# Question
Title: How is it possible for psychometricians to develop IQ tests for people smarter than them?
After all, while psychometricians definitely have above-average IQs, they probably don't have IQs over, say, 160.

If psychometricians already know what the right answers are (and how to obtain the right answer), then wouldn't they score very high on the tests, even if they're not THAT smart? (making it possible for other people to score high on the tests if they studied psychometrics as well?) The format of the test isn't going to vary that much from administration to administration. Of course, the tests are timed, but it's entirely possible that the practice the psychometricians get from writing the tests could easily make them better at each of the subsets of an IQ test.

# Answer
Short answer: construct a usable chair out of toothpicks. Now, I certainly can't do this, but I can verify your answer easily enough

Long answer: Verification of answers is often a lot easier than producing answers. And thats not just anecdotal, there are computational and psychological reasons behind this fact.

Computationally - complexity is different for solving a problem than for verifying it. For example the clad of NP problem cant (probably) be solved in poly time, but they can be verified in poly time.

Psychologically - think recall versus recognition. Recognition just involves your brain lighting up when it sees the right answer, whilst recall or production requires an extensive search at the very least

So the answer is that the test makers don't have produce the answers themselves, and certainly not under test conditions. They simply have to be able to see that the right answer is right (either produced via a group, a computer, or a genius).

Also, remember that no one question separates high IQs from low. Perhaps a 140IQ could answer one of the hardest questions, but not all of them.

Lastly, remember than IQ isn't a single dimension, perhaps the test was written by one expert in each dimension, which could then identify a genius in all dimensions
> 13 votes

# Answer
In a comment above, Kris hopes for a more obvious answer to the question than the \[very good\] answers zergylord gave. So here's one more explanation that strikes me as almost too obvious to mention: test making and test taking are temporally asymmetric. One can spend fifteen minutes creating a question that must be answered in less time. This kind of time differential is extremely diagnostic -- a smart person and a dumb person might both be able to solve the missionaries and cannibals problem, but the smart person does so much more quickly.
> 7 votes

# Answer
Above answers are great, I just wanted to add that you do not need a 'smart' person to create IQ test... you or me could do it... Any set of questions would measure one or more dimensions of intelligence...

You would give your set of questions to many people and their results would show up as Gaussian. You would then assign 100 IQ point at the peak and subdivide the Gaussian into parts by standard deviation where each segment would be your 10 IQ point increment...

P.S Don't get me wrong chances are it wouldn't be the best IQ test but test nevertheless...
> 2 votes

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Tags: intelligence
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# Question
Title: Effects of Game-based Learning on Motivation for Learning
I have already asked this question here, but I did not receive my answer. I found this site a more appropriate place to ask it.

I found many studies discovering the impacts of game-based learning on motivation for learning.

For example, these papers

You can also find a summary of them in this book chapter.

However, it is still unclear to me if this motivation is for entertainment or for learning. I was wondering if there is any study showing that students who are adapted to this method of learning have a higher motivation to pursue learning in other forms. My concern is about long-term effects of this method of learning. What is the intrinsic reaction of K-12 students who have adapted to game-based learning when they start higher education in which game-based learning is not provided? Will they have a higher incentive to study? or a lower incentive, because they prefer to play games rather than studying?

# Answer
I think it depends.

If the game is good, the player will be motivated to learn what they need to succeed in the game. The long term effects may vary depending on different attributes, which I divided into two groups.

This is of course not an exhaustive list.

## Providing learning tools

* Did the game generalize enough, so that the player feels like the more they learn the better they will be in the game? as opposed to being predictable enough so that the user can learn exactly what they need and only that.
* Did the user learn how to learn independently or did they rely on specific instructions in the game?

## providing learning motivation

* Why did the user not want to learn in the first place? If it's because of a sense of incapability, games are great. If it's lack of interest, the long term effect could be negligible.
* Did what the player learn in the game turn out useful for the player in real life? Did the player see it as a positive outcome of learning, or as an outcome of the game?
* Did a motivation swap occur? sometimes the user may be one that learns for fun, and after playing the game they will only learn for points etc.

Since the concepts 'game' and 'learn' are so wide and subjective, I'm doubtful you can get a clear cut answer on this question.
> 2 votes

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Tags: learning, motivation, educational-psychology, gamification, incentives
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title: How does fear paralysis occur?

text:
# Question
Title: How does fear paralysis occur?
(This question is in relation to this question here)

How does fear paralysis occur? What is the mechanism that makes this happen in brain?

# Answer
Taken from here

> The Fear Paralysis Reflex begins to function very early after conception and should normally be integrated before birth. It can be seen in the womb as movement of the head, neck and body in response to threat.
>
> It is sometimes classified as a withdrawal reflex rather than a primitive reflex. If this reflex is retained after birth, it can be characterised by withdrawal, reticence at being involved in anything new, fear of different circumstances, and is often described as the “scaredy cat” child who bears the brunt of teasing by normally adventurous children.
>
> **This behaviour appears to be due to the reflex’s involvement with the parasympathetic nervous system.** **Most of us are familiar with the “fight or flight” adrenalin rush of the sympathetic nervous system, however the FPR taps into the opposing system urging the body to “eat and stay”.** The parasympathetic nervous system is intimately involved with the vagus nerve. This nerve comes directly from the brain to aid the organs. It bypasses the spinal cord, so in the case of spinal injury we are still able to digest our food. The vagus nerve may be mechanically trapped in the skull, chest, abdomen or neck. Release of this nerve entrapment corrects one physical factor that contributes to the retention of the FPR.
> 1 votes

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Tags: fear
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# Question
Title: Activation of brain reasoning areas during dreaming
I would say that dreaming is like any conscious experience, except that objective reasoning is turned off. Do fMRI imaging studies indicate that the reasoning centers of the brain are inactive during REM sleep? Are other sensory sections, like the visual cortex active?

By reasoning centers of the brain, I am referring to area(s) specifically associated with logic.

# Answer
> 3 votes
I cannot answer from fMRI's, but this theory conflicts with other anecdotal evidence from medicine.

In sensory deprivation, or under psychotomimetic substances, you can be awake and unimpaired logically, and still have reality make as little sense as it does in a dream. (People on LSD are not less logical, in an abstract sense. If you can get them to care about an IQ test long enough to take it, they can do just as well.)

To the extent those effects are comparable, I would doubt it is reason that is impaired. It is more likely that reasoning is fine, but the ability to check predictions against perception is lacking.

Antonio Damasio's observations on brain lesions that limit emotion, explained in his popularizing book, seems to indicate that abstract reason needs physical grounding (from the environment or from internal sensations like emotional reactions) or it goes off the rails and does not result in what we ordinarily consider logic, even if the reasoning skills themselves are in order.

Much perceptual feedback and even internal feeback loops are disabled or attenuated by the physiological paralysis in sleep.

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Tags: neuroscience, dreams, fmri
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title: What makes people like other people?

text:
# Question
Title: What makes people like other people?
Humans have this strange behavior: Some people like other people which are disliked by a third party of people. But there are also people that are generally disliked by most ofthe others.

Let me make an example:

Person A, B and C like Person D Person B, C and D like Person F, but A hates F Person A, C and D like Person G, but B hates G Person F and G get along well with C ...

How can you explane this strange behavior?

Some people can't get along with each other, but both have common friends, that get along with both of them. So it can't be like that they are so different from a cultural standpoint, because they have common friends that get along with both.

Do you get the point?

However there are people that are liked by most of the People and there are also people, that are commonly disliked by the majority of people.

What are they doing right/wrong?

If you say, "people like those who are similar to them", how can it be, that A and D don't get along, if both are similar to B, which can get along with both?

What are A and D doing wrong, so that they can't get along. Due to the fact, that B get's along with both, which means, that both aren't bad persons and people you can get along with.

Can you generally say what makes the difference in these groups of people? Where do these preferences come from and why do some not get along? The question is not, what makes someone likable (that is asked here What makes you like someone?), it's more on how to determine wheter two people like eachother or not, on what is this based and can you influence it.

Is maby, the way people get introduced, a factor? What are the big factors

Edit:

As mentioned below, the question might be rephrased to: "How can you describe this behaviour"? (I don't wan't to boil it down to one factor (and it's probably impossible to list all of them), but I would like to get a general Idea, on how this works.

# Answer
is the question: What makes people like other people? or: how can you explain the phenomenon described above?

If it's the former, it has an answer already and this should be marked a duplicate.

I will try to answer the latter, if it can be considered on topic here.

Just because you have a mutual friend, doesn't mean you are going to be friends with someone. Humans are not this simple. The factors involved in deciding whether you like someone is very complicated. As you mentioned, the way you get introduced can definitely affect whether someone likes another person.

Humans have evolved as social creatures and our social interactions have evolved deeply like our intelligence. When you try to logically predict a phenomenon based on ONE factor when there exists a LOT more, you will not get a good prediction. It is not a strange phenomenon, it is quite natural if you consider the personalities that each person and the desires that they have in a friend. A and B might have a completely different type of friendship than B and D, by the way. To me, this question is misguided.
> 2 votes

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Tags: social-psychology, personality
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title: Why I read by hearing the words?

text:
# Question
Title: Why I read by hearing the words?
This question requires very little background: 1. English is my second language and I find myself thinking mostly in it 2. I tend to listen more than to read it

When I read a book from the computer screen (the same screen on which I watch documentaries) about science I tend to read the words but to also hear them in my mind, like if I was reading them out loud (or some documentary speaker was talking) and only after that I perceive the whole sentence. It seems to me that I can not control this process.

My expectations are that reading has nothing to do with hearing.

