I was very concerned about taking help from PapersOwl. To look at this, I, being a fully paid customer for my dissertation, had to stand by for extended periods between my submission and the university's feedback. By the time I asked for modifications based on the feedback, they said I had surpassed the given time for any edits and would not check my paper again. No refunds or help from any end. Having no channels of communication was the worst of all. After they have you in trouble, their writers just step back, and you'll again have to shell out extra money for any adjustments. The service is simply unprofessional and frustrating, for they hide their support behind their policies. PapersOwl puts on airs and graces with false claims; it has earned many an unfavorable review and poor customer service. Spare yourself such trouble and look for alternatives to your academic aide.

Hello! Help me find a video on YouTube. There was a girl, a brunette, talking about the topic. The topic was the application of integrals. I was hoping that I could find this video in the history, but it has disappeared.

Hi, I've been looking for interesting podcasts for a long time but can't find anything

Hey guys, I would like to share a new book that might be interesting to the community!

Graph theorist Reinhard Diestel has written a book with possibly far-reaching implications for mathematical modelling in psychology:

Tangles: A structural approach to artificial intelligence in the empirical sciences
Reinhard Diestel, Cambridge University Press 2024

Publisher's blurb:

Tangles offer a precise way to identify structure in imprecise data. By grouping qualities that often occur together, they not only reveal clusters of things but also types of their qualities: types of political views, of texts, of health conditions, or of proteins. Tangles offer a new, structural, approach to artificial intelligence that can help us understand, classify, and predict complex phenomena.

This has become possible by the recent axiomatization of the mathematical theory of tangles, which has made it applicable far beyond its origin in graph theory: from clustering in data science and machine learning to predicting customer behaviour in economics; from DNA sequencing and drug development to text and image analysis.

Such applications are explored here for the first time. Assuming only basic undergraduate mathematics, the theory of tangles and its potential implications are made accessible to scientists, computer scientists and social scientists.

From the reviews:

“As a sociologist, I am impressed by Diestel’s innovative approach. Tangles open up completely new ways for empirical social research to gain insights that go beyond the usual generation of hypotheses and their verification or falsification. Tangles offer the opportunity to make the ‘big sea of silent data‘ speak for itself.“

Rolf von Lüde Universität Hamburg

Ebook, plus open-source software including tutorials, can be found on tangles-book.com.

The eBook comes in two versions: an abridged 'fun' edition for readers who'd just like to dip in and get a feel for what's new (and there's plenty of that!), and the full eBook edition which includes the mathematical background needed (which is not much).

Table of Contents and an introduction for social scientists (Ch.1.2), are at tangles-book.com/book/details/ and arXiv:2006.01830. Chapters 5 and 13 are specifically about tangle applications in the social sciences.

The software part of tangles-book.com says they invite collaboration on concrete projects. They have made a big effort to smooth newcomers' access - interactive or read-only tutorials, detailed instructions on how to set up the software. The software documentation and tutorials all refer to the book for reference. But if you have that next to you, the tutorials are fun and easy to work through!

https://preview.redd.it/yx6zapc4ue4d1.png?width=2074&format=png&auto=webp&s=7d239465fe86420253ec6371c27c9a5b0880adb5

Hey, math enthusiasts! I'm a little stuck, so I could really use your combined knowledge. I've been looking for the best essay writing service that caters to math students for the 2024–2025 school year. My search has been drawn out and a little disappointing because the more well-known services appear to be spamming other subreddits without providing any concrete evidence of their ability to handle math-related projects. Nor have the reviews I've read been all that compelling. I'm contacting you in the hopes that someone may be aware of a hidden treasure — a dependable, reasonably priced, and experienced writing service for math essays and papers. I would be very grateful for any advice!

https://en.wikipedia.org/wiki/Transition_(linguistics) https://en.wikipedia.org/wiki/Level_of_measurement

Hi all

I am just about to put my research in for ethical approval but calculating the power in order to determine the appropriate sample size is a little confusing.

The primary aim of the study is to identity if any relationships exist among the variables I am using. This analysis is fine I have this part sorted.

A secondary aim is to investigste group differences. When data is collected I will have three groups and they will be tested on multiple measures - In total there are about 7 measures with 5 of them being questionnaires and 2 task based.

One of the tasks I am using in this study is novel in this particular area I am applying it to but effect sizes are considered small. Going by what I remember in stats I'm probably going to have to use a MANOVA. However, the effect sizes for sample calculations change from d to f^2 (if I'm correct). So does this mean I should be putting in 0.105 into the f^2 part of g power with the following

F tests. Priori MANOVA - Global effects

Any help would be appreciated.

