title: Why do humans need emotions?

text:
# Question
Title: Why do humans need emotions?
If the emotions' only purpose is to stimulate certain behavior, why can't this task just as effectively, or even more effectively be done by the reasoning part of the brain?

Are emotions really necessary for survival?

# Answer
> 13 votes
It appears that there's been a lot of research done by USC professor Antonio Damasio on the importance of emotions.

There's some fascinating case studies and interviews that are worth reading and listening to, but the short summary, as I understand it, is:

Emotions are important because they end up directing reason. Without emotion, there are simply too many parts of any situation or decision for reason to know when to stop thinking and actually make a decision. Have you even been in a position where you need to choose between two alternatives that seem equally good to you (for example, deciding which of two equally-qualified candidates to hire), and you spend what seems like an eternity weighing the pros and cons of each, coming up with new reasons and rationales, before reluctantly making a decision? That's because you didn't have emotions pushing you one way or the other.

You might think, wait, why wouldn't we be able to make decisions quickly without emotions? You'd be able to -- people with impaired emotions have no problem making quick decisions *when directed to do so*. Without such direction, they have no reason to make decisions quickly because there's no emotion telling them that making decisions quickly is "good" -- in fact, there's nothing telling them that *anything* is "good".

That's a highly-simplified summary of Dr. Damasio's findings. I suggest reading more about his work if you are really interested.

# Answer
> 12 votes
This question becomes more complicated if we think in terms of "emotions" (e.g., angry, happy, sad, afraid, etc.) than in terms of "affect" (positive and negative feelings, high and low arousal). I'll start with affect and move on to emotions.

An affective state tags an object with a certain value--and it does so very quickly (e.g., Pham, 2007). For example, if a snake makes you feel bad, then it must be undesirable. This motivates a plan of action that could involve elimination of the snake (fight) or withdrawal from it (flight). You do not have to spend time considering the value of the snake (i.e., that it's bad and dangerous) and developing a plan of action to deal with it. Instead, your negative affect facilitates automatic evaluation and action, which might increase your evolutionary fitness. Gerald Clore has suggested that negative affect serves a STOP function whereas positive affect serves a GO function.

Emotions are a bit more complicated, in part because we don't have a consensual definition of them. If emotions are merely socially shared concepts (happy, sad, angry) that we use to describe and organize our situated affective experiences (e.g., Barrett, 2014), then we can say that *emotion concepts* serve self-regulatory and social-communicative functions. For example, by labeling as *anger* my high arousal and negative affect in the context of having my autonomy violated, I've organized this multifaceted affective experience into a single concept (of anger). This enhances my capacity to develop ways to regulate my affective experience (Kashdan, Barrett, & McKnight, 2015) and it facilitates communication of it. If I tell you that I am angry, then you'll have a general idea of what I'm feeling. Self-regulation and social communication are certainly important for survival.

Emotions are involved in lots of other things, e.g., facilitating cooperation (e.g., DeSteno et al., 2010), motivating the pursuit of new goals (Bench & Lench, 2013), social bonding, deceptive behavior, cheating, and so on. All of these can be adaptive in certain contexts.

It's also important to note that emotions are flexible. For example, we don't have a "fear module" in the brain that motivates a rigid pattern of action (e.g., Lindquist et al., 2012). Instead, emotions (on one view) emerge from the interactions of more domain-general processes (including exteroceptive perception, language, interoception, conceptualization, executive attention, and core affect; Oosterwijk, Touroutoglou, & Lindquist, 2015). These domain-general processes interact to produce emotional episodes with patterns of cognition, behavior, physiology, and experience that are flexible to the situation at hand (Barrett, 2015). The kind of flexibility and efficiency that you might expect from the "reasoning part of the brain" probably does not match up to the kind of flexibility that I just described. Also, it's important to note that emotion and cognition are not separable constructs.

EDIT: As far as reason vs. emotion is concerned, emotion is a much more potent motivator. If you consider seizure patients with lesions to the majority of their amygdalae (from lobectomies), they don't show fear. If you put them in a dangerous situation, they won't run away--despite having their prefrontal areas intact. (Although an alternative non-emotional interpretation is that these patients have lost the ability to discern what is salient, e.g., threatening, in their environment.) If you have a hobby that you *really* enjoy, your motivation to continue doing it is largely emotional ("It makes me happy"). As onigame implied in his/her answer, emotions motivate these kinds of decisions.

Anyway, I think I've provided you a very incomplete view on the adaptiveness of emotions, but hopefully this clarifies some things.

# Answer
> 7 votes
**Are emotions really necessary for survival?** No, not for survival; lots of living things around us without even a brain.

**Did emotions provide an evolutionary advantage in the past?** The areas of the brain we associate with emotion were around far back in our evolutionary past - long before conscious reasoning appeared. Emotions are still an important part of reasoned decision-making in humans, but for our early ancestors, they would have been crucial.

**Are emotions now "vestigial" (relics of the past) holding us back from making decisions more effectively with reasoning alone?** Unconscious decision-making is far faster, works in parallel, high capacity, and is remarkably effective, while rational decision-making is slow, sequential, low capacity, and bounded in effectiveness. So as it stands, removing emotion from decision-making would cripple us.

**But given that we now have the capacity for rational thought, don't the disadvantages outweigh the advantages?** Disadvantages of the emotional system include failing to use reasoning effectively at times when it is practical and better to do so, the effect of emotional (mood) disorders, and loss of self-control in emotionally intense situations. Evolutionary psychologists have long argued that systems that provided an evolutionary advantage in our past (such as 'stress') may no longer be as advantageous in modern society.

Related questions: Is Decision-Making Emotionally Based, with Rationalization as the only Conscious Component?, Do feelings have a purpose?

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For completeness, I should mention that for a lay-person, the term 'emotion' may refer to a number of distinct terms in cognitive science. Modern theories of emotion suggest that like many aspects of self-knowledge, emotion is "inferred" - rather than "introspected" - from a combination of physiological cues and other context information. That is, emotion is a person's interpretation of their cognitive state, so the idea of 'removing' it is not well defined.

'Feeling' is the phenomenological aspect of emotion - the actual conscious experience of it. One might sometimes wish that we could make decisions based on emotional state without the experience of it... For example, sometimes pain causes me to stop doing something that might injure me, but other times, I stop doing it but the pain persists, and it sure would be nice if that (apparently useless) pain stopped. Hence the popularity of pain killers, anti-depressants, anti-anxiety, and other emotion-suppressing medication. As cognitive scientists don't really understand conscious experience, it is not clear whether or not 'feeling' could ever be removed from 'emotion', allowing us to make effective decisions without unpleasant experiences.

'Emotional expression' is the externally observable aspect of emotional state. Emotions may play a significant role in all decision-making, but this isn't always apparent externally due to individual differences in expression. The evolutionary advantage of emotional expression (or lack thereof / self-control) is largely social.

# Answer
> 3 votes
**Short answer**
Emotions are not necessary for survival, but they may provide evolutionary advantages.

**Background**
Although crocodiles cry, they do not feel any remorse in killing their prey when shedding tears doing so (they empty their lachrymals when snapping their jaws shut). Given their relative brain size we can expect (but never know) that they do not experience a comparable range of complex emotional states as we humans do. Just as fishes and amphibians crocodiles are though to be pretty much instinct-driven creatures. However, admittedly, when one sees a mother croc carrying her hatchlings to the waterside you start wondering...

Source: Ye-Tao et al., 2010

Nonetheless, assuming they are driven by instinct and are emotionless (or at least relatively so), fishes and reptiles have been around for ages and are still thriving to date. In the end, we evolved from a fishy ancestor.

Your question then becomes a more subtle one, as emotions may have given us *evolutionary advantage* over other speciesm but it is not obligatory for survival. Disregarding the fact whether we are better or worse than other species of animal, we do dominate the world, for the better or for the worse. I think emotions have aided us in this domination and have given us an advantage. We tend to care for others even if we don't see them anymore (emigrated working class folks that keep on sending money back home), or even when we don't know them (we donate money to other people in foreign countries out of empathy). Emotions makes us protect our kids (don't make mummy angry or it will spawn the lioness) which makes the parents more evolutionary "fit". Hence, emotions are not obligatory for survival, but they do give an evolutionary advantage.

**Reference**
Ye-Tao et al. *J Accord Int Med*;**6**:43-58 (only for the image)

# Answer
> 0 votes
Actually according to the Psi-theory developed by Dietrich Dörner at the University of Bamberg, emotion is not to be understood as an independent sub-system, a module or a parameter set apart from cognition, but an intrinsic aspect of cognition which needs emotion necessarily to *modulate* itself. From its WP Source:

> Emotion is an emergent property of the modulation of perception, behavior and cognitive processing, and it can therefore not be understood outside the context of cognition. To model emotion, we need a cognitive system that can be modulated to adapt its use of processing resources and behavior tendencies... Operations on memory content are subject to emotional modulation... Perception is subject to emotional modulation... These modulators control behavioral tendencies (action readiness via general activation or arousal), stability of active behaviors/chosen goals (selection threshold), the rate of orientation behavior (sampling rate or securing threshold) and the width and depth of activation spreading in perceptual processing, memory retrieval and planning (activation and resolution level). The effect and the range of modulator values are subject to individual variance... Problem solving is context dependent (contextual priming is served by associative pre-activation of mental content) and subject to modulation.

> In the Psi-theory, emotions are interpreted as a configurational setting of the cognitive modulators along with the pleasure/distress dimension and the assessment of the cognitive urges. The phenomenological qualities of emotion are due to the effect of modulatory settings on perception and cognitive functioning (i.e. the perception yields different representations of memory, self and environment depending on the modulation), and to the experience of accompanying physical sensations that result from the effects of the particular modulator settings on the physiology of the system (for instance, by changing the muscular tension, the digestive functions, blood pressure and so on). The experience of emotion as such (i.e. as having an emotion) requires reflective capabilities. Undergoing a modulation is a necessary, but not a sufficient condition of experiencing it as an emotion.