The opposite of my situation is to hear something and experience it reading it in your mind on a sheet of paper and after that you perceive the information.

Any idea why this is happening?

# Answer
> When I read a book I tend to read the words but to also hear them in my mind

**Inner Speech** is the general name given to this phenomenon,***1*** although it also goes by imagined speech, silent speech or covert speech, there are few studies and even less definite conclusions as to how it happens and its role in reading comprehension.

> and only after that I perceive the whole sentence.

In general, language comprehension goes through the perception of sounds, words and sentences followed by meaning ( independently or in succession) , in order to comprehend a sentence or paragraph you hold words or sentences in some form of short term memory, for visual language ( written words, sign language ), we have a visual sketchpad and for spoken words we have a phonological loop and other inter related memory structures,these buffers also connect or send information to other associational areas in the brain where meaning is presumably derived.***2***

> It seems to me that I can not control this process.

Another related concept to inner speech is **private speech*****3***, words spoken out loud to oneself. Private speech is both healthy and normal in children from the ages of 2 to 7 ( a student reading in class to himself in whispers for instance ), although we generally stop the public practice when we grow up due mostly to our social environment and other factors. It is then conceivable that our preferred way ( although not the only one) way of reading for comprehension is to use silent private speech which in turn goes through the language comprehension process outlined above.***4***

> My expectations are that reading has nothing to do with hearing.

Yes and no, while the entry point might be a different one ( visual vs auditory), most likely the language centers and higher associational areas get recruited by either one while reading or hearing, furthermore, by translating internally in between domains, you have access to memories specific to that domain, for instance: when perceiving visually a stop sign (and reacting rapidly), reading and imagining a color, or hearing how something smells***5*** .

> Any idea why this is happening?

Languages consisting of ideograms( Japanese kanji ) present some clues along with the previous overview. Even though the kanji ideograms have more in common with pictures than words; the principal speech comprehension centers get predominantly recruited ( there are still differences though).***6*** Language has also been predominantly spoken ( the written word is relatively new to our species ) so it makes sense from an adaptive perspective ( i.e it is more efficient) to have a language comprehension center devoted to the older spoken language ( or general sound deciphering) and co-opt it for reading, this new routing happens with the aid of inner silent speech ( which could be a modification of private speech) and other memory buffers.

*Notes, reference & sources:*

**1.** A great introduction and overview: Inner Speech: Development, Cognitive Functions, Phenomenology, and Neurobiology

**2.** Cognitive Neuroscience( Gazzinga) Has a great overview of language and memory and there are whole fields of Neurolinguistics and Psycholinguistics devoted to language.

**3.** The wiki on private speech and previous sources.

**4.** From Print to sound and meaning (Cognitive Neuroscience of Language -Kemmerer) presents a model of this arrangement.

**5.** A contrasting issue is early blind subjects that read in braile, the entry point here is tactile but ends up recruiting language and visual areas the same. see Visual Cortex Activity in Early and Late Blind People

**6.** Japanese and English sentence reading comprehension and writing systems: An fMRI study of first and second language effects on brain activation Presents a detailed account.
> 3 votes

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Tags: reading, hearing
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# Question
Title: How to induce procrastination in an online experiment?
I am designing an experiment to evaluate potential interventions for reducing procrastination. I am hence looking for one or more tasks to induce procrastination in an online experiment with participants recruited from Amazon Mechanical Turk. Real world tasks associated with procrastination tend to feel meaningless, taxing, unpleasant, frustrating, or boring, and they are often unstructured such that people don't know how to begin, and are unsure about whether and how they are going to succeed.

I have piloted a vigilance task because it is known to be boring but it was insufficient to induce procrastination in people working on Mechanical Turk. Can you think of any more suitable tasks that have some of the characteristics listed above?

# Answer
> 2 votes
Present subjects with a long-form writing task about anything - most likely, you'd be best-off supplying a topic that's difficult to write about (self-analysis, distant dates, self-assumed tardiness, etc) and not fun to think about. Don't set a time limit or a word limit, and stress that it's important they answer fully, completely, and honestly. If you want best results, you should aim to have a secondary control question that's meant to be easy to reply to, or relaxing, or ultimately nice to think about. Maybe pick "The best time of your life" vs. "The absolute worst time of your life".

Then provide them with a procrastinatory task. If you have access to the room they'd be doing this in, provide something hard to notice on first go, but actively distracting - for example, a Spanish soap opera on mute. Up to you on behavioral protocol here - you could watch it while you wait (and risk influencing the subject to watch it too), not appear to notice it, etc. Your goal at this stage is to be able to measure the time spent not working on the task - you might want to make notes of when they look away, create a program that records exactly how much time is spent not inputting data, etc.

You can also choose to make the testing separate and instead only give a single type of test (easy/hard) to a single person, and that person's procrastination responses are recorded. Etc.

The important thing to remember is that you don't need to invoke the purest sense of boredom or negative emotion to make a subject procrastinate; you only need to gently invoke procrastinatory emotions, and then provide a suitable distraction.

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Tags: social-psychology, emotion, motivation, decision-making, procrastination
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# Question
Title: Hard to think when hearing audible speech
I don't know what is the exact cause of this but when there is some song playing around me or someone is talking near me its harder for me to concentrate my thoughts or imagine something. However pure instrumental music is fine

I am a frequent video game player where you need to keep listening to enemy movements and to keep track of their positions.

Playing games like that has led me to be able to operate without concentrating all of my senses on one thing. Like I can do do something carefully by my hands looking at it and at the sane time hear what people are talking about around me, often involuntarily.

But this means when someone talks or a song plays with recognisable speech in it then my concentration involuntarily shift to the audio and it's harder to concentrate on my thoughts. I have to put effort to ignore the sound and keep at my thoughts.

What is this phenomenon called?

# Answer
There are many things at work here. A lot of your sensitivity to human voice can be attributed to the tonotopic organization of the basilar membrane. Check out this image of the cochlea and notice how its sensitivity to different frequencies does *not* decrease linearly over space https://media1.britannica.com/eb-media/98/14298-004-99934987.jpg , this entails that we are most sensitive to frequencies in the 2-4kHZ range, which significantly overlaps with the frequency range of human speech https://users.cs.cf.ac.uk/Dave.Marshall/Multimedia/node271.html .

There is also evidence for using a network of domain specific mental processes to do complex tasks like navigating a room. Specifically, it seems that linguistic processes make spacial navigation tasks easier(https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3572715/ ; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2745626/) --if this is true we could predict that overloading someone's linguistic capabilities by, say, speaking to them, would make spatial navigation tasks harder, which could explain how some stimuli can be "less distracting" than others within different contexts.
> 2 votes

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Tags: cognitive-psychology, hearing, thought, multi-tasking
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# Question
Title: What is the term for when too many choices results in inability to decide?
A common problem is that when offered too many choices, consumers give up and make *no* choice. Too many options results in no sale where fewer options might have resulted in more sales. It's like the cognitive load of so many choices makes the decision impossible.

I've heard it called Decision Paralysis or "Analysis Paralysis" but these terms don't seem to have a root in cognitive science. Is there a formal or generally accepted term for this phenomenon?

# Answer
> 16 votes
It is "paradox of choice". See these resources:

I actually haven't been able to find anything in my search just now, but I remember some claims about a year ago that recent experiments had suggested that the original results were somewhat over-blown.

# Answer
> 4 votes
Overchoice or "choice overload" are the common used terms for

> describing the cognitive process in which people have a difficult time making a decision when faced with many options (Wikipedia on Overchoice)

The same souce refers that the concept was first introduced by Alvin Toffler in his book named Future Shock.

Barry Schwartz as well as Barbara Fasolo and collegues call it "the tiranny of choice".

Nevertheless, recently, some authors have been cast doubts

> on the phenomenon being as pervasive or important as some might have given it credit for. Instead, it appears probable that such choice effects might be limited to particular contexts, assuming they reliably exist in the first place (see Is Choice Overload a Real Thing?)

# Answer
> 1 votes
In HCI terms, which is where I come from, this would probably be called information overload. It is the situation where a person is presented with so much information that they cannot process all of it successfully.

In Cognitive terms, the term probably works best because it is caused, I believe, by insufficient short term memory being available to successfully process what is needed from a page, so the decisions about what to do cannot be made. In a similar way, if too much information is given to a person - even if expressed in terms of "you could do this, or you could do this... " then they cannot dismiss the options enough to narrow it down. Without this, decisions cannot be made.

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Tags: terminology, decision-making, consumer-psychology
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# Question
Title: How to explain utility function from personality, beliefs and ongoing emotions?
Economists widely use utility function (and more broader approach called hedonistic regression) but are there explanations or derivations of utility function from the perspective of cognitive sciences or psychology which can take into account personality, beliefs and experience (memory) and ongoing emotions (affects)? Clearly one should start with the Maslov pyramid that determines goals and needs and then one can proceed to explain why different people value differently one and the same goods or services? Are there such research trends, some keywords, terms, notable researchers in this area?

I have read only about incorporation of altruism in the utility function of neoclassical economics, about hypberbolic discount function and so on. But I have not managed to find more practical explanation of utility function.

