What is the impact of showing the amount of unattended notifications to the user? What is the difference in cognitive effects on the user between simply showing that there are 'some' unattended notifications, or showing how many notifications there are?

Take Facebook's notification count for example,

as opposed to Windows' PC issues notification  in the system tray.

Of course depending on what the notification is notifying you of, people will respond differently, which is not something I'm interested in. However, I suspect there could be a difference based on where it is being displayed, e.g. whether it  is always visible or not. Would one be preferred over the other in different situations?

I'm mainly interested in a comparative study between the two, but any research which might give additional insights in the two separate representations would help me out as well.

For me it is the inference that the more notifications I have the more value I will have in bothering to click it.

That 1 notification on a social networking site might be a request to play some terrible game.. however 24 notifications are likely to yield at least something beneficial for me to justify having bothered clicking it.

The higher the number the increased urgency I feel to check them (maybe the same person is repeatedly trying to make contact and I am unintentionally ignoring them!).

As for there being a notification with no number, that is easier for me to ignore. In the case of the Windows' PC issues (Action Center) in the task-bar, there is always a notification in it for me, so I have learnt to dismiss it.. I have become de-sensitised to seeing that alert. If however the icon increased in number and I detected this change, then I would deem that to be an increase in urgency and would be more likely to click it again.

I think if I kept clicking Action Center and felt it yielded little value to me then I would be able to filter out if the numbers were to keep increasing, but I would find it more difficult to ignore and feel tricked by it just in-case this new state meant something important this time round..


Are there mental states that make the mind more susceptible to other negative mental states? I've recently read the following folk parable and am interested if there is any truth to it.

It is originally in russian(full version here), so I will translate the gist of it:

  The devil displays his Arsenal of tools used to corrupt mortals - anger,
  jealousy, greed, etc. Then he shows the "special" tool which can work
  when all else fails- depression (dismay or broken spirit). He suggests that once depression takes hold, the mind is open to all other tools (jealousy, greed, etc)

This got me thinking - is there some equivalent of a mental "immune system" which keeps the mind healthy and keeps negative, unwanted, maladaptive emotions at bay?

Are there conditions that make the mind more open to other disorders?

Daniel Gilbert actually coined a term called immune neglect (Gilbert et al., 1998), which describes a common bias in affective forecasting.  The idea is that when we try to predict how we'll feel following a negative situation, we fail to take into account (i.e., we neglect) that our psychological "immune system" will kick in and return us to some neutral baseline.  So we overestimate how bad we'll feel.

Indeed, the idea that we have some homeostatic mechanism(s) (or really, allostatic; Sterling, 2014) that brings us back to some baseline has been thrown around in psychology for a while (e.g., Forgas & Ciarrochi, 2002), but emerging theory is starting to take this idea seriously (e.g., Barrett & Simmons, 2015; Craig, 2003; Sterling, 2014; Sterling & Laughlin, 2015).

So to your main question: what kinds of states make us susceptible to other negative mental states? One answer is a negative prior (e.g., Beck, 1979; Huys, Daw, & Dayan, 2015), which shapes your predictions about the world.  For example, if you grew up in a threatening environment, you might develop a prior that yields predictions that the world will be threatening.  Indeed, you might even discount evidence against your predictions (i.e., that the world is not threatening).

This is actually considered adaptive--you're using your prior knowledge ("the world is/was threatening") to make predictions about the world ("the world will be threatening") and to guide how much you weight the errors in your predictions ("the world is not threatening").  After all, if you've only been exposed to threat, then it's good to be hypervigilant to more potential threat and hypovigilant to potential indications otherwise.

So--that's what happens in depression, for example.  You get "stuck" in a negative state because your negative prior leads you to weight negative information more heavily (Garrett et al., 2014) and ignore "the full range of signals from many sources" (Sterling, 2014).  If you fail to change your prior in accordance with new evidence, then you'll behave maladaptively and remain depressed.

I think my answer is slightly incoherent and not complete, and I think there's probably a whole lot more to explain…but I hope it gives you some idea of what's going on.


Is there a formal name for the compulsive and recurrent urges to pick one's nose in order to remove perceived imperfections? Under which formal category does the following condition fall?: the condition of experiencing frequent and compulsive urges to pick one's nose searching for any possible perceived imperfections (be they wet snot, dry snot and even scabs) in order to remove them or "smooth them out" by means of scratching, in a sort of endless pursuit for a sensation of perfect smoothness within one's nose (endless because the sensation of perfect smoothness is never quite achieved and usually the scratching itself leads to bleeding and scabs which paradoxically become the fuel for future nose picking sessions, in an endless cycle). Likewise, are there any formal treatments to cure this particular condition? How effective are these treatments? Are there any studies on this? References are very welcomed.

Excoriation disorder (ED) formerly known as dermatillomania, and more popularly as Skin Picking Disorder (SPD):

  a mental disorder characterized by the repeated urge to pick at one's own skin, often to the extent that damage is caused.

The status as a separate disorder is somewhat controversial, because of high comorbidity:

  There has been controversy over the creation of a separate category in the DSM-5 for excoriation (skin picking) disorder. Two of the main reasons for objecting to the inclusion of excoriation disorder in the DSM-5 are: (1) that excoriation disorder may just be a symptom of a different underlying disorder, e.g. OCD or BDD, and (2) that excoriation disorder is merely a bad habit and that by allowing this disorder to obtain its own separate category it would force the DSM to include a wide array of bad habits as separate syndromes, e.g., nail biting and nose-picking.

Your case seems to be one of those overlapping ones.

There's also a 2017 neuroimaging study which concludes:

  data offer partial support for the view that sub-cortical structures contribute to the pathophysiology of SPD, both supporting its categorization as an OC-Related Disorder in DSM-5 and suggesting that there exists a pathophysiological difference between SPD and OCD.

Wikipedia has a lot to say about various treatments even though

  Knowledge about effective treatments for excoriation disorder is sparse despite the prevalence of the condition.

The usual suspects have been tried: SSRIs, anti-psychotics, anticonvulsants, glutamatergic agents (used to treat drug addictions) etc. As well cognitive-behavioral psychotherapies. It's not clear from there what might be most effective.

Wikipedia aside, there's been a "preliminary meta-analysis" published in 2014, but its review at CRD notes:

  Authors' conclusions:
  Both pharmacological and psychological interventions appeared to be effective in reducing the severity of pathological skin picking; psychological interventions could have a larger effect.

  CRD commentary:
  The authors did not report a quality assessment, so it is difficult to know how reliable the results of the individual studies were. Many of the studies had designs prone to bias. Details about the comparator interventions, in the studies with control groups, were not reported. Given the heterogeneity across the studies, the limited reporting of study details and the unknown quality of the included studies, it seems inappropriate that the results were statistically synthesised, particularly for pharmacological and psychological interventions, for pathological skin-picking severity.

So I wouldn't put too much stock on it.

A more recent (2016) meta-analysis has basically the same conclusions; this one was not reviewed at CRD, but it relies basically on the same small number of studies. Its conclusions were similar to the "preliminary" one: psychotherapy seems to work better than medication based on the limited data available. It only looked at SSRIs and lamictal for medication though.

There's also a 2016 RCT on N-acetylcysteine, published in a high-impact journal, concluding:

  N-acetylcysteine [NAC] treatment resulted in significant reductions in skin-picking symptoms and was well tolerated.

The result was fairly unsurprising given that NAC was previously found effective for trichotillomania (TTM -- hair pulling) as well; both studies with NAC were conducted by the same research group. But there were a few more interesting comments on those papers:

  In striking contrast with what we know about OCD, SSRIs
  have not been proved to be superior to placebo in TTM
  treatment. As for ED, the evidence regarding the efficacy of
  SSRIs is still scarce. So far, there are no US Food and Drug
  Administration–approved treatments for ED and TTM. The
  divergent results of the trials testing either SSRIs or NAC for
  ED/TTM and OCD suggest that treatments may not be used interchangeably
  between OCD and related disorders.

  In addition, results from genetic studies also suggest
  that the neurobiology of pathological grooming may not be
  necessarily related to OCD. It had been demonstrated that
  Sapap3 knockout mice groomed themselves excessively.
  However, using family-based association analyses, Bienvenu
  et al found that variation within the human equivalent of
  the Sapap3 gene appeared associated with ED/TTM, but not
  with OCD.
  The conflicting results regarding the efficacy of NAC for
  ED/TTM and OCD, in addition to the findings of translational
  studies, suggest that the neural basis of repetitive behaviors
  observed in patients with these disorders might not be the
  same. So far, similarities between ED/TTM and OCD do not
  seem to go beyond the characterization by repetitive behaviors
  and some degree of familial aggregation.

(But note that this last statement appears somewhat incorrect in view of the 2017 neuroimaging study--some overlap with OCD seems to exist after all.)

NHS Choices also has consumer-oriented page on "Skin picking disorder
" which doesn't say much I haven't told you already except for some behavioral tips. YMMV.


A subtle test for color-blindness Is there a test method of proving a person being color-blind, without letting the test subject know, that he/she is being tested?

E.g. showing the person cards with colored dots like depicted here is not a valid answer.

Depending on the instructions that you give to the person, you might be able to disguise the fact that they are completing a color-blindness test. You could use stimuli like those in the Ishihara test, but instead ask the subject to do a math problem where they add the previous two numbers. You can also tell them that sometimes no number will be presented, and have a few cases of actual blank tiles mixed in with the regular stimuli. If a subject reports the wrong sum consistently for trials that involve a particular hue, you can be reasonably confident that they are colorblind for that hue.

There is also a short test developed by researchers at City University London which has subjects track a square during a movie. It's probably no more subtle than the Ishihara test, but it is less well known and therefore subjects might not infer what the purpose is. See http://www.city.ac.uk/health/research/centre-for-applied-vision-research/a-new-web-based-colour-vision-test.