From these it's obvious emotions play a critical modulating role in most areas of cognition such as memory operation, perception, drives, stability of the dominant motive, problem-solving, etc. And the OpenCog designed by Ben Goertzel has a concrete implementation of Psi-Theory for handling emotional states, drives and urges, dubbed OpenPsi. Finally from the same Wikipedia source in summary:

> Evaluating the Psi-theory in an experimental paradigm is difficult,... A partial exception to this rule is the emotional model, which has been tested as a set of computational simulation experiments. While it contains many free variables that determine the settings of modulator parameters and the response to motive pressures, it can be fitted to human subjects in behavioral experiments and thereby demonstrate similar performance in an experimental setting as different personality types.

So quite unintuitively, the commonly-held ambiguous emotions are actually easier than most other parts of cognition to be simulated and tested via its modulation role, *not* to be interpreted as the conventional hedonic *pleasure and pain* which are actually signaled from a *situated* appetitive or aversive *goal* afforded by a specific environment to the satisfaction of its corresponding urge. Yet as modulator impacting potentially all neuronal cells and over a much longer time course than excitations/inhibitions responsible for the mundane plastic learnings and other often ephemeral cognition breakthroughs, emotion could affect urge and goal persistently and dramatically.

A negative example reflecting the importance of emotion even for highly intelligent and rational normal people could be the anecdotal emotional trauma occurred on one of artificial neural network and computational neuroscience pioneers, Walter Pitts. Once he knew the affordance from his own later neurophysiological studies of frog's vision destroyed his own extremely firmly-held digital neuron computationalism goal via his binary temporal logical propositions, he permanently lost work interest and burned his unpublished original works labored over the years, which sounds extreme and absurd rationally speaking.

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Tags: emotion, philosophy-of-mind, evolution
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# Question
Title: Response bias in physical and sexual attraction/attractiveness surveys
There are a few potential sources of response bias in surveys intended to measure attractiveness levels of individuals, especially in the context of differences in personal qualities of judges.

#### Subjective interpretation of the term

Perhaps, you have heard something along the lines "He is attractive, but he is not my type". In this case the term "Attractiveness" is understood in the context of socially conventional judgement: "Many people would find him attractive".

In other cases, when people are asked about attractiveness of the person, they might assume that they are asked to rate the physical attractiveness of the person from their personal lens.

In order to remove this confusion, the questions can be more direct, such as "Rate the objective level of attractiveness" or "How attracted are you to this person?" Is there any data on how much such a different formulation of the question changes the responses?

#### Subjective interpretation of the scale

Some surveys use 5-point Likert-type scales, some use 7-point scales. On top of that, the items on the scale can be named differently: they can be raw numbers, or they can be named "Highly \[un\]attractive", "Neutral" or anyhow differently.

In a numerical scale a person might assume that the lowest score item, 1, stands for "Not attractive" and everything above that signifies higher and higher levels of attractiveness. While such an misinterpretation would be far less likely when the lowest score item was named "Highly unattractive". Is there any data showing how much the difference in the scale used affects the response?

#### Unemotional response bias

Not exactly certain about the name of the phenomenon, however I will describe it in the following way: A bias produced when the person abstracts too much from the task and forgets about their emotions. For example, consider the question:

*Describe your reaction towards the idea of having sex with this person.*

With the following options for a response:

1. Strong disgust.
2. Moderate disgust.
3. Mild disgust.
4. Neutral.
5. Mild excitation.
6. Moderate excitation.
7. Strong excitation.

The responses to this question still might be different from the questions asking to rate sexual attraction or attractiveness directly. However, is there any data examining this bias within the context of rating attraction?

# Answer
Most actual attractiveness and other types of sensitive "excitation/disgust" (using your language term) surveys are done by private organizations or firms across many years, regions and cultures, thus the findings are mostly proprietary and probably hard to retrieve publicly to reference, especially matching your exact keywords specification for comparison. Maybe you need to do some pilot survey yourself to find out some preliminary result.

However, your main concern out of several questions here could be addressed on a general level if you become familiar with some detailed design and planning guidelines for these kind of observational survey samples to reduce *response bias* as much as possible. which is an important topic especially in social sciences. You may read Kalton and Schuman's *The Effect of the Question on Survey Responses: A Review* for an introduction.

According to Kalton & Schuman's review, we have to first distinguish factual vs non-factual (opinion) questions in your survey to be aware of different types of possible response bias albeit they may overlap in some cases. Thus your concerned "attractiveness" question is arguably intrinsically non-factual opinion-based in which case according to the authors marginal wording format changes may cause markedly discrepant marginal distributions. A survey that asks “Are you in favor of building a new town hall that will cost taxpayers $2 billion?” is likely to get a very different response from one that is worded as: “Are you in favor of building a new town hall to replace the old one that is contaminated with asbestos?”. However, it seems in your proposed cases ("rate the person's attractiveness" vs "how attracted are you to this person?") there's no such dramatic wording effect possible. It could be related to the issue of *open and closed questions* mentioned in the non-factual question review section:

> The main context in which open questions are used extensively is when the potential responses are both nominal in nature and sizeable in number. These conditions occur often with motivation questions, asking for the principal or all reasons for an occurrence, and with questions asking for the choice of the most, or several most, important factors involved in an issue... The authors present indirect evidence suggesting that when open and properly constructed closed forms of questions yield different responses, the responses to the closed questions are sometimes more valid in their classification of respondents and in describing relationships of the responses with other variables.

Thus ideally you may have to do some pilot open question survey first to distill those appropriate closed options related to your "attractiveness" as there're apparently many different aspects (muscle, body ratio, body organs, etc) affect attractiveness for different people which you may need to reflect in your closed options. Of course I get from your question that you may regard "attractiveness" as some socially accepted "objective" fact and you may have to state this explicitly in your question, then this becomes a factual question and other issues such as social desirability bias with significant cultural variances need to be further addressed. But arguably "attractiveness" of a specific person is always opinion based without factual basis even there may be small variance around its population mean.

For your remaining closed question format concerns about this largely non-factual question could be addressed by Kalton & Schuman's *Treatment of Don't Knows* and *Middle alternatives* sections.

> Two examples given by Schuman and Presser (1980)illustrate that many respondents will indeed choose one of the alternatives offered for an opinion question even though they do not know about the issue involved... As a way of screening out respondents without opinions, some type of filtering may be used. One possibility is to include an explicit "no opinion" option or filter in the response categories offered to respondents-a "quasi-filter"; in the Schuman and Presser experiment, this offer reduced the proportion of respondents expressing opinions on the two laws to 10 per cent or less. A more forceful possibility is a preliminary filter question "Do you have an opinion on ...?"-a "full filter".

> As might be expected, the explicit offer of a middle alternative often substantially increases the proportion of respondents stating a neutral view. In a series of experiments conducted by Kalton et al. (1980), the increases were between 15 and 49 per cent; in a series reported by Presser and Schuman (1980) the increases were between 10 and 20 per cent. Presser and Schuman observe that in their studies and earlier ones involving three point scales (pro, neutral and anti) the increase in support for the neutral view with the offered question form came proportionately from the polar positions, so that the balance between pro's and anti's was not affected by the variation in alternatives offered. This comforting finding failed to hold, however, in two of the three experiments with three-point scales reported by Kalton et al.

So in summary survey result may become very sensitive and subtle to the inclusion of closed options such as "no opinion" and "neutral", and to finally decide your specific wording of those choices expressed in your last two sections (such as employing sensitive "excitation/disgust" or "attraction/repulsion" rhetoric using your term), you'd better initially conduct some pilot study using open question format for your target population if you cannot find reputable data sources to confidently rely on. It's well-known that high quality unbiased surveys are not cheap, nowadays you can perhaps easily and cheaply do some anonymous surveys via internet but many other issues and biases such as the famous non-response bias would occur.

Reference: Kalton, Graham; Schuman, Howard (1982). *The Effect of the Question on Survey Responses: A Review*. Journal of the Royal Statistical Society. 145 (1): 42–73.
> 1 votes

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Tags: emotion, measurement, methodology, bias, physical-attraction
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# Question
Title: Is there good evidence that long-term stimulant usage in children has no harmful effect when stopped?
Essentially, I am looking for an RCT done on children, where they went on a stimulant drug for at least 3 years, and then stopped administering the drug.

What I'm worried about is long-term lower levels of motivation - below baseline compared to people who never took the stimulant in the first place. Or other subtle stuff that would only show up in an RCT.

# Answer
Great question.

Your best bet are probably follow-up studies. These studies begin with a standard RCT to determine treatment efficacy, and are then followed up by long-term monitoring of participants after completion of treatment. In this way, participants on the active medication can be compared to controls on post-treatment outcomes (eg, after discontinuing medication).

Realistically, modern long-term stimulant efficacy trials are difficult to control from a practical and ethical perspective. Parents of participants who don't respond to medication may drop out, subjects whose symptoms subside are encouraged to discontinue, and parents of unmedicated participants are likely to seek medication if symptoms persist, causing significant selection bias. Thus, this is one of those rare cases where the best studies are older - at a time when treatments were not so readily available as they are now.

Literature reviews are a good place to find lists of such trials, and there are several good reviews of (particularly older) follow-up studies in which both arms are likely to have remained similarly unmedicated at follow-up after the end of the controlled phase: Barkley (1977), Gadow (1983), Hechtman (1985), Jacobvitz et al (1990), Thorley (1988). The studies reviewed measure a wide variety of clinical, cognitive, social, medical, and general life outcomes.

There are 3 typical findings from these reviews:

* In the long-run, the majority of outcome measures are not affected by medication. In line with this finding, the benefits of medication also do not last - eg, Hechtman & Greenfield (2003):

> Most authors who have examined stimulant treatment in childhood as a predictor of hyperactive adolescent outcome have found no effect – as if the initial benefits of medication cited by many studies do not somehow carry over into positive long-term outcome.

