participants from psychology, cognitive neuroscience, engineering, medicine, linguistics and other related fields.

When I created the course, my main goal was to help people develop a stronger understanding of EEG analysis, rather than simply teaching them to follow preprocessing pipelines.

One thing that stood out to me was the feedback from participants. Several mentioned that although they had encountered EEG analysis methods before, what they found particularly helpful was understanding why different steps are performed, when certain methods are appropriate, and how those decisions affect the interpretation of the results.

It reminded me how important it is to build strong foundations when learning to work with brain signals, especially when coming from a different research background.

I’m curious whether others here have had similar experiences.

For those who work with EEG, or have tried learning it, what did you find most challenging when getting started?
Was it understanding the signals themselves, preprocessing, choosing appropriate analysis methods, or interpreting the results?

I’d be interested to hear your experiences.

If a sample size of people all live through roughly the same neutral upbringing and exposed to the same sights, would the variance in their cognitive wiring make a meaningful difference on the beliefs (for example, ethical beliefs) that are held by those people when all other factors are controlled?

Hi, I am a post-grad cognitive neuropsych student and I made an an early-stage app for collecting and organizing research papers, with the option to link them to regions on a 3D brain, all while having access to a community forum. I made this app as a student and for the community. I now need your help to find out whether this would be useful to other students and researchers, and what needs to improve before I take the project further.

I am giving access to anyone who wants to use my webapp through this link: https://mapped-brain.pages.dev/

You can organize papers into personal projects and connect them to brain areas. The idea is that users could also share those projects with the community, helping others explore papers by topic or brain region. It links a classic research paper manager with much more. There’s also a community forum which aims to show a heatmap of all contributions as well as host discussions and interactions for students, researchers, and anyone else interested.

The app currently includes a bilateral brain model, along with some other structures around the brain: vascularity, dura and other layers. However, this is still in its experimental phase.

Keep in mind the app is in very early access and I would welcome any and all critique and feedback. I have created a 10 question quickfire survey below for anyone who uses the website, it would help a lot for improving the webapp. You could also reach out to me on the email on the webapp if you have any other thoughts.

https://forms.gle/XjgoT1NyJfgj2LXW9