***This question has the following context****: I am trying to model the economic behavior or BDI agent - agent with Beliefs/Desires/Intentions. BDI is the most popular approach for modelling cognitive agents. Recent research (e.g. http://people.idsia.ch/~steunebrink/) has endowed BDI agents with formalization of emotions (KARO logic framework) and there is scarce but ongoing work to endow BDI agents with personality (e.g IEEE Transactions on Affective Computing and Five Factor Model from personality psychology). So, BDI agents have cognition, personality and affects and they can be used as models of human economic behavior. But there is missing link from economics and cognitive sciences - how humans act as economic agents? What are the models? What goals they can have, how behavior (decision to make purchase) can be infered from agent's goals, beliefs, personality and emotions? How human perception of utility (of particular attributes of particular products, particular designs, particular type of entertainment, etc.) is connected with his personality, experience, beliefs, affects? Utility function seems to be promising proxy in this research - psychology can explain utility function and economics can apply this function to particular transactions.*

*Is there some research along this lines?*

*This should be promising area of research. Because common approach to economic analysis and prediction is connected with Data Science and statistical methods (conjoint analysis and hedonic regression). What I am trying to do is to find logic, semantic, cognitive methods of explanation and prediction of customer behavior. Similar trend - from statistical to semantic methods can be found in other fields of Science as well - e.g. in natural language processing, in search engine engineering and so on.*

# Answer
How to integrate realistic cognitive models of human behaviour and ecomonic models is an open research question, however to tackle it, it might be helpful to break it into a few more easily answerable sub-questions:

* **What models of human planning with emotional influence exist?** There are a number of models in existence that try to capture emotion and personality. However, I don't know any that apply to planning specifically, given that modelling long-term planning is an extremely hard problem in it's own right! My lab-mate Peter Blouw made a rudimentary model of planning with Bryan Tripp that just got accepted to the CogSci2016 Conference. I will update this answer with the link once it is public ally available.
* **What models of human social interactions integrate personality?** Human social interactions are really hard to model, especially at a large scale, consequently most models that currently exist are rudimentary at best.
* **What is the most detailed cognitive model of human behaviour used in economics?** I don't know the answer to this off the top of my head, by I remember this being discussed in Khaneman's "Think Fast, Thinking Slow", where he talks about the diminishing returns that come with including more complicated cognitive factors in economic models.

To get more complete answers to these questions, feel free to ask them as separate questions on this site. I will happily upvote them!
> 2 votes

# Answer
The behaviour of agents can be predicted from utility or value based decision analysis models. Basically, you can model how the agent decides based on multiple objectives (or goals) by means of a multicriteria decision model. A simple multicriteria model consists in the weighted aggregation of value (utility) functions. Value (utility) functions represent how the agent values distinct levels of consequences on each objective.

Multicriteria decision analysis (MCDA) based on utility or value measurement are the fields that you should explore to construct such a model. MCDA has a sound foundational basis and it is the prescritive (normative + descritive) approach to make sound decisions.

A rational agent (decision-maker) faced with alternative courses of actions from which it must select one, it should always select the action with the highest overall expected value (or utility) as computed from the referred multicriteria decision model.

The distinct intrinsic beliefs, personalities, emotions, etc. are modeled by distinct value (or utility) functions and weights; so that each agent (decision-maker) behaves according to a specifc multicriteria model.
> 2 votes

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Tags: cognitive-neuroscience, reference-request, decision-making, consumer-psychology, behavioral-economics
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# Question
Title: What cognitive strategies diminish bias in decision-making beyond those outlined by Larrick?
Larrick (2004; pdf) offers a small number of suggestions for strategies to diminish bias in decision making, which he breaks down into four categories.

1. "Consider the opposite". Tell decision-makers to ask themselves "What are some reasons my initial judgment might be wrong?"
2. Training in rules. Teach statistical and logical principles to decision-makers, e.g. give them lessons on the law of large numbers and regression toward the mean.
3. Training in representations. Teach decision-makers to represent problems in ways that are likely to ameliorate biases. For instance, encourage decision-makers to represent problems in terms of frequencies rather than probabilities, since evidence suggests people reason more accurately when thinking about the former than about the latter.
4. Training in biases. Larrick suggests that this boils down to seeing if teaching decision-makers behavioral decision theory has positive effects. Larrick implies that so far there is little evidence to suggest this method would work.

### Question:

* What other plausible categories of cognitive strategies exist?
* What other strategies exist within the categories Larrick has defined?

### References

* Larrick, R.P. (2004). Debiasing. Blackwell handbook of judgment and decision making, , 316-338. PDF

# Answer
> 3 votes
One possible strategy is to phrase decision-making problems as questions in a foreign language. There's evidence that this attenuates at least certain kinds of cognitive bias (making you more rational and consistent) by distancing the cognitive and affective baggage that comes with the problem. See the following:

Keysar, B., Hayakawa, S.L., & Sun, G.A. (2012). The Foreign-Language Effect: Thinking in a Foreign Tongue Reduces Decision Biases. *Psychological Science*, 23 (6), 661-668.

# Answer
> 1 votes
Jonathan Baron's book, "Thinking and Deciding", presents decision making, and most other thinking tasks, as a combination of searching and judgment. In most cases, expanding the search phase results in better thinking. In the case of decision making, searching further both *for more alternatives before judging* between them and *for better information for judging* between the alternatives.

It isn't specifically about debiasing, but expanding your search has significant debiasing effect.

# Answer
> 0 votes
There are many strategies which diminish bias in decision-making. That is actually the cornerstone of the field called "decision analysis".

Biases, anomalies, heuristics, or any other kind of non-rational behavioral errors that people usually make in their usual everyday decisions is the realm of descriptive (or behavioral) decision-making (Von Winterfeldt and Edwards, 1986).

Normative decision-making studies how decisions should be made if people were completely rational and did not have any limitations or random inconsistencies on their perceptions, reasoning, or acts.

Instead, decision analysis studies how decisions should and could be made better, in practice, by real people. In this sense, it is a prescriptive approach. It fills the gap between descriptive and normative decision theories, drawing from behavioral decision theory research to develop effective decision analysis techniques (e.g. probability, and value elicitation protocols) that conform to normative decision-making (Raiffa et al., 2003; Clemen, 2008; Russo and Schoemaker, 1989).

*Von Winterfeldt D, Edwards W (1986) Decision Analysis and Behavioural Research. Cambridge University Press, Cambridge*

*Raiffa H, Richardson J, Metcalfe D (2003) Negotiation Analysis: The Science and Art of Collaborative Decision. Harvard University Press, Cambridge, MA*

*Clemen RT (2008) Improving and measuring the effectiveness of decision analysis: Linking decision analysis and behavioral decision research. In T Kugler, JC Smith, T Connolly, Y-J. Son (eds.) Decision Modeling and Behavior in Complex and Uncertain Environments, Springer, New York: 3-31*

*Russo JE, Schoemaker PJH (1989) Decision Traps: The Ten Barriers to Brilliant Decision-Making and How to Overcome Them. Doubleday, New York*

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Tags: decision-making, bias
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# Question
Title: Is Decision-Making Emotionally Based, with Rationalization as the only Conscious Component?
My interest is in how problem-solving decisions are made, and what, if any skills could be taught to increase people's ability to make effective decisions? Effective, in this case, means that an external observer finds the decision a sufficient and satisfying solution to the problem given the thinker's current understanding of environment, context, and resources.

I understand the *Information Principle* of emotion takes the position that emotions are a "summary" of all that the unconscious notices about the current situation. (See this link for an excellent overview.) Given the breadth of its perception, and the faster processing time of its systems, emotion is tremendously powerful as an advisor. The reward system is triggered when a desired choice is contemplated, so is the role primary, rather than advisory? **Is conscious thought -- what we come up with when we "think about our decision" -- always *post hoc*, used only for coming up with a way to explain what our feelings told us we wanted?**

Regarding skills to increase effectiveness, I wonder at the value of learning to overcome our initial emotional response to allow the more deliberate conscious processes to make a real contribution. I have heard that trauma victims with absent emotions dither about the smallest decisions, so I'm not talking full suppression. Is anyone aware of research into the effect of trying to achieve some balance here?

# Answer
This is a very broad topic. I'll attempt to quickly summarize the most relevant findings from a wide variety of research areas.

**Post-rationalization:**

There is a fair bit of evidence that explanation follows decision-making, rather than the other way around. Here is a nice quote from Wikipedia attributed to Robert Zajonc: "decisions are made with little to no cognitive process ... we make judgements first, and then seek to justify those judgements by rationalization." My favourite example of rationalization is Choice Blindess experiments, where subjects are "tricked" into believing that they made decisions that they did not actually make, but they nonetheless explain them as if they did.

**Dual Process Theory:**

Part of the implication of this phenomenon is that the human mind is divided into (at least) 2 seemingly independent processes \- an unconscious process that makes the decision, and a conscious process that seeks to justify it after the fact. Emotion plays an important role in some of these theories, though not all. Emotion certainly affects decision-making, and may help decision-making under some circumstances.

**Pre-rationalization:**

The tendency to make decisions unconsciously - biased by emotion and many other cognitive biases \- can be overcome in a straightforward manner. In several experiments, this is done simply by ... asking. For example, Timothy Wilson induced different (rational) decisions from subjects by simply asking them to provide reasons for their decisions before making them. Similarly, Ap Dijksterhuis induced rational decisions from subjects by asking them to think about their decisions before making them.

