Can color blindness be treated with image filtering technology?

Short answer
Lost spectral sensitivity in bichromats or monochromats cannot be made up for by technology. The only thing technology can do is to process the visual image and shift its spectral content such that it falls outside the deficient region in the color-deficient spectrum. Likewise, trichromats can never match the spectral sensitivity of a dodecachromatic shrimp simply because we cannot generate visual acuity de novo. Mantis shrimp can detect UV light and additional cones enhance the spectral reslution in the visible spectrum too. This cannot be emulated with a trichromatic system.

Background
Removing colors from RGB images such that it alludes to the way color-vision impaired people perceive the world is trivial, as the image shows in the question and see Figs. 1 & 2.

Trichromats have three cones: red, green and blue cones, inferring normal RGB vision. Cone opsins are responsible for capturing light and they have overlapping sensitivity spectra (Fig. 3). Dichromats have only two types of cones and lack one cone type. People suffering from protanopia are unable to perceive any ‘red’ light, those with deuteranopia are unable to perceive ‘green’ light and those with tritanopia are unable to perceive ‘blue’ light (source: Colour Blind Awareness). However, while they are said to be color blind, they do not lack the ability to see that one color; in fact they cannot perceive a specific section of the normal visible spectrum.

The most accepted way in which we perceive color is the Hering theory of spectral sensitivity. Basically it states that we perceive color through two opponent systems: a red-green and a yellow-blue opponent system. Without going into detail on this (it's another story...), it's important to realize its consequences. Because of the spectral overlap of the red and green cones and the red-green opponent system in Hering's model, the outcome of either missing the red cones or the green cones is pretty similar. Hence the collective term red/green color blindness, because people with red and green deficiencies see the world in a very similar way. Protanopes and deuteranopes both live in a world of murky greens, where blues and yellows stand out. Browns, oranges, shades of red and green are easily confused. Both types will confuse some blues with some purples and both types will struggle to identify pale shades of most colors. For example, a person with deuteranopia sees red as a shade of green and people with deuteranomaly often misclassify brown as red (source: Colour Blind Awareness).

Now - we cannot regenerate that missing cone with image filtering; removing data from an image is simple (Fig. 1). However, adding it back in is impossible. How can a person without red cones perceive red? It's simply impossible.

To draw an analogy here - when a person at old age is missing the sensory cells in the basal end of the cochlea due to the steady degenerative processes of the hair cells and cannot perceive high frequency tones of, e.g., 8 kHz and up (presbyacusis), it's over. We can amplify with everything we got, but the person simply can't hear it. What we can do, however, is a process called frequency compression. Basically what you do is squeeze the higher frequencies at the front end into the lower frequencies and amplify that through a hearing aid. In this case the person still can't perceive the frequencies, but the information is yet contained in the signal.

Back to your question - what Robin Kramer alluded to is the Chroma device: like the hearing aid example above, this device uses signal processing tricks. Basically it can do three things for you; 1) identify colors, 2) enhance contrast 3) detect edges. Identifying colors can help by replacing reds and greens in red/green color blinds with yellows and blues. Enhancing contrast can be done in red/green color blinds by shifting the red/green spectrum towards the blues and the yellows, much like our acoustic-frequency compression scenario in the hearing aid above. Edge detection can help to improve object detection etcetera in murky images in color blinds. But all it can do is optimally use the limited color information available to the color deficient person. It can not restore normal trichromatic vision in any way.

Then to the mantis shrimp. Six of the additional cones are dedicated to UV vision, which is outside the visible spectrum of ours. The other ones add spectral detail. Extra cones within the visible spectrum add spectral detail. They do not add additional colors as such. UV vision cannot be inferred by technology. What you can do, however, is compressing UV wavelengths into the visible wavelengths. But still we would not be able to 'see' it as such. Further, spectral detail cannot be added by technology either.

Fig. 1. color blindness simulated pictures. source: Vision Salud

Fig. 2. Color blindness. source: Tanuwidjaja et al., 2014

Fig.3. Spectra sensitivity of the cones and the rods. source: wikipedia

Reference
- Tanuwidjaja et al., Ubicomp 2014, September 13–17, Seattle, WA, USA


Is it actually helpful to write todo lists?

There is some research that suggests writing a to-do list will help "unclutter your brain." The research is related to the phenomenon known as the Zeigarnik effect.

The basic observation behind the Zeigarnik effect is that we tend to think more about tasks and goals that are incomplete. This leads to, among other things, increased priming for concepts related to a unfinished goal relative to a finished one (Förster, Liberman, & Higgins, 2005).

Masicampo & Baumeister (2011) investigated whether making a plan to finish a goal, as opposed to actually finishing the goal, could lead to a similar reduction in intrusive thoughts about the goal. They report:

  In several studies, we activated unfulfilled goals
  and demonstrated persistent goal activation over time. Unfinished goals caused intrusive thoughts during
  an unrelated reading task (Studies 1 and 5B), high mental accessibility of goal-related words (Studies 2
  and 3), and poor performance on an unrelated anagram task (Study 4). Allowing participants to formulate
  specific plans for their unfulfilled goals eliminated the various activation and interference effects.
  Reduction of the effects was mediated by the earnestness of participants’ plans: Those who ultimately
  executed their plans were those who also exhibited no more intrusions (Study 4). Moreover, changes in
  goal-related emotions did not appear to be a necessary component of the observed cognitive effects
  (Studies 5A and 5B). Committing to a specific plan for a goal may therefore not only facilitate attainment
  of the goal but may also free cognitive resources for other pursuits. Once a plan is made, the drive to
  attain a goal is suspended—allowing goal-related cognitive activity to cease—and is resumed at the
  specified later time.

In sum, there seems to be some evidence that uncompleted tasks do lead to intrusive thoughts, and that finishing the tasks reduces intrusive thoughts. Alternatively, making and committing to a plan to finish the tasks can reduce the instrusive thoughts even in the absence of finishing the task.

References

Förster, J., Liberman, N., & Higgins, E. T. (2005). Accessibility from active and fulfilled goals. Journal of Experimental Social Psychology, 41(3), 220-239.

Masicampo, E. J., & Baumeister, R. F. (2011). Consider it done! Plan making can eliminate the cognitive effects of unfulfilled goals. Journal of Personality and Social Psychology, 101(4), 667.


Why do children tend to choose the last option in a two-alternative forced choice task?

In general, and not specifically related to children, choosing out of a set of options often depends on people’s memory. In an ideal world, people’s options would be presented simultaneously, but in reality, options are often presented serially over time. Both the delay of time as well as interference from other cognitively demanding tasks are likely to increase uncertainty in one’s evaluations of more distant options compared to more recent options, and therefore increases the amount of regression to an overall category mean. In other words, imperfect recall introduces uncertainty in earlier options and judgments of the earlier presented options regresses as memory decays over time. Relatively desirable options will therefore tend to seem less desirable with time, and relatively undesirable options will tend to seem less undesirable with time.

People therefore tend to select the first option in a set when choosing between generally undesirable options, and will tend to select the last when choosing between generally desirable options (Li & Apley, 2009).

In terms of kids I can add that their working memory span is relatively short, in the order of 10 seconds for a 1-year old (Garon et al., 2008), probably adding to the imperfect recall hypothesis stated above. Further, the executive functioning of kids is limited; most kids between 3 and 6 years are able to recall 4 digits or less in a digit span test (Espy & Bull, 2005), further complicating a seemingly simple task for small children, namely recalling the options, let alone choosing a preferred option between them.

References
- Espy & Bull, Developmental Neuropsychology (2005); 28(2): 669–88
- Garon et al., Psychological Bulletin (2008); 134(1): 31–60
- Li & Apley, J Behav Dec Making (2009); 22: 378–89


What are good introductory books on mathematical psychology?

I also like https://bayesmodels.com/.

I posted the question on twitter, you could check out the responses.

Joachim Vandekerckhove suggested:

Lewandowsky, S., & Farrell, S. (2010). Computational modeling in cognition: Principles and practice. Sage Publications. https://www.amazon.com/Computational-Modeling-Cognition-Principles-Practice/dp/1412970768

Edgar Kausel suggested the following classic that is still good to read:

Coombs, C. H., Dawes, R. M., & Tversky, A. (1970). Mathematical psychology: An elementary introduction.

Daniel Litted mentioned his list that includes over 15 recommendations
https://www.goodreads.com/list/show/121661.Best_Mathematical_Psychology_Books

Here are the first few:


Can lucid dreaming detract from the recuperative effect of REM sleep?

Lucid dreaming is a half-way state between normal REM sleep and wakefulness. Lucid dreaming probably has less of a recuperative effect than normal REM sleep. On the other hand, there is usually less sleep inertia when waking from a lucid dream than when waking from a non-lucid dream.

The function of REM sleep is still a mystery, so very little is known about how lucid dreaming affects sleep quality.

However, going in the other direction, it is known that sleep quality affects the likelihood of lucid dreaming. More specifically, REM rebound following sleep deprivation, going back to sleep after a morning awakening and being sufficiently sleep deprived to take naps before noon are all common triggers for lucid dreams.

In my experience, I have never had a lucid dream after eight hours of unbroken sleep but if I wake up early in the morning and go back to sleep or if I take a daytime nap then I will quite often get a lucid dream, especially if I have consumed caffeine prior to falling asleep.

