This one substance in particular caught my eye - PRL-8-53. There's a study from 1978 called "Enhanced Learning and Subsequent Retention in Humans as a Result of Low Oral Doses of New Psychotropic Agent" claiming sub 0.001 p-values.

The experiment:

A total o f 47 volunteers recruited from the faculty and students

at the university participated in the study. All were normal, healthy adults. All tests were done on a double-blind basis.

Is n too small here to draw any meaningful conclusions? And the population is either students or faculty - how might this skew results?

They go into the mechanics of the verbal test used

he major

testing device was a modification of the serial anticipation test used by R. G. Smith (1967). For this verbal test, a number of word lists were prepared, each consisting of 12 one-syllable, three- or four-letter English words. The lists were matched as to difficulty. A detailed description and discussion of the lists will be presented elsewhere. The recorded word lists were presented audibly to the subjects by the serial anticipation method. The words were heard at 3-s intervals with an 8-s intertrial interval, and each list of 12 words was repeated nine times for each individual session. The number of correct anticipations was recorded for each of eight trials. A com- plete 12 word list was used for an orientation and familiarization session, but no retention scores were recorded. To determine reten- tion 24 h after and 4 days after every test, each subject was instructed to enter on a prepared form all the words, if possible, in their proper sequence, which were recalled from the last test.

Are there problems with this test?

The results show a 40-100+% increase in retention scores for people who took the drug.

https://i.imgur.com/w5U5Yx3.png

I don't know if it is coincidence but I notice the lower the n, the better the score for the drug.

I know I'm probably answering my own questions here, but I want to see what experts think about this study and why it might be wrong.

Not sure if I can post sci hub links here, but the doi is https://doi.org/10.1007/bf00432846

I am a peruvian student really interested in neuroscience and cognitive science, but there are no undergraduate programs here about those fields. Therefore, I would like to know which undergraduate program would be the best to get into these fields. I am currently thinking about medicine and electrical engineering, because I am really good at math.

Hi! I'm taking a master's in cognitive science and posting about my learnings and experience here: https://www.instagram.com/mentescopy/

If you are thinking of pursuing a career in cognitive science reach out! :)

Does anyone know of any interesting work on current LLM models from a cogsci perspective? By that I mean analyzing these models to try to understand how they are similar to and different from humans (and other species). I'm particularly interested in LLMs and memory. I have found one paper on arxiv using research on human memory to try to understand LLM cognition. Wondering if there is other work, academic or otherwise.

I am sorry, I am not sure if it's okay to ask this kind of question.

But I am currently in the final year of B.Sc. Applied Psychology and I am thinking about pursuing a master's in cognitive science or cognitive neuroscience, but I am not sure about what would be the scope or the path post that. Like what would I be able to do, like work in which field or sector after this. I am pretty confused about the future.

I would be extremely grateful if someone could help me with this.

Thank You

I used to feel a lot smarter than I am now. In high school I performed well in my classes, and earned a high GPA. College came around, along with the pandemic, and Long story short I spent a few years clinically depressed, performing abysmal in my classes, and earning a low GPA for a few semesters. I was almost kicked out, but I got my shit together and fixed my GPA. However, I feel stupid, and like I have completely lost my old cognitive abilities.

From some limited research, I've learned that depression can actually lead to a decrease in cognitive ability (processing speed, memory, etc.), and that Long Covid can actually cause brain fog and deterioration in cognitive abilities. I've spent several years clinically depressed, and I've been infected with Covid once before.

I used to feel a lot smarter. I used to pick up on concepts quicker, focus better, and remember things for longer. Now, I have constant brain fog. I feel mentally slow and stupid. It takes me longer to learn and pick up new concepts. All in all, I just feel like an idiot, and I want my own brain back. I even want better abilities than before.