**Bounded Rationality:**

Unfortunately, the results of the above experiments demonstrate that decision-making is not always improved by conscious deliberation before making a decision. This is because sometimes decisions need to take emotions into account to be successful (as in Timothy Wilson's experiments where subjects are asked to predict their preferences), and other times human deliberation is limited by bounded rationality \- we have a very limited capacity for logical thought. Perhaps the best way to overcome some of our cognitive limitations is to use computers to make rational decisions, but at the moment, humans are still better at making decisions than computers in many areas where rule-based decision-making is not practical to use.
> 10 votes

# Answer
An interesting perspective to clarify the interplay between rational and emotional can be found in the 'risk as feelings' approach. Here is the paper and here the abstract:

> Virtually all current theories of choice under risk or uncertainty are cognitive and consequentialist. They assume that people assess the desirability and likelihood of possible outcomes of choice alternatives and integrate this information through some type of expectation-based calculus to arrive at a decision. The authors propose an alternative theoretical perspective, the risk-as-feelings hypothesis, that highlights the role of affect experienced at the moment of decision making. Drawing on research from clinical, physiological, and other subfields of psychology, they show that emotional reactions to risky situations often diverge from cognitive assessments of those risks. When such divergence occurs, emotional reactions often drive behavior. The risk-as-feelings hypothesis is shown to explain a wide range of phenomena that have resisted interpretation in cognitive–consequentialist terms.
>
> Loewenstein, G.F., Weber, E. U., et al (2001) Risk as feelings. Psychological Bulletin, 127(2):267-286.
> 3 votes

# Answer
The human brain is subject to many faults and biases (Kahneman, 2011).

Human beings usually use heuristics, screening, ranking, and other rules of thumb to limit the complexity of a decision.

Kahneman has described the two cognitive systems that drive our judgement and decision making (Kahneman, 2011). System 1 refers to our intuitive system, which is typically fast, automatic, emotional, implicit, and effortless. Despite its long-standing effectiveness in specific decision situations, particularly when quick decisions are of great value (Gladwell, 2007), it can also be the cause of several cognitive biases that hinder our judgements and decisions. On the contrary, system 2 refers to reasoning, which is obviously lazier and effortful than system 1, but more conscious, deliberative, and logical.

For complex high-stakes decisions, it is worthwhile to follow a formal, rational, systematic procedure. This is the aim of decision analysis, helping DMs overcome system 1’s errors and biases towards system 2’s more careful and conscious deliberation (Clemen, 2008). Indeed, considerable research suggests that people will maximize their chances of making the best decision if they use a sound decision analysis method (Russo and Schoemaker, 1989).

If a decision-maker wants to guide his decision making process satisfying a set of simple and transparent axioms, which most people would regard as rational (Goodwin and Wright, 2004), and some logical theorems that follow from them, then the unique standard for correctness is decision analysis (Keeney, 1982; Howard, 2007).

*Kahneman D (2011) Thinking, Fast and Slow. Farrar, Straus and Giroux*

*Gladwell M (2007) Blink: The Power of Thinking Without Thinking. Back Bay Books*

*Clemen RT (2008) Improving and measuring the effectiveness of decision analysis: Linking decision analysis and behavioral decision research. In T Kugler, JC Smith, T Connolly, Y-J. Son (eds.) Decision Modeling and Behavior in Complex and Uncertain Environments, Springer, New York: 3-31*

*Russo JE, Schoemaker PJH (1989) Decision Traps: The Ten Barriers to Brilliant Decision-Making and How to Overcome Them. Doubleday, New York*

*Goodwin P, Wright G (2004) Decision Analysis for Management Judgment. Wiley and Sons, New York*

*Keeney RL (1982) Decision Analysis: An Overview. Operations Research, 30(5): 803-838*

*Howard RA (2007) The Foundations of Decision Analysis Revisited. In W Edwards, RF Miles Jr, D von Winterfeldt (eds.) Advances in Decision Analysis: From Foundations to Applications, Cambridge University Press, Cambridge: 32-56*
> 3 votes

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Tags: decision-making, emotion
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# Question
Title: Are there validated measures to perception of decision making process alignment?
I want to test an idea about how the perception of decision making process alignment with another party influences the tendency to accept information from that party. For example, if my wife and I both consider the cost, nutrition, and the presence of a toy when considering breakfast cereals, I'm more likely to take her cereal recommendation when I go to the grocery store. Likewise, if somebody else only considers taste, I'm less likely to heed his advice.

Looking in the literature, I can't seem to find any measures of decision making process alignment between two people. Does anybody know of a related construct that has been validated?

# Answer
> 1 votes
The following article addresses the issue of decision-making process alignment:

Clemen RT (2008) Improving and measuring the effectiveness of decision analysis: Linking decision analysis and behavioral decision research. In T Kugler, JC Smith, T Connolly, Y-J. Son (eds.) Decision Modeling and Behavior in Complex and Uncertain Environments, Springer, New York: 3-31

Additionally, the following article presents another specific measure which was used to validate the group alignment among the participants by following a particular decision-making process: Supporting Multicriteria Group Decisions with MACBETH Tools: Selection of Sustainable Brownfield Redevelopment Actions. Group Decision and Negotiation

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Tags: decision-making
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# Question
Title: Why can't subjective utilities take probabilities into account?
If my understanding of expected utility theory is correct,

* it is rational for a decision maker to have subjective utilities for objective consequences. For example, it can be rational for a decision maker to value 5 dollars twice as much as 4 dollars
* however, it is *not* rational for a decision maker to value a 100% chance of 5 dollars twice as much as an 80% chance of 5 dollars

That is, probabilities must be "outside" of- not taken into account by- subjective utilities.

Is this understanding correct, and if so, why is the theory structured this way?

Note: My understanding is based on Hastie & Dawes (2010), *Rational Choice in an Uncertain World*.

# Answer
> 2 votes
You certainly did not understand it correctly, because the statement that the second case is either rational or not cannot be stated based only on the information you provided.

The rationality of the second statement depends on the specific decision-maker's attitude to risk only. It is not possible to infer it based only on the information you are providing.

1) **Let's consider the first case: "You value 5 dollars twice as much as 4 dollars"**

A possible decision situation would be a day in which you are so starving that you might well eat one hamburguer as two. However, one hamburguer costs 4 dollars in the store where you are; but there is a promotion in which you can buy two hamburgueres for the price of 5 dollars.

You could represent this situation stating that you value the same: a) 4 additional dollars, in relation to your current *status quo*; b) as the 1 additional dollar, in relation to the moment after receiving the previous 4 dollars.

In this situation, there is no uncertainty involved about the consequences of your decision. If you pay 4 dollars, you eat one hamburguer. If you pay 5 dollars, you eat two hamburguers. In certain situations, you can model your preference by means of a value function (rather than an utility function).

A value function measures only how much you value (i.e. how much you consider attractive, desirable, preferable) distinct consequences, so that consequence levels with higher values mean that you prefer them in relation to consequence levels with lower values.

Utility not only measures this value, but it also measures your attitude in relation to uncertainty (risk). Since this is a certain situation, your utility function is exactly the same as your value function.

2) **Now for the second case: "Why is not also rational to value 5 dollars twice as much as an 80% chance of 5 dollars"**

This situation is distinct from the previous one, because now one of the alternatives is uncertain, there is only a 80% chance of occurring.

A possible decision situation would be the case in which the store has offering a promotion in which you could take a 20% chance of eating your first hamburguer for free, but if you lose you would have to pay 5 dollars for it.

The only possible way that you could "value 5 dollars twice as much as 80% chance of 5 dollars" is if you are indifferent between: a) paying 4 dollars for eating one hamburguer (as in the previous case); b) or accepting this new promotion.

If you are indifferent, then IT IS RATIONAL that you *value 5 dollars twice as much as an 80% chance of 5 dollars*! In this case, it is said that you are risk-neutral, and once more, your utility function is exactly the same as your value function.

If you prefer choice a) then it means you are not willing to risk having to pay 5 dollars for the hamburguer. Then, you no longer *value 5 dollars twice as much as an 80% chance of 5 dollars*! You now *value 5 dollars MORE THAN TWICE AS an 80% chance of 5 dollars*! In this case, it is said that you are risk-averse, and now your utility function is distinct from your value function.

Likewise, If you prefer choice b) then, once more, you no longer *value 5 dollars twice as much as an 80% chance of 5 dollars*! You now *value 5 dollars LESS THAN TWICE AS an 80% chance of 5 dollars*! In this case, it is said that you are risk-seeking, and again your utility function is distinct from your value function.

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PS: Note that utilites (and values) are always subjective, so the term is redundant (that's why I did not mentioned it at all)

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Tags: decision-making, economics, rationality, behavioral-economics
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# Question
Title: What decision problems are better answered with analytic thinking, and what are better answered with intuition?
Most Western philosophy and psychology argue that deliberating carefully and rationally is the right way of reaching a decision. "Don't let emotions turn you" they might say. The splitting of the mind into the two systems (intuitive System 1 vs. rational System 2) have just sharpened this attitude. In contrast, many studies have shown the superiority of making decision by not thinking about it (e.g. Dijksterhuis), or by relying on intuition (e.g. Kahneman and a long list of many others).

It seems that there is no absolute way that is better then the other, rather some decision problems are better handled with careful thinking, while others are solved better with "gut feeling". What are the characteristics that differentiate the two?

# Answer
> 2 votes
**Keith Stanovich** summaries a school of thought on this debate nicely in his review of Kahneman's 'Thinking, Fast and Slow'.

Essentially, system 1 excels in 'benign' environments, where the cues that system 1 is adept at using are reliable indicators of the true state of the world, and no one else is trying to manipulate you by faking these cues. System two is necessary in 'hostile' environments, either because the relationships between cues and reality have changed from what system 1 expects (and has come to expect through natural selection: for millions of years, these cues were reliable), or because there are other agents who are manipulating these cues deliberately (Stanovich cites 'advertisements, or the deliberate design of supermarket floorspace to maximize revenue').

In a similar vein, **Gerd Gigerenzer**, although not strictly speaking a dual process theorist, has long argued the positive role of heuristics (as opposed to the 'heuristics and biases' approach). His main claim is that simple, automatic decision rules often outperform more complex reasoning, or even statistical approaches like linear regression.