How does syntax highlighting affect the learning of a new programming language? What impact does syntax highlighting have on students' progress in learning a new programming language?

I'm looking for studies along the lines of the following hypothetical experiment, which involves two groups of students: one group would code in an editor performing poor to no syntax highlighting of the language in question...

...while the other group would code in an editor performing comparatively richer syntax highlighting.

Do you know of any such studies? Do you have otherwise relevant references? A quick Google search hasn't returned much...

Human-computer interaction describes the creation of programs that use colors effectively. Here we are not discussing how effective the implementation of the HCI colors are, but instead are focusing on the learned pair association and repetition of color training in the psychology of learning programming.

Learned pair association is a way of memorizing information by associating it with another set of information. In the United States and many other countries, when driving, there is an association between red traffic signals and "stop." When people see the color red in a light or at a sign for a distance, they know to stop.

Repetition is the process of repeating the same association with and without exposure time delay until it becomes memorized.

Association with color reinforcements take advantage of the Stroop effect. After sufficient training the brain sees the color green and thinks "comment" or the color purple and thinks "text string":

  Such interference was explained by the automation of reading, where the mind automatically determines the semantic meaning of the word (it reads the word "red" and thinks of the color "red"), and then must intentionally check itself and identify instead the color of the word (the ink is a color other than red), a process that is not automatized.

-wikipedia

Other research has been done with more alternations of text type. Consistency seems to reinforce learning.


Are there innate differences between men's and women's behavior? A stereotypical man is aggressive and competing.
A stereotypical woman isn't aggressive and tends to cooperate rather than compete.

A stereotypical boy have toys like cars and plastic soldiers.
A stereotypical girl have toys like barbie dolls.

So far I believed this is merely the result of the social conditioning, where boys taught to behave like a men, and girls are taught to behave like a women.

But it seems I need to think it again. I recently read this article about the girls in Salinas. They have a weird genetic disorder that makes boys born like a girl and they won't grow a penis until the age of 12.

The interesting thing is that it seems not only their penis grows but also there is a change in behavior (quote from the article above):

  A little girl named Carla is currently going through the same
  transformation, aged nine. Despite being brought up as a girl, his
  mother noticed that from the age of five he was more inclined towards
  the rough and tumble play of little boys. He has recently had his hair
  cut shirt after wearing plaits for years.

Also there are examples of transgender people who have gender dysphoria often describing themselves as a "woman who is lost in a man's body" or vice versa.

Is there any serious scientific research that proves / disprove these perceived differences in behavior?

This is still a matter of fierce debate between the essentialist and cultural camps.

There is little argument that gender differences are shaped by culture, but there is now a growing evidence supporting the hypothesis that gender differences are also partly the outcome of biological disposition.

Much of such biological disposition is the outcome of hormonal influence. Given that such influence was demonstrated even if prenatal, and that the instructions to the postnatal generation of hormones is coded in our DNA, you can argue that such influence falls into the definition of innate.

You may wish to start with these papers:

Alexander, Gerianne M., and Melissa Hines. "Sex differences in response to children's toys in nonhuman primates (Cercopithecus aethiops sabaeus)." Evolution and Human Behavior 23.6 (2002): 467-479.
Hassett, Janice M., Erin R. Siebert, and Kim Wallen. "Sex differences in rhesus monkey toy preferences parallel those of children." Hormones and behavior 54.3 (2008): 359-364.
Maestripieri, Dario, and Suzanne Pelka. "Sex differences in interest in infants across the lifespan." Human Nature 13.3 (2002): 327-344.


In the digit span test, why do some strategies work and others don't? I got a free copy of PEBL, psych experiment software, which includes a couple of standard working memory tests.

When I perform the digit span working memory test, I can repeat the numbers as they appear, and do some basic chunking. For example, I can repeat '3-1-8-4' as "thirty one" "eighty four". But when I try any other chunking or rehearsal technique I find it impossible to do the task. I think my working memory just empties. My question is why that happens? More specifically -

What cognitive functions do more complex rehearsal and chunking techniques tap into? Is there a means to improve / train those specific cognitive functions?

Chunking leverages long-term memory for the chunks, i.e. we recognize and remember much easier the familiar chunks, sometimes algorithmically. This is much easier explained in the domain of letters/words, e.g. we'd recognize USA as substring among random letters. Similarly most would recognize the pattern 1945 (WWII end) by paired association, or 12321 algorithmically.

In the classic case of SF he used sequences familiar to him (running times) to greatly improve his performance. More unusual (cf. Memory Search By A Memorist) is Rajan's ability to recognize 13- to 17-digit chunks visually/syntactically, without assigning them meaning. Ishihara, who could memorize overall longer sequences than Rajan [under controlled settings], but was much slower, used a method to convert them into syllables; he was naturally very gifted at remembering nonsense syllables though (cf. Superior Memory).

A few other association methods exist, including with places ("loci"), which relies on visual memory etc. It's possible to train the average person using these and attain significant improvements; the graph below (3rd one bein relevant, but I've included all for the caption) is from a recent PhD thesis. It takes a significant amount of practice/time to become proficient/fast in the encoding/decoding techniques.

Some clarification in regards to these graphs:

The other two tests are self-paced (just with overall time limit) memorization tests, i.e. how many words/digits in a sequence can be retained in a 5-minute interval with no time pressure for learning each word/digit.
The digit span was tested with 2 second intervals, which probably helped with learning a bit. Other authors have used 1 second, which makes it harder to apply encoding/decoding techniques.
The training protocol (for the blue lines) was fairly involved using the Memocamp software which can display the memorization aids (loci or images), and can also display a configurable metronome.
"SEM" stands for "Standard Error of the Means (the error bars).

The handful of people (not in this thesis/experiment) who have practiced for years (one of SF's colleagues and two in a replication experiment) have reached 80-100 digits in the digit span test. For that level of performance, not only did they use an associative system, but also a hierarchical chunking method. The chunk size was almost universally 4 for these performers (thus the need for hierarchy). On average it took about 500 hours of practice to decrease decoding/encoding time from an initial 5 seconds to the 1 second required by the digit span test (all this cf. The Neuroscience of Expertise, pp. 110-112).


How to stain for orexin neurons in mice? I am interested in studying possible depletion of orexin levels in the brains of some mice, and am wondering if anyone knows of a staining substance/technique for use on slices of fixed mouse brains that would highlight orexin-producing neurons of the hypothalamus such that their number/volume could be examined.

Context - Orexin is also known as hypocretin. It is a neuropeptide associated with sleep regulation that's produced primarily in the hypothalamus. It was originally discovered in rats and it's important to me because it is well-known that depleted orexin-producing neurons are connected to narcolepsy (Tsujino & Sakurai, 2009). I'm part of a large lab studying other conditions that share some symptoms with narcolepsy, and am interested in looking at orexin-producing neurons in our animal models. I don't currently have access to means to use transgenic orexin knockout mice as are mentioned in the article by Tsujino & Sakurai (though if anyone's used them before I'd be interested to know for the future), but I do have means to stain, view, and photograph fixed brain slices from our existing animals.

So, I'm wondering if anyone is familiar with a substance/approach to determine the approximate amount or volume of intact orexin neurons of the hypothalamus in WT mice (for comparison between animals) using a staining technique.

Tsujino, N., & Sakurai, T. (2009). Orexin/hypocretin: a neuropeptide at the interface of sleep, energy homeostasis, and reward system. Pharmacological reviews, 61(2), 162-176.

Let's work backwards.

  I don't currently have access to means to use transgenic orexin knockout mice as are mentioned in the article by Tsujino & Sakurai (though if anyone's used them before I'd be interested to know for the future)

Just so we're on the same page: orexin knockout mice lack orexin receptors. Have you considered finding a highly selective orexin antagonist? Very loosely speaking, a good antagonist ought to instantiate the functional equivalent of the receptor knockout.

  Orexin antagonists, especially those that block OX2 or both OX1 and OX2 receptors, clearly promote sleep in animals, and clinical results are encouraging: Several compounds are in Phase III trials. As the orexin system mainly promotes arousal, these new .... Orexin peptide knockout mice also seem to have severe sleepiness, with an inability to maintain long bouts of wakefulness during their active period (50, 51).

Scammell, T. E., & Winrow, C. J. (2011). Orexin receptors: pharmacology and therapeutic opportunities. Annual review of pharmacology and toxicology, 51, 243-266.

  So, I'm wondering if anyone is familiar with a substance/approach to determine the approximate amount or volume of intact orexin neurons of the hypothalamus in WT mice (for comparison between animals) using a staining technique.

Yes, there's a number of methods. One of the common methods is using Fos immunohistochemistry. A summary is as follows:

An immediate-early gene called c-fos will be expressed as a consequence of neuronal excitation. If you administer an orexin agonist, e.g. modafinil (actually perhaps something far more selective than that) or subject the mice to your orexin-activating intervention, then the neurons where that receptor is present ought to upregulate c-fos express when they fire.

You then cut up your rat into fine slicies and apply a high-contrast stain that will bind to the c-fos protein... It's something to do with the antigen-binding site of the protein, but this is all very vague to me without a reference on hand. You then wash the rat slices. If performed correctly, the stain will remain where the c-fos has been expressed.

You then spend many, many hours counting up the stained dots. You compare this against a control, and the difference allows you to reasonably infer where the orexin receptors are.

The following may help:

Estabrooke, I. V., McCarthy, M. T., Ko, E., Chou, T. C., Chemelli, R. M., Yanagisawa, M., ... & Scammell, T. E. (2001). Fos expression in orexin neurons varies with behavioral state. Journal of Neuroscience, 21(5), 1656-1662.

  I'm part of a large lab studying other conditions that share some symptoms with narcolepsy, and am interested in looking at orexin-producing neurons in our animal models…  So, I'm wondering if anyone is familiar with a substance/approach to determine the approximate

This is what I don't get: why don't you ask your supervisor and say, "yo, is anyone in our lab doing staining?" -- maybe they are, maybe they aren't. If not, you ask your supervisor, "who among your colleagues does staining?" And they will know or know someone who knows. You then get in touch with said researcher. How it works is that you get taught the staining technique, and in exchange, they get free labour. Alternatively, if they're not down with that arrangement, you simply ask them that the next time they're doing staining, if you can drop by and just watch. It's a simple courtesy that they accommodate your request, and I guarantee you that you will learn far more in one afternoon than you could hope to on SE.


What are the personality and behavioural correlates of divorce? I've read quite a few statistics about divorce (sources here and here), and biggest factors seems to be:

Culture - from 71% in Belgium to 2-3% in some traditional societies
Income of the couple
Ratio of the woman's income to the man

Other factors such as education and whether they come from divorced family also plays a big factor.

But what are some behavioral characteristic that have the biggest impact?
For example are shy people less likely to get divorced controlled for everything else? Do people who had less sexual partners in their lifetime are less likely?
 Introverts or extroverts? Which of the 5 big personality traits predict best (Conscientiousness, Openness to experience, Agreeableness, Neuroticism, Extraversion)?
I want some respected studies video lectures very much appreciated :)

This answer speaks mostly to the relationship between personality characteristics/disorders and divorce.

From the Multidimensional Personality Questionnaire, it seems that Positive Emotionality and Negative Emotionality were positively related to divorce risk, while Constraint was negatively related (Jockin, Mcgue, & Lykken, 1996). Note that this study also looks at genetic factors, and finds that “In women and men, respectively, 30% and 42% of the heritability of divorce risk consisted of genetic factors affecting personality, and personality and divorce risk correlated largely as a result of these common genetic influences.”

From a bit of a different perspective, other studies have assessed the relationship between divorce and personality disorders:

  Multiple regression analyses showed Paranoid and Histrionic personality disorder symptoms to be consistently and positively associated with number of divorces across all three sources of personality assessment. Conversely, Avoidant personality disorder symptoms were negatively associated with number of divorces (Disney, Weinstein, & Oltmanns, 2012).

I'll note that the Jockin, Mcgue, and Lykken study is rather old (contextually), and that the relationships that were clear 20 years ago may be different today.

Finally, though not related specifically to divorce, there is a nice article out there that attempts to illustrated differences in personalities for those looking for short-term vs. long-term relationships (Holtzman & Strube, 2013). This article specifically addresses an "array of personality traits (Big 5, Dark Triad, and Schizoid Personality)". A bit tangentially related, but perhaps of interest.

References

Disney, K. L., Weinstein, Y., & Oltmanns, T. F. (2012). Personality Disorder Symptoms Are Differentially Related to Divorce Frequency. Journal of Family Psychology, 26(6), 959–965. https://doi.org/10.1037/a0030446

Holtzman, N. S., & Strube, M. J. (2013). Above and Beyond Short-Term Mating, Long-Term Mating is Uniquely Tied to Human Personality. Evolutionary Psychology, 11(5), 1101–1129. https://doi.org/10.1177/147470491301100514

Jockin, V., Mcgue, M., & Lykken, D. T. (1996). Personality and Divorce: A Genetic Analysis. Journal of Personality and Social Psychology, 7(2), 288–299. https://doi.org/10.1037/0022-3514.71.2.288


Why are humans the only species to maintain relationships with other animals simply for pleasure? In the wild, animals may share waterholes, and grazing areas. Some animals have symbiotic relationships one example being the hippopotamus and the oxpecker bird.
The symbiotic relationships are based on needs; Hunger, shelter, safety.

Human beings, appear to be the only species that has relationships with other species for pleasure. Companion animals and horses for riding are two examples.
Of course, there is also the use of animals to meet the species needs; eg dogs for safety.

In this question I would like to focus on the relationship between human beings and other species as a source of pleasure (or comfort).

What is different within the human brain that allows humans to maintain relationships with other animals purely for pleasure, that is absent in other species?

According to Hal Herzog, humans are the only animals that keep pets. Other animals have also kept pets however it was not under natural settings. These other "pet" relationships were observed in zoos, controlled experiments. etc.

One the main reasons Herzog notes that humans are the only animals to keep pets is the idea of culture. Humans have evolved to form a culture with social rules and norms. They can gather social information from others by relying on certain cultural rules. So it is acceptable for humans to form pet relationships with other animals. Some cultures do not have a word for pet. However ultimately these cultures end up forming relationships with animals that are "pet-like." The reason for this is because of memes. That is, the idea of having a pet spreads across different cultures.


What articles use structural equation modelling in a meta-analytic context to model mediation? I'm interested in structural equation modelling (SEM) meta-analysis and its application to modelling mediation relationships.

Mike W.-L. Cheung has written a few articles on the topic of SEM Meta-Analysis (e.g., Cheung, 2009). He's also written the metaSEM package in R.

One basic approach to SEM meta-analysis is a simple fixed-effect two-step approach. First, you extract a weighted average correlation matrix from the component studies. Second, you analyse that correlation matrix using standard SEM approaches.

However, in reality, things are more complicated.

Correlations often vary between studies by more than just sampling error, suggesting that a fixed-effect approach may be invalid. If a random-effects model is adopted, this raises issues of whether to categorise the correlation matrices into groups of fixed-effects matrices or whether to somehow incorporate the random-effects into the overall SEM.
Some studies may not have all the variables of interest, which introduces a  form of missing data, which in turn raises issues of how to define sample size and the standard error.

So in general, I'm interested in reading examples of SEM meta-analyses applied in psychology to get a sense of how others have dealt with the various issues. In particular, I'm interested in meta-analysis SEM that incorporates the modelling of indirect effects (i.e., mediation).

Questions

What studies have applied meta-analysis SEM in psychology to model indirect effects?
How have they dealt with issues such as (a) fixed versus random effects and (b) missing data?

References

Cheung, M.W.L. (2009). Meta-analysis: A Structural Equation Modeling Perspective. PDF

I'd like to address important issues that Jeromy Anglim raised in the "Personal thoughts" section of his answer, namely that correlation parameters (i.e., true, population, or infinite-sample correlations) often vary and covary among studies, and this between-studies\interstudy heterogeneity implies heterogeneity in studies' parameters for a structural equation model (SEM).  I'll describe a method I've proposed to account for this heterogeneity when estimating and making statistical inferences about a SEM, such as a univariate or multivariate regression model, factor model, path model, or model for structural relations among latent variables, or specific quantities based on such a model (e.g., $R^2$, indirect\mediation effect, fit index).  This approach's core idea is simple:

Use random-effects (RE) meta-analysis to estimate attributes of the between-studies distribution of correlation-matrix parameters (e.g., its mean and covariance matrix).
Transform those results to estimate attributes of the between-studies distribution of SEM parameters.

Many commonly used MASEM methods are based on fixed-effects (FE) models, and although meta-analyzing heterogeneous correlation matrices using certain FE methods performs reasonably well in some situations, it's probably not advisable for making unconditional inferences about a larger universe of studies (Hafdahl, 2008a).

Here I'll just give overviews of the problem and a three-step version of my proposed meta-analytic SEM (MASEM) method -- actually a collection of methods based on different choices for critical tasks at each step.  To keep the exposition relatively accessible I'll gloss over several important technical details.  Although this is an active area of my methodological research, the methods I'll sketch here are based largely on unpublished work (I'll cite references).  Because some aspects of this approach would be prohibitively difficult for many applied researchers to implement, one aim of my work in this area is to develop user-friendly software.  Until that's available, feel free to contact me for help with these techniques; such requests may motivate me to devote more time and other resources to this work.

Overview of Problem

In this section I'll describe my view of the problems for which MASEM is typically used.  Suppose we have from each of $k$ independent studies a sample Pearson-r or Fisher-z correlation matrix among $p$ variables of interest (e.g., $X_1, X_2, \ldots, X_p$), and suppose we're interested in a particular SEM for those variables or a related quantity that can be expressed as a function of the correlation matrix (e.g., 1 or more [standardized] path coefficients, indirect or total effect, squared multiple correlation, fit index).  It's useful to distinguish between each study's correlation-matrix parameter and that study's correlation-matrix estimate from a particular sample of subjects.  Meta-analysts are typically interested in using several studies' estimates to understand their corresponding parameters or distributions thereof.

In this overview I'll mostly ignore several interesting but vexing complications that arise in practice.  For instance, studies might use different versions of one or more variables (e.g., different measures similar to $X_1$), some studies might not contribute all $d = p( p - 1) / 2$ distinct correlation-matrix elements (e.g., due to missing variables or unreported elements), some of a study's correlations might be based on different subsets of its sample (e.g., if some subjects are missing some variables), a study might contribute two or more independent or dependent correlation matrices (e.g., from different groups or the same group in different conditions or at different times), studies' correlations might be influenced by so-called artifacts (e.g., [un]reliability, range restriction, dichotomization), and study-level covariates\moderators might partially account for between-studies heterogeneity.

Now, if we take the RE view that correlation-matrix parameters vary among studies -- that is, our studies are from a universe of studies whose correlation-matrix parameters follow some multivariate distribution -- then most functions of that correlation-matrix parameter will also vary among studies.  In particular, an SEM's path-coefficient parameters and other quantities (e.g., indirect effects) will have a distribution over studies.  To be clear, these distributions' between-studies (co)variation is not due to within-study sampling (co)variation caused by finite samples of subjects; instead, it may be viewed as due to studies' different constellations of features that produce different values for their correlation-matrix parameters and, consequently, most functions of those parameters.  (As a simpler example, consider 1 Pearson-r correlation parameter from each of several studies: If it varies among studies, then so will various functions of it -- its square, coefficient of alienation, Fisher z-transform, common language effect size, etc.)

By analogy with typical tasks in conventional RE meta-analyses, we might be interested in the following for our SEM: estimates of the between-studies mean and variance of each SEM path coefficient or other related quantity (e.g., indirect effect) and inferences about each such quantity (e.g., confidence interval [CI], prediction interval [PrI], hypothesis test).  When we're interested in two or more quantities from the SEM, we might wish to obtain bi- or multivariate generalizations of these estimates or inferences (e.g., confidence or prediction region\set); these might include quantities based on two or more distinct SEMs we'd like to compare, such as fit indices from two or more SEMs.  We might also be interested in the entire between-studies distribution of one or more SEM quantities (i.e., not only its mean and [co]variance [matrix]), which we could in some situations depict graphically (e.g., density plot).

Most commonly used MASEM approaches neglect between-studies heterogeneity in the SEM parameters, which seems especially difficult to justify in the presence of heterogeneous correlation(-matrix) parameters.  Certain aspects of the SEM parameters' between-studies (co)variance might be substantively important, such as if we're interested in how much particular path coefficients, indirect or total effects, squared multiple correlations, or fit indices (co)vary among studies.  For instance, if we use RMSEA as a fit index, we might wish to know how much RMSEA varies among studies, a plausible range of RMSEA values (e.g., prediction interval), or what proportion of studies have RMSEA values within some "acceptable" interval (e.g., below .05).

Moreover, there's reason to treat heterogeneity cautiously even if we're interested in only the mean of SEM parameters: Because SEM parameters are typically nonlinear functions of the correlation matrix, it's unclear what's estimated by applying the SEM directly to a mean correlation matrix, as in most MASEM approaches; that might estimate the mean SEM parameter poorly.  As a simpler example, suppose that for a heterogeneous effect-size parameter $Y$ we know its mean and variance over studies, $\mathrm{E}(Y)$ and $\mathrm{Var}(Y)$, but want to know the mean for $Y$'s square: Because for most distributions of $Y$ it's a fact that $\mathrm{E}(Y^2) = [\mathrm{E}(Y)]^2 + \mathrm{Var}(Y)$, simply squaring $\mathrm{E}(Y)$ gives a value lower than the desired $\mathrm{E}(Y^2)$, especially if $\mathrm{Var}(Y)$ is large.  This basic problem with applying a nonlinear transformation to a mean of heterogeneous effect sizes was addressed for the case of a univariate correlation -- focusing on the z-to-r transformation -- by Hafdahl (2009) and Hafdahl and Williams (2009); the analogous situation was addressed for correlation matrices by Hafdahl (2008b, 2009b), for generic univariate effect sizes by Hafdahl (2011), and for generic multivariate effect sizes by Hafdahl (2009c).

Overview of Proposed Method

In the first paragraph above I mentioned the gist of my proposed two-stage MASEM method.  To facilitate explanation, it's useful to divide the second stage -- transformation of results -- into separate steps for estimation and inference.  For instance, if we start with $k$ studies' estimates of a Fisher-z correlation matrix, the first step might entail estimating the correlation-matrix parameters' between-studies mean and covariance matrix, and the second and third steps might entail transforming those results to obtain estimates of and inferences about the between-studies mean and covariance matrix of the SEM's path coefficients.  Below I elaborate a bit on these three steps.

For convenience, let's use the following notation for correlation matrices from Study $i, i = 1, 2, \ldots, k$:

$\theta_i$: vector of the $d$ distinct parameters in a correlation matrix for $p$ variables, in either the Pearson-r or Fisher-z metric
$y_i$: vector of the $d$ distinct estimates in sample correlation matrix (i.e., $y_i$ is an estimate of $\theta_i$)

For example, if we're interested in the correlation matrix for $p = 5$ variables, then both $\theta_i$ and $y_i$ contain $d = 5(5 - 1) / 2 = 10$ correlations.  In RE meta-analysis we typically assume that $y_i$ has a within-study (sampling\conditional) distribution whose mean is approximately $\theta_i$, and that $\theta_i$ (or just $\theta$) has the same between-studies distribution for all studies, with mean $\mu_\theta = \mathrm{E}(\theta)$ and covariance matrix $\Sigma_\theta = \mathrm{Cov}(\theta)$.  (For simplicity I'll slightly abuse notation for random correlation parameters and estimates -- instead of using $\Theta_i$ and $Y_i$ -- and I'll ignore other quantities used in certain meta-analytic procedures, such as each study's sample size and conditional covariance matrix, whose inverse [i.e., precision matrix] is essentially used as a weight matrix.)

In terms of SEM notation, let's similarly denote the SEM parameters of interest in Study $i$ by $\gamma_i = g(\theta_i)$, where the function $g$ transforms a correlation-matrix parameter into SEM parameters.  This $g$ might be a fairly complicated function, such as for the parameters of a SEM with respect to a particular objective\loss criterion (e.g., ML, WLS, ADF) or a fit index for that SEM.  Also, $\gamma_i$ might be just one number (e.g., 1 path coefficient, indirect effect, fit index) or a vector (e.g., 2 or more path coefficients).  At any rate, our MASEM goal might be to estimate $\gamma$'s between-studies mean or (co)variance (matrix), $\mu_\gamma = \mathrm{E}(\gamma)$ and $\Sigma_\gamma = \mathrm{Cov}(\gamma)$, and make inferences about either distributional attribute; we might also wish to estimate $\gamma$'s entire distribution.

Below are the three steps of my proposed method; Steps 2 and 3 presume we have in mind a specific SEM or related quantity that can be expressed as $\gamma_i = g(\theta_i)$.  This is similar to Hafdahl's (2009a, 2011) univariate methods and Hafdahl's (2008b, 2009b, 2009c) multivariate methods but specific to MASEM.

1. Meta-Analysis for $\theta$: Apply multivariate RE meta-analysis to $y_i$ to obtain estimates of at least $\mu_\theta$ and $\Sigma_\theta$, which I'll denote $\hat\mu_\theta$ and $\hat\Sigma_\theta$, and perhaps $\theta$'s full between-studies distribution; perhaps also obtain a covariance matrix for only $\hat\mu_\theta$ or both $\hat\mu_\theta$ and $\hat\Sigma_\theta$, depending on how inference is handled in Step 3.  Among several proposed methods for estimating $\mu_\theta$ and $\Sigma_\theta$, only a few handle incomplete correlation matrices -- nearly unavoidable in MASEM data sets -- in a principled way (e.g., Hafdahl & Wu, 2011; Kalaian, & Raudenbush, 1996; White, 2011).  In particular, Hafdahl and Wu's extension of Becker and Schram's (1994) EM algorithm permits any one or more of $y_i$'s correlations to be missing, doesn't require imputing values for missing correlations, and yields a posterior distribution for each study's entire $\theta_i$ (given its possibly incomplete $y_i$); it also yields an estimate of $\theta$'s full between-studies distribution as a mixture of the studies' posterior distributions.  Depending on the estimation method, a covariance matrix for $\hat\mu_\theta$ may be obtained by generalized least-squares (GLS) or other methods (e.g., based on Hessian matrix for maximum-likelihood estimators), some of which also provide a covariance matrix for $\hat\Sigma_\theta$.  Hafdahl (2004) demonstrated substantial differences in performance among different techniques for multivariate RE meta-analysis applied to correlation matrices.

2. Estimation for $\gamma$: Use an appropriate transformation method -- based on the function $g$ -- to obtain estimates of at least $\mu_\gamma$ and $\Sigma_\gamma$, which I'll denote $\hat\mu_\gamma$ and $\hat\Sigma_\gamma$, and perhaps $\gamma$'s full between-studies distribution.  One strategy is to use a first- or second-order Taylor series approximation, which essentially entails approximating $\gamma = g(\theta)$ by a simpler linear or quadratic function of $\theta$; estimates of this approximating function's mean and covariance can then be computed from Step 1's $\hat\mu_\theta$ and $\hat\Sigma_\theta$.  Another strategy entails simulation: Sample values of $\theta$ from its distribution estimated in Step 1, transform these to values of $\gamma$, and estimate $\mu_\gamma$ and $\Sigma_\gamma$ from this simulated distribution; we might treat $\theta$'s distribution as multivariate normal -- such as $\theta \sim \mathcal{N}_d(\hat\mu_\theta, \hat\Sigma_\theta)$ -- or permit it to take some other form estimated from the data (e.g., mixture of posteriors from EM algorithm).  Either strategy might also be used to estimate other attributes of $\gamma$'s distribution, such as tail or central areas (e.g., probability that 1 or more path coefficients or other quantities are near 0, positive, large, etc.) or $\gamma$ values bounding regions of interest (e.g., quartiles, middle 95%).  (We could in principle transform $\hat\mu_\theta$ and $\hat\Sigma_\theta$ to $\hat\mu_\gamma$ and $\hat\Sigma_\gamma$ via integration, using the definitions of $\mu_\gamma$ and $\Sigma_\gamma$, but that'll often be intractable analytically and infeasible computationally.)

3. Inference for $\gamma$: Make inferences about $\mu_\gamma$ and $\Sigma_\gamma$, such as CIs, PrIs, or hypothesis tests for single-valued parameters or their multivariate generalizations for vector-valued parameters (e.g., confidence or prediction regions).  One strategy is to use the (multivariate) delta method, which essentially entails using derivatives to transform the covariance matrix for $\hat\mu_\theta$ alone or both $\hat\mu_\theta$ and $\hat\Sigma_\theta$ to a covariance matrix for $\hat\mu_\gamma$ or $\hat\Sigma_\gamma$; the latter covariance matrix can be used to construct CIs or PrIs or test hypotheses.  Another strategy, at least for CIs or confidence regions, is to use a bootstrap technique to essentially construct an empirical sampling distribution of $\hat\mu_\gamma$ or $\hat\Sigma_\gamma$; numerous bootstrap options are available, depending largely on how the sample of bootstrap replicates -- $\hat\mu_\gamma$ or $\hat\Sigma_\gamma$ for each resample from the data -- is constructed (e.g., parametric vs. nonparametric) and how it's used to construct confidence intervals or regions (e.g., standard deviation\error vs. percentile, bias correction or not).

Because this is already a fairly long overview, I'll close with a few remarks.  First, despite its advantages over some other MASEM methods, my proposed method also has drawbacks and limitations; I won't elaborate here on these pros and cons, except to caution that my proposed method might perform unacceptably in certain circumstances.  Second, my proposed method would benefit from considerably more work, such as refining aspects of each step and studying its performance in realistic situations defined by characteristics of MASEM studies (e.g., number of primary studies, distribution of sample sizes, distribution of correlation-matrix parameters, pattern and mechanism of missing data, choice of function g).  To date there's been little evaluation of multivariate RE meta-analysis analytically or by simulation, for either correlation matrices (cf. Hafdahl, 2004, 2008b) or other multivariate effect sizes (cf. Riley, 2009; Riley, Abrams, Sutton, Lambert, & Thompson, 2007), and Hafdahl's (2009c) Monte Carlo studies of meta-analysis for functions of multivariate effect sizes did not include correlation matrices.  Third, Bayesian approaches to meta-analysis, such as Prevost, Mason, Griffin, Kinmonth, Sutton, and Spiegelhalter's (2007) proposed method for correlation matrices, might be especially well-suited to MASEM due to their natural -- though computationally challenging -- strategies for constructing posterior distributions for functions of a study's parameters.

References

Becker, B. J., & Schram, C. M. (1994). Examining explanatory models through research synthesis. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 357-381). New York: Russell Sage Foundation.

Hafdahl, A. R. (2004, June). Refinements for random-effects meta-analysis of correlation matrices. Paper presented at the meeting of the Psychometric Society, Monterey, CA.

Hafdahl, A. R. (2008a). Combining heterogeneous correlation matrices: Simulation analysis of fixed-effects methods. Journal of Educational and Behavioral Statistics, 33, 507-533. doi:10.3102/1076998607309472

Hafdahl, A. R. (2008b, July). Meta-analysis for functions of heterogeneous correlation matrices. Paper presented at the meeting of the Psychometric Society, Durham, NH.

Hafdahl, A. R. (2009a). Improved Fisher z estimators for univariate random-effects meta-analysis of correlations. British Journal of Mathematical and Statistical Psychology, 62, 233-261. doi:10.1348/000711008X281633

Hafdahl, A. R. (2009b, May). Meta-analysis for functions of dependent correlations. In A. R. Hafdahl (Chair), Advances in meta-analysis for multivariable linear models. Invited symposium presented at the meeting of the Association for Psychological Science, San Francisco, CA.

Hafdahl, A. R. (2009c). Meta-analysis for functions of heterogeneous multivariate effect sizes. Unpublished master's thesis, Washington University in St. Louis, St. Louis, Missouri. http://openscholarship.wustl.edu/etd/439/

Hafdahl, A. R. (2011). Translating meta-analytic results: Techniques to express random-effects estimates in other metrics. Manuscript in preparation, Washington University in St. Louis.

Hafdahl, A. R., & Williams, M. A. (2009). Meta-analysis of correlations revisited: Attempted replication and extension of Field’s (2001) simulation studies. Psychological Methods, 14, 24-42. doi:10.1037/a0014697

Hafdahl, A. R., & Wu, W. (2012, February). An EM algorithm for multivariate random-effects meta-analysis with incomplete effect estimates. Manuscript in preparation, ARCH Statistical Consulting, LLC.

Kalaian, H. A., & Raudenbush, S. W. (1996). A multivariate mixed linear model for meta-analysis. Psychological Methods, 1, 227-235. doi:10.1037/1082-989X.1.3.227

Prevost, A. T., Mason, D., Griffin, S., Kinmonth, A.-L., Sutton, S., & Spiegelhalter, D. (2007). Allowing for correlations between correlations in random-effects meta-analysis of correlation matrices. Psychological Methods, 12, 434-450. doi:10.1037/1082-989X.12.4.434

Riley, R. D. (2009). Multivariate meta-analysis: The effect of ignoring within-study correlation. Journal of the Royal Statistical Society—Series A, 172, 789–811. doi:10.1111/j.1467-985X.2008.00593.x

Riley, R. D., Abrams, K. R., Sutton, A. J., Lambert, P. C., & Thompson, J. R. (2007). Bivariate random-effects meta-analysis and the estimation of between-study correlation. BMC Medical Research Methodology, 7, 3. doi:10.1186/1471-2288-7-3

White, I. R. (2011). Multivariate random-effects meta-regression: Updates to mvmeta. Stata Journal, 11, 255-270.


Are there some discussions about Buddhism in science? Buddhists are masters in observing what occurs on their minds, i.e what we call subjective as feelings, thoughts and more first-person stuff.

They really seem to understand how inmaterial world works (it might be obvious to some of you), but they lack of a materialist point of view. For example, they can study feelings occurring on their minds and modify it, but do not have a clear idea regarding the brain-basis of those feelings.

Are there any reviews or essays discussing Buddhists' knowledge and how science can complement it?

Some resources:

Wikipedia has a whole page for Buddhism and science in specific and Cognitive science of religion in general.
Alan Wallace's Buddhism and Science
Tricycle's The Scientific Buddha. Tricycle is a Buddhism-oriented magazine that is quite popular
Mind and Life Institute. One of its founder is Dalai Lama, and it hosts MindRxiv preprint. You can find many preprints in the field there
The mindfulness technique is widely used in therapy


What causes gender differences in responses to farting? Background: By casual observation, I have noticed that males tend to react differently to the sound of a fart. It seems that men are more likely to laugh whereas women are more likely to show signs of disgust.
I thought that perhaps there might be some evolutionary basis for this.

Questions

Is there any serious psychological research on the causes of our responses to farting?
Are there any systematic gender differences?
What are the posited causes of such gender differences?

I was shocked at how difficult it was to find systematic scientific research on the psychology of flatulence. The main empirical paper appears to be one by Lippman (1980). It seems to be hard to get a copy of the original. However, the author of the Neurotic Physiology blog discusses the paper at length.

Lippman study

Lippman asked participants to rank various hypothetical farters based on various factors. With regards to sex differences the blogger writes:

  The sex differences were a little surprising. It turns out that women
  are more forgiving of loud, accidental farts (girls, we've all been
  there I'm sure), and don't ding the farter so much on "politeness"

However, when you think about the problem there are different kinds of sex differences in fart ratings: Men rating male farters; men rating female farters; women rating male farters; and women rating female farters.  This question really only addresses how women rate others in general.

Toilet psychology

Recently, a book was released by Professor Nick Haslam called "Psychology in the Bathroom". There's a good summary in The Psychologist (2012, PDF).
The book specifically aims to redress the lack of psychological research devoted to excretion and related matters.

One particular passage from the book summary in The Psychologist may provide a potential explanation for gender differences in perceptions of farting:

  A common thread running through these differences is that women’s
  excretion is more hidden, emotionally fraught and suppressed than
  men’s. The incompatibility of femininity and excretion is nicely
  expressed in Jonathan Swift’s poem ‘The lady’s dressing room’, in
  which a suitor sneaks into his beloved’s room only to  find evidence
  of her dirty corporeality, including sweaty garments, beslimed towels
  and encrusted combs. Upon discovering her chamber-pot he slinks away
  in horror, lamenting ‘Oh! Celia, Celia, Celia shits!’ The same
  sentiment is expressed less poetically by an American undergraduate:
  ‘women are supposed to  be non-poopers’ (Weinberg & Williams, 2005,
  p.327). Despite our enlightened modern attitudes to gender equality,
  women are still judged more severely for violations of this ideal of
  untaintedness than men.  In one study (Goldenberg & Roberts, 2004), a
  female experimenter who excused herself to use the bathroom was
  evaluated more negatively than one who excused herself to get some
  paperwork: no such difference was found for a male experimenter.

General thoughts

So in summary, research on farting has generally been neglected by the psychological literature.

There is some evidence to suggest that women who fart are judged more negatively than men who fart. However, presumably this would vary a lot with context, who is farting, and who is recognising the farting.
It would also presumably be a cultural phenomena.

One theory to explain gender differences is that a lack of purity is often associated with activities related to excreting, such as farting. Farting loudly or openly may symbolise a lack of control over excretion or remind others that a person is an excreting being. Purity is sometimes  associated with a feminine ideal. Thus, farting may be socially more detrimental for a female than a male. Anyway, the preceding statements are largely based on theory; I'd like to see a lot more empirical evidence before I take them too seriously.

References

Lippman, LG. "Toward a social psychology of flatulence: The interpersonal regulation of natural gas". Psychology: a Quarterly Journal of Human Behavior, 1980.
Goldenberg, J.L. & Roberts, T. (2004). The beast within the beauty: An
existential perspective on the objectification and condemnation of women. In J. Greenberg, S.L. Koole & T. Pyszczynski (Eds.) Handbook of experimental existential psychology (pp.71–85). New York: Guilford.
Haslam, N. (2012). Psychology in the Bathroom. Palgrave Macmillan.
Haslam, N. (2012), Toilet psychology. The Psychologist. 25, 6, 430-433. PDF


What is the difference between social anxiety disorder and shyness? It seems that there are similar symptoms shown by people who suffer social anxiety and shyness, but what is actually the main difference between them? Thank you.

It's a matter of degree.

First of all, "shyness" is not a psychological or psychiatric term, but an everyday English word denoting a commonly observable personality characteristic on a par with courage, cheerfulness, or honesty. The meaning of "shyness" is not exactly defined, and people may use the word "shyness" to refer to different kinds of behaviors, from being introverted (and not interested in social interaction) to being afraid of social interaction.

"Social anxiety disorder", on the other hand, is a psychiatric and clinical psychological term, the meaning of which is clearly and exactly defined by the scientific community. That means that while there is of course some debate about what social anxiety disorder is, there is agreement about what the term currently means.

As for the relation between shyness and social anxiety disorder, shyness is generally conceived as a mild from of social anxiety, which leaves the person able and functioning in his or her life, while social anxiety disorder is such a strong anxiety that the person suffering from it cannot partake in a normal way, is disabled by this anxiety, and suffers from its effect.


Does neurodynamics have any relation to psychodynamics? From Neural oscillation - Wikipedia:

  Neural oscillations are commonly studied from a mathematical framework and belong to the field of "neurodynamics", an area of research in the cognitive sciences that places a strong focus upon the dynamic character of neural activity in describing brain function.

From Psychodynamics - Wikipedia:

  Psychodynamics, also known as psychodynamic psychology, in its broadest sense, is an approach to psychology that emphasizes systematic study of the psychological forces that underlie human behavior, feelings, and emotions and how they might relate to early experience. It is especially interested in the dynamic relations between conscious motivation and unconscious motivation.

When I was watching this artwork, I didn't know about neurodynamics and thought that it was completely about psychodynamics, because I thought the emergence and dissipation of the waves in the artwork were about consciousness and subconsciousness. This thought is strengthened in this letter:

  Mental awareness and ‘I’ emerge as a secondary phenomenon from the primary stream of thinking-in-movement.

I know psychodynamics is regarded as pseudoscience for the lack of evidence, but it's still useful enough for some specific psychological disorders. And as neurodynamics is about oscillation and psychodynamics was inspired from thermodynamics, if we can find the link between the two in physics (which they should), then we will see the link between the two.

So does neurodynamics have any relation to psychodynamics?

Related: Is there a difference between physiological stimulations and psychological stimulations?

I believe I can see where the confusion lies when comparing the interesting artwork with psychodynamic theory.  The artwork you linked uses Neurological data to form images, whereas you may be getting confused with the famous Rorschach test otherwise known as the Inkblot test, and how the inkblot images are interpreted by the viewer.

Many psychologists in the United Kingdom do not trust its efficacy and it is rarely used, however, it is used by some mental health organisations such as the Tavistock Clinic (BBC, 2012).

The artwork with the "emergence and dissipation of waves" is using neurodynamic data and the Rorschach test is a psychodynamic test.

Neurodynamics relates to, or involves the dynamics of communication between different parts of the nervous system (Source) and the earliest document I can find relating to it is an unclassified document held by the DTIC (US Defence Technical Information Center) (Rosenblatt, 1961).  Shacklock (1995) also talks about neurodynamics in the realm of physiotherapy.

Psychodynamics, as stated in the Wikipedia article you linked, is about the dynamic relations between conscious motivation and unconscious motivation that underlie human behavior, feelings, and emotions and how they might relate to early experience.

So the two are not related, and in fact are very different.

A combination of the 2 sets of theories can be seen to be Neuropsychodynamics as in Miller (1990).

References

BBC (2012). Dr Inkblot BBC Radio 4Review available at: http://www.bbc.co.uk/programmes/b01l0kch

Miller, L. (1990). Neuropsychodynamics of alcoholism and addiction: Personality, psychopathology, and cognitive style. Journal of Substance Abuse Treatment, 7(1), 31-49.DOI: 10.1016/0740-5472(90)90034-N PMID: 2179572

Rosenblatt, F. (1961). Principles of neurodynamics. perceptrons and the theory of brain mechanisms (No. VG-1196-G-8) DTIC [Unclassified PDF]Available from: http://www.dtic.mil/docs/citations/AD0256582

Shacklock, M. (1995). Neurodynamics. Physiotherapy, 81(1), 9-16.DOI: 10.1016/S0031-9406(05)67024-1


What is Intelligence? Is it based on prediction capability of an agent? I have the intuition that at a first level is the capability of an individual to predict outcomes of her/his/its own actions in a environment were he/she/it has familiarity with (don't you feel stupid when you find yourself stranded in unknown environment?).

Then at higher level intelligence should correspond to be able to predict the outcomes of the actions of the other agents in the same environment, especially if they have a significant impact on the subject autonomy.

Does my intuition make sense?

There is no agreement among scholars about what intelligence might be. You must keep in mind that we cannot directly see intelligence. We don't even know if it actually exists! The idea of intelligence was invented to describe what we think might be the cause of certain observable behaviors, but there are others who think that we are throwing together disparate things in one pot that are caused by a whole variety of different powers.

Think of it like you think of love. Does anyone really love anyone? Or do we only follow a bunch of emotions like sexual attraction, fear of being alone, and so on? And even if there is "true love", what is it? Is it the love of a mother for her child? The love of a person for his job? The love of a teenager for an unattainable celebrity?

Similar confusion and doubts pertain to the concept of intelligence. Is there such a thing as a unified intelligence? Or is it made up of a set of partial intelligences (verbal intelligence, mathematical intelligence, spacial intelligence, and so on)? Or is there no "intelligence" at all and only things like memory capacity, processing speed and so on? And even if there were such a thing as general intelligence, is it the actual current ability to solve a problem, or the level of the most difficult problem you could potentially solve if you were trained to your maximum? Is it the ability to find a solution fast, or to find the best solution?

Psychology tries to avoid the pitfalls of these concepts and rather investigates the observable behavior itself: e.g. mate choice in the case of love, or problem solving behavior in the case of intelligence. Because that way we can all agree on what we are talking about, and it can be measured and quantified and interventions can be planned.

Intelligence is not a really useful concept.


Dual systems theory: in what sense are the systems 'unreal'? In the course of explain dual systems theory, Kahneman (2011) discusses the Heider study, in which:

  you see a large triangle, a small triangle, and a circle moving around
  a shape that looks like a schematic view of a house with an open door.
  Viewers see an aggressive large triangle bullying a smaller triangle,
  a terrified circle, the circle and the small triangle joining forces
  to defeat the bully.

Viewers perceive the movements of the shapes in causal terms. Kahneman then goes on to say:

  The psychology of causality was the basis of my decision to describe
  psychological processes by metaphors of agency, with little concern
  for consistency. I sometimes refer to System 1 as an agent with
  certain traits and preferences, and sometimes as an associative
  machine that represents reality by a complex pattern of links. The
  system and the machine are fictions; my reason for using them is that
  they fit the way we think about causes. Heider’s triangles and circles
  are not really agents—it is just very easy and natural to think of
  them that way. It is a matter of mental economy. I assume that you
  (like me) find it easier to think about the mind if we describe what
  happens in terms of traits and intentions (the two systems) and
  sometimes in terms of mechanical regularities (the associative
  machine). I do not intend to convince you that the systems are real,
  any more than Heider intended you to believe that the large triangle
  is really a bully.

I've never been able to understand the distinction he is trying to draw there. In what sense are the two systems 'unreal'?

Kahneman, D. (2011). Thinking, fast and slow. Macmillan.

What Kahneman means is that these are nothing but comparisons, or metaphors, to indirectly explain cognitive processing. Neither you nor I have physically seen these two systems exist, BUT the existence of such systems may be implicated through neural representations. Kahneman has used economic metaphors (concept of resources and their allocation) in his capacity model of attention (1973). (1)

The following excerpt from John B. Best's Cognitive Psychology, fifth edition (2) , might help. Here, he talks about the rationale behind using a metaphor (the information-processing or computer metaphor) in cognitive psychology:

  Because the information-processing viewpoint is necessarily abstract, why use it to study cognitive processes, especially when it might be possible to study the underlying neural processes directly? In other words, the students seem to suggest that we simply cut to the chase and study neural processes themselves. What stops us? Information-processing theories might argue that an abstract analysis is necessary because of discoverability problems at the neural level. It's true that specific relationships may always be discoverable between specific neural activities or locations, and particular cognitive or mental events... Despite this reservation, however, cognitive psychologists working in the information-processing tradition maintain that there are no guarantees that a general, discoverable neural representation inevitably underlies all specific cognitive events."

TL;DR: It's a metaphor, an indirect comparison to how cognitive processes occur, for ease of understanding.

1) There has been considerable debate regarding whether the concept of using strict, dichotomous systems is a good way to represent cognitive processing and why and how dual-process theories like Kahneman's fail in some aspects. Highly recommend reading some of the highlighted points in The Mythical Number Two by Melnikoff and Bargh (2018), published in Trends in Cognitive Sciences.

2) An edition of the book is available to borrow on archive.org and you may find this quoted text in the first introductory chapter.


Do we have technology to ethically measure the changes of interstitial space in sleeping humans? Lulu Xie et al write in their paper Sleep Drives Metabolite Clearance from the Adult Brain:

  Using real-time assessments of tetramethylammonium diffusion and two-photon imaging in live mice, we show that natural sleep or anesthesia are associated with a 60% increase in the interstitial space

Do we have technology to do the similar measurements in humans and not only in mice? Have those measurements been done?

It is possible to extract samples of interstitial fluid in humans.
The method has been described in detail.
Is it ethically permissible to extract the entire contents of the interstitial space? No. It is vital for survival.

Technically, it seems possible to perform the procedure before and after sleep. I am not sure if this has been done though. To what extent this is an invasive procedure I do not know, but it appears to involve surgery.

In addition, local interstitual fluid pressure (IFP) can be
measured in humans under anesthesia.

Both methods pose ethical challenges and it appears that these methods are relevant in clinical settings, where the cost-to-benefit analysis would suggest a benefit to the patient.


Name of a cognitive test assessing whether a child can think independently I am looking for a names / examples of a tests that determine if the child is thinking on its own (I have tried searching on wikipedia topics about cognitive sciences/ psychology etc. with no luck). To be more specific, long time ago I saw a video that described two tests:

In the first, a teacher was given two cookies, and the kid just one. Teacher asked the child
"Is it fair that I have two cookies and you just one" and the kid replied as it was supposed to "No".
Teacher then has split kid's cookie in half and asked again "Is it fair now?" and kid stated "Yes, cause we both have two now".

My memory fails at accurate description of second test, but I remember that it was supposed to determine if the kid is thinking 'What I know is what everybody knows'.

It's like if you put two kids in the same room with a large amount of 'sth', and asked one, let's call it 'Martha', to leave the room.
Then, you make the kid that stayed take for example two instances of 'sth' and afterwards ask him 'Do you think the kid that left the room knows that you took it?" and kid kid replies "Yes" because it cannot differentiate 'Martha's' knowledge from it's own.

I'd appreciate any ideas where to seek.

The first one is a test if a child has understood conservation of matter. It is an example of a conservation task. These belong to the tests used in the framework of Piaget to test what stage of development a child is in.
Here is a video demonstration of the cookie task. Here is another question on this site pertaining to a different conservation task.

The second one is an example of a false-belief task test for the Theory of Mind. See here for some criticism of the false-belief task, questioning if it truly tests what it aims to test.

I don't think it's appropriate to say either of these is used to test if children are "thinking on their own" for what it's worth. They're about developmental stages.


What can we learn from the neural networks of C.elegans to understand human brains? Recently I am reading some works about Caenorhabditis Elegans. A C.elegans has 302 neurons and we already know the function and connection of every one of their neurons so that we can exactly reconstruct it by computer programs. Also, C.elegans have a couple of sensory inputs, and a kind of intelligence (they can learn something).

The question is: what can we learn from C.elegans, so that we can understand Human brains?

To specify, I want to know:

What's the relationship between C.elegans and Human beings (or primates)? Is there any route in the terms of evolution?
Does the neural system of C.elegans still exist in human brains, as either a working or an idle part?

I am a computer scientist working on artificial intelligence. My purpose is to clarify the mechanisms of the Human brain. I hope C.elegans could help but I don't know much about biology.

Caenorhabditis elegans is probably not an ancestor to Humans. As found in Sponge proteins are more similar to those of Homo sapiens than to Caenorhabditis elegans, certain sponges were found to have more similar protein structures to humans than C. elegans suggesting the sponges are the ancestor.

For your second point, it depends what you mean. The actual, exact sort of network in C. elegans doesn't exist as a "part" in Human brains nor would it even if humans had C elegans as a direct ancestor; the network would have evolved considerably.

The basic structure of the neural network as a connected net of neurons and the basic functionality has of course remained through all organisms with developed neural networks. Like all life, certain related developmental processes exist between humans and C. elegans, which is why studying them (and most any other animal) is often relevant to Human research of similar biological or psychological areas of interest.

C. elegans can even learn, showing it is a very complete model of a neural network, despite it's simplicity.

C. elegans is not studied because it is an ancestor or because it has any particular relation to the human mind, but because it has an extremely simple, completely functional neural network. There are complete maps of C elegans' neural network and even online tools to search the synapses of C elegans. There are whole websites dedicated to research on C. elegans.

Similar to fruit flies, C. elegans also provide a great vector for developmental research because they have a lifespan of a couple weeks instead of decades.

Some great further reading is the introductory Why use C. elegans to study the synapse ?


Acoustic and light wave coherency? I know how some music notes combinations sound pleasing, yet others do not. Does the same occur with different frequencies of light (colors)? Since spectral color and acoustic pitch are both defined by their respective wavelengths (frequencies), it made me think that the psychological properties of the two may be related.

So do light waves, for example one with the same wave length as a mid-C and another with a mid-F wave, look nicely together? Or do certain colors when viewed simultaneously, or side by side, appear pleasing?

First I have to say that the wavelengths of light are on a totally different order of magnitude than sound. So the parallel drawn in your question "do light waves, for example one with the same wave length as a mid-C and another with a mid-F wave, look nicely together?" may seem logical, but is on closer inspection not easily maintained. Instead, one way to address this question more appropriately would be to talk about wavelength differences; i.e., light with wavelengths differing X octaves compared to acoustic tones differing X octaves.

Having said that I think it is worthwhile to sidestep the theoretical approach and take a closer look at how auditory and visual sensory information is actually processed at a neurophysiological level.

Sound is processed in the inner ear (the cochlea), which basically works as a Fourier transformer, and specifically a frequency-to-place converter. The spatial distribution of the characteristic frequencies (the tonotopy) on the basilar membrane of the cochlea follows a pattern where one octave spans about 2.5 mm (Greenwood, 1990). For the approximately 10 octaves the human ear can hear (~20 Hz - 20 kHz) we have 16,000 inner hair cells. See the following picture for a rolled-out cochlea with the tonotopy illustrated:

source: what-when-how.com

The eye, on the other hand, analyzes light frequencies using just 3 colors (red, green, blue) by means of three cone classes (as opposed to 16,000 hair cells each sensitive to a slightly different acoustic frequency). Although the visual system does a great job in combining this sparse frequency information into a spectrum of colors, it is not a frequency analyzer as such. In fact, it is more of a frequency "combiner", as it combines the ratios of activation of the cone classes and runs it through a system of color opponency. By weighing the relative contributions of the three colors (RGB) using the opponent system (R-G, Y-B) the visual system estimates the color of the object you are looking at. Below on the left is illustrated the spectral frequency sensitivity of the three cone classes (note the unequal, non-octave distribution of the three across the dynamic spectrum range), and on the right the color-opponent (Hering) model.

source:huevaluechroma.com and giantitp.com

The opponent nature of human vision (blue-yellow and red-green axis) results in a 2-dimensional color space which is very different from the 1-dimensional frequency space of the cochlea (Mather, 2006). Note that the third visual dimension is brightness, comparable to the second dimension of loudness in the ear.

In all, based on a neurophysiological signal-processing point-of-view, hearing and seeing frequencies are two completely different things. Comparing octaves between the two is worse than comparing apples and oranges, as apples and oranges share at least the same dimensionality.

It probably doesn't answer the question, but it may answer the question why it cannot be answered in any logical, comprehensive and straightforward way without loosing oneself in subjective monologues about "I like this and this combination of timbres but I don't like this color combination so much" kind-of-thing. Admittedly, it can be experimentally addressed by inquiring about the subjective 'pleasantness' of a set of combinations of colors and pitches in a study population. But from a physical and pysiological perspective, comparing the two entities of light and sound through equal-octave comparisons doesn't make sense since: (1) the two entities are processed through completely different neurophysiological principles as described above, ('1D acoustic frequency Fourier analyzer' versus '2D spectral combiner'), and (2) they represent entirely different physical entities altogether (photons/EM waves versus air pressure differences). Their only commonality is that they have an oscillatory nature, but that's where the parallel starts, and ends.

References
Greenwood et al. JASA 1990; 87:2592-605
Mather. Foundations of Perception 2006


How common are single-author research articles in cognitive psychology? Are single-author research articles looked down upon?

To target the question in the question body here:

This question is quite opinion-based, because everyone can decide to frown upon something. However, I think that, in general, it's much more about:

Who the author is, i.e., what is their track record in the field;
What their affiliation is;
What the quality of the article is;
What the quality (e.g. impact factor) of the journal is.

These considerations are, at least to me, much more important than how many authors follow or precede someone in the author list. I would never judge an article on the number of authors per se. Lastly, (invited) review papers are often authored by just one author, even in the STEM sciences.


Is there an upper limit to the number of channels in an EEG system that would provide additional useful data points? A quick scan of search results seems to indicate that the highest density EEG systems on the market are limited to 256 channels such as Neuvo, SynAmps, and others.
Is this an arbitrary limit (either due to system design or practicality of sensor placement) or is it perhaps a case of diminishing returns with any decreased sensor spacing not providing much in the way of additional information in the data points?

If not 256 channels then what would be an upper limit? Am I correct in assuming that the summation of electrical activity that is expressed at the scalp would be spatially quantized due the raised gyri on the brain surface (with any activity in the intervening sulci being masked)? Then again if that were the case would there still be benefit in having the extra spatial resolution running atop the contours of the gyri?

I'm wondering if a system consisting of a full mesh of electrodes would have merit as a project? [this would be for those such as myself that due to either nature or grooming don't have to work around any hair on there scalp]

For EEG, mostly diminishing returns. Because the contacts themselves are relatively far from the neurons generating those signals, there is a lot of correlated signal on adjacent channels. Statistical methods like clustering are often used to treat signals of interest coming from multiple channels as just a single signal. Increasing the spatial sampling density just means more channels will be included in that cluster.

There could be some improvement to SNR, but that would really only be helpful in the case of single bad contacts (effectively you are adding redundancy) and uncorrelated noise. It's also worth considering that more channels means more data to store and analyze which can increase processing time.

There's also the practical issue that equipment for recording EEG can be quite expensive, so if a lab owns a 256-channel system, increasing the density to allow for 512 channels is a substantial investment. If most EEG labs' systems allow for no more than 256 channels to be recorded, there is little incentive to create commercially available caps that record more than 256 channels, which further makes it less likely for someone to upgrade to more than 256 channel recording, etc.

If you move recordings closer to the brain, like in ECoG, or with depth electrodes, much higher sampling densities are relevant, but in that case for human subjects placement will be based on clinical needs rather than some theoretical limit.

As far as the contributions of sulci versus gyri to EEG signals, that's an active field of research and in summary "it's complicated": a lot depends on which neurons in cortex are involved, and how spatial patterns of activity are organized. You can make general statements about, for example, EEG versus MEG, but otherwise you are just making claims about relative contributions. Increasing sampling density on the scalp will have little to no impact on which brain regions contribute most to the signal.