From what I've gathered from reading discussion posts and research pertaining to this topic, your IQ is set in stone, and cannot be changed. This bums me out. I'd hoped there could be some things I could do to recover and maybe even raise my IQ, but it seems this was a false hope. Now, my question is, does this same answer apply to cognitive ability? I'd assume if your IQ cannot be changed, nor recovered once decreased, your cognitive ability must follow suit, as IQ is linked to cognitive ability. Is there any hope for me? Can I get ny old brain back? Or am I doomed to be slow and stupid forever?

Full disclaimer of self promotion here. Our research team is developing the new gold standard for online IQ testing (test + administration software). We are relaying our mission to groups of researchers + psychologists to get some eyeballs on what we are doing. Please poke holes, ask questions, follow along, or even message us directly. We would love to chat.

If you'd like to read more about our research team please visit our website or Discord

Chief scientist is Dr. Russell T Warne

PS: We are launching version 1 of the RIOT test & software in a couple months

I’m wondering if anyone has the same problem as me: Lately I’ve been forgetting my words A LOT & I’m only 21 years old. I feel like I’ve always struggled with my communication (I simply can’t express myself verbally even though I know what to say, but I do better expressing myself in a written form). Anyway, it’s becoming more & more worse. Last night I forgot what a bowl was and told a family member of mine to “fill the dogs bucket” (we have a chihuahua and he has the TINIEST bowl). I forgot what a broom was and had asked someone to “pass me the sweep”. I also forgot words whenever i’m trying to talk or tend to skip over them. This tends to happen whenever it’s in the moment. Mind you, I don’t smoke, I don’t drink & I’ve never been in an accident or played a sport that could’ve involved brain trauma. This is TRULY an insecurity of mine & im afraid doctors wouldn’t want to rule out anything serious because of my age. I don’t go out & I have little friends because I’m so insecure of trying to talk to someone and sounding like I’m barely learning how to speak english. It’s embarrassing and I just want to know if anyone has been diagnosed with something that involves similarities as to what i’m going through 🥲

Yeah... people have a tantrum or jump to mania or mental illness when I mention a lack of credentials so I don't know how I'm supposed to ask this.

Have some type of Colombo/Sherlock Holmes thing from hypervigilance and reading people. Think untreated PTSD. Brought my anxiety way down and now uh I can think a lot more clearly. It was fun for a while, until I noticed uh...

Neuroscientist Benjamin Libet’s research showed that the brain begins preparing for action milliseconds before we consciously decide to act, suggesting that free will may be an afterthought.

Yes. I have ruled out the alternatives. Observing it in writing via interpersonal communications makes it easy to repeatedly see. Knowing what I want to do(act altruistically in this specific case), subconsciously being pulled towards it and using a tool/technique to achieve it. There is a delay and my conscious mind assigns an alternate reasoning to get to the same place that is less...innovative. Then in retrospect, the actual subconscious motivation rises to the surface.

Not a distress post. Pretty cool to see the science. Want to know if anyone else has this...Well I'm sure people have it. But do they notice it.

Hi everyone,

I’m exploring possibly pursuing a BSc in Cognitive Science and would love your insights. I have an associate degree in Computer Science but recently realized that Cognitive Science resonates much more with my interests. I’m fascinated by the interdisciplinary nature of the field—how it bridges neuroscience, psychology, computer science, and philosophy—but I’m also trying to understand the career paths it can open up.

From a practical standpoint:

• What are some unique or lesser-known job opportunities that a BSc in Cognitive Science can lead to?
• Which specialization courses or tracks (e.g., AI, neuroscience, HCI, etc.) have you found to be the most rewarding or in-demand?

I’m particularly interested in fields that blend cognitive science with creativity, but I’m open to exploring other technical possibilities like Human-Computer Interaction or Computational Neuroscience.

(Also, I’m a dual citizen of the US and Barbados and currently doing an internship in Sweden. Right now, I’m looking at Osnabrück University for Cognitive Science, but if anyone knows of other great programs, courses, apprenticeships, scholarships, or universities—especially ones that are financially optimal—please feel free to share!)