Hi mathpsych,

As a part of an exam project at my CogSci bachelors I am conducting a research experiment that investigates the effect of hormonal contraception on perserverance in a series of cognitive battery tasks (anagrams, HMT-S etc). The study is based on a previous study by Sarah Hill (link), but I want to approach the analysis from a baysian perspective.

Now to my question: In my model, I want to take both reaction times and accuracy into account. When I do research on this, decision diffusion models are by far the prevalent search result - however, as far as I can tell it is only applicable to fast-speeded 2-choice decision tasks (whereas some of my cognitive battery tasks are multiple choice, some are free, and reaction times will most likely vary form 30 secs to 90 secs). Is there a way to apply a decision diffusion approach to this kind of data, or should I just stick to a baysian model based on informed priors and treat the RT data in a shifted log-normal distribution?

TL;DR: I am in doubt how widely applicable decision diffusion models are, and if they can be applied to cognitive battery tasks with long reaction times and multiple choices.

I'm shootin from the hip here. Anyone know something substantial? My best guess so far...

• cortisol, dopamine, serotonin, adrenaline (more?) = "brain chemicals" = fuel for the "hypo/mania engine"
• The "hypo/manic engine" activates when "brain chemicals" exceed some arbitrary "initiation threshold"
• The "hypo/manic engine" itself supplies the brain with an increased supply of "brain chemicals"...more sensitive to stimuli
• An episode will escalate (like from hypomania to mania to psychosis) as the fuel for the "hypo/mania engine" increases
• An episode will terminate when the fuel runs out...when the "brain chemicals" reach an arbitrary "termination threshold"
  • the "termination threshold" is significantly lower than the "initiation threshold" and time plays a factor too...the engine shuts down slowly. AKA the "hypo/mania engine" can idle on less fuel than it takes to start it
• An episode can also terminate when the brain/body reaches some arbitrary level of strain or fatigue. (possibly adrenal fatigue?)
• The "brain chemicals" feed into eachother. For instance, an increase in cortisol means in increase in dopamine and serotonin.
  • If you block the receptors for one of the sources of fuel (antipsychotics) the engine sputters out
• There is something like a refractory period after an episode is terminated. Perhaps some inhibitory mechanisms prevent the engine from starting for an arbitrary duration.

​

I'm mostly just lookin for some theories on mania. HMU with whatever you got please :-)

similar to base ten digits being mirror neuron groups and you can think of a few digits at a time.

Hello, r/mathpsych!

I am planning to introduce a manipulation of an effort discounting task as a part of my PHD dissertation. However, I am having a lot of trouble understanding how is the subjective value computed from the choice data? As case in point, I am looking at this article: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004116

Let's take for example the simplest model, linear. On a given trial, subjective value V = M - kC, where M is reward for this trial, C is effort cost for this trial, and k is parameter to be estimated. We know M and C, but how do we know V? Further in the article, the authors say: "the softmax function was used to transform the subjective values V1 and V2 of the two options offered on each trial into the probability of choosing option 1.", but I really don't understand what is the use for it if we don't know the V in the first place. My question might sound stupid, and I apologize if that's the case, but I'd greatly appreciate if anyone could help me.

In other words, how do we get from basic information about trials and choices to the k parameter?

Hello everyone. I have a model (or theory) about perspective and knowledge representation, and I would like to have your review. Basically "perspective" is just another word of "schema", but by doing so I think this model gives us three advantages:

• Intuitive and imagery. It is simple, so it can be used to explain in therapy, or can connect with folk psychology ("look at the problem in a new perspective", etc)
• Be an intermediate framework to connect with other fields. From my understanding, the most successful model to represent knowledge is connectionism. However, its applications are still limited to purely cognitive problems (e.g. dyslexia), not to problems in other fields. It is hardly to imagine how to apply this model to explain stylistic devices or communication.
• Provide new mathematical structure to current models. The new structure in here is plane, which represents perspective. In other models, once a piece of information is regarded as a node, it is always be a node. If it regarded as an edge, it is always be so. More than that, the structure of the network is fixed, even though you can work around it by turning the nodes on or off. However, with plane, you are freely to regard it as node or edge, and the structure is fundamentally changed in each plane.