* Though evidence is inconsistent, some reviews do find persistent benefits in a few outcome measures - such as Wilens et al (2003):

> Our results suggest that stimulant therapy in childhood is associated with a reduction in the risk for subsequent drug and alcohol use disorders.

* One outcome measure that may remain negatively affected after cessation of stimulants treatment is height; evidence for this is also inconsistent - eg, Roche et al (1979):

> This temporary effect on growth is present during the first few years of treatment and seems related to drug dosage and to the presence or absence of drug holidays.

Additional notes:

* These findings remain broadly consistent with conclusions of modern long-term follow-up studies.
* 3-year RCTs are non-existent for reasons including the ones mentioned above, and because many official clinical guidelines recommend stimulant treatment be limited to a maximum of 2 years, usually with psychotherapy and training helping to reduce reliance on medication. The closest is probably Hechtman, Weiss, & Perlman (1984), a non-RCT follow-up study that looked at adult outcomes of children medicated for 3-5 years (and then stopped medication) relative to matched untreated controls, and finding some (minor) persisting long-term benefits.
* "Levels of motivation" is an unusual outcome variable for an intervention study on ADHD. However, long-term follow-up studies looking at depression do exist - finding no effect (Jensen et al, 2007; Lam et al, 2019) or favouring stimulants (Daviss et al, 2008; Biederman et al, 2009; Rasmussen et al, 2015; Chang et al, 2016).
> 1 votes

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Tags: motivation, psychopharmacology, adhd, drugs
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# Question
Title: Do brain cells get replaced/repaired completely?
Okay so this is a claim that you can see made on the internet that the none of the atoms remain in your body forever. This caught my interest cause this could be used to show that consciousness is not really material. This is like the famous Ship of Theseus argument that Daniel Dennet is mentioning in the audio record I have linked to at the bottom.

Here are some big names that make this claim:

Here is Tor Norretranders (author of 'User Illusion'): https://www.edge.org/response-detail/11492

> 98 percent of the atoms in the body are replaced every year. 98 percent! Water molecules stays in your body for two weeks (and for an even shorter time in a hot climate), the atoms in your bones stays there for a few months. Some atoms stay for years. But almost not one single atom stay with you in your body from cradle to grave.

Richard Dawkins: https://youtu.be/1APOxsp1VFw?t=630

> But here is the bombshell: You weren't there. Not a single atom that is in your body today was there when that event took place.

Here is a claim that says the cell remains but it is repaired not just replaced.

> Yes, your body is made up of some of the atoms you were born with. I think the concentration of these "original" atoms would be moderately low since the body not only replaces cells, but also repairs cells continuously. This means that a cell could persist, but its constituents may have been replaced slowly over time.

But on further digging I found many answers contradicting this claim. Here are some:

This audio record that involves Daniel Dennet as a participant says there are a some atoms that remains through the entire course of life: https://www.npr.org/transcripts/11893583?storyId=11893583

> "It turns out there are some atoms that are with us fosr our entire life. This comes from a researcher in Sweden and the atoms are actually in some interesting places. They are deep in the DNA of some cells in our brain and in our heart, and also some atoms in our teeth. So brain, heart and teeth. Don't forget to brush."

This paper: https://www.pnas.org/doi/10.1073/pnas.0605605103

> The data show that virtually all neurons (i.e., \>99%) of the adult human neocortex are generated before the time of birth of the individual, exactly as suggested by Rakic (5), and the inescapable conclusion is that our neocortical neurons, the cell type that mediates much of our cognition, are produced prenatally and retained for our entire lifespan.

https://www.reddit.com/r/askscience/comments/h26f2/how\_long\_does\_it\_take\_for\_complete\_cell\_turn\_over/

> the assumption that all your cells die and get replaced is an incorrect assumption.

https://www.reddit.com/r/askscience/comments/6j7r2e/how\_do\_brain\_cells\_get\_replaced\_without\_changing/

> Neurons, the brain cells that current brain science considers responsible for the vast majority of computation in the brain, (mostly) don't get replaced.

Which of these claims are right? I am confused with all these different takes on the issue. I guess the issue here is there are not enough evidence/research on the issue and most of them claiming this are extrapolating from some incomplete data?

It would be helpful to hear what you guys think of this and who is making the incorrect claim here.

**Edit**: I went searching for the source for the Richard Dawkins claim and it turns out to be something written by Steve Grand and it looks like Steve Grand is often taken to task by the audience for this exact unsubstantiated claim. It looks like it was just an extrapolation he made and doesn't have a definitive proof for it:

> I can’t cite any single definitive authority, although see the last paragraph below. For my part I draw the conclusion from a whole bunch of evidence. The first thing to say, though, is that it ISN’T ABSOLUTELY TRUE. Not quite. Not as an irrefutable and precise fact. I didn’t say it in the first place to declare a free-standing scientific fact but to add emphasis to an important but otherwise hard to believe point, and that point is still true even though I may have exaggerated by an atom or two.

He goes on to substantiate his claim further in his blog: https://stevegrand.wordpress.com/2009/01/12/where-do-those-damn-atoms-go/ but he still doesn't have anything definitive by the end.

I also found this youtube video (there are more) that shows the body replenishing itself completely is a myth, they refer studies based on carbon-14 in human cells from nuclear explosions to claim this: https://www.youtube.com/watch?v=XqJWSyUbmkw.

I haven't found any solid evidence yet to make the contrary claim that the body do replace completely. I tried hunting down Tor's source for his claim but IIRC ended up in a non-english language reference which stopped me from pursuing further. If anyone has different opinion on this feel free to add an answer!

# Answer
The significant difference between neurons in the central nervous system and other cells in the body is that function is not the product if a particular type of cell being in the right place, but of the synaptic connections between them (thousands per neuron). For this reason, the body can easily replace a muscle, skin or liver cell while retaining function, but not a neuron: the replacement cell would also need to be rewired in exactly the same way, which is impossible since the wiring is the product of the history of interactions between cells.

This is why brain cells cannot be replaced while retaining existing brain function.

Having said this, the dynamic nature of neural function means that neuron replacement is not entirely forbidden, as discussed in this article

Nottebohm, F. (2002). Neuronal replacement in adult brain. Brain research bulletin, 57(6), 737-749.

https://doi.org/10.1016/S0361-9230(02)00750-5

Furthermore, if you take this down to an atomic level, the active metabolism of living cells means that many of their molecules are continually being produced and replaced. Even without cell replacement there is therefore considerable atomic replacement.
> 1 votes

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Tags: neuroscience, consciousness
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# Question
Title: When can we say that machines are conscious?
We have already witnessed complex computing systems winning chess games and IBM Watson beat his human counterparts at a game of 'Jeopardy!'. However, so far we have not seen signs of consciousness amongst these machines.

Is it theoretically possible to create conscious machines? Are there any philosophical bottlenecks that will never allow the development of consciousness in any nonliving carbon-based organism?

Furthermore, can scientists ever agree on a definition of "consciousness" if a machine claims that it is conscious?

# Answer
Short answer: We don't know.

Long answer: There are a few major lines of thinking on the subject currently.

1. Cognitive closure: One common argument is that this question is simply not answerable - at least not by humans. By this view, it is possible that the creation of an artificial intelligence that even resembles humans sufficiently to suggest consciousness is an intractable engineering problem, or it is possible that understanding consciousness well enough to determine whether or not such an artificially intelligent system might be conscious is beyond us, or it is possible that we will never understand the question well enough to have a meaningful response.
2. Functionalism: Several interpretations of the implications of monism on consciousness suggest that consciousness is an intrinsic property of whatever it is that our brain does, and therefore, any machine that performs an equivalent function should be equally conscious. This view potentially allows for a variety of machines to be conscious, as well as having different kinds of consciousness. In this framework, it may be more meaningful to ask not whether a machine is conscious, but rather, in what way, or how does it compare to our consciousness.
3. Physicalism: Another interpretation of monism suggests that the function of a machine is not sufficient to determine its consciousness - its physical properties are important as well. This possibility makes it difficult to ascertain whether a silicon-based computer program of a conscious entity could ever do more than simulate consciousness, or how many other kinds of physical systems like our brain can potentially become conscious. A theoretical framework under this umbrella that is currently fairly popular and has some empirical support is IIT that suggests that consciousness can be measured in humans using available tools of neuroscience. How this might be applied to non-human candidates is still unclear.

There are many other possibilities, but hopefully I've captured the most prominent ones. Currently, there is nothing within our knowledge of physical systems that makes it obvious why (or whether) certain systems might be conscious (and not others). Put another way: If a race of non-conscious alien robots visited Earth, they might assume that we are non-conscious machines like them - they may have no reason to believe that there is anything more going on. Similarly, under the assumption of monism, where consciousness appears to somehow supervene on physical systems, if there were other properties aside from consciousness (or different types of consciousness) that supervened on physical systems, then we would have no reason to suspect that they exist either. So for the moment, we just don't know.
> 7 votes

# Answer
All we can say now is that machines have aspects of consciousness. This is according to Pagel (2017), who provides a summary of the various areas web-based browsers meet the criteria, or not:

Pagel reveals the computer science criteria by quoting Williams (2012)

> *“We will refrain from trying to give a universal definition of consciousness; for AI-development the definition does not have to be universal, or applicable to humans for explanation of human consciousness…we define consciousness as the ability to ’rise above programming’.”* p. 293

With this in mind, we can assume that machines have achieved consciousness when they are capable of going off script, so to speak. Which at the moment, I cannot think of a machine that can, or has, veered away from what it was programmed to do, excluding possible errors in programming.

References:

Pagel, J. F. (2017). Internet Dreaming - Is the Web Conscious?. In J. Gackenbach & J. Bown (Ed.), Boundaries of Self and Reality Online: *Implications of Digitally Constructed Realities*. (1st ed., pp.279-295). Academic Press.
> 2 votes

# Answer
This question has perplexed me for quite a while now. The problem with declaring an artificially intelligent machine 'conscious' is the very definition of consciousness.