Thanks in advance for your thoughts and experiences! 😊

So I've made the leap from BSc Computer Science to MSc in Cognitive Science, and I'm getting some serious imposter syndrome.

My compsci course somehow managed to completely skip almost all maths, statistics, machine learning, data analysis and algorithms and instead spread itself over a bit of software development, human-computer interaction, databases, game development etc etc.

Now I'm doing cogsci in Tübingen and I feel like all the knowledge I developed in compsci was completely useless. Since I haven't done any algebra, calculus or statistics for 5 years, I'm completely struggling to do the most basic maths required in my machine learning module, which is just embarrassing considering I was incredibly good at maths when I was still in school.

My data analysis skills are also quite poor as we did almost none of that during my bachelor's. I feel like I offer a very limited skillset compared to my other peers with computer science backgrounds, and I also feel like a tiny baby in all things psychology compared to people with psychology backgrounds who are now doing Cogsci. The other problem is that my university provides almost no HCI modules.

I want to go into research after my Masters, but I really just feel so unqualified compared to many of my peers. I find cognitive psychology and vision stuff particularly interesting, but I just feel so underqualified compared to other people. I probably won't do anything very AI related as it would take too much time to catch up. Does anyone have any advice for me?

What voluntary courses should I take to increase my chances of being accepted to the CogSci Master's program in Osmabrück? I have a Bachelor's degree in intercultural business psychology. The Bachelor's degree had a very high psychological component. I know that I have to upload a proof of English at least B2. When I wrote to the mentoring team, they also told me to write a cover letter explaining in detail why I want to do CogSci. I have to explain voluntary courses and further training in detail. Personally, I find the composition of the modules super exciting. I would like to focus on neurosciences and AI. I wrote my bachelor's thesis on the effects of LLM on the world of work. I'm currently reading a textbook on cognitive neuroscience by Gazzaniga. I find the basic mechanisms of the brain particularly interesting.

Does anyone have any tips or recommendations on how I can increase my chances?

So my working memory is in 5th percentile, my other index scores based on the WAIS IV test I did last year, for when I got the my adhd diagnosis range from average to above average. With the subtests from PSI ranging from average - below average and PRI scores above average - below average.

I’m 21 and since graduation I’ve been trying to get my high school equivalency diploma. I kind of thought that once I was medicated and received accommodations I would be able to lean into my “strengths” but it’s still been so incredibly difficult especially with writing assignments- I was aiming for higher education too but I don’t know if that’d even be possible or smart of me to do now.

I’m hoping that someone might be able to offer some insight. To me, a WMI in the 5th percentile seems “bad”. But I’m also worried that I’m hung up on a number or being fatalistic. At the same time I’m worried that I might be in denial and stubborn.

I’m definitely planning on talking to a doctor about this as well, but I switched psychiatrist so I’m on a wait list for now.

Hi! I am a cognitive science major currently working on getting my undergraduate degree! I am loving every part of this degree so far but I am really struggling in linguistics, particularly with morphology and syntax. I was wondering what fields of linguistics are most focused on in cognitive science? From what I gather it seems to be morphosyntax and semantics but I wanted to ask somebody with a background in cognitive science. If I am not particularly good with the hard details of linguistics will that get me into trouble in the future? Did anybody else struggle with linguistics, and if so, how did you manage to get a better understanding of it? Does anybody have any recommendations on further readings in linguistics and its role in cognitive science so I can zero in on the most relevant aspects of linguistics? Thanks! (Particularly concerned because I just got a D on a morphology and syntax exam lol)

I just see these people that try to kill themselves or other people because they don’t want to be gay because of the cult of heteronormativity in this country and I wonder if it’s possible to help them.

I have no idea what to make of all this. Some people say the IQ tests are racially profiled while others say it isn't. Some say IQ isn't fixed while others say it is. Some say it completely genetic while others say it is significantly increased by education. Some say that the SES has a significant effect on IQ while others just say it's genetic. Please give me some articles, books, or studies to separate the truth from the fluff.

I asked a question on Quora to this dude name Brian White and he edited his post to respond to me with a very thorough and detailed response. Overall I am quite convinced by all the evidence he presents but since I am ignorant on this stuff, I don't think it would be good to come to a full conclusion based off one persons writings. So that is why I'm asking yall if this this response was completely accurate and if it wasn't, then what was wrong with it?

Some of the points he makes is that IQ is largely fixed and that there is different IQ levels among races that are attributed to genetics. This is because in some studies where adopted asians in white families perform 10 IQ points better than the family they are adopted into even with possible malnurtition in early life before adoption. He also says that African Americans in adopted white families are 10 IQ points below the family they were adopted into.

This seems somewhat unsettling but he provides plenty of links to several studies and it seems like a reasonable conclusion with all the evidence he provides with it. I don't know enought about it at the moment to make a good educated conclusion and thats why I am asking y'all.

Hi, does anyone have recommendations for schools in the US for intersections of cognitive science (social psych, cognition, research on social inequalities) and communication (media related research). I have background in philosophy with focus on social epistemology and philosophy of mind, i also have a good amount of work in documentary filmmaking (themes: marginalisation, visibility, performativity, queer cultures, intersectionality, decolonisation).

I have applied to a couple of social psych phds but now i am considering a few communications, computational social sci, social anthropology (like UCLA, Michigan, UPenn, Princeton). Do you have any recommendations for schools across US, UK, Canada with Phd programs around this focus? I might also consider a funded MA if I can find a good program. It's important for me that the program/dept is interdisciplinary and has collabs with labs or research centres using different research methods.

The main reason for moving away from philosophy is to develop skills other than critical thinking and analytical writing.

Any suggestions are welcome! all my application materials are almost ready so i just want to consider a few new options before i submit.

Thank you.

Our research team has gotten countless questions about this, so we just wrote it up to clarify misconceptions around how modern IQ is calculated. Hopefully some of you find this useful or interesting at the least.

So, the way IQ has been calculated has shifted since IQ's inception.

The First IQ Formula (Stern's)

The original IQ formula was:

IQ = (Mental Age / Chronological Age) × 100

• Mental Age: The cognitive age at which someone performs. Example: A 10-year-old solving problems typical for 12-year-olds has a mental age of 12.
• Chronological Age: The actual age in years.

Seems straightforward, right? But here’s the catch and issue...

The Problem with Stern's Formula

IQ wasn’t consistent as kids aged when using this formula...

Example:

A child 2 years ahead of their peers would see his/her IQ drop over time for no reason:

• At age 6 with mental age of 8: (8/6)×100=133
• At age 10 with mental age of 12: (12/10)×100=120

Even though they remained 2 years ahead of their peers in mental ability, their IQ dropped.

Enter Modern IQ Calculations Stage Left

Modern IQ scores compare test performance to statistical norms, not mental vs. chronological age. This involves:

1️⃣ The Mean (M): The average score in a population.
2️⃣ Standard Deviation (SD): How spread out scores are from the mean.

Together, these help measure how far an individual’s performance deviates from the average.

Z-Score for Each Subtest

So, IQ tests are constructed by a series (a.k.a. battery) of smaller tests called "subtests". You get a z score for each subtest you complete. We start with the z-score, which tells us how far your raw score is from the mean in units of SD:

z = (x − M) / SD

Example:
A test with M=50, SD=10

If your score is x=70, then...

z = (70 − 50) / 10 = 2.0

You’re 2 SDs above the mean.

Sum the z Scores

Then... since modern IQ tests like the RIOT have multiple subtests. Each produces a z-score. These z-scores are summed to create a composite score.

Example:
Verbal: z=1.0
Spatial: z=2.0
Memory: z=−0.5

Total:
z=1.0+2.0−0.5 --> 2.5

Final Steps to Get IQ Score

Lastly, we convert to IQ Scale

To align scores with the IQ scale (mean = 100, SD = 15), we use:

IQ = z · 15 + 100

Example:
If total z=2.5, your IQ is --> ~138

IQ = (2.5 · 15) + 100 = 137.5 ≈ 138

We will leave out a few extra things in this section that relate to the Score Extremity Effect. You can read here if you want more detail on this concept and additional step.

That's it! IQ Calculated ✅

This method of calculating IQ is called the "Deviation IQ", which it is highly superior to Stern's original Quotient IQ

Why do we use this now?
- Consistent: Across age groups
- Fair: No arbitrary age assumptions
- Accurate: Reflects relative standing in a population

Deviation IQ is now the standard in tests like the WAIS and RIOT

Hope you guys found this interesting. Reply with any questions, our research team will happily look through them and engage. Cheers all.

I got severe whiplash from attempting to discuss psychological phenomena on machine learning, AI, and computer science subreddits. Even in ExperiencedDevs, there is strong resistance to telling people that the very software they work on can potentially do their job. And I don't think this is philosophical enough for the philosophy subreddit.

Furthermore, when I go to an artificial intelligence subreddit, I get very opinionated individuals bringing up LeCun, and Chollet (foundational figures in the development of Neural Networks) disagree with me.

If you don't know, LeCun and Chollet are notable experts in AI who both contend that LLMs and Transformer based models are incapable of reasoning or creativity.

And they might be right. But I thought this deserved a more nuanced discussion instead of appeals to authority.

In a 2024 interview with Lex Fridman, LeCun stated: "The first is that there is a number of characteristics of intelligent behavior. For example, the capacity to understand the world, understand the physical world, the ability to remember and retrieve things, persistent memory, the ability to reason, and the ability to plan. Those are four essential characteristics of intelligent systems or entities, humans, animals. LLMs can do none of those or they can only do them in a very primitive way and they don’t really understand the physical world. They don’t really have persistent memory. They can’t really reason and they certainly can’t plan. And so if you expect the system to become intelligent just without having the possibility of doing those things, you’re making a mistake. That is not to say that autoregressive LLMs are not useful. They’re certainly useful, that they’re not interesting.."

The argument that LLMs are limited is not that controversial. They are not humans. But LeCun's argument that LLMs can't reason or understand the physical world is not self-evident. The more you train transformers, even text-based LLMs, the more cognitive features emerge. This has been happening from the very beginning.

We went from predicting the next token or letter, to predicting capitalization and punctuation. Then basic spelling and grammar rules. Paragraph structures. The relationship between different words not only syntactically but semantically. Transformers discovered the syntax of not just English, but every language you trained it on, including computer languages (literal code). And if you showed it chemical formulas, amino acid sequences, it could predict their relationships to other structures, concepts. If you showed it pairs of Spanish and English phrases, it could learn to translate between English and Spanish. And if you gave it enough memory in the form of a context window, you could get it to learn languages it had never been trained on.

So, it's a bit reductive to say that no reasoning is happening in LLMs. If you can dump an textbook that teaches obscure language into an LLM, and if that LLM is capable of conversing in that language, would you say it's not capable of reasoning? Would you say it's simply learned to translate between other languages and so it's just doing pattern recognition?

So, then you get a well-regarded expert like LeCun who will argue that because an LLM doesn't have a persistent memory, (or a variety of other seemingly arbitrary reasons), that LLMs can't reason.

Thought Experiment

This is where anterograde amnesia becomes relevant. People with anterograde amnesia:

• Cannot form new long-term memories.
• Cannot learn new information that persists beyond their working memory.
• Are limited to their pre-amnesia knowledge and experiences.

And yet we wouldn't say that people with anterograde amnesia are incapable of reasoning because they can:

• Draw logical conclusions from information in their working memory.
• Apply their pre-existing knowledge to new situations.
• Engage in creative problem-solving within their constraints.

So would LeCun and Chollet argue that people with anterograde amnesia can't reason? I don't think they would. I think they simply are making a different kind of argument - that software (neural networks) are inherently not human - that there are some ingredients missing. But their argument that LLMs can't reason is empirically flawed.

Take one of the most popular "hello world" examples of implementing and training an artificial neural network (ANN). That ANN is the Exclusive OR (XOR) neural network which is a neural network implementation of a XOR logical circuit that basically says either this or that, but not both.

And as a software developer you can implement this very symbolically with a line of code that looks like this:

Func<bool, bool, bool> XOR = (X,Y) => ((!X) && Y) || (X && (!Y));

with a truth table that looks like this:

 X | Y | Result
 ==============
 0 | 0 | 0
 1 | 0 | 1
 0 | 1 | 1
 1 | 1 | 0

The XOR example is significant because it demonstrates both statistical and logical thinking in one of the simplest neural networks ever implemented. The network doesn't just memorize patterns. It's learning to make logical inferences. And I will admit I don't have direct proof, but if you examine an LLM that can do a little bit of math, or can simulate reasoning of any kind, there is a good chance that it's littered with neural "circuits" that look like logic gates. It's almost guaranteed that there are AND and OR circuits emerging in small localities as well as in more organ-like structures.

Some people might ask whether this has anything to do with causal reasoning or statistical reasoning, and the answer is undoubtedly yes. Dig deep enough and you are going to find that the only reasonable way for LLMs to generate coherent inferences across configurations of words not in the training data is not to memorize those configurations, but to "evolve" inference.

The Mathematical Definition of Creativity. Thank you Anterograde Amnesia.

Let's go a bit further. Are we willing to say that people with Anterograde Amnesia are incapable of creativity? Well, the answer is not really. (Do a quick Google Scholar search).

LLMs don't really have persistent memory either (see LeCun), at least not today. But you can ask them to write a song about Bayesian Statistics in the Style of Taylor Swift, in a sarcastic but philosophical tone using Haitian Creole. Clearly that song wasn't in the training data.

But if it doesn't have agency or persistent memory, how can it reason or be creative? Hopefully by now, it's obvious that agency and persistent memory are not good arguments against the ability of transformer based AI to exhibit creativity and reasoning in practice.

Creativity can be viewed mathematically as applying one non-linear function to another non-linear function across a cognitive space. In a more practical formulation it's the same as saying to an LLM that trained on pirate talk and poems to write a poem in pirate talk. The training set may not have poems with pirate linguistic features, but the space in between exists, and if the "function" for creating poems and the function for "speaking like a pirate" can be blended, you get a potentially valuable hallucination.

Creativity = f(g(x)) where f and g are non-linear transformations across cognitive space

But since these functions can be any transformation, just as we can say that f generates poems and g generates "pirate talk", we could say f infers probability and g provides a context and that f(g(x)) = Reasoning.

An important thing to note here is that this application of a non-linear function to another across a cognitive space explains both human creativity and artificial creativity. It also mathematically explains inference and reasoning. Yeah, it's hand-wavy, but it is a clean though-experiment.

We went from trying to understand human memory through metaphors like tape recorders to computer metaphors like RAM and processors. Each generation of technology gives us new ways to think about how our minds work.

This mathematical view of creativity and reasoning - as functions transforming information across cognitive spaces - explains both human and artificial intelligence. Yeah, it's simplified, but it gets at something important: these capabilities don't require mystical human qualities. They emerge from basic operations, whether in brains or neural networks.

So we're left with a choice: either accept that reasoning and creativity can emerge from mathematical functions in transformer architectures, or argue that people with anterograde amnesia can't reason or be creative. The second option doesn't hold up to what we know about human cognition.

AI models today, especially large language models (LLMs), are fantastic at predicting the next word in a sequence, but they’re still largely stuck in the realm of statistical token prediction. My research explores how to push beyond this limitation and transform AI into environment reasoners — systems that don’t just predict the next token but actively understand, adapt, and reason about their conceptual environments.

This paper introduces a novel framework I call AI Geometry. Inspired by classical geometry, where Euclid laid the groundwork for spatial reasoning, AI Geometry formalizes the internal structures of neural networks using graph theory principles.

Key Highlights

1. Reimagining Neural Networks:
   • Rather than treating neural networks as static systems, I propose viewing them as dynamic graphs. Nodes represent concepts, edges denote relationships, and clusters capture higher-level abstractions.
   • This perspective allows AI models to go beyond token-level predictions, enabling deeper pattern recognition and conceptual reasoning.
2. The Dual Nature of Networks:
   • Neural networks can be treated both as graph structures and probability spaces. This dual perspective lets models navigate uncertainty and learn from complex environments.
   • Techniques like Gaussian and Monte Carlo methods are leveraged to enhance conceptual learning and generalization.
3. Introducing the Rhizome Optimizer 🌱:
   • A novel optimization technique focusing on graph-theoretic metrics (clustering coefficients, centrality, node degree) instead of traditional loss functions.
   • The Rhizome Optimizer dynamically adapts the model’s internal graph, enhancing conceptual connectivity, reducing overfitting, and improving adaptability.
4. Topology-Based Backpropagation:
   • An extension of traditional backpropagation, incorporating topological gradients. This allows the model to adjust not just weights but also its internal structure, optimizing nodes, edges, and clusters during training.
5. Bridging Physical and Virtual Environments:
   • By treating neural networks as environments governed by probabilistic rules, we eliminate the distinction between virtual and physical learning. This opens the door to AI systems that learn and reason more like humans do.

Why It Matters

This research is aimed at moving AI beyond token prediction to become more adaptive, self-organizing, and capable of deeper reasoning. By integrating concepts from graph theory, topology, and probability, we can build AI systems that are not just token predictors but genuine environment reasoners.

Read the Full Paper:

Check out the video so you don't have to read!: https://youtu.be/Ox3W56k4F-4

The full research paper is available here: Beyond Tokens Transforming Neural Networks Into Adaptive Graph Based Environment Reasoners ( 1) : Free Download, Borrow, and Streaming : Internet Archive

Have there been any breakthroughs in research on bilingualism / multilingualism and it’s effect on the brain? we’ve known for at least ten years that there’s a positive correlation with attention, memory, social skills and cognitive reserve, but have there been any other interesting findings ?

Greetings everyone,

I've recently completed my 3 year psych undergrad, My line of interest hovers around the intersection of Psych & Philo , perhaps some rudimentary neuro as well. As result i figured cogsci might be up my alley. I am considering opting into a Masters program abroad , however i'd like to get a picture of the field prior to going all in.

I was wondering if my peers & seniors can aid me in this regard by pointing me to few vital resources.

I understand that depending on one's line of interest the field can vary . therefore kindly consider the aforementioned interests whilst sharing your suggestions.

Thank you.

So I have recently had some cognitive testing done for an adhd assessment. I am 23F, have always been a high achiever so adhd was never considered despite the internal struggles I have faced.

I did above average in most areas, except I got less than below average in spatial reasoning/awareness. Looking back I have always struggled at this. I’m good at maths but if you give me blocks or ask me to determine what complex shapes will look like built from a 2D picture etc I do poorly. This seems to make sense as I have bad sense of direction. I can walk into a public bathroom but when I walk out into the hallway afterwards, I can’t remember which way I turned in and sometimes walk the wrong way.

Is this common with adhd folk? I haven’t received a diagnosis yet but was curious to know if this is a common occurrence.