Applications will range from cognitive linguistics, memory, cognitive therapy, social psychology and clinical psychology. They will be:

• Analogy
• Writing
• Finding the balance point
• Communication and perspective taking
• The cold gaze

To be specific, here are the questions that each of them trying to answer:

• Analogy: Why do analogies help us understand a problem we don't understand? How to reason with analogy without making logical fallacy?
• Writing: How to explain a concept when the novice really lacks background? What does it mean to have a transformative writing? What does "big picture" really mean?
• Finding the balance point: Why are efforts to be adaptive become maladaptive? Why is it hard to balance between disciplinary and flexibility? How to stop the indecisiveness without worrying of doing wrong?
• Communication & perspective taking: Why do people keep misunderstand each other? Why do others keep distorting our words? Why don't we realize that we are distorting theirs? How to solve it when it happens?
• The cold gaze: How to see your core value when your mind is clouded with fantasies, ruminations, resentments, or fears?

The underlying philosophies are Taoism, Buddhism, postmodernism, and perhaps romanticism. The discussion section will scramble a bit about the nature of information, metaphysics, epistemology, neurocognition, semantics, and physics. However, these are just minor points; you don't need to know them, and I don't claim that I know them. You can also read my another post that is tuned for folks studying Eastern philosophy.

Here is the link: A theory of perspective. Thank you for your reading. Hope you enjoy it.

I'll be entering a quant Psych PhD program next year. I'll already have an MA in experimental psych and a minor in stats.

Instead of doing the masters in psych that many do within their first 2 years, I thought it might be beneficial to do an MS in stats. I would like to teach in both the psych and stats departments one day, and think it might also be helpful if I decide to enter industry one day.

Any thoughts/insights would be super helpful!

What are books that would help and entrepreneur on mathematical psychology or in general?

I just finished reading "An Introduction to the logic of psychological measurement" by Joel Michell where he presents fairly scathing criticisms of modern measurement theory. I've discussed this book with a few quantitative psychologists who mostly seem to think the whole axiomatic approach to measurement is silly. I was curious if anybody here is a fan of Michell's work.

Hi,

During my PhD I was using a task that is similar to the Stroop Task: there are two possible responses, three cue-distractor compatibility levels [compatible, incompatible, baseline] and I was measuring RT and accuracy. In most studies, each subject contributed 30-60 data points per condition [totaling 120-240 data point per person].

Now I want to model the results, if that is possible. I know that most of the time computational studies use a large N for each condition.

The question is, can I still use these data or I need to conduct new experiments for bigger sample? Can I pool across participants, let's say by normalizing the data?

Thanks is advance.

P.S. I don't have to use specifically LBA, I just assumed that because it has less parameters than other models it can deal better with a smaller sample size.

Hello everyone. I have an interdisciplinary paper and would like to ask mathematical psychologists for feedback. I think it is interesting for you because:

• It gives me a unified view about "meaning". It seems that currently there is no satisfactory theory about it from established knowledge.
• Anecdotally, it has helped me find my maturity, so it should have large impact on personal psychology.
• It connects deeply to other areas of pure math and theoretical physics.

You can read the paper at https://osf.io/m3x2q/. Below are my elevator pitches and excerpts from it. Thank you so much for your time.

#Elevator pitches ##For kids

What is the first step to put a giraffe into a fridge? Open the fridge. Why is that? Because at the very moment you look into the fridge, your perspective changes, and your mind is ready to think outside the box.

##For dynamical systems theorists

• When all oscillators align in the same phase, the system is clearest. However, it only becomes "meaningful" only when we see the whole trajectory of the system.
• Under a different perspective, a trajectory will be distorted and can become a point of another trajectory. This is actually special relativity theory.

##For mathematicians

Do you have any questions that you still can't answer? Maybe the applications of the irreducible representation of PSL(2,ℝ) in harmonic analysis can explain why the answer hasn't come yet.

The diagram

#Excerpts ##Choosing books When choosing books I usually imagine the book is a painting, yet I forget to bring my eyeglass. If every time I close my eyes and reopen them I see a new painting, yet I still don't feel vague with it, then that book is worth reading.

##Describing personality disorders as turbulent flow (psychodynamics)

When a smoke begins to smoulder, it first maintains its stability. But with just a little turbulence, the smoke becomes an uncontrollable chaos. Swirling currents will be generated to radiate heat outwardly, which rolls together and causes more and more energy to be lost. And after the energy is completely depleted, it will dissolve into the surroundings and leave not even a single mark behind.

##Quotes:

• The difference between the almost right word and the right word is really a large matter—it's the difference between the lightning bug and the lightning. (Mark Twain)
• Mathematics is the art of giving the same name to different things (Poincaré)

Poetry is the art of giving different names to the same thing (unknown poet responding to Poincaré)

I’m going to define love. I do not mean the love you are familiar with – the love synonymous with a variety of different emotional and mental states, but rather I’m going to repurpose the term “love” for a different concept that I’ve been trying to define mathematically.

Love L is the negative partial derivative of mindstream M with respect to extent of transformation E at constant verbal report and arousal. That is,

L = – (∂M/∂E) V,A.

It follows that love is positive for the spontaneous transformations from one subjective experience to the next if time in the positive direction correlates with higher assigned values for the mindstream.

Now let me explain what the equation means:

The partial derivative exists when a function has several variables and yet we just look at the derivative with respect to one of those variables. In this case, there are many variables going into producing the value of a mindstream at any given point, but we are just looking at the derivative with respect to E. The derivative means the sensitivity to change of the mindstream with respect to E. You can visualize a tangent line to a mindstream function: If the tangent line is closer to flat, there is little sensitivity to change, if it is very sloped, then there is high sensitivity to change.

Now, what the hell is a mindstream? A mindstream is simply defined by the values for the brain. Imagine that you can describe all characteristics of a brain that distinguish it from any other brain, and then assign a value to that unique configuration. Each configuration of a brain correlates to a configuration of mind/consciousness, and if we had a complete understanding of the brain, we might be able to plot all the different possible states in a single dimension. This is state 1, this is state 2, this is state 5946294, etc. There exist very similar brains/minds, like you at the beginning of this sentence and you at the end of this sentence. So this might be a transition from state 24 to state 25, say. While very different brains, like comparing a snapshot of your brain and a snapshot of my mom’s brain would be very far apart in their respective assigned values.

So what is E? E is the extent of transformation from one brain state to the next. Consider the transformation

A↔B *

Suppose an infinitesimal amount dE of the configuration A changes into B. The change of the amount of A can be represented by the equation dnA = -dE, and the change of B in dnB = dE. The extent of transformation is then defined as

dE = dni/vi

where ni denotes the value of the i-th configuration** and vi is the number that balances the i-th configuration to all the other configurations (in case the difference between 4 and 5 is different than 5 and 6 for some reason.) In other words, E is the amount of configuration that is being changed when a brain/mind state becomes another brain/mind state. Considering finite changes instead of infinitesimal changes, one can write the equation for the extent of a transformation as

ΔE = Δni/vi

The extent of a transformation is defined as zero at the beginning of the frozen snapshot. Thus the change of E is the extent of transformation itself.

E = Δni/vi = (ntransformed – ninitial)/vi

*(remember that according to the laws of physics, both brains/minds equally exist; there is no flow of time from now A to now B that can’t also be reversed.)

** the i-th configuration is just some configuration between brain A and B.

The constant verbal report and arousal simply mean that the mindstream/brain would constantly be able to narrate “Here I am, there’s something going on.” And if you poke the sensory inputs connected to the brain, she would say “ouch.” These are simply some of the most reliable indicators of consciousness today. But these may be replaced with other constants that are more fundamental to explaining consciousness in the future. The reason we need consciousness to be constant in this sense is because if it wasn’t, then the mindstream function would be different in off states and on states.

So the love I defined refers to the tendency of a mindstream/brain configuration to be joined with another different configuration by transformation.

I'd like to find a sample DISC (behaviour profiling) test with instructions on how to manually score it - i.e., the algorithm for calculating the different values in the DISC graphs.

There are 'classic' DISC surveys, where the respondent has to select a 'most' and a 'least' option in each topic, sometimes referred to as 'ML' surveys. Other DISC tests offer the respondent a 'grading scale' for each sentence, with 5 to 7 steps from 'strongly agree' to 'strongly disagree' - or similar. These types of survey are sometimes referred to as 'R4'.

I have already managed to find a very old version of the DISC test - ML, single-word based - but this is not indicative of current DISC usage. Ideally, I'd like to find a book or (academic) paper that uses an R4, full-sentence based questionnaire.

Not interested in discussing the validity or reliability of DISC itself, or comparing it with other testing instruments - only need information to help me understand and implement a current algorithm.

If this is an inappropriate post for this sub-reddit, I apologise, and thank you in advance for pointing me in the right direction!