His examples include:

* A recognition heuristic - whatever the decision (predicting elections, sports, stock performances, consumer behaviour, etc.), the response you recognize is more likely to do well than the one you don't.
* Fluency - If you recognize both, the one that was easier to recognize will do better.
* If you're not presented with multiple choice responses, the first response to come to mind is likely the best.
* One-reason choices - it's more effective to rely on one reliable cue than to analytically compute all the available information (example: running to catch a high ball, try to modulate your speed to keep the angle between the ball and the ground constant).

There's plenty more in this review (warning: paywall).

Finally, a really interesting recent approach has been to compare decisions 'made by the head' to analogous choices 'made by the hand', or in other words, design fast, action-based analogues to classic decision making tasks (see this review).

I'll not go into details here, but essentially, we're extremely good at intuitively computing the probabilities involved in executing movements under uncertainty, presumably because mammals have been doing it for millions of years, but can be pretty awful at explicitly working out the right strategy for the same problem.

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In summary, \**automatic, innate, intuitive processes work well for the evolutionary old tasks they were designed for, like movement planning, and a lot of real world decision making (for a certain value of 'designed': natural selection, etc.), but can lead up astray in evolutionary novel situations that human culture places us in, ranging from supermarkets, to psychology labs.

Hope this helps, sorry about the rambling!

# Answer
> 0 votes
Analytical thinking should be used instead of intuition for new high-stakes and complex decisions only, since it has a cost, particularly in terms of the time and other valuable resources used in the process. In sum, the expected gains from analytical thinking must surpass the respective expected costs.

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Tags: decision-making, intuition
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# Question
Title: How can one describe personal space and its comfortable area
I have noticed people get a little frustrated or uncomfortable when a unknown or known person invade the personal space . How can one define Personal Space . Why do people get uncomfortable .

# Answer
> -1 votes
Personal Space may be seen as one's working area for carrying out autonomous actions and thoughts. When someone invades that space, you have decreased capacity to act autonomously. For example, when someone is physically too close, you have decreased freedom of locomotion and movement. When someone is being nosy or intellectually getting into your business, they are interfering with your ability to act by your own feelings and beliefs. The reason is that you begin to consider how that person might judge your actions and motivations. The key problem is interference with autonomy.

Put another way, invading someone's personal space interferes with that person's life, liberty, and pursuit of happiness by imposing external opinions or forces the individual does not desire in his or her life. Naturally social actions and interactions are meant to accommodate the feelings and desires of bystanders, but some actions are purely personal.

The situation is a bit different when we are talking about close friends or relatives. Presumably the reason here is that these external parties have already been screened for agreeability and likeability, so not only is there less likelihood of opposition, but there is also a personal desire to find agreement. Moreover, the less agreeable a person appears to be, the less you would want that person interfering with or even knowing your personal feelings, plans, and circumstances.

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Tags: personality
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# Question
Title: Is speed up of arithmetic calculations as a result of memorisation?
In elementary school, it is typical for a student to learn "times tables" to increase their speed of mental multiplication. What is the mechanism for the increased speed of calculation resulting from the repetition of "times tables"? Is it memory based? That is, the pattern is recognized and the result is retrieved from memory. Is it optimisation based? Does the brain actually just become faster at multiplication? The same question could apply to the progression of addition as a result of memorised counting.

This is assuming a naive, limited approach without mental imagery optimisation, such Chisanbop, where a mental abacus is used.

# Answer
> 1 votes
I asked this question back when I was building a neural model of progression through counting strategies \[\]. The quotes in this answer are taken from that publication.

Psychologically, there is some evidence of addition speedup being a result of memorisation.

> Typically, children progress through various strategies before finally memorizing the results of addition, as shown in Table 1 (Siegler, 1987). The Counting strategy involves choosing the larger number and incrementing it a number of times equal to the smaller number. The Recall strategy is where the two numbers form an association with a previously memorized answer, which is then recalled from long-term memory. Other strategies have been identified, although we focus here on recall and counting \[...\]
>
> \[The\] recall strategy \[...\] improves reaction time and accuracy, consistent with the data from Tables 2 and 3, respectively.

There is also some evidence from the Cognitive Neuroscience point of view which ends up being supported by the neural model I built:

> First, when doing mental calculation, age is correlated with the use of parietal brain areas. However, it is inversely correlated with the use of prefrontal and hippocampal brain areas, as well as the use of the dorsal basal ganglia area (Rivera, Reiss, Eckert, & Menon, 2005). Within the model, this transition can be framed as older children using fewer iterative processes (no use of working memory in prefrontal, no iterative process control requiring the basal ganglia, and no loading of instructions from the hippocampus) and more memorization.
>
> Second, those with dyscalculia show greater activation of the prefrontal cortex compared to individuals with normal numeracy (Kucian & von Aster, 2015). Although this model make no claims about why dyscalculia occurs, given that it is a complicated disability usually accompanied by various comorbidities and no direct cause (Rubinsten & Henik, 2009), it does provide part of a possible explanation as to why such compensation occurs. Specifically, those with dyscalculia are unable to consolidate the functional role of the prefrontal cortex during the counting task within the frontal-parietal region and must instead rely on their working memory. Given an excessively noisy input, inaccurate feedback, or inappropriate modulation of the error signal, the Fast-Net could fail to learn the mapping between addends and sum. Consequently, there would be limited progression from counting to recall and a continued dependence on working memory.

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1. Improving With Practice: A Neural Model of Mathematical Development

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Tags: cognitive-psychology, memory, mathematical-ability
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# Question
Title: Study about how position of head influences thoughts
I'm wondering if there are any studies on how (or if) the position of the head changes our thoughts or dreams. Mainly I mean when lying down, as I experienced it is hard to keep the thoughts that where there moments ago when I turn from one side to another in bed. I guess this can be explained by how the blood flows in our brains, that one side gets more blood than the other when lying on your side. But does that mean you could influence your dreams by your sleeping position? Would you react differently to outside influences depending on what your head position is?

I have to mention I have no knowledge at all in these kind of stuff and I'm just asking out of curiosity. Does anyone know about something like this?

# Answer
> 6 votes
Hue,

It sounds like you are interested in a field of research known as embodied cognitive science. It is a fascinating field that looks at how we use our bodies and environments in conjunction with or instead of our brains to complete some cognitive task.

I suggest you start by reading Risko & Gilbert (2016) for a recent review of a phenomenon known as "cognitive offloading" which I think will give you some interesting insights to some of the questions you posed. Evan Risko has done some interesting work on the effects of head tilts on cognition which I think you'll find interesting. If you can't access the article due to the paywall, let me know, and I can send it to you.

Unfortunately, I do not know any research on dreams and such.

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Tags: cognitive-psychology
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# Question
Title: Where are scientific references for the claims made in "Thinking Fast and Slow"
I am currently reading fast and slow and the author mentions the results of **a lot** of studies. However, these are not directly cited, although some can possibly be googled with the information given.

I was wondering if there exists a version with all the references **or** an academic book which provides similar information and references. I don't have much knowledge on the subject, so I do not know the technical terms for some of the phenomena explained in the book, and thus I do not know how to look for the papers pertaining them. (A suggestion of how familiarize myself with such terms is also welcome).

# Answer
Just found the book I used to study decision making. It's called Judgement and Decision Making and is written by Daniel and David Hardman. It is a perfect introductory book on the topic with references to pretty recent literature. It covers much of Tversky and Kahneman's work (the latter being the author of Thinking Fast and Slow), but also refer to literature of many other good research. I think this book may be the best scientific approximation of Thinking Fast and Slow.

Hardman, D. K. & Hardman, D., (2009). Judgment and decision making: Psychological perspectives (Vol. 11). John Wiley & Sons.
> 5 votes

# Answer
If I recall correctly, the result of the work of Kahnemman and Tversky leaded is the Prospect Theory (Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the econometric society, 263-291.). The main components of that theory are include in that book ("Think fast and Slow"). So, I believe that that paper you can provide you with more reference on the work of these two authors.
> 1 votes

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Tags: cognitive-psychology, experimental-psychology, decision-making
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# Question
Title: Difference between the cingulate gyrus and the cingulate cortex
I am doing an analysis of the brain of the Creature in Frankenstein by looking at its actions and experiences from a scientific perspective. For this, I was wondering:

What is the difference between the Cingulate Gyrus and the Cingulate Cortex? What is the function of these structures?

# Answer
> 2 votes
**Short answer**
The cingulate gyrus and cingulate sulcus are the two components that make up the cingulate cortex. The cingulate cortex is involved in emotions, self-control, and problem solving.

**Background**
In terms of the anatomy of the cingulate area in the brain the following nomenclature is important:

* **Cerebral cortex**: the few-millimeters of superficial layer of the brain with its characteristic foldings in higher *animalia*;
* The cortical foldings result in the formation of ***sulci*** and ***gyri*** (plural for *sulcus* and *gyrus*). Sulci are grooves, gyri are ridges. These wrinkles increase the cortical surface and allow more neurons to be packed onto the brain. Cortical neurons are important for higher cognitive functions, explaining why higher primates have a high density of sulci and gyri.
* The **cingulate cortex** comprises one sulcus and one gyrus: the cingulate sulcus and cingulate gyrus (Fig. 1) (Kandel *et al*., 2000). Hence, the cingulate gyrus is part of the cingulate cortex.

Regarding the functions associated with the cingulate region - The cingulate cortex is an integral part of the **limbic system**. The limbic system is the emotional control center of the brain. The cingulate cortex as such is involved in emotional self-control, focused problem solving, error recognition, and adaptive response to changing conditions (Allman *et al*, 2001). Especially the anterior cingulate cortex (ACC) has received special attention, as it connects the limbic system with the “cognitive” prefrontal cortex. The ACC is thought to integrate the neuronal circuitry for affect regulation and a locus responsible for affective disorders (Stevens *et al*, 2011).