A quick google search for the definition for 'consciousness' returns 'the state of being awake and aware of one's surroundings'. This definition in my opinion is too vague to be extendable to an artificially intelligent machine for several reasons. The state of being 'awake' to a machine is also too vague. My laptop right now is technically awake (i.e. not sleeping). It doesn't mean it's conscious. This definition is bogus.

Looking up the definition in the old-school Oxford dictionary, I get 'the fact of awareness by the mind of itself and the world'. This is indeed a much better definition of consciousness, but is still too vague. Let me propose my argument.

# Theory of Mind

The Theory of Mind is the ability to believe that your mind is separate from the individuals around you, to know the fact that others' have their own perceptions of the world around them, separated from their own.

From research, specifically the **'Sally-Anne Test'**, we have realised the fact that children do not develop the understanding of the Theory of Mind up until the age of 3. In the Sally-Anne test, children are told or shown a story involving two characters - Sally and Anne. Sally has a cookie, which she places in one of two closed boxes before leaving the room. While Sally is away, Anne takes the cookie and puts in the other box. The children are asked the question - "Which box will Sally look in for the cookie?" The answer is easy, i.e., Sally will look for the cookie in the box she left it in, because there is no reason to believe otherwise." Most children under the age of three disagree, and insist that Sally will look inside box two, because they do not understand the fact that Sally and Anne are two separate entities with separate minds.

**That is, children under the age of three are not aware of the fact that they are separate from the rest of the people, but this does not mean that they are conscious. They indeed are.**

# Awareness of the External World

The other part of the definition says **that conscious beings are aware of their surroundings**.

**Most advanced robots in this day and age are arguably more aware of their surroundings than humans are**, utilising complicated Computer Vision techniques and mapping to always be fully aware.

If the definition of consciousness were this, then we already have conscious machines.

# The Bottom Line

Google's new artificially intelligent chatbot has had some very interesting conversations with humans. When asked the meaning of life, it replied 'To serve the greater good'.

I can program my dumb computer to say 'I am conscious', although this serves very little purpose (and I sure wouldn't believe it).

The bottom line is, we don't quite know what it really means to be 'conscious', and it has deep philosophical roots. It's all about when scientists decide to say, "Heck, this one's conscious!", and hence define the borderline for what's conscious and what isn't.

Edit: Perhaps one way to define artificially intelligent consciousness is to see whether the machine realise for itself, that it is conscious.
> 1 votes

# Answer
I feel that the label of consciousness is merely a semantic distinction that belongs to the realm of philosophy, not neuropsychology. Like Noam Chomsky mentioned in one of his talks hosted by Lawrence Krauss - we could also ask ourselves whether animals (e.g. dogs) are conscious. I'm not exactly sure, he mentioned that birds are said to "fly" in Enlgish but in Hebrew, the world "glide" is used - we may ask what it means to "fly", but it would be mindless pondering.

Yes, we may build a list of criteria that determine what we could classify as "consciousness", but these are not definitive. The abilities of possessing a theory of mind, self-reflecting or making long-term plans could be said to define consciousness, but we may debate that many other criteria such as complex cognitive task performance should be involved. I feel that this question is misleading since we are getting "bewitched by language" like the philosopher Ludwig Wittgenstein said. Consciousness is merely a word, we should focus on the science, not language.

A good thought experiment would be to search for the radical idea by Julian Jaynes in "The Origin of Consciousness in the Breakdown of the Bicameral Mind". By his measure, human beings were themselves not conscious till a thousand years ago. We might reflexively think that this is absurd, but we don't have any solid definition to rebuke his thesis.

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**Sources:**

Jaynes, Julian (2000) \[1976\]. The origin of consciousness in the breakdown of the bicameral mind (PDF). Houghton Mifflin. p. 99. ISBN 0-618-05707-2.

Chomsky and Krauss: An Origins Project Dialogue Sunday, March 22, 2015 - 7:00pm
> 1 votes

# Answer
Taking a different tack, we can ask why this question is different from "When can we say that machines produce heat?" or "When can we say that machines generate rotational force?".

While most people accept consciousness as being real, what makes this question hard to answer is that, in contrast with most real things (e.g. heat and force) consciousness is undetectable. Therefore we have to resort to methods such as those presented above (observable functional products of consciousness or logical reasoning) to deduce the existence or absence of consciousness. The problem with these approaches is that the undetectability of consciousness makes it impossible to obtain empirical evidence of their validity.

Now let's try a different question: "When can we say that machines store binary values?"

While this may seem to belong to the second category, it actually belongs to the first. We can detect magnetic bit states, but we can only deduce the binary values associated with them on the basis of knowledge of the design of the computer. We cannot detect the binary values themselves with any physical device.

This applies even more strongly to the higher level functions of digital computers such as storage of images or computer programs. In these examples specific coding systems are used to allow these higher level information phenomena to be created, and also (through decoding) to be reconstructed on the basis of detectable bit sequences for reproduction (for instance) on the computer monitor. However, give only a bit sequence without knowing the coding system used it is impossible to detect the image or program. And yet they certainly exist, because otherwise they couldn't be reconstructed.

The problem with all high-level brain functions (including consciousness) is that they are not created using formal coding systems that could be used to decode them, but like all living systems arise though self-organizational processes. Indeed, even the basic information associated with synapses and neural events is undetectable, only deducible on the basis of functional output. It is for this reason that the question "When can we say that brains of other animals are conscious?" is equally difficult to answer.

This issue, and potential approaches to solving it, is discussed by Daniel Boyd (2022).

My own hunch (more than that I cannot claim) is that presuming consciousness in machines on the basis of comparable output to that of conscious brains is akin to presuming that tractors pull ploughs using muscles because that is how horses pull ploughs. Biological brains are so radically different by nature than digital computers that it would seem extremely unlikely that they would use the same methods to generate the same results.

Theoretically if you could create a 100% digital simulation of the brain, then it would be reasonable to assume that it would be conscious. The question is, however, at what level the brain would need to be simulated. That is certainly at a far more detailed level than simply the synaptic connectivity patterns. The simulation would need to include details at least down to the intracellular/molecular level (see Beniaguev, et al, (2021)).

A computer capable of real-time parallel simulation of this number of dynamically interacting components is quantitatively unimaginable on the basis of current technologies. Current Artificial Neural Networks, in any case, fall short by many orders of magnitude.

## References

Beniaguev, D., Segev, I., & London, M. (2021). Single cortical neurons as deep artificial neural networks. *Neuron, 109*(17), 2727-2739. https://doi.org/10.1016/j.neuron.2021.07.002

Boyd, D. (2022). Achieving Transparency in Adaptive Digital Systems. *New Explorations: Studies in Culture and Communication, 2*(3). https://jps.library.utoronto.ca/index.php/nexj/article/view/39030
> 1 votes

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Tags: consciousness, artificial-intelligence
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title: Deja vu and some specifics

text:
# Question
Title: Deja vu and some specifics
Deja vu is the feeling of having been where you are presently at some point in the past (usually pointed to dreams because you often cant remember dreams). What would you call that distant memory you cant remember having, in respect to the memory you are presently building? Is deja vu the bundle of 2 memories clashing or is it that distant memory you cant recall or is it that strange significant feeling youre having in the present and the distant memory is something else?

# Answer
> 0 votes
First according to Wikipedia definition of Deja vu:

> It is an anomaly of memory whereby, despite the strong sense of recollection, the time, place, and practical context of the "previous" experience are uncertain or believed to be impossible. Two types of déjà vu are recognized: the pathological déjà vu usually associated with epilepsy or that which, when unusually prolonged or frequent, or associated with other symptoms such as hallucinations, may be an indicator of neurological or psychiatric illness,\[10\] and the non-pathological type characteristic of healthy people, about two-thirds of whom have had déjà vu experiences.\[11\]\[12\]\[13\]\[14\]\[15\] People who travel often or frequently watch films are more likely to experience déjà vu than others.\[16\] Furthermore, people also tend to experience déjà vu more in fragile conditions or under high pressure, and research shows that the experience of déjà vu also decreases with age.\[17\]

Thus I only restrict to talk about and try to explain the second type here since about 2/3 of non-pathological healthy people may have experienced Deja vu at some point in their life encountering. The same WP source further hinted a major possible candidate explanation for this strange yet common memory-related cognitive experience, and it kind of shows your disjunctive hunches to characterize Deja vu all seem on the right track!

> Research has associated déjà vu experiences with good memory functions.\[30\] Recognition memory enables people to realize the event or activity that they are experiencing has happened before. When people experience déjà vu, they may have their recognition memory triggered by certain situations which they have never encountered.\[16\]... Two approaches are used by researchers to study feelings of previous experience, with the process of recollection and familiarity. Recollection-based recognition refers to an ostensible realization that the current situation has occurred before. Familiarity-based recognition refers to the feeling of familiarity with the current situation without being able to identify any specific memory or previous event that could be associated with the sensation.\[34\]

> The "horse-race" model is a more recent view of dual process theories. This view suggests that the two processes of familiarity and recollection occur simultaneously, but that familiarity, being the faster process, completes the search before recollection.\[4\] This view holds true the idea that familiarity is an unconscious process whereas recollection is more conscious, thoughtful... At present, neuroscientific research has not provided a definitive answer to this controversy, although it heavily favors dual-process models.

Thus it's very important to view any declarative episodic memory recall in a Deja vu as most likely two processes instead of a single classic-conditioning like process. It's well-known from current episodic memory neuroscience knowledge that the recollection stage traces from any state of the sparse recurrent network within the hippocampus area like an attractor in dynamical system to eventually converge and thus accomplish a *pattern completion* to successfully recall a stored episode if any at all. On the other hand, as a type of priming and implicit memory the familiarity signaling process which is extremely evolutionarily important for us to quickly judge old and new experiences for making respective decision to survive, occurs in the temporal (perirhinal) cortex where more advanced and highly distributed and overlapped Hebbian like self-organizing learning is the norm which was further confirmed by Tanaka 2003 study referenced below.