  Smith, B. V., & Blagrove, M. (2015). Lucid dreaming frequency and
  alarm clock snooze button use. Dreaming, 25(4), 291-299.
  http://dx.doi.org/10.1037/drm0000012

  Dresler, Martin et al. “Neural correlates of dream lucidity obtained
  from contrasting lucid versus non-lucid REM sleep: a combined EEG/fMRI
  case study.” Sleep vol. 35,7 1017-20. 1 Jul. 2012,
  https://dx.doi.org/10.5665%2Fsleep.1974


A list of books on the big ideas of psychology

Yeah, it's too difficult to say ALL the books, because there are thousands.  Some good ones I've come across lately are "Thinking Fast and Slow" by Kahneman.  His ideas have been enormously influential, especially since he won the Nobel Prize, and it's easy to read.

Another book I've been reading lately is "The Evolution of Childhood" by Melvin Konner.  It's not really psychology, more behavioral biology and evolution, but it's relevant to psychology and interesting.

I'll come back and post more as I think of them.


How to determine which scale to use: Nominal vs Interval?

I'd recommend an ordinal scale.

A nominal scale just labels classes without a < or > relation. Using this you would be limited to "immersive" and "not immersive" type of answers. This runs into the danger that experimental subjects might classify all your conditions as immersive or non-immersive respectively. That is, you might get little information.

An ordinal scale is a set of ordered levels with a < or > relation. A 5-point Likert scale like "not immersive at all / a little immersive / moderately immersive / highly immersive/complete immersion" would be an example. In case immersiveness is binary, it would show as all answers falling into only two levels of your scale. That is, you do not lose anything. As an experimental subject, I'd understand such instructions and a rating scale. This is what I'd take.

An interval scale requires a quantitative comparison. For example, the difference between 350ms and 370ms reaction time is as large as between 200ms and 220ms. For immersiveness I do not know of such a scale. Therefore it does not seem an option for you.

There is a further rational scale. But it is even more demanding.

A nice explanation of scales is on https://www.mymarketresearchmethods.com/types-of-data-nominal-ordinal-interval-ratio/


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


Are there any cognitive test (or test suites) available on the iPad?

FingerFriendlySoft has created an app for all iOS devices (that is, iPod touch, iPhone and iPad) that is called N-back Suite. This is, as the name suggests, an app which lets you take the n-back test.

Included are both the single and the dual n-back test and you can chose different amounts of n (from 1 to 10), five different speeds, and different type of stimuli for the two tasks (color, letters, images, 3x3 matrixes, and sounds).

You can also export your results to a tab delimited text file that you can analyse using statistical software (or by hand if you feel a bit masochistic), for example R.

So, I guess, with this example in mind, that the answer to your quesion is "yes" :)


Short answer
Because tacit knowledge is defined as knowledge we cannot describe, researchers apparently adopt a strategy of just interviewing and listening for signs of tacit knowledge, as far as I understand it.

Background
Tacit knowledge can be curtly defined as ‘that which we know but cannot tell’.

For example, a person may be very aware of being able to ride a bicycle and able to describe how they learned to do it. However, the person may not be able to describe critical aspects of biking, such as swift, reflex-driven adjustments due to imbalance, and subjects like the steadying effect of the gyroscopic motion of the wheels, may not be consciously realized either.

Still tacit knowledge can be made explicit by either making the knower to learn to tell, or by the researcher telling and seeking respondent. In other words, one has to facilitate the ‘telling’ of the untold. Both methods require the researcher to construct an account.

Eraut (2000) claims that tacit knowledge can be examined through regular interactions, encouraging persons to freely describe what they know. This can be facilitated by an informal relationship, where one can freely interact with the person about their knowledge.

Reference
- Eraut, Brit J Edu Psychol (2000); 70: 113–36


Collective term for fun, relaxation and escapism

I do not believe that there is an actual term that encompasses the three, but if you look at the functions they fulfil, you will find that they tend to give you a "break" from the stimuli that memory is repeatedly exposed to and allow the brain to process those stimuli from memory.

My thoughts on it are: re-processing information that was processed before, trying again to find the patterns and bring the information you are exposed to onto consciousness is hard work that the brain shouldn't have to do. Instead, the same stimuli can be brought from memory and processed as such during lack of exposure.

The only scientific thing that comes to my mind is a thing that comes from imagery of the visual cortex during significant activities (memorising a picture) versus non-significant activities (like being exposed to a blank picture instead of repeating the significant picture). It is called refreshing. More about it, for the scientifically-minded, here

In laymen terms, at times of non-exposure to the real stimuli your brain will automatically expose you to self-made renditions of those stimuli. This particularity of the brain involves complex cognitive functions, of which visual imagery is just an example.

This might be an assumption and an over-generalising of the findings in the paper quoted here, but it might also be true that all the terms you mentioned in your question (fun, relaxation and escapism),can be linked to periods of time of non-exposure to the stimuli that need refreshed, aka. brought to memory by a cognitive representation rather than by repeated exposure.

Now, the big difference between my proposed term and the terms you used is the fact that, scientifically, attentional refreshing has only been  proven for stimuli that the brain was exposed to moments ago. For complex pieces of information and longer periods of time, it would be hard to measure the effect that non--exposure has, because of the nature of the experiments we are now conducting in cognitive and neuro psychology.


There are situations as you mentioned where light can be from below, but they are extremely rare in our evolutionary past. Fire is quite recent: remove that one. Reflections from water or snow would require very strong light from above, so those are essentially removed as well. I think the creepiness comes from the unfamiliarity. Also see the Uncanny Valley, which describes similar feelings in response to near-human representations https://en.wikipedia.org/wiki/Uncanny_valley

A good test would be with non-human animals. Do they find bottom-lit faces fear-inducing?


Does learning too much useless information weaken my memory?

Current evidence suggests that internet access is not weakening memory, but changing what information is prioritized.  This study referenced below suggests that when people expect to have future access to information, they are less likely to remember the information itself and more likely to remember where or how that information can be found.

Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips
Betsy Sparrow, Jenny Liu, Daniel M. Wegner Science 5 August 2011: Vol. 333 no. 6043 pp. 776-778.

DOI: 10.1126/science.1207745


CBT approach and why we generate unrealistic thoughts

Another way to phrase these questions would be:

Why do humans feel fear and anxiety?
Why are humans particularly sensitive to potential losses See: https://en.wikipedia.org/wiki/Loss_aversion

In the broadest terms, the answer is that we evolved to have these mental characteristics because they probably gave our ancestors a survival and/or reproductive edge over their contemporaries. In the past, humans needed to be socially accepted in a small tribe; exclusion meant actual death as it was too hard to survive alone. Hence, we evolved to be hyper-sensitive to signs of social exclusion.

Your final question might be:

How come we can't control our feelings with our thoughts?

See the first point above. It didn't help us survive to be calm and at peace.


What is the study that identified that kids growing up without fathers were angrier and more depressed?

A report in the Daily Mail in 2013 had the headline:

  Growing up without a father can permanently alter the BRAIN: Fatherless children are more likely to grow up angry and turn to drugs

  Dr Gabriella Gobbi, who carried out the research with colleagues at the medical faculty at McGill University in Canada, said: ‘This is the first time research findings have shown that paternal deprivation during development affects the neurobiology of the offspring.’ (Spencer, 2013)

After some searching, I believe the source article is:

Bambico, F. R., Lacoste, B., Hattan, P. R., & Gobbi, G. (2013). Father absence in the monogamous California mouse impairs social behavior and modifies dopamine and glutamate synapses in the medial prefrontal cortex. Cerebral Cortex, 25(5), 1163-1175.DOI: 10.1093/cercor/bht310

and the research was carried out on California mice.

  Abstract:

  The role of the father in psycho-affective development is indispensable. Yet, the neurobehavioral effects of paternal deprivation (PD) are poorly understood. Here, we examined the behavioral consequences of PD in the California mouse, a species displaying monogamous bonding and biparental care, and assessed its impact on dopamine (DA), serotonin (5-HT), and glutamate (GLU) transmission in the medial prefrontal cortex (mPFC). In adult males, deficits in social interaction were observed, when a father-deprived (PD) mouse was matched with a PD partner. In adult females, deficits were observed when matching a PD animal with a non-PD control, and when matching 2 PD animals. PD also increased aggression in females. Behavioral abnormalities in PD females were associated with a sensitized response to the locomotor-activating effect of amphetamine. Following immunocytochemical demonstration of DA, 5-HT, and GLU innervations in the mPFC, we employed in vivo electrophysiology and microiontophoresis, and found that PD attenuated the basal activity of low-spiking pyramidal neurons in females. PD decreased pyramidal responses to DA in females, while enhancing responses to NMDA in both sexes. We thus demonstrate that, during critical neurodevelopmental periods, PD leads to sex-dependent abnormalities in social and reward-related behaviors that are associated with disturbances in cortical DA and GLU neurotransmission.

References

Spencer, B. (2013) Growing up without a father can permanently alter the BRAIN: Fatherless children are more likely to grow up angry and turn to drugs. In: Daily Mail [Online]
Available at: http://www.dailymail.co.uk/sciencetech/article-2518247/Growing-father-permanently-alter-BRAIN-Fatherless-children-likely-grow-angry-turn-drugs.html


Here's why people need to sleep:

The brain does its "cleaning up" during sleep. As cells in the nervous system are active, waste products are produced. In the rest of the body, waste removal is carried out by the lymphatic system, but this system does not extend to the brain. Instead, cerebrospinal fluid (CSF) must be pumped through the brain tissue in order to flush waste back into the circulatory system where it can be excreted by the liver. Pumping CSF seems to require a great deal of energy, and researchers speculate that this energy expenditure is incompatible with the intense metabolic demands of brain cells during wakefulness. Also, cells in the brain shrink considerably during sleep, resulting in a 60% increase in interstitial space which allows CSF to wash more freely through the brain tissue. The consequence is significantly faster clearance of neurotoxic waste (e.g., β-amyloid).

http://www.sciencemag.org/content/342/6156/373
http://stm.sciencemag.org/content/4/147/147ra111


Consciousness - on/off switch or spectrum?

On/off is indeed not the commonest view of consciousness. In Hindu philosophical circles, consciousness is thought of as continuous across living systems.  Humans have 'budding'consciousness, (plus free will), animals 'shuttered' and plants 'covered'consciousness, and these are points along a continuum.  (Reference example:  Hinduism and Science, by T D Singh; a paper in “Science and Religion: Global Perspectives”, June 4-8, 2005, in Philadelphia, PA, USA, a program of the Metanexus Institute).


What salient features of a {conditioned stimulus,unconditioned stimulus} pair are represented in the lateral amygdala?

The lateral amygdala appears to be involved in representing fear memories after extinction (Hobin, Goosens and Maren, 2003). The extent of the lateral amygdala's involvement in representing these appears to revolve around the context of the {CS, UCS} pair. The authors state the following in their abstract:

  Similarly, the majority of LA neurons exhibited context-dependent spike firing; short-latency spike firing was greater to both CSs when they were presented outside of their own extinction context. In contrast, behavioral and neuronal responses to either non-extinguished CSs or habituated auditory stimuli were not contextually modulated. Context-dependent neuronal activity in the LA may be an important mechanism for disambiguating the meaning of fear signals, thereby enabling appropriate behavioral responses to such stimuli.

You appear to be right to suspect the hippocampus' involvement.  In a subsequent study by Maren and Hobin (2007) using a similar Pavlovian methodology to the previous study, the authors reported that hippocampal activity was associated with regulation of context-dependent lateral amygdaloid activity and concluded the following:

  After saline infusion, rats froze more to the CS when it was presented outside of its extinction context, but froze equally in both contexts after muscimol infusion. In parallel with the behavior, lateral nucleus neurons exhibited context-dependent firing to extinguished CSs, and hippocampal inactivation disrupted this activity pattern. These data reveal a novel role for the hippocampus in regulating the context-specific firing of lateral amygdala neurons after fear memory extinction.

Finally, a context-related function of the lateral amygdala seems to provide a simple and plausible explanation for why fear extinction is far more difficult in human patients than laboratory rats: humans encounter a virtually infinite and certainly unpredictable space of possible contexts, whereas lab rats, by the nature of their unfortunate lot in life, tend to encounter relatively few and predictable contexts.

(I found it somewhat difficult to cover all the bases for your question and subquestions, so please comment if I need to expand on something.)

References

Hobin, J. A., Goosens, K. A., & Maren, S. (2003). Context-dependent neuronal activity in the lateral amygdala represents fear memories after extinction. The Journal of neuroscience, 23(23), 8410-8416.
Maren, S., & Hobin, J. A. (2007). Hippocampal regulation of context-dependent neuronal activity in the lateral amygdala. Learning & Memory, 14(4), 318-324.
Chicago


Introductory resources on developmental psychology/neuroscience

For an introduction to neurotransmitters (and the field of neuroscience) a good book to start with is Principles of Neural Science (Kandel, Schwartz, and Jessel, 2000). It is a standard reference used by many undergraduate and graduate courses in neuroscience/related fields. The next edition is due for publication in October. During development neurotransmitters act different than in adults, so you may also be interested on a review in this topic (Herlenius & Lagercrantz, 2004); Although the time frame is earlier than what you are looking at.

In general I think you should start off by looking for possible neural mechanisms and any associated brain regions underlying the specific phenomenon you are interested in (in your example, empathy, or other subcomponents of social-emotional competence measured by the Infant-Toddler Social and Emotional Assessment (Carter et al., 2003)). I.e. Singer (2006) reviews the neural basis of empathy (as well as Theory of Mind); one section talks about implications for development.

After knowing what brain regions to focus on, you can look into anatomical/physiological research for better understanding of cellular type/structure, connectivity, synaptic characteristics, and neurotransmitters specific to these regions as well as genes that may affect normal function of a transmitter (i.e genes that control protein channels, enzymes/transmitter metabolism etc.). Here are two newer reviews, which link empathy to neuronal circuits and genes (Chakrabarti et al., 2009; Walter, 2012).


Research on how problem, presented in practical, real world format, is solved much more easily?

Pretty sure you're talking about the Wason selection task, and the bar variation is generally attributed to Leda Cosmides and John Tooby (1992).

https://en.wikipedia.org/wiki/Wason_selection_task


Why compare a target group to a control group when population norms are available?

Say your juvenile offender are on average 16 year old boys from Britain, and the control group consists of (on average) 16 year old British boys who haven't done anything wrong. The only difference between these groups are the fact one has a criminal record or not. If you would look at the true norm, you could be looking at the world wide norm of both boys and girls from both Britain and other countries that may or may not have a criminal record.

The control groups (and actual participants) are thus chosen (and tested) specifically to factor out unforeseen variability. Depending on the question authors want to answer, different control groups may be necessary.


The origins of dreams have been studied by many different people and your approach to understanding the origins depends on your approach, however, dreams seem to originate within the unconscious part of the mind.

This is a huge subject and therefore the following is only a taster to lead you into further reading on the subject.

In the psychodynamic approach, Sigmund Freud studied dreams and in 1900, he wrote a book on his theories.  If you are unfamiliar with the psychodynamic approach and psychoanalysis, it may be worth reading the full article at SimplyPsychology, however if you are familiar with terms such as ego, id and superego along with Freud's theroies of the unconscious mind, then you can skip to the Dreams Analysis bit.

Basically, Freud believed

  dreams to be the royal road to the unconscious as it is in dreams that
  the ego's defenses are lowered so that some of the repressed material
  comes through to awareness, albeit in distorted form. Dreams perform
  important functions for the unconscious mind and serve as valuable
  clues to how the unconscious mind operates.

Repressed material is referred to being disturbing or threatening thoughts which are repressed by the ego by an unconscious mechanism called the ego defence mechanism.

In the person centred approach, Carl Jung talked about the compensatory nature of dreams and Carl Rogers said in his book A way of being (Boston: Houghton Mifflin, 1980)

  In relating to the client a Jungian will want to relate to the
  unconscious as well as the conscious side of the client's personality.
  This means that it will be natural to give special attention to such
  things as dreams, fantasies, and projections, which from a Jungian
  point of view are our chief means of contact with the unconscious.

  Dreams will be given special significance, since the dream provides a
  view of the client's problem that is different from his or her
  conscious view, yet is still an aspect of the client's view.

Putting these ideas together in a summary, dreams

can sometimes be pre-recorded in the form of memories of what was said or thought - e.g. in nightmares relating to past traumatic events such as war or abuse - although they can sometimes be a little skewed towards the person's understanding of what happened, and
they can sometimes be a product of repressed or unrepressed thoughts and feelings just as the article you linked to in your question suggests.

I would also like to draw on a word of caution mentioned in the SimplyPsychology article.

  'Dream dictionaries', which are still popular now, were a source of
  irritation to Freud. In an amusing example of the limitations of
  universal symbols, one of Freud's patients, after dreaming about
  holding a wriggling fish, said to him 'that's a Freudian symbol - it
  must be a penis!'

  Freud explored further and it turned out that the woman's mother, who
  was a passionate astrologer and a Pisces, was on the patient's mind
  because she disapproved of her daughter being in analysis. It seems
  more plausible, as Freud suggested, that the fish represented the
  patient's mother rather than a penis!

It is believed from both approaches that dreams perform important functions for the unconscious mind, and these dreams can be beneficial in therapy if carefully and correctly examined by the therapist with the client, as they can open you up to a different view of how things happened in the past or are happening in the present.

Edit: to reflect alteration of OP question

On reflection on my answer above you concluded that Freud's work covered

  the process of converting an underlying wish into a dream, an
  experience that satisfies an unconscious - possibly immoral - desire
  in a morally acceptable way

Whilst dreams can reflect unconscious immoral desires, the desires can be moral but repressed for other reasons such as personal prejudice.  Repression is a powerful ego defence mechanism whereby it can prevent a thought or memory which can be psychologically damaging from reaching the conscious.

Repression is unconscious. When we deliberately and consciously try to push away thoughts, this is suppression.

The combination of the answers given in this posting point to the fact that dream creation can occur either before dreaming, at the start or during dreaming.  Repression and suppression of thoughts and memories can lead to your unconscious creating the dream when in sleep as your ego defenses are lowered.


Is the logic of "Herrnstein's syllogism" sound, and are its premises true?

Herrnstein's Syllogism, as shown above, comes from the 1971 article written for The Atlantic by psychologist Richard Herrnstein (Herrnstein, 1971). It argued that while socioeconomic status of individuals may be caused by various factors, one of those factors must be differences in inherited genetics. The article ignited a big controversy.

As a personal note, I must admit that I don't quite understand the controversy (Well, at a scientific level). It was already accepted by most psychologists in the 1970s that general intelligence was a heritable trait. Further, it was accepted that general intelligence statistically predicted success in education as well as in professional life. Therefore, it should not be surprising if genetic differences acting on intelligence would not be distributed evenly across the social environment.

I will first deal with the premises. Is general intelligence heritable? Then I will merge the two next premises. Does intelligence predict mobility in success and social standing? I will also look directly at the conclusion of the syllogism. Do differences in genetics that influence differences in intelligence vary as a function of socioeconomic status?

Was Herrnstein's conclusion scientifically justified back then? That could be argued, but ultimately it doesn't matter. Today we have much more evidence available. We can now directly look at molecular genetics that is known to influence intelligence. As I will show, the best current evidence absolutely supports Herrnstein's conclusion. This does not mean that intelligence is everything, or that genetics is everything, but that it is a part of the story.

Heritability of Intelligence

Is intelligence, educational success and similar variables partly heritable traits? In fact, this is not even questioned by the relevant experts anymore. From a landmark Nature editorial (2017), I quote:

  it’s well established and uncontroversial among geneticists that together, differences in genetics underwrite significant variation in intelligence between people.

In an expert review on five findings regarding genetics and intelligence, the first noted finding is the heritability of intelligence (Plomin & Deary, 2015).

The question remains: how do we know that intelligence is partly heritable? Quantitative genetics is a field which attempts to address such questions. In particular, methods have been developed that attempt to estimate aggregate genetic influence without finding the influence of particular genetic variants.

The most highly used method for humans is the twin study. Identical twins are more genetically similar than fraternal twins. Identical twins also tend to be more similar than fraternal twins on various variables such as facial similarity, height, and even on psychological variables. From this increase in genetic similarity coupled with the increase in phenotypic similarity, an estimate of heritability can be calculated. This method can be extended to compare not just similarities between twins, but in general siblings of all kinds, cousins and more in pedigree studies. For example, based on a massive Dutch birth cohort a team of researchers estimated a heritability of educational achievement of 85% (Schwabe et al, 2017).

Another method is the adoption study. This method compares the correlations of children with their adoptive parents and with their biological parents. Based on this an estimate of heritability is calculated. Adoption studies show substantial genetic influence on intelligence (McGue et al, 2007). One of the most powerful variants of the adoption study is a study that compares the similarity of identical twins separated at birth and reared apart. For example, the Minnesota Study of Twins Reared Apart found a very high heritability estimate (Bouchard et al, 1990). Identical twins are highly similar in intelligence regardless of whether they are reared together or not.

These are classical methods of estimating heritability, yet more and newer methods have been developed. Genome-wide Complex Trait Analysis (GCTA) estimates narrow-sense heritability by directly quantifying the chance genetic similarity of unrelated strangers and comparing it to their measured similarity on a trait. A study of approximately 20,000 individuals found a substantial heritability of general intelligence with this method (Hill et al, 2018). In general, based on this finding and all the other findings together, one can conclude that genetic similarity is a better predictor of similarity in intelligence than environmental similarity is. For example, identical twins reared apart are much more similar than adoptees raised together.

There are far more studies one could cite, and other lines of evidence. For example, inbreeding has been shown to lower intelligence, and this effect is larger the higher degree the inbreeding is (Fareed & Afzal, 2014). This should only be viewed as a snippet of the evidence from which researchers conclude that it is "uncontroversial" that genetics influence intelligence.

One thing is to estimate the aggregate genetic effect; another thing entirely is to find the actual genetic variants at play. This is done using genome-wide association studies (GWAS). It has been found that intelligence is a highly polygenic trait, meaning that it is influenced of many genes each of very small individual effect. This means that huge sample sizes are required to unlock the individual variants that affect the trait. However, in the last few years, GWA studies have found many variants affecting intelligence, see e.g. (Savage et al, 2018). By aggregating the effects of many of the variants thought to affect the trait one gets a polygenic score, such that a higher polygenic score is associated with higher average intelligence. Of course, most of the genetic variants have yet to be found, and hence only a moderate portion of the variance in intelligence can be captured directly by molecular genetics currently.

Practical Implications of Intelligence Differences

It can be difficult to establish causal effects in the life sciences. Does a correlation between higher pay and higher intelligence mean that higher pay causes higher intelligence? Or how about vice versa? Or is a third variable at play? One of the best ways of providing causal evidence is through longitudinal studies. For example, measure the intelligence of a sample at age 11 and see if that predicts later success.

Higher intelligence longitudinally predicts higher grades. For example, a meta-analysis of 240 independent samples with 105,185 participants finds that intelligence is a very strong predictor of school grades (Roth et al, 2015). In a massive longitudinal study of 70,000+ English children the association between intelligence measured at age 11 and educational achievement later was tested. Educational achievement was calculated as a general factor of GCSE results and they found a strong correlation of 0.81 between measured intelligence and educational achievement (Deary et al, 2007). Studies that attempt to break down whether teachers, schools, or individual student factors matter most for student educational success find that individual factors account for the vast majority of the variance (Detterman, 2016).

General intelligence has been found to be a big predictor of job success, especially in jobs of higher complexity (Gottfredson, 1997; Schmidt, Oh & Shaffer, 2016).

General intelligence longitudinally predicts financial well-being (Furnham & Cheng, 2017). A large-scale longitudinal analysis showed that intelligence affected salary growth whereas socioeconomic background primarily affected starting salary (Ganzach, 2011). Intelligence predicts upward socioeconomic mobility (PEW Upward Intergenerational Economic Mobility in the United States).

Individuals tested and assessed to be gifted at young age longitudinally predicts profound achievements academically and professionally (Makel et al, 2016).

Highly intelligent individuals are much more likely to become inventors (Aghion et al, 2017). This study found that intelligence "matters more than all family background variables combined".

We already know that intelligence is highly heritable, and hence these effects are expected to be partly due to genetic differences. Yet, some posit that these effects may be due to some socioeconomic variables that are not controlled for.

Testing this hypothesis is possible with within family sibling analysis. By restricting studies to only look at the outcomes of intelligence differences between siblings who are raised together, one can establish that these effects are not due to different rearing environments. In fact, restricting analyses to siblings only slightly changes the associations (Murray, 2002). The study on the origins of inventors mentioned above similarly does a brother-only comparison, and they conclude "This suggests that these coefficients capture an effect of IQ on the probability of inventing which is largely independent of unobserved family background".

Herrnstein's Conclusion: Societal Stratification of Genetics

Taking the two previous sections together, I don't see a plausible way in which it would not be the case that genetic variants influencing intelligence would distribute unevenly across socioeconomic lines. However, today we have far more direct evidence in the form of molecular genetics. As I already alluded to in the section on genetic influence on intelligence, we now know many variants influencing intelligence. From the DNA of an individual person one can then sum up the effects of the variants into a polygenic score associated with that individual. Indeed, as I will show, these polygenic scores have been show to predict various outcomes and have been shown to not be distributed evenly (which is exactly Herrnstein's conclusion in other words).

One cause of increased genetic stratification is assortative mating. Assortative mating is when couples are more similar than random strangers. In fact, intelligence is the psychological variable that has the highest degree of assortative mating (Plomin & Deary, 2015). This has not just been found at the phenotypic level, but also at a genetic level. In a study of 24,662 spousal pairs a team of researchers found a genetic correlation of educational attainment between spousal pairs of 0.654 (Robinson, ..., Visscher, 2017). Since intelligence is heritable, assortative mating of intelligence/educational attainment makes it even more likely that intelligent parents pass on genes that are associated with increased intelligence.

In a large sample, a team of researchers showed that spousal pairs where both were university educated had children with higher polygenic scores than pairs where only one or none were university educated. Furthermore, polygenic scores predicted educational mobility. Children of educated parents who went on to become highly educated themselves had higher polygenic scores than children who did not become educated like their parents. On the other hand, children of non-educated parents who went on to become educated, unlike their parents, had higher polygenic scores than children who, like their parents, did not become educated (Ayorech et al, 2017).

It is well known that students of some schools do better on average than students of other schools. But the question remains: is it because of the schools? Studies already referenced earlier showed strong evidence that individual factors account for the largest amount of variance. In a large sample of students from different types of schools, a team of researchers found that indeed students do better in more expensive private schools. However, once they controlled for various pre-selection factors there remained no statistical difference. In other words, they did not find evidence that it was because of the schools that the students did well, but rather the schools had selected students who already were good. But furthermore, they found that differences in exam performance between the schools mirrored the genetic differences between them. That is, they found that pupils attending expensive selective schools had higher polygenic scores on average (Smith-Woolley et al, 2018).

Intelligence has been shown to predict later wealth as shown in the previous section. However, whether this is partly due to genetics can be tested directly be examining whether polygenic scores predict later wealth. A large sample study examined exactly this and they concluded that "genetic endowments linked to educational attainment strongly and robustly predict wealth at retirement" (Barth et al, 2018).

Lastly, do longitudinal studies show that polygenic scores predict upward social mobility? A major study on this was recently published. They took data from five samples of more than 20,000 individuals and showed that polygenic scores predicted longitudinal socioeconomic mobility. In sibling-analyses the sibling with higher polygenic score was more likely to be upwardly mobile. Not only that, individuals of lower socioeconomic status had lower average polygenic scores (Belsky et al, 2018).  That is a validation of Herrnstein's conclusion in modern words: namely that differences in social standing is partly due to genetic differences.

Conclusion

As I have shown, there is more than a little evidence that Herrnstein's premises and conclusion were correct. However, this does not mean that intelligence or genetics is all that matters. Many of the studies above, and others like them, also show effects of non-genetic origin. However, to suggest that genetics is part of the explanation is scientifically justified.


What is a realistic mathematical function to model link decay in a social network?

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


Paul Ekman suggests that in near-accidents, fear motivates people to hit their car brakes. Is the fear response fast enough to do this?

This was much longer than I expected! There's quite a bit of ground to cover, but I try to go over it quickly.

So, there are two implicit theoretical assumptions in your question:

We have an "affect program" for fear in our brain (e.g., Ekman & Cordaro, 2011).  When the fear program is activated, a specific pattern of changes in experience, behavior, physiology, and cognition occur (i.e., the fear response).
Present emotions are the primary causes of future behavior.

I'll briefly examine these assumptions, and then I'll draw a conclusion.

Assumption #1:  Affect programs

By "affect program" I mean a discrete module or circuit in the brain that, when activated, automatically produces a fear response.  So if I see a car about to hit me, it might activate my fear program, which will produce my fear response.

Your question uses this assumption by asking about the speed of the fear response.  How fast can my fear program activate my fear response (which would motivate me to hit the brakes)? Does my fear program trigger a response too slowly, such that my fear response will occur after I hit the brakes?

In recent years, the "affect program" idea has been called into question (e.g., Barrett, 2006; Lindquist et al., 2012).  As it stands, it's a fairly controversial assumption that I would argue has iffy empirical support and explanatory power.

If there isn't a "fear program," then what is there? Well…that's tricky. I could get into a whole lot of theory, but that would take up a lot of space (although if you're interested, see Satpute, Wilson-Mendenhall, & Barrett, 2015).  I think the point that I want to make is that it's difficult to talk about the speed of the fear response.  Without having a consensual definition of "emotion," we can't talk very easily about the nature of fear (i.e., what it is, what it looks like, when it begins, when it ends, its function, its neural basis, its evolution, etc.).

Assumption #2: Present emotions cause future behavior

The idea that fear causes me to run away, that anger causes me to approach, or that sadness causes me to withdraw seems very intuitive.  In fact, despite its mixed empirical support, many researchers seem to accept this idea without reservation.

On the other hand, Roy Baumeister and colleagues have argued that present emotions affect future behaviors indirectly (Baumeister, Vohs, DeWall, & Zhang, 2007). In fact, they argue that behavior tends to follow emotions, not the other way around.

To make a long story short, Baumeister argues that anticipated emotions are the more direct causes of present behavior.  So If I anticipate feeling relieved as a consequence of hitting the brakes, then I will hit the brakes.  That is, I will hit the brakes because this will lead to future relief. My fear is not motivating this behavior; instead, my expectation of relief is. Indeed, a recent small-scope meta-analysis supports this theory (DeWall, Baumeister, Chester, & Bushman, 2015)

Conclusion

So I made things pretty complicated! I posed two questions:

Can we meaningfully talk about the speed of the fear response without a consensual definition of emotion?
Do present emotions cause future behavior (e.g., fear causing me to hit the brakes)?

My answers to both questions were no.  However, I think if we ignore the yuckiness of the first question, we can say that fear probably doesn't cause us to hit the brakes, but rather the expectation of relief does (or some other anticipated feeling).

When and how quickly the fear response occurs during this situation is hard to answer (but can be resolved somewhat depending on your theoretical perspective). If we assume that emotions are multi-componential, then we might imagine that the experience component need not occur at the same moment as the behavioral component (mirroring studies of emotion coherence, e.g., Mauss et al., 2005).  Thus, the experience of fear could (hypothetically) occur before or after the braking behavior.


The answer to your question is yes, our memories are very malleable. Look into the research of Elizabeth Loftus. She is kind of the pioneer on this topic and has done a ton of research into false memories.

Here is a TED talk by her that you might find interesting and here is a review article


For depression diagnoses, does one make a clear distinction between endogenous and exogenous causes of depression?

Whew, OK. I am going to take a crack at this. The question asks about depression diagnoses, so I will interpret and focus on that accordingly. I think we can all agree that making any clear distinction between endogenous and exogenous causes of depression is difficult, since 1) liken to the Nature vs. Nurture question, people generally agree that Major Depression is a result of multiple factors, and 2) clinicians disagree on which (exogenous vs. endogenous) tends to be the "primary" cause of depression [1].
When it comes to making a clinical diagnosis, psychiatrists and other mental health professionals will use the multi-axial system in documentation. Axis IV is a part of the system, and is where exogenous factors can be reported as psychosocial and environmental problems that may affect the diagnosis, treatment, and prognosis [2]. In addition, the clinician should know to keep in mind that psychosocial problems may develop as a consequence of the patient's psychopathology!
Examples of commonly listed psychosocial and environmental problems include family problems (illness, death), social environment (isolation), educational/occupational problems, economic problems, etc., etc.

The evaluation of stressors is based on a clinician's assessment of the stress that an average person with similar sociocultural values and circumstances would experience from psychosocial stressors [1].
The judgement is based on the degree of change that the stressor causes in the person's life, the degree to which the event is desired and under the person's control, and the number of stressors.

Tidbits

To make a quick note about endogenous factors, it is interesting to note that the most consistent abnormality in those with depressive disorders is increased frequency of abnormal hyperintensities in subcortical regions of the brain...
A long-standing clinical observation is that stressful life events more often precede first, rather than subsequent, episodes of mood disorders.
^ One theory proposed is that the stress accompanying a first episode results in long-lasting changes in the brain's biology... so a person has a high(er) risk of experiencing subsequent episodes of (a) mood disorder(s), even without an external stressor [1].
While some clinicians believe that life events play the primary or principal role in depression, others suggest that life events have only a limited role in the onset and timing of depression.
What may seem to be a relatively mild stressor to outsiders may be devastating to the patient due to particular/personal idiosyncratic meanings the patient attach to the event.
Edward Bibring thought perhaps depression is [the result of] the phenomenon that sets in when a person becomes cognizant of a discrepancy between his high ideals and the inability to meet the goals appropriate to reach said ideals.

I suppose I did not provide an adequate answer to your question. Given what I have looked into, it appears that individuality plays a ginormous factor in determining one's personal cause(es), and not only do clinicians disagree on the source of depression, but it appears that when clinicians make their diagnosis/diagnoses, a clear distinction between endogenous and exogenous influences is not made. At least, generally speaking...
References

Kaplan and Sadock's Synopsis of Psychiatry
DSM-IV-TR® Diagnostic and Statistical Manual of Mental Disorders


Is doubting a claim more taxing in terms of energy spent by the brain?

Overall, the claim seems to be that people prefer holding their current beliefs. This is well-documented, for example in a big literature on https://en.wikipedia.org/wiki/Cognitive_dissonance . Note that this may be culturally dependent; some Buddhist traditions explicitly value holding contradictory beliefs.

It's possible the study being referred to in the quoted passage is reported in this paper: https://journals.sagepub.com/doi/10.1111/j.1467-9280.2008.02226.x 'When the Truth Is Not Too Hard to Handle: An Event-Related Potential Study on the Pragmatics of Negation' (One author is at Harvard).

If so, the quoted speaker's comments are inconsistent with the findings. There was earlier work suggesting statements with negations might be more difficult to understand, but this paper doesn't find that. Their results "...suggest that negation poses no principled obstacle for readers to immediately relate incoming words to what they hold to be true."

I think it's fair to say that humans limit the mental effort they put in; this perspective is also summarized in the theory: https://en.wikipedia.org/wiki/Cognitive_miser

A word of caution: the quoted text seems to rely heavily on vague appeals to authority (e.g., Harvard) rather than references to specific empirical findings. This can be a red flag for argument quality.


What are different ways to determine centroids of fMRI activation, their drawbacks and perks?

This question gets close to something that might alternatively be posted to stats.stackoverflow.com. Personally, I've always felt that application of Null-Hypothesis Significance Testing (NHST) methods to neuroimaging data does a particularly good job of highlighting their scientific deficiencies.

Personally, these days I'd use Generalized Additive Modelling (GAM) to analyze data from an individual's scan and if aggregating multiple spatially normalized scans I'd use Generalized Additive Mixed effects Modelling (GAMM). In both approaches, I'd use an AIC-adjusted likelihood ratio to evaluate the strength of evidence for differences amongst groups/conditions in the activity patterns discerned by GAM. A posteriori investigation of any evidence-supported differences can be achieved by visualization and parametric bootstrapping of confidence intervals.

If you really want to explicitly evaluate evidence for a group difference in the distance between activation areas, then one approach is to assume an a priori model for the activation, say a mixture of two trivariate gaussians with a parameter that specifies their separation, then use something like hierarchical Bayesian modelling to establish credible intervals on the difference between groups on the separation parameter.


Are psychometrics used in the public sector?

The wide majority of companies have different tests to measure different cognitive and personality features.

First of all IQ (intelligence quotient) - is a total score derived from several standardized tests designed to assess human intelligence.

It helps filter applicants before even meeting them. (IQ is the highest predictor of educational and work success)

Secondly, there are personality tests. Imagine you're looking for a lawyer. A lawyer preferably should be very emotionally stable and argumentative. In psychometric language, this can be measured with Big Five Personality Test - Low agreeableness and low neuroticism.

There are cooler psychometric tests that are in essence better than Big Five Personality, but they are not commonly used, because of their length or difficulty of analysis.

I'm going to list my personal favorite psychometrics here:

Minnesota Multiphasic Personality Inventory
Millon Clinical Multiaxial Inventory
The Alternative DSM-5 Model for Personality Disorders

But this psychometrics are more useful for clinical/forensic settings rather than Human Resources.

Hope this helps.


Is it possible that listening same music with your partner can synchronize emotionally?

I think that one of the most basic and universal responses a person can have to music is engagement. When listeners are engaged with music, they follow the sounds closely connecting in an affective way to what they hear.
Moreover, music listening and music therapy is becoming an important topic of social neuroscience scientific inquiry (Kamioka et al., 2014).
This branch of research is facilitated by Hyperscanning EEG which provides the opportunity to explore dynamic brain activities between two or more participants (Liu et al., 2018).
I'd like to suggest to you the following study by Lindenberger and colleagues (2009).
The authors recorded EEG from the brains of pairs of guitarists playing a short melody together to investigate the rate of cortical synchronization in the course of music-driven actions.
In order to measure the rate of brain synchronization, they implemented the phase-locking index (PLI) which reflects invariants of phases across trials measured from single electrodes. As you can see in the first figure both the participants playing and listening to each other music showed fronto-central activity and Synchronization measured by the PLI index suggested that the activity is the highest at fronto-central sites. Especially, in the image below you can see the PLI distribution in the fronto-central brain regions at the theta frequency.

Finally, they used another index called Interbrain Phase Coherence (IPC) which represents the degree of constancy in phase differences across trial between two electrodes measuring the activity from two different brains.
In the figure below you can see synchronization between brains as measured by IPC: coherence was also strongest for fronto-central connections.

I hope that this paper partly answered your doubts. I do not know any study directly investigating brain synchronization in romantic couples, but if you think that romantic couples can be a discriminating factor to study brain synchronization while listening or practising music you could put on an experiment that considers romantic couples as an experimental group and friends/acquaintances as a control group.
Always remember, however, that you must justify the choice of your experimental groups with strong literature.
REFERENCES

Kamioka H, Tsutani K, Yamada M, Park H, Okuizumi H, Tsuruoka K, Honda T, Okada S, Park S. (2014)Effectiveness of music therapy: a summary of systematic reviews based on randomized controlled trials of music interventions. DOVEPRESS. http://dx.doi.org/10.2147/PPA.S61340
Difei Liu, Shen Liu, Xiaoming Liu, Chong Zhang, Aosika Li, Chenggong Jin, Yijun Chen, Hangwei Wang and Xiaochu Zhang. Interactive Brain Activity: Review and Progress on EEG-Based Hyperscanning in Social Interactions. Front. Psychol., 08 October 2018 | https://doi.org/10.3389/fpsyg.2018.01862
Ulman Lindenberger, Shu-Chen Li, Walter Gruber and Viktor Müller. Brains swinging in concert: cortical phase synchronization while playing guitar. BMC Neuroscience 2009, 10:22 doi:10.1186/1471-2202-10-22


What is the best way to structure an ethnographic study?

I have used ethnography for similar purposes several times. Structuring the interviews so that you are an apprentice and your subject plays the role of a master artisan works well.

The book, "Contextual Design" by Beyer and Holtzblatt provides several modeling techniques for expressing the information in some ways that add significantly to essay.

http://www.amazon.com/Contextual-Design-Customer-Centered-Technologies-ebook/dp/B003VM7GEC/ref=sr_1_1?ie=UTF8&qid=1382970418&sr=8-1&keywords=holtzblatt


Word for person showing psychological projection

Projection is both normal (we all do it always) and (in excess) a symptom of many different psychological disorders.

There is no name for such a person, as that symptom never occurs in isolation.

It is not correct that extreme self-importance makes someone a narcissist. Again, extreme self-importance can be found in many different disorders, and narcissism has many additional symptoms.


Difference between parallel processing done by human brain and by computers

There is a passage in On intelligence about the differences between parallel processing in human versus computers :

  From the dawn of the industrial revolution, people have viewed the
  brain as some sort of machine. They knew there weren't gears and cogs
  in the head, but it was the best metaphor they had. Somehow
  information entered the brain and the brain-machine determined how the
  body should react. During the computer age, the brain has been viewed
  as a particular type of machine, the programmable computer. And as we
  saw in chapter 1, AI researchers have stuck with this view, arguing
  that their lack of progress is only due to how small and slow
  computers remain compared to the human brain. Today's computers may be
  equivalent only to a cockroach brain, they say, but when we make
  bigger and faster computers they will be as intelligent as humans.

  There is a largely ignored problem with this brain-as-computer
  analogy. Neurons are quite slow compared to the transistors in a
  computer. A neuron collects inputs from its synapses, and combines
  these inputs together to decide when to output a spike to other
  neurons. A typical neuron can do this and reset itself in about five
  milliseconds (5 ms), or around two hundred times per second. This may
  seem fast, but a modern silicon-based computer can do one billion
  operations in a second. This means a basic computer operation is five
  million times faster than the basic operation in your brain! That is a
  very, very big difference. So how is it possible that a brain could be
  faster and more powerful than our fastest digital computers? "No
  problem," say the brain-as-computer people. "The brain is a parallel
  computer. It has billions of cells all computing at the same time.
  This parallelism vastly multiplies the processing power of the
  biological brain."

  I always felt this argument was a fallacy, and a simple thought
  experiment shows why. It is called the "one hundred–step rule." A
  human can perform significant tasks in much less time than a second.
  For example, I could show you a photograph and ask you to determine if
  there is cat in the image. Your job would be to push a button if there
  is a cat, but not if you see a bear or a warthog or a turnip. This
  task is difficult or impossible for a computer to perform today, yet a
  human can do it reliably in half a second or less. But neurons are
  slow, so in that half a second, the information entering your brain
  can only traverse a chain one hundred neurons long. That is, the brain
  "computes" solutions to problems like this in one hundred steps or
  fewer, regardless of how many total neurons might be  involved. From
  the time light enters your eye to the time you press the button, a
  chain no longer than one hundred neurons could be involved. A digital
  computer attempting to solve the same problem would take billions of
  steps. One hundred computer instructions are barely enough to move a
  single character on the computer's display, let alone do something
  interesting.

  But if I have many millions of neurons working together, isn't that
  like a parallel computer? Not really. Brains operate in parallel and
  parallel computers operate in parallel, but that's the only thing they
  have in common. Parallel computers combine many fast computers to work
  on large problems such as computing tomorrow's weather. To predict the
  weather you have to compute the physical conditions at many points on
  the planet. Each computer can work on a different location at the same
  time. But even though there may be hundreds or even thousands of
  computers working in parallel, the individual computers still need to
  perform billions or trillions of steps to accomplish their task. The
  largest conceivable parallel computer can't do anything useful in one
  hundred steps, no matter how large or how fast.

  Here is an analogy. Suppose I ask you to carry one hundred stone
  blocks across a desert. You can carry one stone at a time and it takes
  a million steps to cross the desert. You figure this will take a long
  time to complete by yourself, so you recruit a hundred workers to do
  it in parallel. The task now goes a hundred times faster, but it still
  requires a minimum of a million steps to cross the desert. Hiring more
  workers— even a thousand workers— wouldn't provide any additional
  gain. No matter how many workers you hire, the problem cannot be
  solved in less time than it takes to walk a million steps. The same is
  true for parallel computers. After a point, adding more processors
  doesn't make a difference. A computer, no matter how many processors
  it might have and no matter how fast it runs, cannot "compute" the
  answer to difficult problems in one hundred steps.

  So how can a brain perform difficult tasks in one hundred steps that
  the largest parallel computer imaginable can't solve in a million or a
  billion steps? The answer is the brain doesn't "compute" the answers
  to problems; it retrieves the answers from memory. In essence, the
  answers were stored in memory a long time ago. It only takes a few
  steps to retrieve something from memory. Slow neurons are not only
  fast enough to do this, but they constitute the memory themselves. The
  entire cortex is a memory system. It isn't a computer at all.

The point made here is that the computing paradigm (that is, the way the whole thing works) of the brain and the computer are completely different. The computer is a Turing machine, and the brain is something else, possibly a memory system if you think that Jeff Hawking is right. Whatever it is, the brain is not a Turing machine.

To go back to your question:

  Why can't human brains be used to do massive parallel processing in
  the same way computers are doing today?

It has to do with the way the human brain works. If you assume that the brain will do any task in a parallel fashion, and the more neurons involved, the better the performance; then in order to maximize your performance you should use your whole brain. 1 task: 100% performance, 2 tasks: 50% performance, 3 tasks: 33% performance, and so on.

But if you add an "attention switching cost" to go from one task to another, then you are better off just focusing on one task where the switching cost is zero.

So you can multitask, but it won't be efficient.


What happens in your brain when a concept "clicks"?

With the aim of fleshing out Jeff's link, insight is the broad concept you are talking about. More specific terms which focus on the apparent abrupt shift include Eureka effect and Aha! effect.

The Kounios and Beeman (2009) article mentioned by Jeff discusses potential neural models of the Aha moment. Kounios and Beeman state that

  Although the experience of insight is sudden and can seem disconnected
  from the immediately preceding thought, [studies using EEG and fMRI] show that
  insight is the culmination of a series of brain states and processes
  operating at different time scales. Elucidation of these precursors
  suggests interventional opportunities for the facilitation of insight.

More broadly, you ask why there is such a threshold for understanding. One answer is that the phenomena by definition only applies to phenomena that operates that way. Most understanding does not involve an Aha moment. Problems are routine, or they involve searching for an answer by reading materials, or they involve asking someone, or they involve working through a process, and so on. Furthermore, most problems do not have single discrete insights that are going to make a huge difference. Rather, successful performance is the culmination of many smaller tasks. All this is just to say, insight by definition involves a particular dynamic of impasse and then discovery.

Some literature on the Aha moment focuses on problem solvig techniques to both explain the occurrence of the impasse and the insight. For example, problem solvers upon reaching an impasse may start to re-arrange knowledge or engage in various restructuring processes. The literature also discusses incubation processes which in theory may permit more distal knowledge structures to be integrated with the problem.

References

Kounios, J., & Beeman, M. (2009). The Aha! Moment The Cognitive Neuroscience of Insight. Current Directions in Psychological Science, 18(4), 210-216. PDF


Thinking Fast and Slow: Similarity of Linda problem and Dinnerware case

Question Restatement:

It seems that "have exactly the same structure" is actually a reference to whether the underlying syllogisms are similar - in both research designs.

Another assumption: Given the absence of a quote from Daniel Kahneman's, "Thinking, Fast and Slow" - the question seems to be hoping for answers that will make an inference as to the goal of the questions/research.  Otherwise, this would be moot as Kahneman probably explains in context.

Possible Answer - Identifying Bias when Rational Responses aren't Possible:

Both research designs exhibit the same challenge to BOTH a respondent, and a researcher -

  "What conclusions are they inclined to, (bias) - when it is impossible to respond reasonably?" (E.G., like be faced with a false dilemma logical fallacy).

The first tests Respondent's behavior; the second tests Researcher's behavior.

The Linda Problem - Measuring Respondents:

In the first case, the question speaks about a "philosopher", and an expectation that they would be "objective" in their role as a banker.  Regardless, a rationally minded respondent would immediately notices the absence of an obvious third answer, "None of the Above".  Obviously, "Probability cannot be inferred."

Note: Because respondents feel a generous inclination to "help" the researchers - only a very small number of respondents are likely to protest the validity of the test.

The Dinnerware Case - Measuring Researchers:

In the second case, the question is framed computationally, seeking a rational answer.   Regardless, a rationally minded researcher immediately notices the absence of an obvious third explanation, "Set Theory" ... Obviously, "Cups" rings "dissonant" and does not have as strong a connection as the others do.  So, the research design wrongfully disregards the primal valuation of "belonging" - which might have a higher valuation than "market value", and even "utility".

Note: Because researchers feel an obligation to make SOME inference - only a small portion might protest the validity of the test.

Conclusion:

In other-words, I feel that the similarity between the designs is not at all about "averaging" or "valuation" - but their similarity is due to the "dilemma" that both researchers AND respondents are faced with - when confronted with an irrationality:

How much does cognitive dissonance contribute to the even distribution of bias?  (The "Data" in the question suggests an inverse effect.)
And, if there is no even distribution of bias - when a rational answer cannot reasonably be provided, (given the logical errors of the circumstance) - then what biases do people tend towards? (The "Data" in the question suggests a universal bias towards "belonging, association", rather than rationality, personal values, etc.)

It would be very interesting to see the full implementation of this instrument - as it might inform political campaigns, (IMHO).

If the data from the two questions here is actually correct - then it might be consistent with what we observe in politics, (and relationships, etc).


Length of questionnaire for long-string analysis

Here are a few general principles that I would apply:
Does the scale have a mix of positively and negatively worded items? If so, you'd need fewer items to identify strange cases. If it is all positively worded, then it's possible that someone could for example agree to a fairly large number of items.
Does it measure multiple constructs or does it measure a single dimension? If it measures more than one dimension (e.g., like a five factor personality test), then you'd expect to see more diversity in responses.
From first principles, it can be helpful to consider whether there is any conceivable reason to provide such a sequence of responses that would reflect conscientious survey completion.
You may also want to think about your survey design and the incentive structure to consider what incentives there are to respond in given way. Some survey designs make that mode of responding more efficient.
Another general strategy is to calculate an index and plot the distribution of cases on that index. If you see a break in the histogram, this can suggest that a discrete process (i.e., non-conscientious responding) has caused the data on the other side of the break. This can be helpful when looking at things like survey completion times. In the case of providing only one response option to a set of items, you can obtain a count of the number of response options and tabulate and look to see whether the vast majority provide all the responses. Alternatively, if it looks like a common response type, then you'd be less likely to conclude that it is problematic.
Here is one way to get this in R assuming data is your dataset and items is a vector item names.
data$unique_responses <- apply(data[,items], 1, function(X) length(unique(X))
table(data$unique_responses)


How is the relationship between hypothalamus and homeostasis & fear and how does it evolve across human development stages?

TLDR: the hypothalamus is involved in fear processing, but not in the way you think.
Wikipedia has a decently sourced article on the hypothalamus and it does play some role in fear processing:

Fear processing
The medial zone of hypothalamus is part of a circuitry that controls motivated behaviors, like defensive behaviors.[29] Analyses of Fos-labeling showed that a series of nuclei in the "behavioral control column" is important in regulating the expression of innate and conditioned defensive behaviors.[30]
Antipredatory defensive behavior
Exposure to a predator (such as a cat) elicits defensive behaviors in laboratory rodents, even when the animal has never been exposed to a cat.[31] In the hypothalamus, this exposure causes an increase in Fos-labeled cells in the anterior hypothalamic nucleus, the dorsomedial part of the ventromedial nucleus, and in the ventrolateral part of the premammillary nucleus (PMDvl).[32] The premammillary nucleus has an important role in expression of defensive behaviors towards a predator, since lesions in this nucleus abolish defensive behaviors, like freezing and flight.[32][33] The PMD does not modulate defensive behavior in other situations, as lesions of this nucleus had minimal effects on post-shock freezing scores.[33] The PMD has important connections to the dorsal periaqueductal gray, an important structure in fear expression.[34][35] In addition, animals display risk assessment behaviors to the environment previously associated with the cat. Fos-labeled cell analysis showed that the PMDvl is the most activated structure in the hypothalamus, and inactivation with muscimol prior to exposure to the context abolishes the defensive behavior.[32] Therefore, the hypothalamus, mainly the PMDvl, has an important role in expression of innate and conditioned defensive behaviors to a predator.
Social defeat
Likewise, the hypothalamus has a role in social defeat: Nuclei in medial zone are also mobilized during an encounter with an aggressive conspecific. The defeated animal has an increase in Fos levels in sexually dimorphic structures, such as the medial pre-optic nucleus, the ventrolateral part of ventromedial nucleus, and the ventral premammilary nucleus.[36] Such structures are important in other social behaviors, such as sexual and aggressive behaviors. Moreover, the premammillary nucleus also is mobilized, the dorsomedial part but not the ventrolateral part.[36] Lesions in this nucleus abolish passive defensive behavior, like freezing and the "on-the-back" posture.[36]

Just based on that it's easy overemphasize the importance of the hypothalamus in fear processing; it's actually not that central. If you want the popsci version, then the amygdala gets the center stage in regulating fear. From a review of the "Neuronal circuits for fear and anxiety":

I don't see why the homeostasis circuitry would be involved in fear processing (as you propose), and as far as I can tell, it isn't.


How many items do I need to design a measure?

Cross Validated has plenty of info on this topic; arguably it's a duplicate of some question over there. Check out "Under what conditions should Likert scales be used as ordinal or interval data?" and my synopsis of Bollen and Barb (1981) in this answer. If you have more to say about why this is a question specific to Cognitive Sciences (beyond the presumable involvement of subjective judgments and self-reporting on attitudes), you should say it; I might be able to add unique substance to this answer. (BTW, there is plenty of literature out there on this question. Frankly, I'd guess you haven't tried very hard in looking for yourself. If you have, you could also improve answers by noting what you know so far.)

As for non-unique substance that mostly approaches the problem from a statistical perspective, consider what you'll probably want to do once you have the data. If your theory of your latent construct is typical, you probably want to estimate individuals' scores on a continuous dimension, not just rank people. I.e., you probably want to be able to say how different one person is from another, not just who ranks higher and how many people are between them.

The basic problem with Likert scales is that they don't really allow you to do this inherently. E.g., the difference between ratings 3 and 4 isn't necessarily the same as the difference between 2 and 3 even if you use exactly opposite anchors like 2/4$=$dis/agree and 3 is given a completely neutral anchor. Therefore you can't really assume the sum of a few items has this property (interval-level measurement, basically) if you just treat these ratings as numbers.

Another halfway decent place to start reading is Wikipedia's classical test theory (CTT) page. This might not be the gentlest introduction to the underlying issues, but it's probably nicer than sending you straight to IRT. IMO, that's where one ultimately has to go to understand latent dimension estimation in terms of thresholds between Likert scale points...but if you can satisfy CTT assumptions, you can avoid a lot of complexity, which I assume you want to do, for better or worse.

Bollen and Barb (1981) demonstrate that Likert scales with $\ge5$ rating options can be treated like interval measures without weakening correlations too terribly much, but more is diminishingly better. Fewer simplifies the rating task somewhat in general, but can sometimes make it harder for respondents with nuanced opinions that can't be reflected accurately with fewer options...and either way, it costs you info. Conversely, having tons of options might start to elicit arbitrary noise rather than meaningfully distinct responses...so consider whether additional options actually mean something different from existing ones. If they do, you're probably not going to do any harm by adding them.

Another technical issue for which I have no better reference than my former advisor (he never told me where he read it): more scale options tends to increase the size of the general factor (in a factor-analytic sense). I suspect this has something to do with response biases like acquiescence bias and extreme response style. If you know what you're doing, you can control for "nuisance factors" like these, but it's advanced stats. See "Factor analysis of questionnaires composed of Likert items" for more.

As for the number of items, it depends somewhat on similar concerns: your abilities to approximate and estimate a continuous dimension, and your ability to differentiate items meaningfully. A minimum of three items are necessary for statistically meaningful estimation of latent factor scores (this may be true of latent score $(\theta)$ estimation using IRT as well; I'm not sure, but I think it should be). Two doesn't permit identification of a unique solution in factor analysis – there's no indication of how to weigh your two items as measures of any common factor(s). If you're trying to measure more than one latent construct (or trying to separate out nuisance factors from your construct of interest), you'll need at least one item for each that only really measures that construct, and three that measure each. That's only four items for two correlated constructs, but six for two unrelated constructs.

Again, more items is generally better for the sake of approximating and estimating a continuous latent dimension...but if you're just going to sum or average Likert ratings, you're implicitly assuming that all items measure the latent construct equally well, or that their inequalities at least balance out overall. Therefore you don't want to just add more items if they're going to be substantially worse, unless you're willing to do some nontrivial statistical work to sort them out properly.

I'd also hesitate to add overly duplicative items, because that could encourage inattentive responding and thus engender both arbitrary noise and (more) systematic bias. Using semantic opposites might be defensible: e.g., 1) Tumblers are better than pumpers; 2) Pumpers are better than tumblers...? This can help manage acquiescence bias if it's done properly, but it can also irk respondents and introduce nuisance multidimensionality.

Multidimensionality is yet another problem that can arise with too many items...but again, it's not necessarily a problem if you know what you're doing statistically. There are ways of separating factors of interest from others that might influence responses if you have enough items. If you or someone else might actually figure out how to do this someday (it's not THAT hard), it's better to have the items from the outset than to have to add or change them later, because you probably want to maintain a consistent stimulus across all data you collect over the long-term life of the measure.

However, if it's important to keep it simple for the sake of avoiding complex calculation or for the sake of reducing respondent fatigue (yet another source of inattentive responding, noise, and bias), you might want to limit yourself to 10–20 short, clearly-phrased, single-idea items. Some questionnaires have 100–500 items, but that requires some serious compensation for a participant's time and concentration; people do not like to think that hard for that long. Consider the concerns of your target population and any distracting elements in your response collection scenario, and try piloting your first draft of the test with a few people to see how it comes across and whether it takes as long to complete as you think it should. Several stages of revision aren't uncommon when this is done properly, but improper practice isn't uncommon either, nor is it much easier to fail completely than it is to pull it off perfectly.


Recording motor-evoked potentials (MEPs) in response to transcranial direct current stimulation (tDCS)?

Compared to TMS which is usually brief and high-intensity, tDCS stimulation protocols are fairly weak and long-duration. This protocol doesn't elicit MEPs by itself, because you need a stronger stimulus.

In some studies (see references below), tDCS has been shown to modulate MEPs, but it is probed by using TMS to elicit the MEP, and comparing the evoked MEPs with and without tDCS.

The mechanistic idea is that tDCS is modulating neuronal thresholds, but you still need some sort of a "kick" to actually evoke a MEP.

Nitsche, M. A., & Paulus, W. (2000). Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. The Journal of physiology, 527(3), 633-639.

Nitsche, M. A., & Paulus, W. (2001). Sustained excitability elevations induced by transcranial DC motor cortex stimulation in humans. Neurology, 57(10), 1899-1901.

Nitsche, M. A., Fricke, K., Henschke, U., Schlitterlau, A., Liebetanz, D., Lang, N., ... & Paulus, W. (2003). Pharmacological modulation of cortical excitability shifts induced by transcranial direct current stimulation in humans. The Journal of physiology, 553(1), 293-301.


Why is swearing such an in built part of the mind?

The mind is a large landscape as you are finding. Speech centers where new or current ideas are formed into words rely on other areas where conversion elements have already been worked out some time ago. Ready made phrases for non-new ideas and reliable conversation can be brought up with little or no effort. With these peppered into speech one can sound as if they are otherwise fully conscious and thinking about what they are saying.

You can see this when speaking with people when they cannot stop adding the same phrase to their sentences. Such as "...if you know what I mean." Though a complex phrase it has its own quick 'subroutine' to send it to the mouth. After hearing this over and over you would guess that the speaker is not as self aware as you first suspected. This can reflect the effort made as they cast about for ideas and how to express them.

The foul language which would be rare in earlier days probably reflects her frustration and anger with her difficulties in forming speech. Normally there are filters to prevent using bad language if only to spare feelings of listeners. These can also be compromised when the effort is to get any speech together. You can think of her speech as letting you in on parts of her internal, normally private, dialogue as she struggles to express herself.

With patience this should improve as she re-maps her speech abilities along with the boundaries of what to say and not say.

I suggest you read The Man Who Mistook His Wife for a Hat by Oliver Sacks to see how disparate the areas of the mind can be. Good luck to you both.


Can information be lost while traveling through neurotransmitters?

Synapse Information Processing

In the Neural Engineering Framework (NEF), synapses are:

Leveraged to provide filtering of spikes at different time scales. This allows for managing heterogeneity in neurons, as well as allowing to trade-off speed of response for information integration over time.
Used to create dynamical systems using these filtered spikes including oscillators, integrators and temporal memory networks.

In all of these cases, information can be lost. However, this is always framed as a trade-off between informational integrity (which can be measured various ways, such as how well a signal is classified, how well a dynamical system is approximated, how much information is compressed over time) and resource use.

Retransmission as Information Recovery

In the context of the brain, TCP doesn't really make sense. TCP assumes there is a copy of the information that is desired to be transmitted somewhere, but that's usually not the case in the brain. That being said, there is something called the phonological loop which appears to be a short term store of what was heard. Consequently, you could interpret this replay as a retransmission and consider it to be information recovery.


For more context, the passage in Al-Krenawi & Graham (2006) reads as follows on page 12.

  Previous research suggests that education and attitudinal acceptance of polygamy are inversely correlated (Heaton & Hirschl, 1999; Maziak et al., 2002; Nevadomsky, 1991); other findings are less conclusive (Nevadomsky, 1991). The Bedouin-Arab community is in a massive state of transition, part of which includes higher attainment for youth. Respondents in monogamous marriages therefore may have had greater opportunity for education; and the potential for less acceptance of polygamy may be modestly influential on the cohort.

My interpretation is that research indicated by 3 papers suggests that where education is high, acceptance of polygamy is low, and where education is low, acceptance is high.  The acceptance is shown through attitude.

As @ArnonWeinberg pointed out, "attitude" would normally refer to "expressed attitude" measured via a survey or interview or similar report.  In this case it was questionnaires.

  Questionnaires were structured; data collectors were present during the interview, completed the questionnaire forms with the respondent and, for those with limited reading or writing skills, the researchers read to the respondent and filled in the questionnaire according to the given responses (pages 7—8).

References

Heaton, T., & Hirschl, T. A. (1999). The trajectory of family change in Nigeria. Journal of Comparative Family Studies 30(1), 35-55. Retrieved from http://www.jstor.org/stable/41603608

Al-Krenawi, A., & Graham, J. R. (2006). A comparison of family functioning, life and marital satisfaction, and mental health of women in polygamous and monogamous marriages. International Journal of Social Psychiatry, 52(1), 5-17. DOI: 10.1177/00207640060061245

Maziak, W., Asfar, T., Mzayek, F., Fouad, F. M., & Kilzieh, N. (2002). Socio-demographic correlates of psychiatric morbidity among low-income women in Aleppo, Syria. Social science & medicine, 54(9), 1419-1427. DOI: 10.1016/S0277-9536(01)00123-X

Nevadomsky, J. (1991). Attitudes of Nigerian students toward marriage and family relationships. International Journal of Sociology of the Family, 21(2), 201–212. Retrieved$nbsp;from http://www.jstor.org/stable/23029820


If light travels at c, and the human nervous system's speed/perception speed<c, why aren't we not seeing or blind at some times?

I think you are confusing speed with rate.  The speed of light is how long it take to for light to get from one point to another, not how often light "events" arrive at the eye.

To give an analogy, imagine a highway.  There are 500 cars on that highway.  They are traveling at 70 mph.  Those cars then exit the highway onto a residential rode.  They are then travelling at 30 mph.  Does this require some of the cars disappear?  No, they are all there, just moving at a different speed.

So for vision, imagine a certain light source emits a burst of 500 photons.  The light source is 1 meter away from the eye, so it takes about 3 nanoseconds for the photons to arrive at the eye.  Then they are converted into electrical impulses by the eye, and processed by the brain.  Let's say this takes 100 milliseconds (I am just making that number up).  That is about 30 million times longer.  But it is still processing the same 500 photons, it just takes longer for them (or the electrical impulses they create) to get from one point to another.


Can calmness happen during the fight-flight response?

With looking at the possibility of calmness during stress and trauma, you only have to look at the situation with rape victims and soldiers witnessing severe traumatic events who dissociate (Ellert, et al. 2011)(Waller, et al. 2001) during the event.

Dissociation is a psychological defence mechanism (Cardeña, 1994) which helps the person to get through the situation with as little harm as possible.  Think about it like throwing a switch on conscious awareness.

Whilst some define dissociation as a combination of 2 distinct psychological mechanisms (Brown, 2006), the psychobiological mechanism of dissociation is little understood; but, when someone is dissociated from the event, the detachment of conscious awareness from the event can make the person appear very calm.

References

Brown, R. J. (2006). Different types of “dissociation” have different psychological mechanisms. Journal of Trauma & Dissociation, 7(4), 7-28.DOI: 10.1300/J229v07n04_02

Cardeña, E. (1994) The domain of dissociation. In: Dissociation: Clinical and theoretical aspects, Edited by: Lynn, S. J. and Rhue, J. W. 5–31. New York, NY: Guildford Press.

Ellert R. S. Nijenhuis PhD & Onno van der Hart PhD (2011) Dissociation in Trauma: A New Definition and Comparison with Previous Formulations, Journal of Trauma & Dissociation, 12(4), 416-445.DOI: 10.1080/15299732.2011.570592

Waller, G., Hamilton, K., Elliott, P., Lewendon, J., Stopa, L., Waters, A., ... & Hargreaves, I. (2001). Somatoform dissociation, psychological dissociation, and specific forms of trauma. Journal of Trauma & Dissociation, 1(4), 81-98.DOI: 10.1300/J229v01n04_05