# Question
Title: What is the current "accepted" science behind dream interpretation?
I'm doing some casual reading about dream interpretation (meaning I'm reading the wikipedia entry) and the article mentions that there are several ways of thinking about dreams from a psychological standpoint:

> In modern times, various schools of psychology have offered theories about the meaning of dreams.

But the text offers no judgement of which of these approaches is currently believed to be most "accurate". It simply lists various scholars and their theories.

What I want to know is: which approach is considered the most "correct" by the scientific community and why? Is there a reason to take one of the listed methods of interpretation more seriously than another as a casual layman?

Note that I'm not asking for your subjective answer. I'm looking for any evidence as to whether a specific method is more successful or more pursued by the profession and community in general.

# Answer
> 33 votes
As far as I know, there is no accepted science to dream interpretation. In fact, there's no science to it at all. Evidence has shown that indeed, dreaming draws material from people, places, and things in our lives, but there's absolutely no scientific data out there (that I'm familiar with) that links dreams to anything meaningful in our actual daily lives.

If anything, dreaming and sleep is an opportunity to "replay" patterns of neural activation that occurred during the day, leading to consolidation of long-term memories. I wouldn't take anything on that Wikipedia page seriously.

**Other arguments for why we sleep and dream:**

(1) We may sleep to repair our brain or maintain homeostasis within the system. This would suggest that there's a substance in our body which requires sleep that increases its need over time. This possibility is evidenced by the fact that you can die of sleep deprivation -- but evidence against is that some amount of deprivation doesn't set you back all too far. (Also, why does sleep feel restorative, if it's only maintaining homeostasis?) There's increasing amount of evidence to suggest that improved circulation of cerebrospinal fluid in the brain during sleep serves to clean the brain from toxic metabolic byproducts of daily activity.

(2) It's also possible that sleep and dreaming serves the body's overall safety. This argument stems from value long-ago in sleeping during the dangerous nighttime, when predators and other dangerous animals generally have the upper hand over man. This seems reasonably vestigial in humans, though...

(3) It also may be the case that sleep and dreaming is effective for conserving our neurak energy over the course of the day. Indeed, slow-wave sleep is associated with reduced cortical activity -- but then again, REM sleep features high activity.

(4) An even more powerful possibility is that neural reprogramming is dependent on sleep. This is evidenced by consolidation effects that occur while we rest, and the fact that less interference occurs during the sleeping hours (see Jenkins & Dallenbach, 1924, if you want to read a real classic). There are many minimal sleepers out there, though, that are high functioning, so what does that mean for this theory?

# Answer
> 14 votes
As far as I know, dreams are meaningless information, strung into a story or series of events and interpretation are therefore highly subjective. The theory that I know best is that dreams are a result of **memory consolidation** during sleep. Of course, this is still controversial.

Memory consolidation is explained (simple version) as follows:

during waking, your senses bind information from the different senses into a single "experience" in the hippocampus. This is stored as **episodic memory**. When you sleep at night (and even during the day, stream of consciousness may be associated with this effect) your hippocampus is consolidating the episodic memories into (for instance) **semantic memories** in the temporal lobe. The hippocampus actually sends signals all over the neocortex, but the semantic memories are the best understood and most easily graspable to laymen. The basic idea is that all your experiences with an apple (episodic memories) get consolidated to some generalization of what an apple is (semantic memory).

It is thought that as these streams of information travel from hippocampus to the rest of the brain, bits of its randomly interact with parts of our brain that are semi-conscious, but probably not in any significant way. In other words, the meaning and episodes experienced in dreams probably has no direct resemblence to the declarative memories they are encoding.

**sleep and memory consolidation in general:**

Nature 437, 1272-1278 (27 October 2005) | doi:10.1038/nature04286

# Answer
> 6 votes
We could have scientific clinical study of the reports of dreams. Given that the dreams don't relate to specific real world events and often have very bizarre properties there's no reason to believe the report has much to do with what really happened. Therefore, from a clinicians standpoint they're useful in that you're in a relaxed state when they occur and you're interpreting things according to your current psychological condition. I'm sure any study of them from the clinical end that did occur would reflect some correlations of general features to chronic anxiety or clinical depression because of your interpretation of what happens. That's sort of a gimme.

Then again, it's still hard to interpret because depression and anxiety cause more sleep events (Kimura, Müller-Preuss, Wiesner, Flachskamm, Wurst, Holsboer & JM Deussing, 2009). Perhaps that alone changes reporting.

# Answer
> 4 votes
I was influenced by this lecture. I will jump ahead to the pertinent part: http://www.youtube.com/watch?v=2Ei6wFJ9kCc&t=59m20s

My interpretation is that there are two general areas in the brain responsible for memory formation: 1) hippocampus and 2) basal ganglia. According to the speaker, a study shows a direct correlation between the amount of hipocampal learning done during the day and the amount of REM sleep that occurs the following night. I.e., a sort of encoding happens through dreams. No correlation exists for learning done with the basal ganglia.

My rough interpretation of the differences between the two types of learning are the hipocampal is higher thoughts and logic like math concepts where the basal ganlia records more basic cause and effect type correlations. I'll appreciate being corrected where I'm wrong; especially if you site your source. Thanks.

# Answer
> 1 votes
I don't know if you want this type of answer, but I would like to focus on conceptual issues as well that I thought as necessary. Well, to ask if a certain approach to the meaning of dreams is better or more accurate, one has first to ask if dreams have meaning and this also requires a definition of meaning.

As for the first question, there is an approach by Allan Hobson that says dreams have no meaning. Dreams come to be when neural impulses from the brainstem are received by the cortex which reacts producing images thoughts and feelings relating to what lies in the memory storage. So dreams may be personal but there is not any meaning to them. The other approach is by Anttio Revonsuo who supports the meaning of dreams in the sense that they have evolutionary value. Dreams in this case, enrich our experiences relating to dangerous events and allow us to be more careful in a consequent dangerous situation. And of course psychodynamic theories of which you are aware, support the meaning of dreams but the interpretations differ depending on the psychoanalyst which makes the whole thing seem suspicious.(for an overview of the above check Kolb and Whishaw's "An introduction to Brain and Behaviour", it has a chapter about sleep and dreams)

But what do we mean by meaning is not all that clear.The concept of meaning has been a waste basket of evaluative and aesthetic judgements, as well as used in divergent trends and in different contexts. Also, meaning is a concept that cannot be discussed without intentionality, that is meaning has to be meaning to someone that only he/she can feel directly. You can have meaning in the level of society or civilization but again it reduces to personal experience. Because meaning is subjective, I don't know if it can be studied scientifically. Qualitative research that studies meaning is quite informative and interesting to do but it can hardly be viewed as scientific. That is what dream interpretation is. A qualitative analysis of experiences. So when a psychoanalyst claims he can uncover the meaning that my dreams have to me, what he does is fit the reported experiences in a set of theoretical constructs. In other words, he constructs a meaning of my dreams imposing a psychodynamic frame of reference which is no different from a common man's frame of reference in terms of validity. After all, if one gives their own meaning to their dreams, isn't that a meaning also?

I hope that this makes clear that no approach of dream interpretation can be considered better or worse because the act of interpretation is subjective and although we can study (through psychological research methods) what mechanisms and resources are used by people to construct meaning, there is no evidence that could be used to support a kind of meaning over another. It is like asking what literary approach is more accurate or more accepted to interpret a surrealistic poem. Literature theorists can develop different trends the acceptance of which depends on sociological factors and not on empirical validation.Thus, anyone can interpret the poem as he wants and his meaning will be accurate for him.

# Answer
> 1 votes
There can't be an objective, scientific answer to 'the meaning of dreams' because the effects are purely subjective. I've found by experience, however, that a dream is something like a 'meaning' without a stimulus event to cause it, like a semantic without a word. Usually or very often next day a waking event does occur and the dream meaning then becomes very clear by that event's occurrance. Neurologically, as the scientists suggest, the past days' events are incorporated into memory and in a relatively stable social environment will be recalled with the appropriate stimulus as might be expected. Subjectively, I think it is otherwise known as deju vu or very like it. In this sense a dream is predictive but, to avoid misunderstanding, only in this sense. For a 'scientific' enquiry into dream interpretation I recommend the theories of the psychologist C G Jung but it is no way a simple subject.

# Answer
> 0 votes
I believe that the current view is that when asleep, the mind processes the events of the day, the issues of note, and classifies them, storing the relevant events in memories linking these to others.

Dreams are then the process of replaying and connecting events to others, making links to other memories, which is why sometimes solutions can come when asleep, because conenctions re made. Also, this is where nightmares come from, as links are made to other negative or disturbing experiences.

In my experience, this makes sense, which holds no scientific basis whatsoever, but I find it a comfort.

# Answer
> 0 votes
This is not "accepted" science behind the meaning of dreams. From a physiological perspective , the role of sleeping (suspended neural activity ) in relation to cleaning the brain of toxic byproducts of daily activity is undeniable (every open system, like a cell, generates residue, entropy laws ), but this does not explain the associated dreams and their meaning (if any). It might have something to do with the perception of time, in other words, consolidating the information extracted from past experiences and successfully managing/predicting decision making in future challenges (which is essential for survival from an evolutionary perspective) might explain this phenomenon. In deep learning (and big data science) the training of a neural network is an essential process (and it takes time). The brain trains itself on past experiences, in order to better manage/handle future challenges. That's what the brain does when you sleep. It's all about training the neural network, in order to be more efficient when faced with future challenges. So I would not put too much weight on the "meaning " of dreams, there isn't any. And I suspect that this training that the brain performs is more advanced than the standard neural network training required in pattern recognition.. The brain performs training in simulated scenarios, this is dynamic training.

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Tags: cognitive-psychology, dreams, jung, psychoanalysis
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# Question
Title: Could human decision making have evolved from food finding?
One of the things human's do (and possibly other animals) is consider various options, imagine what they would be like, and then make a choice. This is called a "tree search". For example, "should I take the job offer A or B"?

Now, I wonder if this abstract decision making, may have evolved from food finding. And what is the smallest animal that does this sort of thing?

For example, imagine a small monkey or squirrel climbing a tree. When it gets to a branch it has a choice of taking the left branch or the right branch. It has to guess which branch is likely to lead to the best crop of fruit.

Once it has got fruit from one branch it may have to retrace its steps to get to the next branch.

So I would suggest that this sort of "tree search" would probably have evolved in mammals brain before humans repurposed these brain pathways to do more abstract decision making involving language.

I am trying to think of other animals that this would apply to. For example a rabbit may have a choice between branching tunnels.

A bear might have a choice of paths up a mountain and so on.

Rats are good maze finder so they must have this ability to make choices.

I wonder what other animals do this sort of thing? Rather than making a choice at random? It would have to be an animal at least as advanced enough to have a memory.

# Answer
*C. elegans* is the simplest model organism whose nervous system has been studied extensively. *C. elegans* certainly makes decisions; I would feel pretty confident in saying that decision making is an ubiquitous feature in the nervous system of freely moving animals.

It is also non-controversial to consider more complex behaviors as having evolved from more simple ones (finding food/evading predation/mating). You can even reduce a lot of complex behaviors directly to those simple ones, and certainly neural mechanisms for grouping and distinguishing between different patterns (an essential component of language but also many other behaviors) are conserved across domains.

However, it is not correct to refer to all decision making as a tree search: a tree search is a particular family of algorithms and not what you describe. Even tree-like problems need not be solved with a tree search. Specifically, tree search is a computational algorithm that works well in the serial processing environment of computer science. Analogies between brains and computers often break down at the level of algorithms, because brains are massively parallel. That doesn't mean tree search-like algorithms are absent from the brain, but you should not take such a narrow view *a priori*.

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Christiansen, M. H., & Kirby, S. (2003). Language evolution: Consensus and controversies. Trends in cognitive sciences, 7(7), 300-307.

Faumont, S., Lindsay, T. H., & Lockery, S. R. (2012). Neuronal microcircuits for decision making in C. elegans. Current opinion in neurobiology, 22(4), 580-591.

Fitch, W. T. (2005). The evolution of language: a comparative review. Biology and philosophy, 20(2-3), 193-203.
> 1 votes

# Answer
**Q: Could human decision making have evolved from food finding?**

**A:** The answer is yes, it probably could. But the more probable answer is that decision making in humans and other animals has evolved to increase the survival chance of humans and those animals and of their species. To increase the survival chance certainly requires good decision making to find food, but it also requires good decision to avoid/evade danger, to find fit mates, to protect/raise their offspring, etc. Thus, decision making has evolved to do all these necessary activities, not only food findings, with the end, common goal of to increase the survival chance (of the animals and their species).

**Q: What is the smallest animal that can do decision makings? … I wonder what other animals do this sort of thing? Rather than making a choice at random?**

**A:** Decision making in the advanced form like that exists in humans requires a nervous system with an integration center. The group of animals or Metazoans that have a nervous system is Eumetazoa, but only animals in the subgroup called Bilateria (which comprises many phyla) have an integration center (see the picture).

The integration center in the basal (more primitive) animals of each phylum is in the form of a nerve ring, a ganglion, or a group of ganglia; only in more advanced animals of each phylum is it in the form of the brain, which can be rudimentary or well developed (like those in mammals). \[1-3\]

So, the earliest animals that can make decisions are probably the basal animals in each phylum in Bilateria. C. elegans, which Bryan Krause mentioned in his answer, is one of these animals. It is a small, basal animal in phylum Nematoda (marked * in the picture). It has only a nerve ring connected with ganglia as an integration center. It probably can make only simple decisions and most likely without consciousness, which is different from us, human, because the structure of its integration center is too simple to create a complex function like consciousness. The earliest animals that probably can make discussions with some form of consciousness is arguably lampreys.\[3\] They are small, basal animals in the phylum Chordata (marked * in the picture), class Petromyzontida. \[1,3\]

However, it should be noted that decision makings have been existing in more primitive forms in other animals and other living organisms for a very long time before the brain emerged. These animals/organisms are animals that have a nervous system that is only a nerve net without the brain (e.g. jellyfish, hydras, sea anemones), animals that do not have a nervous system (sponges, Trichoplax), single-cell animals, and even plants, fungi, bacteria. All these living organisms have mechanisms (such as a nerve-net or certain cellular mechanisms) to detect information in the environment and in themselves, process the information, and make appropriate (non-random) responses without using the brain.\[1-6\] So, with these abilities, it can be considered that they, too, can do decision makings (but in the most primitive or most basic forms). If so, it can be considered that bacteria are the smallest organisms that can make decision makings (in the most primitive form).

> Roth G & Dick K \[Ref 1\]: Nervous systems and brains have a dual function, i.e., the maintenance of inner “vital” functions of the organism and the control of behavior of that organism within a given environment. Unicellular organisms exert the same functions and exhibit remarkably complex behaviors, although they do not possess, by deﬁnition, a nervous system. Bacteria sense nutritive substances (e.g., sugar) or toxins (e.g., heavy metal) in their environments through a large number of chemoreceptors as well as obstacles through mechanoreceptors. This diverse information is integrated and, through a chain of complex chemical reactions, drives their ﬂagella for movement. … The bacterium has a mini-memory, by which it can compare the incoming information with previous information, and this “knowledge” determines the behavior. Thus, even in these most primitive organisms we ﬁnd the three basic components for adaptive control of behavior, i.e., a sensorium, a motorium and in between information storage and processing.”

**References:**

1. Roth G, Dicke U. Evolution of Nervous Systems and Brains.In: Galizia CG, Lledo P-M (eds.). Neurosciences - From Molecule to Behavior: A University Textbook. Berlin Heidelberg: Springer-Verlag; 2013:19-45. ISBN: 978-3-642-10768-9. DOI 10.1007/978-3-642-10769-6\_2.
2. Jorgensen EM. Animal evolution: Looking for the first nervous system. Current biology. 2014 Jul;24(14):R655–R658. DOI: https://doi.org/10.1016/j.cub.2014.06.036
3. Feinberg TE, Mallatt J. The evolutionary and genetic origins of consciousness in the Cambrian Period over 500 million years ago. Front Psychol. 2013;4:667. DOI: 10.3389/fpsyg.2013.00667.
4. Renard E, Vacelet J, Gazave E, Lapébie P, Borchiellini C, Ereskovsky AV. Origin of the neuro-sensory system: New and expected insights from sponges. Integr Zool. 2009 Sep;4(3):294-308. DOI: 10.1111/j.1749-4877.2009.00167.x
5. Brenner ED, Stahlberg R, Mancuso S, Vivanco J, Baluska F, Van Volkenburgh E. Plant neurobiology: An integrated view of plant signaling. Trends Plant Sci. 2006 Aug;11(8):413-419. DOI: 10.1016/j.tplants.2006.06.009.
6. Camilli A, Bassler BL. Bacterial small-molecule signaling pathways. Science. 2006 Feb 24;311(5764):1113-1116. DOI: 10.1126/science.1121357.
> 1 votes

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Tags: decision-making, animal-cognition, free-will
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title: Understanding the 'Wiring Catastrophe'

text:
# Question
Title: Understanding the 'Wiring Catastrophe'
## Introduction:

In 'Wiring optimization in the brain'(2000), Dmitri Chklovskii and Charles Stevens analyse the dependence of the complexity of cortical circuits on the number of synapses per neuron using virtual perturbations. The authors clarify what they mean by a perturbation in their introduction:

> To calculate the optimal wire fraction, we start with a real cortical region containing a fixed number of neurons, a mm cube, for example, and imagine perturbing it by adding or subtracting synapses and the axons and dendrites needed to support them. The rules for perturbing the cortical cube require that the existing circuit connections and function remain intact (except for what may have been removed in the perturbation), that no holes are created, and that all added (or subtracted) synapses are typical of those present; as wire volume is added, the volume of the cube of course increases.

Starting from a reasonable set of physiological relationships approximated as algebraic equations they derive a formula for $\theta$, the ratio of the number of synapses per neuron in the perturbed cortex to that of the real cortex as a function of $\phi$, the volume fraction of the perturbed cortical region made up of wires(synapses + dendrites), and $\lambda$, a constant that multiplies the length of wire associated with each synapse:

\begin{equation} \theta(\phi) = \frac{1}{\lambda^5} \cdot \Big(\frac{1-\phi}{1-\phi\_0}\Big)^{\frac{2}{3}} \cdot \frac{\phi}{\phi\_0} \tag{1} \end{equation}

In the paper, $\lambda$ is assumed to be equal to $1.0$ and I must say that the authors explain the derivation clearly. It doesn't require more than highschool algebra and each assumption is carefully justified. By calculating $\frac{d\theta}{d\phi}$ you may then find that ,if $\lambda =1$, $\theta$ is maximal when $\phi = 0.6$. This much makes sense to me but the authors then try to rationalise this value in a manner that appears to lack careful justification:

> Why does complexity reach a maximum value at a particular wire fraction? When wire and synapses are added, a series of consequences can lead to a runaway situation we call the wiring catastrophe...At this point(phi=0.6), adding wire becomes impossible without decreasing complexity or making other changes - like decreasing axon diameters - that alter cortical function. The physical cause of the catastrophe is a slow growth of conduction velocity and dendritic cable length with diameter combined with the requirement that the conduction times between synapses (and dendrite cable lengths) be unchanged in the perturbed cortex.

## Question:

The kind of justification that appears necessary to me concerns data on brain development. I understand that significant synaptic pruning occurs during adolescence so $\theta$ should vary and I think this poses a significant challenge to the account given by Chklovskii and Stevens. More precisely, this variation isn't accounted for by variations in $\phi$ in their model so I have doubts concerning the temporal and causal validity of their model.

To quantify the variation in $\theta$ we may use numbers from 'Synaptic Pruning Mechanisms in Learning' by Deborah Sandoval:

> At infancy, each neuron averages around 2,500 synapses and at the peak of exuberant synaptogenesis (around 2-3 years of age) the number increases to around 15,000 (The University of Maine, 2001).

I must add that I have spent some time googling 'Wiring Catastrophe' but this doesn't return anything besides the original paper by D. Chklovskii and C. Stevens.

These observations lead me to the following questions:

1. If equation (1) is not an exact relationship, how can the extremization(i.e. differentiation) procedure be justified within the context of applied mathematics?
2. Is there any biological evidence of runaway wiring catastrophes occurring in real brains?

## Discussion:

To clarify how synaptic pruning fits into the discussion we may associate a perturbation with a temporal variation in the number of synapses per neuron in the brain as follows:

\begin{equation} \theta(i \rightarrow f) = \frac{\langle \text{synapses}\_{t=f}/\text{neuron}\_{t=f} \rangle}{\langle \text{synapses}\_{t=i}/\text{neuron}\_{t=i} \rangle} \approx \frac{\sum \text{synapses}\_{t=f}}{\sum \text{synapses}\_{t=i}} \tag{2} \end{equation}

since the number of neurons in the brain is approximately constant. My argument is essentially that significant variations in $\theta$ aren't explained by the variations in $\phi$ which stands in contrast to the authors' assertion in their introduction that *the relation between relative complexity $\theta$ and wire volume fraction $\phi$* is given by equation (1).

## Addendum:

I just received a pretty comprehensive reply from Charles Stevens via email. In particular, he advised me to read the final version of their paper: *Chklovskii, D.M., Schikorski, T., and Stevens, C.F. (2002) Wiring optimization in cortical circuits. Neuron 34:341- 347.* This is on my reading list for the day.

## References:

1. D. Chklovskii, C. Stevens. Wiring optimization in the brain. NIPS. 2000.
2. Gal Chechik ,Isaac Meilijson. Neuronal Regulation: A Mechanism For Synaptic Pruning During Brain Maturation. 1999.
3. Deborah Sandoval. Synaptic Pruning Mechanisms in Learning. 2015.

# Answer
## The role of synaptic pruning and brain development:

In an email I received from Charles Stevens of the Salk Institute, one of the co-authors, he explained that they were trying to make predictions about cortical structure and function in fully developed brains and that they weren't trying to model brain development. This answers my first question.

## Empirical observations of the 'Wiring Catastrophe':

He also advised me to read the final version of his paper \[2\] which is a lot more thorough and contains a number of predictions based on three different thought experiments. Each thought experiment leads to a function whose extrema correspond to a scenario where the volume of axons and dendrites occupy approximately 60% of a cubic cortical region.

Instead of referring to a 'Wiring Catastrophe' the final paper simply focuses on the predictions of each thought experiment which are backed by the following observation:

> We also measured the wire fraction from three mouse cortical regions and find that its actual value is not significantly different from the special value of 3/5.

To be precise, using an electron microscope they analysed cubic cortical regions from the Neocortex, Piriform cortex and Hippocampus of adult mice.

Finally, I must add that the discussion section and experimental methods section contain very insightful caveats.

## References:

1. D. Chklovskii, C. Stevens. Wiring optimization in the brain. NIPS. 2000.
2. Chklovskii, D.M., Schikorski, T., and Stevens, C.F. (2002) Wiring optimization in cortical circuits. Neuron 34:341- 347
> 2 votes

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Tags: neuroscience, theoretical-neuroscience, neurophysiology, brain-development, connectionism
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# Question
Title: Which brain areas are linked to Hyperactivity in ADHD?
I'm looking for some research identifying which brain areas or biological processes are behind specifically the hyperactivity symptoms of ADHD? I have done a preliminary search but most of the research I have found is about general ADHD symptoms/ how the brain of an individual with ADHD is different I'm struggling to find stuff focusing on the hyperactivity aspect.

# Answer
I think the studies that you were after have only recently been published earlier this year. 38 subjects with ADHD had fMRI scans and compared with a database. The study found that -

> More severe hyperactivity was associated with increased functional connectivity in the left putamen, right caudate nucleus, right central operculum and a portion of the right postcentral gyrus within the auditory/sensorimotor network.More severe hyperactivity was associated with increased functional connectivity in the left putamen, right caudate nucleus, right central operculum and a portion of the right postcentral gyrus within the auditory/sensorimotor network. https://bmcpsychiatry.biomedcentral.com/articles/10.1186/s12888-019-2031-9
> 2 votes

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Tags: abnormal-psychology, reference-request, attention
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# Question
Title: The relationship between semantic memory and concepts/procedures
I try to analyse my memory and cognition while learning coding and generally interacting with a computer to develop more meta cognition. I found many times that I remember the words and some associations regarding some procedure, but I do not really remember how to execute it. I will give an example from statistics. I was doing an Anova and the rule states that if the Mauchly's test is significant you should use Greenhouse Geiser adjustment, however if epsilon is greater than 0.75 you use a different adjustment. I have executed this rule a bunch of times, however when I run into it again the only thing I remember is Greenhouse Geisser and 0.75, I always have to check the relationship between them and what to do next.

My semantic memory keeps the information about the association of the two words. What is the term used for the memory of procedures to execute? I know about procedural memory, though that is defined as implicit so it does not encompassing this concrete example.

This happens in many other situations as well with more difficult procedures or concepts, I remember the words and the associations to it, but for the actual procedure or concept I always have to look up again.

# Answer
> 2 votes
You are right, **procedural memory** is defined as implicit. **Semantic memory** on the other hand is part of *explicit memory*. It includes **declarative knowlegde** (knowledge of facts or ideas) that can be made conscious. As far as I know there is no particular relationship between the two but they will definitely interact during cognitive processes. For example, experts often possess declarative knowledge thas has been proceduralized, which means that the declarative knowlege is incorporated in automatic procedures without making strong demands on working memory.

It is believed that **concepts** ("a concept is a mental representation used to group similar events, ideas, objects, or people into a category", see Woolfolk, Educational Psychology) are part of semantic memory and thus explicit memory. The term for "memory of procedures to execute" comes closes to two things that are part actually procedural memory:

* **Scripts:** actions sequences or plans of action stored in memory. For example, we have scripts for ordering food at a certain type of restaurant.
* **Productions** (or condition-action rules): if A happens than B happens. If good behavior occurs than reinforce that behavior if you want it to occur again. Or if you want to ski faster than you automatically lean back.

In order for the procedure to become more easy to execute or even automatic for you as an individual, you need to proceduralize your declarative knowledge. So explicit knowledge has to become implicit. These skills are often referred to **automated basic skills**. For this to happen you will need to practice the skill a lot in a realistic context. However, not all skills can be come expert. Some skills such as driving can be automatic but you will still need to monitor the traffic consciously while driving, in other words you cannot do this automatically. Such a conscious procedure is called **domain-specific strategy**. After reading your example, I think the skill your are referring to can become automatic, however you need to practice each individual procedure (how and when to do a certain statistical test) for it to become automated.

I know this might not entirely answer your question but I think the information is usefull nevertheless.

Reference used: Educational Psychology by A. Woolfolk, Chapter 8.

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Tags: cognitive-psychology, working-memory, semantic-memory
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# Question
Title: How can higher concepts get unrolled with upward and feedback connections differing?
In the neocortex, input patterns are compressed hierarchically. Sensory inputs in the lower levels are combined by higher levels to form abstract concepts. However, there are even more feedback connections ranging from higher levels downward to lower ones. Those are assumed to unroll higher concepts back into lower patterns that could have caused them in the first place.

If patterns could flow up and down through the same synapses, it would be easy to understand how higher patterns cause lower patterns similar to those that could have caused them. In a mathematical sense, traversing the connections in the inverse direction would cause the inverse of the upward transformation.

But synapses are mainly directed. So patterns cannot flow downward through the same synapses. How do the feedback connections still learn transformations similar to the inverse of the upward connections?

# Answer
> 2 votes
## Caveats

There's some points to keep in mind when thinking about this:

* While it is true that connections in cortex are reciprocal, this does not mean that they are symmetric. Cortical connectivity is quite elaborate and the neurons that send bottom-up signals to the next higher area are not necessarily the same neurons that receive the feedback. The forward signals seem to originate mostly from cortical layers 2/3 and 5, whereas the targets of feedback are primarily layers 1 and 6 (see for example Roelfsema & Holtmaat (2018) figure 2 and Shipp (2007)). On the other hand, an axon that terminates in layer 1 may be contacting dendrites of neurons with cell bodies in other layers, so the story is not that simple (see Larkum et al. (2018)).
* The connections may not be monosynaptic. Many of the communication between cortical areas happens via the thalamus and other subcortical structures (see the same papers as in the last point).

## Learning symmetric connections

I will ignore these two points for now and just assume that a set of neurons in a lower-level cortical area (for example, V1) and a set of neurons in a higher-level cortical area (let's say V2) have reciprocal, monosynaptic connections. So how can a neuron $y\_j$ in V2 ensure that its feedback to V1 generates roughly the pattern that excited $y\_j$ in the first place? One possibility is to use **Hebbian learning**. Essentially, if synaptic strength $w\_{ji}$ in the forward and $w\_{ij}$ in the feedback pathway both grow when $x\_i$ in V1 and $y\_j$ in V2 fire synchronously / with strong rates, the connections will tend to become symmetric over time.

### Instar-outstar learning

Pure Hebbian learning ($\Delta w\_{ji} \propto x\_i \cdot y\_j$) tends to be unstable, so one needs to add a decay term to the weights. This way you arrive at instar-outstar learning (see for example Grossberg (2013), section 1.5), which has been used for exactly this purpose: learning appropriate connections between higher-level categories and lower-level inputs.

The forward connection would perform **instar learning** ($\Delta w\_{ji} \propto y\_j \cdot (x\_i - w\_{ji})$). This means that the weights of all synapses going into neuron $y\_j$ compete. Over time, $w\_{ji}$ will tend to the mean input vector $\langle \mathbf{x} \rangle$.

The feedback connection would perform **outstar learning** ($\Delta w\_{ji} \propto x\_i \cdot (y\_j - w\_{ji})$). This means that the weights of all synapses going out of neuron $y\_j$ are in competition. Note that this may be difficult to implement in a biological neural network, since it means that synapses on different neurons $x\_i$ have to compete with each other. Over time, $w\_{ij}$ will also tend to the mean output vector $\langle \mathbf{x} \rangle$. Since the input of the forward connection is the output of the feedback connection are identical, the weight vectors should become symmetric.

### Oja learning

Note however, that this means the weight matrices are transposes of each other, not inverses. Also, you would need to ensure that different neurons in V2 do not all learn the same pattern, for example by inhibitory connections between them.

One way to overcome both of these problems is to use the **Oja subspace rule** (see Oja (1992, 1997)): $\Delta \mathbf{W}\_{ji} \propto (\mathbf{x} - \mathbf{W} \cdot \mathbf{y}) \cdot \mathbf{y}^T $ This rule ensures that the weight matrix is orthogonal, so that the transpose will be an inverse. However, note that this involves some more competition between synapses on different neurons, so it is not biologically plausible. Note also that the weight vectors will now tend to principle components of the input patterns, not to mean values.

## Beyond the simple scenario

These learning rules make it possible to learn symmetric connections in a very simple scenario, assuming directly connected, rate-based neurons. The proofs to show that instar-outstart approximates the mean and Oja's rule performs PCA also assume linear neurons, which is a further simplification. So coming back to the caveats above, in cortex the story is probably not so simple. Still, these principles (Hebbian learning with competition in both directions) may provide the basis for an explanation.

For example, despite caveat 1 Hebbian learning may still be useful since neurons across all layers of a cortical column tend to respond to the same stimuli. So as long as the rates are similar enough, the connections could still learn to be somewhat symmetrical or at least to have the kind of concept unrolling function you have in mind.

Also, while I only talked about rate-based networks, something similar may work for spiking networks as well (see Clopath et al (2010) for a spike-time dependent plasticity rule that can lead to the formation of reciprocal connections).

## TL;DR

Connections in cortex may not be directly symmetrical. Making several simplifying assumptions, it is possible to learn symmetrical connections with variants of Hebbian learning.

## References

* Clopath et al. (2010). Nature Neuroscience 13(3), 344-352. link
* Grossberg (2013). Neural Networks 37, 1-47. link
* Larkum et al. (2018). Frontiers in Neuroanatomy 12, 56. link
* Oja (1992). Neural Networks 5(6), 927-935. link
* Oja (1997). Neurocomputing 17, 25-45. link
* Roelfsema & Holtmaat (2018). Nature Reviews Neuroscience 19(3), 166-180. link
* Shipp (2007). Current Biology 17(12), R443-R449. link

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Tags: neuroscience, cognitive-neuroscience, theoretical-neuroscience, cognitive-modeling, neural-network
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# Question
Title: Does selfishness increase in periods of extreme anxiety
Does an individual going through a period of extreme anxiety, show increased levels of selfishness during that period ?

When this period ends, does the individual returns back to normal levels of selfishness ?

The following extract is from this article, which describes the personal experience of a person with anxiety/depression.

> When we’re anxious, we become lost in our own thoughts, waging a battle so distracting in its intensity that it makes it nearly impossible to notice the outside world. It’s not that we don’t care, but rather that we’re so distracted by our discomfort that it is only with great effort that we’re able to look out and truly see others.

This somehow verifies my intuition, that extreme anxiety increases selfshiness. There is so much anxiety related activity in the head, that other aspects like other people, are neglected.

This is just a personal report though, I need more solid research data.

# Answer
It is hard to argue that anxiety makes you more "selfish". Your definition of "extreme periods anxiety" is rather vague and hard to operationalize, same goes for selfishness in this context. The article you cited is not based on scientific literature but much rather a personal look on anxiety and selfishness. Therefore it is in no way conclusive evidence.

Anxiety is a natural body response to certain dangers. When anxiety becomes abnormal it can be diagnosed as an Anxiety disorder (see DSM V for criteria for certain anxiety disorders). There has been a lot of research on General Anxiety Disorder (one of the anxiety disorders) and the worry-process. However, too my knowledge there are hardly any studies that describe individuals become more selfish. Like I said, this is also definition question. Certainly people with anxiety worry more and are more focused on the thoughts in their head. However, as far as I know there is no convincing evidence that people with strong anxiety do not care about others except for themselves. On the contrary some people with Anxiety disorders excessively call their loved ones to make sure that they are alright.

Further reading: Butcher, Hooley and Mineka, Abnormal Psychology 16th Edition, Chapter 6.
> 2 votes

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Tags: personality, anxiety
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title: What works for procrastination?

text:
# Question
Title: What works for procrastination?
Procrastination seems universal and well understood by the public. However, there seems to be very little research into techniques and approaches that works to reduce and manage procrastination.

The previous aversion therapy thread cited a 2012 review that found minimal research into procrastination. It is 2019 now and I was wondering whether anyone knows of any recent research? - Effectiveness of aversion therapy for procrastination

Another thread will address procrastination in ADHD - Treatment and strategies for procrastination for ADHD

# Answer
I found this 2018 meta-analysis of 24 studies by Wendelienvan & Klingsieck on procrastination intervention - Educational Research Review article.

They used Steel's 2007 definition of procrastination as an irrational and acratic behavior. Procrastination is a voluntary delay an intended course of action despite expecting to be worse off for the delay. Wendelienvan & Klingsieck found that **cognitive-behavioural** therapeutic approaches had a greater reduction in procrastination than **self-regulation** and **strength & assertiveness training** interventions.

Quite an accessible and interesting read.
> 1 votes

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Tags: attention, procrastination
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# Question
Title: How synapses are hold in place if they're not phisically attached?
When two neurons connect to form a synapse, between the pre-synaptic and post-synaptic cells some space is found (synaptic cleft). How does this keep in position if there is no physical connection between the two?

# Answer
> 4 votes
There is a physical connection.

Synapses are held together by adhesion molecules, like cadherins and neuroligins/neurexins.

Missler, M., Südhof, T. C., & Biederer, T. (2012). Synaptic cell adhesion. Cold Spring Harbor perspectives in biology, 4(4), a005694.

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Tags: neuroanatomy
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# Question
Title: Hebbian theory and synaptogenesis - how to identify the subsequently actioned neuron?
Given Hebbian theory stating that two neuron cells wich fire subsequently tend to wire together, what is the underlying mechanism by which they do so? How does the cell identify the subsequently actioned neurons? How do the two find their ways to connect with each other?

Do they already need to be very close to each other, or even have already some synapses formed? In this case, do synaptogenesis forms randomly and spontaneously between adjacent cells?

# Answer
> 2 votes
Hebb's actual statement is:

> Let us assume that the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular changes that add to its stability. ... When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased.

So, in Hebb's case we are talking about neurons that are *already connected* that then *increase their strength of connection* when firing of Cell B follows after excitation by Cell A.

Biologically, the plasticity mechanism best associated with Hebbian plasticity is called *spike timing dependent plasticity (STDP)*. STDP utilizes a calcium signal mediated by NMDA receptors.

NMDA receptors are glutamate receptors but depend on simultaneous depolarization of the membrane to displace a magnesium block that is ordinarily present. NMDA receptors are best activated when they are already bound to glutamate (released from Cell A) when Cell B fires an action potential; the action potential propagates back into the dendritic tree as well as down the axon, and this displaces the magnesium block, allowing calcium to enter the cell.

The subsequent signaling cascade results in increases in post-synaptic AMPA receptors, which causes long term potentiation.

If cell B fires but cell A did not fire before it, then the action potential still propagates back into the dendrites but there is no glutamate present at synapses between cell A and cell B so those synapses are not potentiated.

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Hebb, D.O. (1949). The Organization of Behavior. New York: Wiley & Sons.

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Tags: neurogenesis
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title: Do animals know about choices?

text:
# Question
Title: Do animals know about choices?
A mouse goes through a maze and gets to a T-junction. Does it "know" there is a choice to be made between left and right or does it just continue following it's nose? Perhaps using some heuristics?

An ape is given a choice of toys to play with. Does it know there is a choice and consider each in turn? Or just grab one on instinct?

A human gets to a T-junction in a maze. And you can garuntee that he or she will stop there for a while considering their options and imagining the best route forward.

So I wonder if "choice" is a thing that only humans (and maybe apes and crows) understand because they are the only ones who can make use of this by considering the future events?

Also, apart from the artificial example of a T-junction in a maze, what other examples of "choices" exist in the wild.

# Answer
What (with proper null and experimental method) do you mean by "heuristic"?

What (with proper null and experimental method) do you mean by "instinct"?

What (with proper null and experimental method) do mean by "considering"?

This is a situation where psychological language meets the imperfections of experimental neuroscience. Typical neuroscience tasks are simple because they attempt to isolate some particular behavior in the most parsimonious way possible. Generalization to behavior in the natural state is far removed from this. e.g. consider the humble mouse. It takes 100's-1000's of trials to learn a 2AFC task with visual feedback. It takes ~6 trials (even in absence of cortex and hippocampus via Markus Meister) to learn a complex maze given the right reward condition. Context is everything and ethological context is missing from most modern behavioral assays. A mouse will escape from a looming stimulus with 0 training.

So, although I'm not a mouse myself (but I am an ape), I would say: the mouse "knows" just as well as you do.
> 1 votes

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Tags: animal-cognition, free-will
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# Question
Title: The effects of online chat on written language skills
With the advent of texting and online social networking. There has been a dictionary of new words and acronyms, based on abbreviations. I have noticed that I will misuse there, they're and their, hear and here... and the incidence of my grammatical errors has increased with the increase in longevity of my online and text communication.

I understand this is anecdotal, but I am wondering:
Are there studies showing the effects of online social networking and texting on people's written language skills?

# Answer
> 3 votes
The research on this topic is limited; it seems, however, that the advent of online and mobile communication may assist in improving literacy skills of children and people learning a second language.

The use of phonetic abbreviations, assists the learner in developing the connection between verbal and written language. The use of acronyms relies on the users knowledge of how words are spelt. Which requires a presumption of literacy.

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Although some studies are inconclusive, the trend shows that texting and online chat assists in child literacy. Enforced limitations on text size, and the use of a, rapidly, evolving language, promotes creativity in the use of written language.

> Recognising that many, if not most, textisms are some form of phonetic abbreviation, Plester, Wood and Joshi (2009) argue that to produce and read such abbreviations requires, in the texter, a level of phonological awareness (and orthographic awareness). While spelled incorrectly in a conventional sense, many textisms are phonologically acceptable forms of written English. Decades of research has demonstrated a consistent association between different forms of 3phonological awareness and reading attainment. David Crystal, honorary professor of linguistics at the University of Wales, believes that sending frequent texts can actually help children to read and write because of the abbreviations used. “People have always used abbreviations ... They do not actually use that many in texts but when they do they are using them in new, playful and imaginative ways that benefit literacy” (Leake, 2008).
>
> Crystal (2008) believes that the widespread concern about the impact of texting on children’s literacy is unfounded. The brevity of the text style, and the 160 character constraint of an SMS, requires the author to write economically, inventively and playfully – doing this is good practice when learning to read and write. Wood, Plester and Bowyer (2008) concur that “when texting, the children have the freedom to ‘play’ with the construction of language that they are learning about at school, and are creative in their use of it. They also have regular engagement with it.” Plester, Wood and Joshi (2009) believe that any engagement with the written word (as opposed to the spoken word) – including reading and writing textisms in digital form on mobile phones – is beneficial for children. Wood, Plester and Bowyer (2008) posit that “children’s use of this technology appears to have a positive impact on their developing literacy, as it provides children with an additional resource for learning about and experimenting with letter-sound correspondences and language, and for reading and ‘decoding’ text.” They conclude that “If our children are showing difficulties with reading and spelling attainment, it would seem that this is in spite of the contribution of textism use, not because of it.”

....

> Evolution of language Language changes constantly. To illustrate this point, at the beginning of each school year Cindi Rigsbee, a sixth- and seventh-grade reading-resource teacher in the USA, shows her learners texts from Old English, Middle English, contemporary English from the time of Jane Eyre, and a MySpace page. Throughout the year, Rigsbee often refers back to this lesson to remind her learners of the different forms of writing (Bernard, 2008). Other teachers have contrasted IM lingo with Shakespeare to demonstrate how English has evolved (Lee, 2002)

*(The effects of texting on literacy: Modern scourge or opportunity? \- Shuttleworth Foundation)*

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The use of texting and online chat has also promoted Second Language Acquisition (SLA). The emphasis being on the written language, and the inability to utilize body language, and facial gestures, as a bridge to overcoming learning difficulties.

> It was found that text-based online chat promotes noticing more than face-to-face conversations, especially in terms of learners’ noticing of their own linguistic mistakes.

....

> Philp’s (1999) findings pushed the agenda of research on noticing away from general evidentiary studies on the positive effect of noticing on SLA towards exploratory studies on contextual factors that affect noticing. Realizing the potential effect of learners’ memory capacity on noticing as laid out in Doughty’s (2001) cognitive comparison framework, Mackey, Philp, Egi, Fujii, and Tatsumi (2002) designed a study to explore the relationship between learners’ working memory (WM) capacities and their noticing of interactional feedback. They found that there was a positive relationship between WM capacities and their noticing of interactional feedback, and that learners at lower developmental levels exhibited more noticing than those at higher levels.

*(NOTICING AND TEXT-BASED CHAT, Chun Lai and Yong Zhao, Michigan State University)*

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It is unclear what the advent of technological communication has upon the literacy of adult native language. Any links to current research on this would be welcome.

*Note I would like to acknowledge that I have used the links provided in the comments to answer this question.*

*Will update answer upon acquisition of new material.*

# Answer
> 0 votes
I have come across some literature in the gaming arena which I thought was relevant. Gaming is full of texting however there is also voice chat (so this might be a bit off-topic). