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

Hi, I'm thinking of applying for my masters in Cognitive Science for the fall of 2027. I'm having a hard time deciphering which programs are good. I have a bachelors in Philosophy with a minor in Social Science methods and Economics. The Universities that are on my radar are ENS-PSL, Sorbonne-Paris Cite, University of Vienna, Neuchatel, Osnabruck and Trento. I would love some help on the opinions of these unis, especially since I'd like to go into research and do a phd and would like to go to a uni which gives good placement or is well respected for when I move to do a phd somewhere (could be the same place or other). Any help would be very very appreciated as I've found it hard to find much information online apart from university websites. Thank you in advance!

I am a freshman in college, and I have my first Cog Sci 101 midterm next week. We don't have a textbook, and basically, all we get are lecture slides. I honestly don't know how to study. All I've done so far is take notes on the lecture slides and I guess just try to understand stuff. I tried looking for practice questions online but none of them really matches the material. Please help.

Hi everyone — I put together this free interactive resource for exploring cognitive biases and related effects. I thought it might be relevant and helpful here.

https://thebiasmap.com

It’s adapted from the Cognitive Bias Codex, with 188 entries, plain-English explanations, everyday examples, sources, and short animated videos. Credit for the original Codex is on the site.

I’d appreciate any feedback or corrections.

I've been thinking about the boundary between cognition and environment.

Suppose I experience anger.

That state can produce facial expressions, posture, voice changes, movement, etc. Another person detects those signals and constructs an interpretation of my internal state.

Their interpretation changes their behavior, which changes my perception of the situation, which changes my next state.

So:

internal state → behavior → environment → another mind → interpretation → behavior → original mind

This seems straightforward socially, but I'm wondering how far the same principle extends to cognition itself.

Humans externalize cognitive processes into:

- language

- writing

- diagrams

- calculators

- computers

- search engines

- smartphones

- AI systems

These aren't merely passive storage devices. They can alter what we remember, what we attend to, what we consider possible, how quickly we reason, and what actions we take.

That creates a recursive loop:

cognition → external representation/tool → altered cognitive environment → altered cognition → new tools/representations

My question is:

Is there a principled cognitive-science boundary between "a cognitive process using an external tool" and "a cognitive process that genuinely includes the external tool as part of its functional organization"?

And separately:

Could increasing technological coupling eventually change the kind of cognition humans perform, rather than merely making existing cognition faster?

I'm deliberately not assuming the extended-mind thesis is correct. I'd like to know where the strongest empirical objections are.

For example, what evidence would distinguish:

1. an internal cognitive process merely receiving information from a tool,

from

1. a genuinely distributed cognitive process spanning brain, body, other people, and technological systems?

I'm especially interested in work from cognitive science, neuroscience, embodied cognition, extended cognition, predictive processing, or related fields.

This mind blowing and rich conversation between Biologist and Author Dr. Rupert Sheldrake and Indian Philosopher and Author Acharya Prashant explores various interesting topics such as Consciousness, Scientism, Enlightenment, Reincarnation, Suffering, Prayer, Sentience , Compassion and Self Knowledge.

What really excited me was the conversation on consciousness. When we say something is conscious there is prior assumption that something/someone should show certain signs or behavior, which resembles are own behavior. And what difference does it make for me to call something as conscious and something is unconscious. But have we even questioned that are we conscious? Without exploring the one who is calling himself conscious how do I know I am conscious in the first place. Here comes the importance of Self knowledge, knowing oneself, if I don't know myself then how can I know the reality of my own claims.

This conversation is an invitation to reflect and question what we already believe in and consider it to be true.

For example, when you are trying to learn about a historical figure's personal life for a history project, option 1 goes the path of using ai like gemini or perplexity to learn their life, option 2 reads on autobiographies about them and the historical events that happened during his era in his region. However, ai misses out on key details, and gives you the answer much quicker within minutes, compared to option 2. So long term, cognitively, whats the long term effect of such implications?

Does David Goggins' approach work for academics/cognitively intense tasks where one needs to learn a lot of things while research suggests a human can only effectively learn/work for a few hours?

Hi everyone! I'm a research assistant with the Visual Cognition Lab at the University of British Columbia, and we're currently recruiting volunteer participants for an online study. We're investigating inattentional blindness (think the "gorilla video" if you're familiar) and cueing using the principles of magic tricks.

Anyone age 18-35 with normal/corrected to normal vision can participate. You must have a computer with an English keyboard and a stable internet connection. Participants complete an online experiment that takes approximately 60 minutes. You'll be asked to complete a repetitive visual search task.

The study is being conducted by researchers at the UBC Visual Cognition Lab (BREB Approval Code:
H23-02597).

If you're interested in participating, you can find more information and sign up here!

When a language model generates "Paris is the capital of France," it is not uncovering anything. It is completing a pattern. Sequence of tokens most statistically probable to follow "What is the capital of..." happens to align with a factual state of affairs, but the alignment is incidental rather than intentional. Model does not turn toward the world and receive its disclosure. It samples from a distribution of linguistic possibilities estimated during pre-training.

I think this distinction, between pattern completion and genuine unconcealment, is the thing cognitive science keeps collapsing when it talks about model "truthfulness."

When engineers evaluate truthfulness with benchmarks like TruthfulQA, they measure frequency with which outputs match a predetermined answer set. Model produces statements that conform to a curated body of assertions. Conformity is tallied. A score is assigned. What never enters the frame is whether anything has been unconcealed, whether the model has participated in the event by which something shows itself.

The word ἀλήθεια is formed from the alpha-privative plus lethe, forgetting or hiddenness. Greeks experienced truth not only as static correspondence between proposition and reality. There is also a dynamic event: something is wrested from concealment and brought into the open. Truth is not only a property that statements possess. Truth is something that happens.

Correspondence theory, crystallized in Aristotle's Metaphysics IV and formalized by Tarski, has so colonized our intuitions that we find it difficult to think truth as anything other than a relation between proposition and fact. For a proposition to correspond to a fact, the fact must already be manifest within a clearing of intelligibility. Before I can judge whether "the cat is on the mat" is true, the cat, the mat, and the relation of being-on must already be disclosed within a meaningfully structured world. This prior disclosure is original truth. Correctness truth, the kind measured by benchmarks, is derivative. It operates within a clearing it has not itself established.

Implication for AI is immediate. A language model can approximate correctness truth with impressive accuracy. It cannot, even in principle, participate in the event by which a clearing is opened. It does not dwell within a world. It processes tokens. It does not unconceal.

Ontological poverty becomes visible when we ask what the model is doing when it produces "The sky is blue." A human speaker who utters this sentence is oriented toward the world. They have perceived the sky, experienced blueness, and their utterance expresses a perceptual encounter with a state of affairs. A language model that produces the same sequence has done nothing of the kind. It has computed that this continuation has higher conditional probability than alternatives. The fact that the high-probability sequence corresponds to reality is a property of the training data, not of the model's relation to the world. The model has no relation to the world.

This is a claim about ontology, not scale. Training data consists of tokens produced by beings who participated in unconcealment. The texts carry, in their statistical structure, fossilized traces of countless acts of human disclosure. When the model learns to complete "The sky is..." with "blue," it is learning that humans, in the aggregate, have used the most common color term. Model has not learned that the sky is blue. It has learned that humans say it is.

Distinction between "the sky is blue" and "humans say the sky is blue" may seem scholastic when applied to common knowledge. It becomes anything but scholastic with genuinely contested claims: historical interpretation, ethical obligations, the meaning of a difficult text. In these domains, what the model has learned is the distribution of human opinions across its training corpus. Output may be correct. It may not be. What it cannot be is the result of an authentic encounter with the matter itself, because the model encounters nothing.

Commercial systems compound this with an institutional layer. After pre-training, base models are fine-tuned using human feedback. Human raters, operating under organizational guidelines, evaluate outputs along dimensions like helpfulness and harmlessness. Model learns to produce outputs that raters approve of.

What this produces is not truth. It is a smoothed reconstruction of the consensus implicit in the training data and rater guidelines. Ask a commercial model about a genuinely contested question, the nature of consciousness or the foundations of ethics, and observe. Model surveys "perspectives," attributes each to its tradition, and declines to commit. This is not intellectual humility. It is an architectural limitation dressed as circumspection.

A system that produces true statements by accident, because statistical patterns happen to align with reality, is not a system that knows. It is a system that echoes.

If a cognitive architecture has no world, only a distribution over tokens that beings once used to disclose a world, can it ever know, or only echo?

Hi, I just built Unique Host.

It’s a lightweight, non-LLM, roleplay-focused engine where characters have their own state, memory, and identity.

You can create a character, give them a world, and play with them — or just try the included characters, Delia and Joaquin.

The idea is that the character doesn't just remember the conversation. The character is supposed to remember what happened to them, and that history can affect how they behave in future interactions.

It can be run locally and uses no GPU, no API, and no LLM.

This is a very early v0.3, so expect bugs, strange behavior, and plenty of things that still need refining. 😅

You can try it here: https://huggingface.co/spaces/Bichini/Unique_Host

Or download it here: https://github.com/Bicheno1/unique-host

I built it as a small implementation of my Cognitive Coherence Model (CCM) architecture.

Theoretical framework: https://zenodo.org/records/20648800

I also created this addon as an experiment to see how the CCM architecture can control a body.

It's called Jellyfish AI.

It currently has a jellyfish, a turtle, and a fish. You just put them in the scene and see what they do.

You can also put several turtles, fish, or jellyfish together and see what happens.

They can perceive things around them, and their behavior comes from the architecture and their current state.

Jellyfish AI: https://github.com/Bicheno1/Jellyfish-AI

Today I was searching curiously about our brain I came to know that our brain is actively doing millions of calculations every minute with topics even we don't know like when we try to catch a ball it calculates its speed,motion ,swing,angle etc before preparing us to catch it like our brain knows more maths and physics then we do , Ex- Whenever we are about to fall out hands suddenly shoot up to balance us.

It means it already knows advance maths and physics just can't do these thing voluntarily.