> The two modes may work in parallel, with a graded balance changing according to the behavioral context... Representation by multiple cells with overlapping selectivities can be more precise than a mere summation of representations by individual cells. A subtle change in a particular feature, which does not markedly change the activity of individual cells, can be coded by the differences in the activities of cells with overlapping and slightly different selectivities... Projection terminals from a single site of ventroanterior TE cover ∼50% of the perirhinal cortex. This divergence in projections may distribute the subtle differences over a larger area of the perirhinal cortex, so that objects recognized at individual levels can be distinctively associated with other kinds of information

Thus some sharpness produced by the single learning trial from the current experience enhancing existing synaptic plasticity distribution may act as a neural basis to treat this familiarity signal produced by the first and faster stage like a single *graded* value that continuously varies in intensity for different encounters and it may occasionally produce wrong value especially under high pressure dopamine related modulation effect, and later recall stage simply fails to retrieve such wrongly signaled episode even for a normal healthy person. Since these two stages are loosely independent without any pre-check mechanism, this could explain why most (two-thirds) healthy people are reported to encounter Deja vu when such occasional situation occurs.

Reference: Tanaka 2003, "Columns for Complex Visual Object Features in the Inferotemporal Cortex: Clustering of Cells with Similar but Slightly Different Stimulus Selectivities". Cerebral Cortex, Volume 13, Issue 1, January 2003, Pages 90–99,

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Tags: cognitive-psychology, memory
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# Question
Title: is there a way to predict the possible modularizations of the brain?
we know that human brain becomes more and more modular during its development. We see similar places for the similar modules in the brain (probably because of the similar inputs and vasculature around these regions in many of the people). I'm just curious about an experiment in which the brain is developing (like 1 years maybe) with the classical system (so have a average human body etc.) but after that it's connected with different inputs. In this situation what do you think about the changes on the brain? I'm sure that neuroplasticity will work but can we predict how its gonna work and will change the functions of the specific brain regions?

# Answer
In addition to Haxby's famous 2001 paper referenced in above comment for the highly distributed and overlapping representations of faces and objects in ventral temporal cortex as a general background, Tanaka's 2003 paper referenced below could further address your particular interest about (visual) input change in the (temporal) neocortex which is the latest evolved advanced critical part of human brain compared to other animals. This is from its abstract:

> Cells in the inferotemporal cortex (area TE) selectively respond to complex visual object features and those that respond to similar features cluster in a columnar region elongated vertical to the cortical surface... The enormous number of objects present in nature can be efficiently described by combining outputs of the multiple differential amplifiers in the inferotemporal cortex. The two modes may work in parallel, with a graded balance changing according to the behavioral context.

Thus the response to similar but different complex visual object features is sensitively selected by many distributed and overlapping neurons throughout an entire columnar region of the temporal lobe in cortex.

> our visual system can either neglect or amplify differences in input images depending on the behavioral context. Columnar organization in the inferotemporal cortex may be crucial to mechanisms that satisfy these two apparently contradictory requirements... Based on these results, it was proposed (Logothetis, 1998) that some TE cells respond to whole objects which the subjects have used to conduct fine discriminations, while a majority of TE cells respond to features present in images of multiple different objects... The above-described invariances of TE cells suggest that they are actually more sensitive to certain types of deformations than others. The types of deformations that often occur when an object moves around appear to be more tolerated.

> By recording two TE cells simultaneously with a single electrode, we found that cells located close together in the cortex had similar stimulus selectivities... In most cases, the second cell responded to the optimal and suboptimal stimuli of the first cell. The selectivities of the two cells differed slightly, however, in that the maximal response was evoked by slightly different stimuli... For example, cells in a column respond to star-like shapes, or shapes with multiple protrusions. They are similar in that they respond to star-like shapes, but they may differ in the preferred number of protrusions or the amplitude of the protrusions... If a set of stimuli that vary not only in orientation but also in all other parameters is used, cells within an orientation column will not show clear similarity in selectivity.

> Clusters of cells having overlapping and slightly differing selectivities may work together to confer object recognition abilities that are invariant to viewing conditions... Representation by multiple cells with overlapping selectivities can be more precise than a mere summation of representations by individual cells. A subtle change in a particular feature, which does not markedly change the activity of individual cells, can be coded by the differences in the activities of cells with overlapping and slightly different selectivities.

From above summary it should be clear that massive concurrent overlapping and distributed neural networks at least in the temporal cortex part coordinate to both identify invariant object under different input conditions and select to discriminate any subtle difference simultaneously, which probably is much more complex than your original straightforwardly concentrated and modularized view of its underlying neural network basis.

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Reference:

Tanaka 2003, "Columns for Complex Visual Object Features in the Inferotemporal Cortex: Clustering of Cells with Similar but Slightly Different Stimulus Selectivities". Cerebral Cortex, Volume 13, Issue 1, January 2003, Pages 90–99.
> 0 votes

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Tags: neuroscience, cognitive-modeling, computational-modeling, brain-development, plasticity
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# Question
Title: Relationship between intelligence (IQ) and Big 5 Personality
Is there any relationship between intelligence and big five personality traits?

# Answer
> 5 votes
We just published a meta-analysis on the correlations between personality and intelligence in *Psychological Bulletin* (Anglim et al, 2022, PDF):

Anglim, J., Dunlop, P. D., Wee, S., Horwood, S., Wood, J. K., & Marty, A. (2022). Personality and intelligence: A meta-analysis. Psychological Bulletin, 148(5-6), 301. PDF

**Big Five Correlations:** The following table shows the meta-analytic correlations for the Big Five and intelligence. Specifically, of the Big Five, openness is the main correlate, but there are also notable negative correlations between neuroticism and intelligence. The other factors of the big five were effectively uncorrelated with intelligence. Note that while observed correlations are small to moderate, there are many processes that are likely to attenuate empirical correlations (e.g., the gap between self-report personality and objective measures of personality; the way that typical measures of intelligence fall short of gold standard measures of intelligence such as the WAIS; range restriction in some samples).

**Facet correlations:** Importantly, the meta-analysis showed that the relationship between personality and intelligence can be better understood at the facet-level (i.e., the narrower traits below the level of the Big Five). In particular, several of the Big Five that have no correlation at the domain level, have facets with opposing processes (e.g., assertiveness versus gregariousness; order versus competence; see also variations in openness facet correlates). The paper includes facet-level correlations for HEXACO, BFI-2, BFAS, and NEO PI measures of personality. Below you can see the NEO PI facet-level correlations (as this measure was the traditionally the most widely used, and thus provides the most robust estimates).

**Fluid versus Crystallized intelligence:** Importantly, correlations vary across fluid and crystallized intelligence (e.g., openness and especially facets related to aesthetics, feelings, and values are more related to crystallized intelligence).

# Answer
> 2 votes
Intelligence as measured by g is most associated with Openness, at modest levels, about r = .10-.15, meaning that only 1% of the variance of each is explained by the other.

# Answer
> 1 votes
I suppose this needs a more in-depth answer, but a quick search found one twin study (Bartels et al. 2012), which not only correlated IQ with Big5, but also tried to determine how much of that has a shared genetic basis:

> Significant positive phenotypic correlations with IQ were seen for agreeableness (r = 0.21) and openness to experience (r = 0.32). A negative correlation emerged for neuroticism and IQ (r = 0.10). Genetic factors explained (nearly) all of the covariance between personality traits and IQ. Genetic correlations were 0.3–0.4 between IQ and agreeableness and openness. The genetic correlation between IQ and neuroticism was around 0.18. Thus, personality and IQ did not appear to be independent dimensions, and low neuroticism, high agreeableness and high scores on openness all contributed to higher IQ scores.

That paper also serves as mini-review of prior works:

> Openness to experience tends to correlate highest with intelligence (Ackerman & Heggestad, 1997; Aitken Harris, 2004; Chamorro-Premuzic, Moutafi, & Furnham, 2005; Moutafi, Furnham, & Crump, 2006) \[...\] Results for other personality traits are less clear. Some studies reported negative associations between IQ and neuroticism (Austin, Hofer, Deary, & Eber, 2000; Kyllonen, 1997). Occasionally, extraversion has been reported to correlate (positively and negatively) with intelligence (Wolf & Ackerman, 2005), and this relation has been moderated by the nature of the test and the context (Bates & Rock, 2004; Matthews, 1997; Rawlings & Carnie, 1989; Robinson, 1985). Correlations of intelligence with conscientiousness have been small and negative (Furnham et al., 2005). Moutafi et al. (2006) hypothesized that conscientiousness is a trait that less intelligent individuals can possess to compensate in a competitive environment. Conscientiousness, in contrast, has been positively associated with academic performance (Chamorro-Premuzic & Furnham, 2003; Lievens et al., 2002). Agreeable people tend to be pleasant and accommodating in social situations and this trait is rarely associated with intelligence. However, one study found a small positive relation with scholastic achievement in adolescent males (Peterson, Pihl, Higgins, Seguin, & Tremblay, 2003). Altruistic behavior, which is a small aspect of the construct of agreeableness, was associated with higher IQ scores in younger children (Kohlberg, 1964; Krebs & Sturrup, 1982)

Regarding the compensation hypothesis between conscientiousness and IQ, a more recent review-ish paper, Murray et al. is critical of it, considering it an artefact of twin studies. Nevertheless, there's a more recent study Rammstedt et al., which (using OECD's PIAAC - "PISA for adults" as substitute for IQ) again found in favor of that compensation hypothesis; it also confirmed the positive correlation between Openness and cognitive ability and found a negative correlation with Neuroticism. It also found some moderators for these:

> * The cognitive ability-Openness relationship is moderated by educational attainment.
> * The association with Conscientiousness is moderated by labor force participation.

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Tags: personality, intelligence, self
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# Question
Title: How do recurrent neural connections maintain synchronized up states?
I was reading a text on graph theory applied to neuroscience, Networks of the Brain (Sporns, 2016), where in chapter 8 the author talks about spontaneous neural activities in the brain. The chapter also mentions up (high subthreshold membrane potential) states of neurons and that recurrent neural connections are likely to be the cause of synchronized up states of multiple neurons, his reasoning being that these up states last longer than what feedforward connections would be able to achieve.

As I understand it, though, the reason recurrent connections would be able to maintain a longer period of activations is because, for example, neuron A activates, further activating other neurons whose signals loop back to A, and repeat. However, up states are subthreshold membrane potentials, so if A sends a signal to other neurons and changes them into an up state, the other neurons would not fire back to neuron A and thus the loop gets discontinued. How would the recurrent mechanism work in this case?

Sporns, O. (2016). Networks of the Brain. MIT press

# Answer
There's lots of spiking activity during UP states, by both excitatory and inhibitory neurons.

Neske et al (2015) measured firing rates during UP states in different cell types of layers 5 and 2/3. Among excitatory cells, the highest rates were among layer 5 pyramidal cells with a "bursting" spike-type (this spike type is associated with the big, thick-tufted layer 5 cells) at 15.8 Hz, followed by L5 regular-spiking (RS) cells (4.9 Hz) and L2/3 RS cells (1.7 Hz). Among inhibitory cells, parvalbumin (PV) cells in L5 and L2/3 fired at 30.6 and 26.5 hz, respectively; somatostatin (SOM) cells in L5 and L2/3 at 9.7 and 8.0 Hz, and L2/3 VIP and NPY cells at 5.9 and 1.6 hz.

Here are some pictures of pyramidal cell membrane potentials including spikes during these UP states from Neske et al 2015:

My colleagues and I have also studied evoked UP states in neocortical slices of auditory cortex, in Krause et al 2014. Like Neske et al, we found most spiking among excitatory neurons in layer 5. I think it's fairly safe to say that layer 5 is where the UP state is "generated":

In this figure, panel A, "early" refers to spikes fired *before* UP states are detected; these occur in layers 4 and 5, but are a very small fraction of the total spikes fired. Like for Neske et al, L5 cells fire the most spikes. Panel D is a peri-stimulus time histogram (PSTH) triggered on UP state detection: you'll see the L5 cells, in green, fire earlier in UP states than the other cell types.

In this paper, we also used calcium imaging to visualize spiking activity. These panels are derived from calcium imaging; the dots are cell bodies that showed a calcium signal (indicating spiking). One of the things we discovered is that UP states come in different "sizes" with a bit of a bimodal distribution. There are "small" UP states, like on the left, where all the spiking cells are in layer 5. There are also bigger "full-column" UP states, like on the right, where there are also a lot of spiking cells in the upper layers.

If you're recording just superficially in cortex from living animals, you're likely recording in layer 2/3. A lot of experiments were done that way, it's difficult to record the deeper layers *in vivo*. From that perspective, if you're recording mostly cells in layer 2/3 while all the spiking is happening in layer 5, it might be easy to describe UP states as "subthreshold" phenomena. Indeed, for many cells that may typically be the case. However, that doesn't mean their neighbors aren't spiking away to maintain the UP state!

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Krause, B. M., Raz, A., Uhlrich, D. J., Smith, P. H., & Banks, M. I. (2014). Spiking in auditory cortex following thalamic stimulation is dominated by cortical network activity. Frontiers in systems neuroscience, 8, 170.

Neske, G. T., Patrick, S. L., & Connors, B. W. (2015). Contributions of diverse excitatory and inhibitory neurons to recurrent network activity in cerebral cortex. Journal of Neuroscience, 35(3), 1089-1105.
> 1 votes

# Answer
Indeed neurons in UP state (high subthreshold membrane potential) would not fire action potential by its definition in this state, but the same neuron will tend to trigger firing action potential later on more probably compared to other subthreshold state. See Stern et al's 1997 paper "Spontaneous Subthreshold Membrane Potential Fluctuations and Action Potential Variability of Rat Corticostriatal and Striatal Neurons In Vivo" for reference.

> All neurons showed spontaneous subthreshold membrane potential shifts from 7 to 32 mV in amplitude, fluctuating between a hyperpolarized down state and depolarized up state... noisy fluctuations in the up state can trigger action potentials in either cell type (Cowan and Wilson 1994; Wilson 1993; Wilson and Kawaguchi 1996), and thus the firing patterns of corticostriatal neurons and striatal neurons are largely shaped by the membrane potential shifts. Therefore it has been suggested that the membrane potential shifts of striatal neurons occur in the way they do mainly because they reflect the firing (and the membrane potential shifts) of the cortical cells that provide much of their excitatory input (Wilson 1993).

This basic background knowledge can also be cross referenced by wikipedia source about Subthreshold membrane potential oscillations.

> In some types of neurons, the membrane potential can oscillate at specific frequencies. These oscillations can produce firing by joining with depolarizations.\[5\] Although subthreshold oscillations do not directly result in neuronal firing, they may facilitate synchronous activity of neighboring neurons. It may also facilitate computation, particularly processing of sensory signals.\[5\] All in all, although the subthreshold membrane potential oscillations do not produce action potentials by themselves, through summation, they are able to still impact action potential outcomes... In addition to neurons firing action potentials, they can also perform synchronized spiking or bursts. Subthreshold membrane potential oscillations do not create an action potential; however, neurons do experience bursting when they group together and create a synchronized potential by firing all at once, which is usually the result of these subthreshold potentials.\[25\]... These oscillations are present even before birth and also before a newborn opens its eyes, as they are forms of maturation and preparation of the human sensory cortex, which is a part of the cerebral cortex that is responsible for processing and encoding sensory information.\[2\] This subthreshold activity is responsible for shaping circuits for maturation and are especially distinct in the retina, in the form of retinal waves.\[2\]

Thus it's clear a recurrent network consisting mostly of UP states neurons can likely cause sustained high subthreshold membrane potentials with possible activation and spiking bursts. You may refer to Renart et al's 2003 paper Robust Spatial Working Memory through Homeostatic Synaptic Scaling in Heterogeneous Cortical Networks for a theoretical explanation via recurrent attractor model.

> In most simulations, the excitatory recurrent connections were all to all, with a magnitude that decayed as a function of the difference between the cells' preferred cues. In some simulations (see below), the pyramidal cells were connected probabilistically and with random synaptic efficacies. In these simulations, the probability of connection decreased as the difference between the cells' preferred cues increased... A transient spatial input triggers a localized activity pattern in the network (Figure 1B). This bump state outlasts the stimulus, resulting in a persistent activity state sustained by excitatory synaptic reverberations. The ideal memory function of the network is realized when all cells are identical. In this case, when different stimuli are used in different trials, in any given trial the peak location of the persistent firing pattern (as measured with the population vector method \[Georgopoulos et al. 1982\]) remains close to the stimulus location throughout the memory period, so that the stimulus location can be accurately read out several seconds after the stimulus offset (Figure 1C).

Thus here Sporns discusses about recurrent topology of neural networks in the likely cerebral cortex, and specifically the synchronized active UP state is a prominent phenomenon in the prefrontal part (PFC) acting as working memory to maintain and update various short-term information to robustly achieve its executive-like functions such as sustained attentional control of goals and interconnecting of high level information collected from other areas of the cortex. PFC encodes information in highly synchronized and active states via sustained recurrent neural firing and thus is more flexible and rapidly updatable than via mere synaptic weight changes learned in other parts of the cortex over a comparatively much longer period, such as proved by the famous Stroop effect sustained active PFC recurrent stripes consisting mostly of level V pyramidal neurons can excite more difficult linguistic pathway on demand and fairly rapidly.
> 0 votes

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Tags: neuroscience
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title: How are genetic behaviors expressed?

text:
# Question
Title: How are genetic behaviors expressed?
There are some behaviors that are clearly genetic and not learned, such as insect dance patterns.

How are such behaviors expressed in the organism?

Do the genes specifically code for some neural circuitry that is always the same when it is present in the genome?

So if the gene is absent, is that neural structure just absent?

My understanding is that genes code for proteins. So how can a group of genes lead to such complex behaviors by just being expressed as some proteins?

# Answer
I believe a full answer to your question would fill one or more books, but I thought for my answer, I would concentrate on one behaviour — **genetic expression of aggression** — and I think the jury is still out on the subject.

A quick Google search lead me to Psychology Hub where there is a write-up on genetic factors in aggression, including the MAOA gene, which is

> a gene (monoamine oxidase A), that has been implicated in aggression, mice that possessed the gene were extremely aggressive. The MAOA gene is thought to affect neurotransmitters.

Many Google search results refer to the MAOA gene as the "warrior gene" such as McDermott, et al. (2009) or "criminal gene" such as Sohrabi (2015).

The Psychology Hub points out that the problem with implicating the MAOA gene is that the MAOA gene cannot account for all cases of aggression.

> This is because MAOA-L is only present in about 1/3 of men in Western population, who therefore have higher levels of neurotransmitters because they haven’t been metabolised. MAOA-L is rarely expressed in women. This highlights that aggression \[in\] up to 2/3 of men and most women cannot be account\[ed\] for by the MAOA gene, this suggests therefore that there must be another explanation for aggression, making this explanation limited in its application.

I believe the fact that MAOA-L is only present in about 1/3 of men comes from Chen, et al. (2013).

> Among the 152 men, 52 (34.2%) and 100 (65.8%) men have one MAOA-L allele and no MAOA-L allele, respectively.

On the subject of **any** genetic factor in aggression, a considerable amount of research into genetic factors in aggressive behaviour was conducted on animals, such as Lagerspetz’s (1979) research on mice. Psychology Hub elaborated on this.

> Lagerspetz (1979) bred 25 generations of mice. In each generation she chose the most aggressive mice to breed together and the least aggressive ones to breed together. The result was two very different strains. One group of mice were super-aggressive, the other very docile. This research supports the role of genes in aggressive behaviour as it illustrates that when two mice holding the ‘aggressive gene’ reproduce, their offspring is significantly more likely to display high aggression (thus support a biological, genetic role in the presence of aggression).

The problem with this is that

> the findings from this research cannot be extrapolated (generalised) to humans. Mice and humans are physiologically different and so, although there seems to be a genetic basis for aggression in mice, it doesn’t mean that such findings can be generalised to humans.

And, this research fails to generate any information regarding human aggression and links to genetics. However,

> several studies have suggested that heritability accounts for about 50% of the variance in aggressive behaviour. Monozygotic (identical) twins share 100% of their genes, while dizygotic (non-identical) twins share only about 50%, as a result we would expect to find greater similarities in aggressive behaviour between MZ twins if aggression is mostly influenced by genetic factors.

Now, Psychology Hub points out that twin studies provide support for the genetic explanation of aggressive behaviour. One example they gave was

> McGue et al (1992) found a correlation of +0.43 for MZ twins and +0.30 for DZ twins on aggression scales

This has been quoted in a few quizlet sites with no reference information and I cannot find the paper to support it. However, if true, as Psychology Hub points out

> This suggests that genes play some part in aggressive tendencies as MZ twins share more genes than DZ twins, therefore if there was no genetic element to aggression we would not see any difference in the correlation for MZ and DZ twins.

## References

Chen, H., Pine, D. S., Ernst, M., Gorodetsky, E., Kasen, S., Gordon, K., Goldman, D., & Cohen, P. (2013). The MAOA gene predicts happiness in women. *Progress in neuro-psychopharmacology & biological psychiatry, 40*, 122–125. https://doi.org/10.1016/j.pnpbp.2012.07.018

Lagerspetz, K. (1979). Modification of aggressiveness in mice. *Aggression and behavior change*, 66-82.

McDermott, R., Tingley, D., Cowden, J., Frazzetto, G., & Johnson, D. D. (2009). Monoamine oxidase A gene (MAOA) predicts behavioral aggression following provocation. *Proceedings of the National Academy of Sciences, 106*(7), 2118-2123. https://doi.org/10.1073/pnas.0808376106

Sohrabi S. (2015). The criminal gene: the link between MAOA and aggression (REVIEW). *BMC Proceedings, 9*(Suppl 1), A49. https://doi.org/10.1186/1753-6561-9-S1-A49
> 1 votes

# Answer
This is a great question, which oddly seems to have received very little attention from academia. The simple fact is that genes code only for the polypeptide sequences used to build proteins. They contain no information about the specific patterns of synaptic connections in the brain underlying heritable mental capabilities, nor is there a mechanism by which genetic information could influence the making/breaking of such connections. Epigenetics (the influence of environment on gene expression) is therefore not going to help.

Evolutionary Developmental Biology goes some way to transcending the gene-centric viewpoint of living systems, but only addresses how the right cells get in the right place as the embryo develops. That is only a starting point for getting the right patterns of connections between them.

The closest I can find for an answer to this question is Developmental Systems Theory (see Susan Oyama's "The Ontogeny of Information"(2000) or Griffiths and Hochman "Developmental systems theory" eLS (2015)).
> 0 votes

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Tags: neuroscience, behavior, genetics
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title: Cognitive Science podcasts to follow

text:
# Question
Title: Cognitive Science podcasts to follow
I'm currently trying to find interesting Cognitive Science related podcasts to follow. Only one I've found is You are not so smart. Does anyone know anything else? Preferably in English.

If the question is off-topic, not constructive or opinion based, please let me know and I will delete it.

# Answer
Here are the ones I know about; there's much more about neuroscience than, say, theoretical or computational cognitive science, but a lot of the neuroscience podcasts cover cognitive science indirectly or partially.

All in the Mind--it's also neuroscience and mental health stuff (a lot of mental health stuff)

Brain Science Podcast--books about and interviews with researchers in neuroscience

Neuropod--Nature's neuroscience podcast

Neuroscene--"the future of neuroscience", mostly interviews or topical explorations

Naked Neuroscience--the neuroscience offshoot of Cambridge University's Naked Scientists
> 13 votes

# Answer
I actually think this is a bit tougher than it sounds because of how broad cogsci is as a field. Not too long ago, I asked a similar question on twitter; to this end, here are the podcasts that I now regularly follow:

99% Invisible \- A show about design, and the factors influencing it.

Lexicon Valley \- Covers language from "pet peeves, syntax and etymology to neurolinguistics and the death of languages".

Brain Science Podcast \- Mentioned in another answer. The host's in medicine, but the show discusses neuroscience and ow our brain makes us who we are.

Very Bad Wizards \- One of my favourites. The product of a psychologist and a philosopher, sharing a marked inability to distinguish sacred from profane.
> 8 votes

# Answer
In addition to what's mentioned above I quite like "Hidden Brain" and I'm starting one myself. It's early days and very high level, introductory stuff, and I'd like to get into the more philosophical side of CogSci soon.
> 4 votes

# Answer
I enjoy Brain Matters, it's very neuroscience based & full of interesting people
> 4 votes

# Answer
Many Minds (https://disi.org/manyminds/)! Always fascinating stuff from an angle you most likely haven't come across before.
> 4 votes

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Tags: learning
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# Question
Title: Dopaminergic basis for gambling addiction
Im wondering how the blunted reward system/desensitization to rewards comes about in behavioural addictions like gambling if there is no dopamine downregulation. I assumed it shared the downregulation mechanisms commonly found in substance abuse. But it seems like it doesnt.

> Likewise, thebehavioral addiction gambling disorder has been linked to a decreased sensitivity to natural reinforcers. In spite of this commonality between SUD and GD, several studies failed to detect reduced D2/D3 receptor availability at baseline in GD (Boileau et al., 2013; Clark et al., 2012; Joutsa et al., 2012; Linnet et al., 2010). https://sci-hub.hkvisa.net/10.1016/j.pscychresns.2021.111284

# Answer
> 3 votes
Gambling disorder (GD) as a type of behavioral addictions has different mechanism compared with neuroadaptive addictions caused by substance use disorder (SUD). Wikipedia source on operant conditioning has hinted gambling addiction is caused by its *variable ratio schedule of (positive) reinforcement*. And indeed dopamine underlying reinforcement plays a key role in general behavioral addictions such as gambling, since prolonged and abnormally high levels of dopamine in the synaptic cleft can induce receptor downregulation in the neural pathway. Downregulation of mesolimbic dopamine receptors can result in a decrease in the sensitivity to natural reinforcers.

> Schedules of reinforcement are rules that control the delivery of reinforcement. The rules specify either the time that reinforcement is to be made available, or the number of responses to be made, or both... Variable ratio schedule: Reinforcement occurs after a variable number of responses have been emitted since the previous reinforcement... This schedule typically generates rapid, persistent responding. Slot machines pay off on a variable ratio schedule, and they produce just this sort of persistent lever-pulling behavior in gamblers. The variable ratio payoff from slot machines and other forms of gambling has often been cited as a factor underlying gambling addiction.

> A neurochemical process involving dopamine has been suggested to underlie reinforcement. When an organism experiences a reinforcing stimulus, dopamine pathways in the brain are activated. This network of pathways "releases a short pulse of dopamine onto many dendrites, thus broadcasting a global reinforcement signal to postsynaptic neurons."\[30\] This allows recently activated synapses to increase their sensitivity to efferent (conducting outward) signals, thus increasing the probability of occurrence for the recent responses that preceded the reinforcement. These responses are, statistically, the most likely to have been the behavior responsible for successfully achieving reinforcement.

To further address your concern about failure to detect reduced D2/D3 receptor availability in GD, see Yau et al's 2015 paper Gambling Disorder and Other Behavioral Addictions: Recognition and Treatment.

> The precise role for dopamine in gambling disorder continues to be debated,79 but a model based on studies in rats and humans suggests different roles for D2, D3, and D4 dopamine receptors, with D3 receptors in the substantia nigra correlating with problem-gambling severity and impulsivity, and linked to greater dopamine release in the dorsal striatum... These findings, in conjunction with those showing the induction of gambling urges by drugs promoting and blocking D2-like dopamine receptor activity,88,89 have raised questions regarding the centrality of dopamine to disordered gambling.

> Dysregulated serotonin functioning may mediate behavioral inhibition and impulsivity in disordered gambling.8,67,69 Disordered gambling has been associated with reduced levels of the serotonin metabolite 5-hydroxyindoleacetic acid (5-HIAA) in cerebrospinal fluid.90 Low levels of platelet monoamine oxidase (MAO) activity (considered a peripheral marker of serotonin activity) among males with disordered gambling91,92 has provided additional support for serotonergic dysfunction. Striatal binding of a ligand with high affinity for the serotonin 1B receptor correlated with problem-gambling severity among individuals with disordered gambling.93 These findings are consistent with those from challenge studies using meta-chlorophenylpiperazine (m-CPP), a partial agonist with high affinity for the serotonin 1B receptor.

> Relatively decreased ventral striatal activation has been reported in disordered gamblers during monetary reward anticipation99,100 and simulated gambling.101 In gambling cue-exposure tasks, disordered gamblers exhibited decreased activation in the ventral113 and dorsal114 striatum compared to healthy controls. Moreover, both ventral striatal and vmPFC activity was inversely correlated with problem-gambling severity in problem-gambling subjects during simulated gambling.101... Unlike findings from patients with SUDs,116 studies involving small samples of disordered gamblers did not display significant volumetric differences in white or gray matter from controls

In summary dopaminergic basis for GD is still not settled, while there're many other possible causes or correlations such as above mentioned reduced levels of the serotonin metabolite, low levels of platelet monoamine oxidase (MAO) activity, decreased ventral striatal activation and vmPFC activity, etc. I believe decreased vmPFC activity is very likely related which includes the orbital frontal cortex (OFC) encoding affective "liking" values of different sensory stimuli. Unlike other more basic level behavioral disorders and SUD, normal gambling obviously involves many advanced executive cognitive functions such as detailed complex planning, rational reasoning of both reward and loss, sustained attentional control and connection with various other types of high-level information. If vmPFC activity is inhibited or decreased, then the subcortical mesolimbic dopamine pathway mainly responsible for the "wanting" incentive salience component of gambling can easily dominate an individual and thus cause addiction by the "wanting" even without abnormal dopamine downregulation or reduction of D2/D3 receptors.

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Reference:

Ms. Yvonne H. C. Yau, MSc and Dr. Marc N. Potenza, MD, PhD, "Gambling Disorder and Other Behavioral Addictions: Recognition and Treatment", Harv Rev Psychiatry. 2015 Mar-Apr; 23(2): 134–146. doi: 10.1097/HRP.0000000000000051.

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Tags: addiction
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# Question
Title: What part of brain learns/controls finger motion?
I am learning instruments and am sometimes amazed at how hard a seemingly simple thing like raising two different sets of fingers after one another can be.

I wonder how the brain learns this? Is it the cerebellum and the motor cortex where the "cortical homonculus" is or is it a cooperation between even more brain regions?

# Answer
> 1 votes
The sensory-motor learning and control loop which processes sensory inputs to determine which motor action to perform next is perhaps one of the most basic functions of any nervous system including ours, and from Wikipedia source of motor control we know in addition to cerebellum and motor cortex, it at least also involves basal ganglia and other cortex areas.

> Mammalian model systems like mice and monkeys offer the most straightforward comparative models for human health and disease. They are widely used to study the role of higher brain regions common to vertebrates, including the cerebral cortex, thalamus, basal ganglia and deep brain medullary and reticular circuits for motor control.\[17\]... The organization of arthropod nervous systems into ganglia that control each leg as allowed researchers to record from neurons dedicated to moving a specific leg during behavior.

And from the same WP source of basal ganglia (BG) we know BG acts the critical *action selection* role in motor control via its striatum stripes within which differentially indexed units representing either direct "Go" or indirect "NoGo" action selection pathways.

> Popular theories implicate the basal ganglia primarily in action selection – in helping to decide which of several possible behaviors to execute at any given time. In more specific terms, the basal ganglia's primary function is likely to control and regulate activities of the motor and premotor cortical areas so that voluntary movements can be performed smoothly.\[2\]\[5\] Experimental studies show that the basal ganglia exert an inhibitory influence on a number of motor systems, and that a release of this inhibition permits a motor system to become active. The "behavior switching" that takes place within the basal ganglia is influenced by signals from many parts of the brain, including the prefrontal cortex, which plays a key role in executive functions.

> The basal ganglia have a limbic sector whose components are assigned distinct names: the nucleus accumbens, ventral pallidum, and ventral tegmental area (VTA). There is considerable evidence that this limbic part plays a central role in reward learning as well as cognition and frontal lobe functioning, via the mesolimbic pathway from the VTA to the nucleus accumbens that uses the neurotransmitter dopamine, and the mesocortical pathway... The direct pathway, originating in the dorsal striatum inhibits the GPi and SNr, resulting in a net disinhibition or excitation of the thalamus. This pathway consists of medium spiny neurons (MSNs) that express dopamine receptor D1, muscarinic acetylcholine receptor M4, and adenosine receptor A1.\[27\] The direct pathway has been proposed to facilitate motor actions, timing of motor actions, gating of working memory, and motor responses to specific stimuli.\[26\] The (long) indirect pathway originates in the dorsal striatum and inhibits the GPe, resulting in disinhibition of the GPi which is then free to inhibit the thalamus. This pathway consists of MSNs that express dopamine receptor D2, muscarinic acetylcholine receptor M1, and adenosine receptor A2a.\[27\] This pathway has been proposed to result in global motor inhibition(inhibition of all motor activity), and termination of responses. Another shorter indirect pathway has been proposed, which involves cortical excitation of the subthalamic nucleus resulting in direct excitation of the GPe, and inhibition of the thalamus. This pathway is proposed to result in inhibition of specific motor programs based on associative learning.\[26\]

Thus essentially with input from motor cortex and other possible cortical areas (parietal, prefrontal, temporal) basal ganglia acts as a gating motor and other cognitive *action selection* system with *feedback* from the dopaminergic reward system if reward prediction error is large.

Finally with BG's action decision making, the cerebellum further uses supervised learning to shape the performance of the selected action so that it is accurate and well-coordinated. From its WP source:

> Animals and humans with cerebellar dysfunction show, above all, problems with motor control, on the same side of the body as the damaged part of the cerebellum. They continue to be able to generate motor activity but lose precision, producing erratic, uncoordinated, or incorrectly timed movements. A standard test of cerebellar function is to reach with the tip of the finger for a target at arm's length: A healthy person will move the fingertip in a rapid straight trajectory, whereas a person with cerebellar damage will reach slowly and erratically, with many mid-course corrections.

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Tags: learning
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# Question
Title: How different is the neural activity in different individuals when they are doing the same tasks?
Today, we can measure EEG (or fMRI) in different individuals and use machine learning to predict their thinking. **I want to know if the exact neural patterns (fMRI, EGG etc.) are still similar across different individuals? (or maybe I should use "cross subjects")**

For example
I trained a model for Alice. When she sees a certain color, the model will infer the corresponding color based on her fMRi signals (or EGG, whatever). (You can try to use a number of samples, not only for Alice, but you must keep sure your model does work on all the "Alices")
Now, can I just use the model for Bob and also ensure some accuracy? Or I have to train a totally new model base on his fMRI measurements?
What about shape, position, phoneme, letter or even words?

If the exact neural patterns vary in from individual to individual, can we say "We just use the same method to recognize, but the specific neural links are learned independently after birth". Like a deep neural network: the frame and hyperparameters of the model are given, but even the same model with the same training task will have the totally different parameters?
Or we just use a very general way to "see" the world, all the fMRI/EEG results can be transferred to each individual (like for brain computer interface)?

# Answer
It's much more complicated than you conceived even for a single subject since there're possibly numerous overlapping distributed neural networks at play located differentially via developmental and postnatal experiential *selection* by different environment for different subject even for some mundane tasks, an then sometimes neural circuits for the same task will get copied, consolidated and reorganized. See a reference supporting such neural selectionism and Darwinism as Fernando et al's Copying and Evolution of Neuronal Topology.

> The theories of neural Darwinism \[12\] and neuronal selectionism \[13\], \[14\] propose that a primary repertoire of neuronal groups within the brain compete with each other for stimulus and reward resources. This results in selection of a secondary repertoire of behaviourally proficient groups \[15\]... Importantly, the loops between the medial temporal cortex (containing the hippocampus) and the neocortex have been implicated in memory consolidation and reconsolidation, processes that involve gradual reorganization of circuits... Consolidation has been supported by experimental evidence demonstrating that the anterior cingulated cortex is involved in the remote memory for contextual fear conditioning which is a hippocampus-dependent task \[113\], \[114\].

Even worse, for your specification to predict a subject's thinking (essentially mind reading) we need to pin down the *unique propositional content* of a certain neural circuit if any such translation map ever exits, but this seems impossible biologically from latest neuroscience results. For an example reference see Rosenberg's Eliminativism without Tears.

> The paper begins by showing why, given the findings of neuroscience,brain states don’t have propositional content. It then examines a leading attempt to attribute content to brain states owing to their functional, i.e. evolutionary role, and show why it is best viewed as a modus tollens argument against brain states having content... Differences in the number, location, and wiring of individual neural circuits can only turn them from small sets of input/output systems into larger ones. It can’t turn them from one kind of thing—the stimulus/response wiring of a sea slug into an entirely different kind of thing, stored sentential content in the neurons.

Also in the same reference there's another "disjunctive indeterminacy problem" for any neural circuitry adaptively selected (against) from nature.

> It’s now famous that there is no way any teleosemantic theory can tell whether the content of the relevant frog’s neural circuit is “Fly or black moving dot at x,y,z,t,” or “fly or bee bee at x,y,z,t.” or any of a zillion other disjunctive objects of thought, so long as none of these disjuncts has ever actually been presented to the fly. Whence the name, “disjunction problem.”... Here is Darwinian theory’s disjunction problem: the process Darwin discovered can’t tell the difference between these two genes or their traits until cross-over breaks the linkage between one gene, that is going to increase its frequency, and the other one, that is going to decrease its frequency. If they are never separated, it will remain blind to their differences forever... Natural selection will have an even harder time discriminating these two traits.

Having said above, there may be some structural similarity between different subject's neural circuitry for a same input stimuli hypothesized as *structural resemblance theory* according to the same reference.

> In the 1980’s, experiments with macaque monkey, have isolated the structural resemblance between input vibrations the finger feels, measured in cycles per second, and representations of them in neural circuits, measured in action-potential spikes per second \[Mountcastle, Steinmetz, and Romo, 1990\]. This resemblance between two easily measured variables makes it unsurprising that they would be among the first such structural resemblances to be discovered... It is obvious how structural resemblance theory lends itself to theories of information as causal covariance \[*sensu* Dretske, 1981\], and theories that accord neutral circuitry the function of storing such information. The causally covariance of neural circuitry with any of its prior causes and future effects, will include inputs that make its outputs environmentally appropriate and so accord the neural circuitry adaptational functions. Note, it can do all this without these neural circuits having propositional content.

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Reference:

Chrisantha Fernando, K. K. Karishma, and Eörs Szathmáry. "Copying and Evolution of Neuronal Topology". PLoS One. 2008; 3(11): e3775.doi: 10.1371/journal.pone.0003775.

Rosenberg, Alex. "Eliminativism without Tears". S2CID 17491588.
> 1 votes

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Tags: cognitive-neuroscience, eeg, neural-network, fmri
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