Fig. 1. The cingulate cortex comprises one sulcus and one gyrus. source: Wikipedia

**References**
**-** Allman *et al*, Annals NY Acad Sci (2001); **935**: 107-17
**-** Kandel *et al*., The anatomical organization of the central nervous system. In: *Principles of neural science* 4the ed. (2000)
**-** Stevens *et al*., *J Neuropsychiatry Clin Neurosci*; **23**(2): Spring 2011

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Tags: cognitive-psychology, neuroscience, cognitive-neuroscience, neuroanatomy, brain
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# Question
Title: Does a true hallucination need to go away while plugging one's ears?
Can an auditory hallucination be true hallucination even if the person clàims that it doesnot reduces in volume when he plugs his ears. Similarly can a visual hallucination still be a true hallucination even when it doesn't disappear when person closes his eyes??

# Answer
A hallucination is a perception in the absence of external stimulus that has qualities of real perception and hallucinations can occur in different ways.

Visual hallucinations can occur with the eyes open or closed (see also). Visual hallucinations can also occur with people who have lost their sight

Just like visual hallucinations, auditory hallucinations can also occur with the ears open or plugged. To highlight the point, I suffer with tinnitus and I hear the noise with my ears plugged or open. Auditory hallucinations can also occur in people with hearing loss
> 3 votes

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Tags: hallucinations
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# Question
Title: Do antidepressants provide a permanent solution for depression and anxiety?
Antidepressants works temporarily (as long as you take them), and also affects the cognitive behaviour. Should they be seen as a permanent solution for depression? Shouldn't we find a solution at the psychosocial level, which actually works rather than suppressing the thoughts?

# Answer
Your assumption that antidepressants works by "suppressing the thoughts" is not supported. Arguably it's quite the opposite. But let us not comment any further.

Now onto your question

## Do anti depressant provide permanent solution for depression and anxiety?

At least for Major Depressive Disorder (MDD), they normally do.

* According to a brochure from the Boynton Health Service of the University of Minnesota:

> Once you respond to an antidepressant, we recommend that you continue to take the medication for at least six months. If you stop taking the antidepressant before six months have passed there is an increased chance that your depression will return. For depressions that have lasted a long time (a year or longer), or for a recurrent depression, longer periods of treatment should be considered. If you wish to discontinue your medication, we recommend that you do this during times of decreased demands, such as during breaks or during the summer months.
>
> Source: "Common Questions About Antidepressants" (2005) - page 2
* According to an article available from the Massachusetts Institute of Technology healthcare service MIT Medical:

> Clinicians usually recommend that people continue taking antidepressants for about six months after they begin feeling better. Although it is tempting to stop taking the medication as soon as you feel better, abruptly stopping will greatly increase your risk of relapse.
>
> Source: "FAQ: Antidepressants" question #7: "How long should I continue to take antidepressants?"

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* From an article published by the Journal of Clinical Psychiatry, doctor David Louis Dunner has this to say:

> According to the AHCPR Practice Guidelines(1) for the treatment of major depression, acute episodes of depression should be treated for almost a year and recurrent episodes should be treated somewhat longer.

However, Dr. John M. Zajecka states:

> Clinicians should perceive depression as potentially being a lifelong illness that may require treatment indefinitely in many patients rather than as an illness that can usually be cured with a single, short course of treatment like an antibiotic.

Dr. Michael Edward Thase notes:

> There is a difference between “forever” and “indefinitely.” In the future, treatments that target the altered pathologic mechanisms of recurrent depression may be available and may have a more curative effect than the present medications, which suppress illness activity.

While it's debatable whether *all* "present medications" simply suppress depressive symptoms, consider that this article was published in 2007.

--

Source: "Preventing Recurrent Depression: Long-Term Treatment for Major Depressive Disorder" - PMC 1911177. PMID 17388700.

References:

(1) Clinical Practice Guideline. Number 5: Depression in Primary Care, vol 2. Treatment of Major Depression. Rockville, Md: US Dept Health Human Services, Agency for Health Care Policy and Research; 1993. AHCPR Publication 93-0551.

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* Last but not least, from Wikipedia:

> Studies have shown that 80% of those suffering from their first major depressive episode will suffer from at least 1 more during their life,(1) with a lifetime average of 4 episodes.(2) Other general population studies indicate that around half those who have an episode recover (whether treated or not) and remain well, while the other half will have at least one more, and around 15% of those experience chronic recurrence.(3) Studies recruiting from selective inpatient sources suggest lower recovery and higher chronicity, while studies of mostly outpatients show that nearly all recover, with a median episode duration of 11 months. Around 90% of those with severe or psychotic depression, most of whom also meet criteria for other mental disorders, experience recurrence.(4)(5)
>
> Recurrence is more likely if symptoms have not fully resolved with treatment. Current guidelines recommend continuing antidepressants for four to six months after remission to prevent relapse. Evidence from many randomized controlled trials indicates continuing antidepressant medications after recovery can reduce the chance of relapse by 70% (41% on placebo vs. 18% on antidepressant). The preventive effect probably lasts for at least the first 36 months of use.(6)
>
> Those people experiencing repeated episodes of depression require ongoing treatment in order to prevent more severe, long-term depression. In some cases, people must take medications for long periods of time or for the rest of their lives.(7)
>
> ---
>
> References:
>
> (1) Fava GA, Park SK, Sonino N (2006). "Treatment of recurrent depression.". Expert Review of Neurotherapeutics. 6 (11): 1735–1740. doi:10.1586/14737175.6.11.1735. PMID 17144786.
>
> (2) Limosin F, Mekaoui L, Hautecouverture S (2007). "Stratégies thérapeutiques prophylactiques dans la dépression unipolaire \[Prophylactic treatment for recurrent major depression\]". La Presse Médicale. 36 (11–C2): 1627–1633. doi:10.1016/j.lpm.2007.03.032. PMID 17555914.
>
> (3) Eaton WW, Shao H, Nestadt G, Lee HB, Lee BH, Bienvenu OJ, Zandi P (2008). "Population-based study of first onset and chronicity in major depressive disorder". Archives of General Psychiatry. 65 (5): 513–20. doi:10.1001/archpsyc.65.5.513. PMC 2761826. PMID 18458203.
>
> (4) Holma KM, Holma IA, Melartin TK, Rytsälä HJ, Isometsä ET (2008). "Long-term outcome of major depressive disorder in psychiatric patients is variable". Journal of Clinical Psychiatry. 69 (2): 196–205. doi:10.4088/JCP.v69n0205. PMID 18251627.
>
> (5) Kanai T, Takeuchi H, Furukawa TA, Yoshimura R, Imaizumi T, Kitamura T, Takahashi K (2003). "Time to recurrence after recovery from major depressive episodes and its predictors". Psychological Medicine. 33 (5): 839–45. doi:10.1017/S0033291703007827. PMID 12877398.
>
> (6) Geddes JR, Carney SM, Davies C, Furukawa TA, Kupfer DJ, Frank E, Goodwin GM (2003). "Relapse prevention with antidepressant drug treatment in depressive disorders: A systematic review". Lancet. 361 (9358): 653–61. doi:10.1016/S0140-6736(03)12599-8. PMID 12606176.
>
> (7) "Major Depression". MedlinePlus. 10 March 2014. Retrieved 16 July 2010.
> 2 votes

# Answer
Most studies exploring the impact of medication for the treatment of depression track patients in the short term. Also, these clinical studies are typically not as comparable to the general population as many of exclusion criteria are strict, and rule out individuals who have co-morbid conditions like an anxiety disorder.

http://psych242.class.uic.edu/Articles/Zimmerman,%20Sampling%20in%20depression%20research,%20AmJrPsy,%202002.pdf

Overall, medication without any psychological intervention is largely a short-term fix. Most studies have shown that the most effective course is to have a combination of antidepressants and therapy, like CBT. In my experience, getting to the core of a psychological issue requires exploration through therapy, while medication is important for managing some symptoms, specifically in the case of severe depression. It is important for a person to be stable before engaging in intensive psychotherapy, hence, medication should be the first course of action, along with more behavioral approaches such as behavioral activation, positive coping skills, etc.

"In any comparison between psychotherapy and medication, it is important to examine whether the sample was responsive to medications and whether pharmacotherapy was adequately implemented. Among more severely depressed participants in this trial,ADM significantly outperformed placebo through 8 weeks of treatment. There were no significant differences in outcome between ADM and placebo for the less severely depressed participants, consistent with findings from numerous other studies (Hollon et al., 2002). In the absence of a demonstrated drug effect for such patients, there may be little justification for prescribing psychoactive medications when there are comparably effective psychosocial alternatives free of side effects."

Among more severely depressed patients, behavioral activation was comparable to antidepressant medication, and both significantly outperformed cognitive therapy.

Depression occurs for many reasons. One route is commonly due to negative and distorted core beliefs (in line with CBT theory). These beliefs and narratives need to be brought out of automatic processing and explored, and ultimately changed or replaced with beliefs that are not distorted. Also, depression occurs when automatic thoughts are highly negative, bringing about debilitating emotions. Often, people attempt to suppress these thoughts by avoiding situations or even resorting to means of distracting themselves by using drugs.

Depression can also occur when one is no longer living in line with their core and true values, and rather responding to life based upon avoidance of feared outcomes, often irrational and unexplored fears. This is in line with Acceptance and Commitment (ACT) theory, which proposes that through mindfulness and learning to observe the process of your mind and body rather than getting attached to the content, you will gain freedom from the impact of the thoughts. Then, you can start to focus on your values, and slowly identify how you interrupt your pursuit of a meaningful life.
If depressive symptoms are severe, and a person is struggling to get out of bed, than medication can help provide reduce these symptoms enough to motivate the person to engage in therapy.