Can new emotions be created or discovered? Related: Are there emotions that only some people can feel?

Is there any known observed or theoretical process by which new emotions could be observed or discovered?

Although one may argue over the exact number of emotions that exist today or the exact criteria that differentiate one from another, there is broad agreement on the general types of emotions that exist (e.g. most psychologists would agree that happiness, sadness, envy, anxiety, excitement, anger, disgust, lust, pride, etc. are emotions while pecan, horseradish, choke, stupid, chair, delta, redshirt, appoiapgpadvap, asdf, and yooe caph garharheq are not emotions). Those minor disagreements/edge cases are not what I am talking about, but a brand new emotion. Hypothetically, I can imagine that one might result from novel stimuli (or patterns of stimuli) never before experienced by humans (and thus resulting in a never-before seen affect on the brain), but I am not aware of any actual cases of this occurring or any known (outside of science fiction) theory of what sort of stimuli patterns might accomplish this or what such an emotion might be like.

If it has been shown that one of the "standard" emotions today arose after the advent of anatomically-modern humans (e.g. Cro-Magnon), I would consider that an answer.

In response to a comment by Bryan Krause, I am aware that there is a linguistic argument here. I'm imagining this as similar to the linguistic/neurological divide in color taxonomy. Famously, Russian does not have a single word for "blue" (instead, dividing it up into two separate colors), while Japanese historically did not distinguish between what English speakers call blue and green, but I believe it is fairly well understood that these distinctions happen at a fairly high level in the brain, rather than representing truly distinct forms of cognitive evolution. The color equivalent to my question could be whether it is possible for the brain to evolve, or for novel stimuli to generate, an entirely new color that no one has ever seen before and that cannot be reasonably described as a variant or subtype of an existing color. Some science fiction authors, famously H. P. Lovecraft, have tried to imagine a "new" color, so the idea that new cognitive experiences could be found is certainly not new.

Another way to ask this is whether the process of coming up with new words to describe emotions represents enhancements in the understanding of current emotions or whether it represents truly new emotions never before experienced or observed.

The atlas of personality, emotion and behaviour catalogues over 3,000 emotions (Mobbs, 2020).  The atlas's approach taken to identifying and cataloguing the emotions was to start with existing lists of emotions and then using thesauri to identify synonyms and antonyms of emotions, which are themselves likely to be emotions.

I expect that over time, existing emotions will be subdivided into more specific variants which will then need to accepted by the curators of each dictionary.  At the point these variants are included in dictionaries, the new emotion will be deemed to discovered.

Mobbs AED (2020) An atlas of personality, emotion and behaviour. PLOS ONE 15(1): e0227877. https://doi.org/10.1371/journal.pone.0227877


Why do errors such as typos happen? Why do typos happen?
Why do we sometimes type the same word right, and sometimes wrong?

For the same reason, why does a player of Flappy Bird for instance crash at pipe 138? Why not any of the 137 other pipes, and why not any later?

Short answer
Multiple factors are in play that affect error making. Cognitive load is an important element that increases the chance of making errors. Other factors are the presence of distraction, levels of stress or level of vigilance.

Background
It's a matter of chance. In psychophysics, the field of research I'm spending most of my time in at the time of this writing, models subject responses with psychometric curves. These curves represent the correct rates as a function of the level of the physical stimulus presented (Fig. 1).

Basically, it shows that at high enough stimulus levels the %correct score increases steadily, up to the point where the response is correct for all the trials. In the figure % seen is plotted, for example in a test where image brightness (gamma correction) is varied on a computer screen and the subject is asked if a stimulus is seen yes or no. The characteristic that is mostly reported in such an experiment is the 50% correct point, i.e., the steepest point in the curve. This means that the subject was correct in 50% of the trials.

Now, back to your question - if one not plots intensity, but task difficulty on the x-axis, a similar plot is obtained. Think, for example, speech recognition in noise - when the signal-to-noise ratio increases, %correct will increase. Or, consider a task where cognitive load is slowly decreased, i.e., the task is made easier and easier - then at a certain point the %correct response will also approach 100% (time constraints). Thus, when you are typing, you're typing a lot of characters per minute as you're in a hurry to meet that deadline. And you're thinking about the problem and the solution of the assignment you're working on behind your laptop (cognitive load), your roommates are talking (distraction), and all the while the cat keeps on nestling herself on top of your keyboard (distraction). You might have been working all-nighters, which increases sleepiness that reduces your cognitive abilities overall. These conditions increase mental load and promote incorrect responses (incorrect key strokes) and hence increase the number of typos you make.

If your assignment's deadline was only 3 months from now, and you were already well on your way and you've set yourself the goal of writing one sentence per hour, you wouldn't make any typos at all, because you can look at the keyboard and find every single individual key before pressing it.

It's a matter of cognitive load. Which responses go wrong is all a matter of chance. As Fig. 1 shows, it's a matter of chance that overall results, after many repeats, in a nice psychometric curve.

Fig.1. Psychometric curve. source: Kalloniatis & Luu C (2005)

Reference
- Kalloniatis & Luu Principles of Vision. In: Kolb H, Fernandez E, Nelson R, editors. Webvision: The Organization of the Retina and Visual System [2005]. Salt Lake City (UT): University of Utah Health Sciences Center


Classical conditioning paradigm for hippocampal learning I wanted to know what a suitable classical conditioning experiment would be to analyze learning and memory capabilities in rodent models with respect to hippocampal long-term potentiation (LTP).

For example, I want to subject the animal to conditioning and test the behavioral readout at a certain time point after conditioning. Many , like fear conditioning experiments, are mostly attributed to amygdala processes.

I wish to know some classical conditioning task where I can understand primarily hippocampal processes.

Also is there a way to monitor hippocampal CA1-CA3 firing pattern after the training period or during the training ?

That is, I want to check if LTP will sustain after the training period or is there is any increased sustenance of LTP post training.

But if I am to do this electrophysiological experiment in hippocampal brain slices, how will I incorporate the conditioned and unconditioned aspect to this ?

Read this paper

Storage of Spatial Information by the Maintenance Mechanism of LTP

Eva Pastalkova*, Peter Serrano*, Deana Pinkhasova, Emma Wallace, André Antonio Fenton†, Todd Charlton Sacktor†
http://www.sciencemag.org/content/313/5790/1141.short

It uses a conditioning task that is primarily hippocampal as it is a spatial navigation task.  They monitor the strength of CA3-CA1 connections during and after training using the fEPSP (field excitatory post-synaptic potential) slope.

Also, for a review of hippocampal dependent tasks, see

The role of hippocampal subregions in memory for stimulus associations

Rosamund F. Langston, Cassie H. Stevenson, Claire L. Wilson, Ian Saunder, Emma R. Wood

http://www.sciencedirect.com/science/article/pii/S0166432810004857


Biological Plausibility of FORCE training In "Supervised Learning in Spiking Neural Networks with FORCE Training" by Wilten Nicola and Claudia Clopath. The authors create a learning rule for learning non-linear dynamics from populations of spiking neurons. The learning rule only seems to depend on:

an error signal
the firing rates of each neurons
an approximate inverse correlation matrix calculated from the firing rates of each neuron

Is it possible to calculate the inverse correlation matrix of using another population of neurons or is there some biological mechanism that could be used to explain this? Additionally, is this calculation sensitive to delay? Often, forgetting to take delay into account is the undoing of many learning rules and cognitive architectures.

From the paper:

  Although FORCE trained networks have dynamics that are starting to
  resemble those of populations of neurons, at present all top-down
  procedures used to construct any functional spiking neural network
  need further work to become biologically plausible learning rules
  [Sussillo and Abbott, 2009, Boerlin et al., 2013, Eliasmith et al.,
  2012]. For example, FORCE trained networks require non-local
  information in the form of the correlation matrix $P(t)$. However, we
  should not dismiss the final weight matrices generated by these
  techniques as biologically implausible simply because the techniques
  are themselves biologically implausible. More work should be done in
  implementing either FORCE, NEF, or spike-based coding networks using a
  biologically plausible learning mechanism based on synaptic plasticity
  or homeostasis [Bi and Poo, 1998, Pfister and Gerstner, 2006, Clopath
  et al., 2010, Graupner and Brunel, 2012, Babadi and Abbott, 2016,
  Vogels et al., 2011]. This has been resolved for spike-based coding
  networks and linear dynamical systems for example [Bourdoukan and
  Deneve, 2015]

Basically, calculating the correlation matrix $P(t)$ requires every neuron to know what every other neuron is doing, so the FORCE algorithm isn't biologically plausible. I'm not sure what alternative there would be calculating the correlation matrix. Maybe there's some mathematical way to approximate it gradually over time?


Is there a name for the natural human guilt complex? Humans tend to have guilt complexes, which prevents them, for example, from taking too many samples from Costco. Is there a name for this phenomenon?

Short Answer: I'm not aware of a specific name for this, but I would describe it as guilt-proneness, and it is almost always associated with a high degree of conscientiousness.

Long Answer: Conscientiousness is one of the five major personality traits. People with high levels of conscientiousness feel strongly compelled to adhere to societal norms and rules, and will experience more guilt when they fear they've broken them. If extreme, in combination with other criteria, it could be classified as Obsessive-Compulsive Personality Disorder (OCPD). The DSM 5 lists eight diagnostic criteria for OCPD, one of which is "overconscientious, scrupulous, and inflexible about matters of morality, ethics, or values." However, in the absence of OCPD criteria, if I was writing a psychiatric impression of someone like you describe, I would probably say that person demonstrates and high degree of conscientiousness and is prone to guilt reactions in the face of perceived violations of his or her morals.