Overall, medication alone is not a long term fix to psychological problems, although it can be highly effective and necessary for certain populations. Medication also communicates that depression and anxiety are things to be fixed; they are not. All humans, at some point in their lives, will struggle psychologically. Moreover, we all do our best and have ways of responding that we typically learned as children and that helped us to survive at certain points in our lives. The issue becomes that when our context changes, some become stuck in old ways of responding to a new context. Adaptation is key, and to adapt you have to become self-aware in order to have the choice to make a positive change.
> 2 votes

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Tags: cognitive-psychology, depression
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# Question
Title: What is a realistic mathematical function to model link decay in a social network?
I'm wondering (as a non-psychologist/cogsci programmer) what would be a **good mathematical function to model the decay human relationships**, or in other words, how people forget about people they don't interact anymore.

**The background**: I'm building an agent-based model where agents (representing people) interact, converging on a semi-stable network. If a link between two agents is not used, it should weaken, and eventually fade. Currently the growth and decay of a link between two agents is linear between 0..1 (more interactions -\> stronger link, up to 1; less interactions -\> link gradually weakens, evetually drops to 0). While my current decay function is linear, for the part that controls who interacts with who, I'm using an S-shaped function - there's a higher chance to interact with agents one knows, and who their "friends" know, but the sum of these values is then "bent" on an S-curve.

The concept of **time** in the model (counted in iterations of interactions) is relative, so it's not about minutes, days or years. I'm rather interested in a formal model that would capture the dynamics of forgetting people they don't see around anymore. I have a gut feeling it's not linear - one won't start to forget their mother, and the relationship won't weaken, if they don't see her for a little while. But if you just ask for directions on the street, chances are you'll forget the person rather quickly (unless he/she was hot or something; but the agents in my model don't have properties pertaining to such attributes).

A few **alternatives** could thus be: a **sigmoid/S-curve** (but the "steepness/curviness" would be a question still; green line below), **logarithmic** (also looks nice, but steepness of the curve would be a question; yellow), **exponential** (doesn't seem likely though; orange), perfect memory until **arbitrary cutoff** (probably not; red), compared to simply linear (blue).

If an curve could be decided on for the decay, then I suppose I would also use it for link growth (e.g., on an S-curve a slow growth in the beginning up to a point (strangers) and towards higher values (friends past the get-to-know phase)). It might be useful to think of the lines on the plot as trajectories of a relationship, with the stength on the y-axis and relative time (either way) on x.

**The question** then: which one of these functions (and which parameters; or some other function) would be best to model the dynamics of how quickly people start to forget about/lose their connections with other people when they don't interact - or in other words, how a relationship weakens due to sparse contact? I assume there's literature on the subject, but I would have no idea where to start (googling generic keywords like relationships and decay function does not help), so a good answer could cite relevant sources or otherwise show the superiority of one function over the others.

# Answer
> 2 votes
Here's a nice paper that explores the question in depth: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4626528/. The authors measure the emotional closeness as frequency of contact and number of different activities done together for a group of 25 people over the period of 18 months. As far as I can tell from the charts, the decay curve is closer to an exponent than to anything else for friends. However, for the kin the emotional closeness increases over time - though, again, in an exponential way.

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Tags: social-psychology, memory, social-networks, agent-based-modeling
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# Question
Title: "Nested Neurons": Are they biologically plausible?
Is there any evidence for neurons existing within other neurons in humans or other organisms? From what little I've learned about physical neuroscience while learning to build neural nets, I don't see how one neural cell could actually contain another, as a nested structure implies; on the other hand, I figured I'd ask, because neuroscientists have discovered such a wide variety of neuron types, exhibiting all kinds of fascinating behaviors and structures. Furthermore, I suppose it's conceivable that the complex firing of neurotransmitters might be interpreted in a hierarchical way, akin to nested neurons.

The reason I'm asking is entirely practical: I've recently discovered that there may be certain advantages to treating neurons as nested structures when performing data and object modeling of neural nets. I'm interested in finding out if such nesting could be biologically plausible and if so, how I could go about finding more information on the topic. I've performed Internet searches and looked through the threads at StackExchange and can find nary a reference to the term "nested neuron."

Edited to Add (from the comments section, by request):

I'm asking whether neuroscientists currently possess any evidence for biological nested neurons. It's a very simple yes-or-no thing; either professional neuroscientists have such evidence in hand or they don't.

# Answer
> 5 votes
Disregarding the feasibility of neurons within neurons *in silico*, the answer to

> Is there any evidence for neurons existing within other neurons in humans or other organisms?

from a practical, biological point of view is *no*.

**The literature point-of-view**
The strongest argument against the neuron embedded (I'll deliberately not say "nested" as it has a computational and statistical taste to it) in another neuron is: I've never heard of it. I could cite every single neuro textbook I have *not* mentioning the phenomenon, but I'll wager that doesn't make too much sense, hence this unreferenced argument.

**The physiological view**
Neurons need a membrane potential to operate, *e.g.*, to fire action potentials, or change their membrane in a graded manner. That membrane potential is, with perhaps an exception or two, always negative. That is, inside negative, outside neutral. A neuron within a neuron would provide inhospitable living conditions for the former. The latter wouldn't be too happy either. The former will not be able to build up the normal gradient of high Na+ outside the cell, and a high K+ inside, as the enveloping neuron would try to do exactly that same thing. The inner cell would thus build up a gradient (push Na+ out) and the enveloping cell would again transport the Na+ out. For K+ the reverse would happen (see Na+,K+-ATPase for background). In short, the inner cell would not be able to build up a membrane gradient, while the outer cell would have to deal with a pesky parasite messing up its ionic homeostasis. The inner cell would die, as many processes vital to the cell need a gradient (ion transport of nutrients along the membrane, disposal of garbage) and the outer cell would then finally be a happy neuron, barred the debris in its insides.

**The teleological view**
Physically embedded neurons don't make sense. Simply, because neurons exist, basically, to convey information from one neuron to the next. The textbook example of a neuron (Fig. 1) has an input part (the dendritic region), a space-traversing part to cover distance (the axon) and an output part (the axon terminal). Any processing occurring within the neuron is done primarily by logic operators such as summation, subtraction and integration (through the dendritic tree collecting information) and gain control (inhibitory or excitatory influences in dendritic region, cell body or axonal part). But in the end, a neuron is designed to get information from A to B. Therefore, a neuron within a neuron doesn't make sense from a neurophysiological vantage point.

Fig. 1. Textbook example of a motor neuron. The neuron receives information from higher motor areas in the dendritic region and stimulates the muscle using action potentials. Rate code is used to regulate the strength of muscle contraction. source: Obes DVR Lists.

**Reference**
**-** Purves *et al*. (eds.) *Neuroscience*, 2nd ed. Sunderland (MA): Sinauer Associates (2001). The Ionic Basis of Action Potentials

# Answer
> 3 votes
Here are some notes and ideas, and hopefully an answer to your question:

1. New membranes are produced by a process during divisions as you read in genetics; sometime calls embryogenesis.It is just the divisions of cytoplasm. The layer than separates nuclides are suggested to polarize toward nuclides inside a single cytoplasmic they all are. So, as it might be guessed by the definition of the word "cell", normally in final form of a tissue there will be no cell inside a cell (or its cytoplasm). They will be split into two cells, as always in science this is what "usually" happens (This article provides some backgrounds on how this mechanism works).
2. There are however vesicles, simply bags of some chemical stuff (e.g. neurotransmitters). They fuse into the cell membrane at its terminals and some N.t. would be released. They are not living cells though, but their outside layer is similar to the ones in a real cell, made in form of phospholipid bilayer.
3. Rather than neurons, there are some more cells in the nervous system, called *Glias*, even more than neurons. Their main role seems to support neurons and synapses. You may think of them as supporting cells, joint with a neuron or a synapse helps them being activated or deactivated. There are recent studies, investigating the role of Glias in the brain.
4. Neurons (for example in cortex, which is the most influential area of the brain interested computer scientists) do not perform activations alone. But they form a network; in fact networks of networks. In cortex, they together form columns, super columns, and in human around 6 layers. They usually study groups, columns or bigger forms of interconnected neurons, except in invasive single neuron imaging techniques. So neurons have been in some sense studied doing computations *"on average"*, but not alone. Even the activation in neurons (action potentials) are generated at a place called axon hillock, that does not rationalize having two cells can simple generate two independent signals and then merge them together; this can be done easier at synapse level since nature is just a sloth and select best random outcomes.
5. Synapses are where information is stored in artificial and natural neural networks. So even a cell within a cell (connected), does not provide further improvements (like new trends of providing computing power necessary for deep nets). By cell-within-cell in ANNs you just add another connection and possibly a new layer. You can do the same by defining new layer as in deep networks.
6. Action potentials to send signals of activation forward in cell are performed and developed in places outside the cell body (soma). There are usually occurs in dendrites and mostly in axons. So biologically speaking. A cell body within a cell body does not help or intervenes in computations or for communication. If it happens in real world, there are just mutations, or wrongly or in-lab developed ones (It is possible, right?); similarly other solutions perform same mechanism, for example like a single neuron, or two connected neurons normally through a single synapse.
7. Though the idea could be investigated by self-similar systems (fractals), dimension of self-similarity measures, dynamic systems, and in general chaos theory. In neural system this fractal dimension is an integer at cell level (a single cell). You can however study how this self-similarity works for artificial neural nets :-).

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*I've tried to look at you question from biologically-inspired computer science perspective...*, and hope it helps :-)

# Answer
> 2 votes
As the other answers point out, there is no such thing as a neuron inside another neuron, i.e. a literal nested neuron.

However, from an artificial neural network point of view, there is evidence that a single pyramidal neuron can operate as a hierarchical two layer network thanks to its dendritic tree. To quote the paper's abstract

> Local supra-linear summation of excitatory inputs occurring in pyramidal cell dendrites, the so-called dendritic spikes, results in independent spiking dendritic sub-units, which turn pyramidal neurons into two-layer neural networks capable of computing linearly non-separable functions, such as the exclusive OR.

This means that the neuron behaves as if it had many nested subunits, which are in fact only branches of the dendritic tree, not real neurons.

I am not an expert in that field but you should look at Boris Gutkin's work who is presently at Moscow.

# Answer
> 0 votes
As I understand the idea of parallel computing, the idea of massive parallelism is to reduce the (computer) node sizes while maximizing connections between nodes. If you look at the evolution of supercomputers, initially they were build of very powerful superscalar processors. But, as time goes, they are trying to build of more interconnected lighter processors. That is because communication is the bottleneck in the supercomputing. Powerful nodes compute too much information that they need to stand by to receive/send more to/from their (distant) neighbours. It makes no sense to make very powerful nodes, but, instead to spend the machine space to more cabling.

Neural networks are the ultimate parallelism machines. Their compute nodes, neurons, are as simple as possible. The paradigm, known as connectionism, means that computation is achieved through emergence of results when simple nodes communicate with each other. It consists mostly of cables. In NN the computation is made by thouthands of inter-neuron synapses rather than neuron itself. The idea is that you should connect directly with as much other neurons as possible. You can consider a number of connected neurons as one neuron (subnetwork) but why and, for sure, you do not want to isolate any neurons by a physical wrapper to force them to connect only to each other and prevent possible connections the rest of the neurons. It seems unnecessary, difficult to implement and not only wasteful but adverse limitation.

There is hardly a room for other (sub)neural network in the neuron. You may decide yourself, analyzing the function of neuron how can you implement it. As far as I know, biologically plausible spiking neurons are made of simple capacitor+resistor+something firing a spike if capacitor charge exceeds some threshold. Nothing indicates that you either can fit another neural network into the neuron or need it to simulate it.

Why not to hypothesize existence of humans inside the neurons or smaller heads in human head? A head, made of other heads can give more power.

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Tags: neuroscience, theoretical-neuroscience, neural-network, neurophysiology
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# Question
Title: Difference between reverse inference and decoding (e.g. MVPA) in fMRI
*Reverse inference*, i.e. the use of brain activation (fMRI) data to infer the engagement of a certain mental function, is heavily criticised (e.g., Poldrack 2011, Neuron).

At the same time, *decoding*, the process of applying machine learning algorithms to such brain activation patterns in order to infer something about the mental function that elicited this response (e.g. to distinguish between two different conditions in an fMRI experiment), is extensively used and actively developed - multivariate pattern analysis (MVPA) being just the most widespread of these techniques.

The two concepts appear, however, to be one and the same; or rather, that the second (decoding) is a special case of the first (reverse inference). Is there an additional nuance to their difference? Also, why is decoding not criticised in the same way as reverse inference?

# Answer
Short answer: Decoding is not a special case of reverse inference.

The difficulty with interpreting neuroimaging results is that there is a tremendous amount of variability (noise) in the data. For example, say we attempt to determine the brain areas associated with the emotion of romantic love by showing subjects images of close friends (condition 1), or images of their loved ones (condition 2), and comparing the results. Each brain scan may show 5-10 active regions, which regions are active and to what degree varies between subjects even in the same condition, and there is even variability in brain scans of the same subject across multiple trials.

To deal with this variability, the first step in just about any neuroimaging experiment's data interpretation process is a statistical analysis. This can range from an "averaging" or "noise-cancellation" analysis, to a multi-voxel / multi-frame machine-learning pattern-matching classifier (MVPA). The data analysis is used to determine a predictor of the independent variable, and also to calculate significance (p-value) based on the consistency level in the data. For example, we may learn that subjects viewing images of their loved ones (condition 2) have 5 areas of their brain reduce activity and 4 areas increase activity relative to subjects viewing images of other close relations (condition 1). Note that the decoding step does not involve "labelling" the pattern as any particular mental state - a classifier like MVPA does not care what the patterns mean, it's just a mathematical function used to distinguish between them.

In a typical *cognitive* neuroscience experiment, the next step is to attach a label to the mental state defined by the pattern found - this is forward inference. To do this, researchers typically determine the mental state through some other validated measure - for example, a questionnaire or some other test that is known to reliably measure love. The researchers may conclude: "These regions of the brain are associated with the emotion of romantic love when subjects view images of their loved ones." Immediately, a constraining factor arises: This pattern of activation is relative to the control condition only!

While forward inference involves labelling the pattern from the independent variable, reverse inference is used to label the independent variable from an activity pattern. Reverse inference gets into trouble when it is used to label a particular pattern of activation in a different context. For example, "the same areas of the brain associated with the emotion of love in an experiment involving showing subjects images of their loved ones, also show the same pattern of activation when subjects view their iPhones, and therefore we conclude that subjects are in love with their iPhones." Note that statistical analysis of the data (decoding) must take place first in order to be able to make either kind of cognitive inference.

Reverse inferences suffer from several potential pitfalls, among them:

* A particular pattern of activity usually indicates different mental states in different contexts, so assuming that the same pattern indicates "love" when applied to iPhones is invalid.
* Patterns isolated using a statistical analysis usually do not fully describe the mental state, so assuming that the pattern is sufficient to indicate "love" is also invalid.
* Mental state labels themselves are context dependent, subject-dependent, and even temporally variable, so assuming that subjects would interpret their own mental state as "love" is invalid as well.
* Mental states are not discrete, as evidenced by all the "noise" in neuroimaging data, so subjects may identify their mental state using a variety of different labels depending on what part of their mental state they attend to, "love" being only one.

Reverse inference is subject to these issues no matter what statistical analysis is used, MVPA is not immune. However, many of these issues can be overcome using a variety of techniques. MVPA is very valuable for dealing with the first issue - thanks to much higher "resolution" (level of detail, scope of data, linearity, etc), this decoding technique is significantly less likely to confuse different mental states that appear to have the same activity pattern at a lower resolution. Another important strategy is using meta-analysis (data gathered from many different studies) to determine how useful a particular pattern is for reliably inferring a mental state. Solving the remaining problems will likely require per-subject training data, and even more advanced analysis techniques, but already reverse inference has been a useful methodology in many studies, when carefully applied.

An additional interesting side-effect of machine-learning pattern analyzers is that they tend to remove the temptation to make unwarranted reverse inferences. In the early days, when patterns of activation were "informal" - based on gross anatomy - it was too easy to make sweeping statements such as: "... the insular cortex of the brain, which is associated with feelings of love ..." With MVPA, patterns of activation are practically impossible to describe in human language, so machines are required to make inferences, which are then inherently more objective.
> 1 votes

# Answer
*Note.* I initially scan read the question, I have rewritten my answer as a consequence, and due to the comments given.

As highlighted by others here Multi-voxel pattern analysis (MVPA) is an application of machine learning, used for decoding vast quantities of complex information (neural activation patterns to particular asks). This is a form of decoding may be used to infer a cognition, otherwise known as *reverse inference*.

The problem of *reverse inference* is largely summed up by the following comment from Poldrak's (2011)...

> The use of reasoning from activation to mental functions, known as “reverse inference”, has been previously criticized on the basis that it does not take into account how selectively the area is activated by the mental process in question.

Poldrack (2011) goes on to explain that *informal reverse inference*, which is based on a researchers knowledge, is flawed because an individuals knowledge is limited by what the remember and have read. Additionally poor interpretations are compounded from one researcher to another.

The problem with reverse inference comes not from understanding general cognitive processes, such as sight, movement, language, decision-making etc. General patterns for general processes have been established, the problem as Poldrack (2011) points out is when we interpret patterns to more well defined cognitions, for example instead of looking at merely reward processing, we might want to compare patterns of pleasure derived from seeing highly palatable e.g. cake, and less palatable food e.g. fruit. At this level, comparing the data requires far more specific analytical approach. If we were a researcher making an inference based on our knowledge, we would have a strong chance of error.

MVPA deals with a far higher resolution of data than an individual could deal with, and compares data to prior trials or previous experiments, see Fig 1. However it is critical to remember that we are comparing participants within similar contexts.

Fig 1. MVPA diagram of testing and inference Norman et al (2006)

Poldrack (2011) gives an excellent example of how to use MVPA, which was conducted by Kay et al (2008). Simply Kay et al (2008) scanned participants viewing natural images *n*= 1750. In the following trial 120 images were added, MVPA of the neural data was able to accurately predict which images were being viewed. This method has seen substantial development to the point that using a similar method researchers have accurately analysed neural patterns observed in a participants waking brain into decoding what participants are dreaming. Basically the quantity and quality of when taking account of context may be used to conduct reverse inference, but this does require previous data, **within context**, for the MPVA and other machine learning methods to compare too.

This does not mean that machine learning can not be wrong if incorrectly applied. It is a statistical method in which humans set the parameters, the post below by Arnon Weinberg accurately defines the issues and pot holes that must be avoided to make this method viable for reverse inference.

Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. http://doi.org/10.1016/j.tics.2006.07.005
> 2 votes

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Tags: neuroimaging, fmri
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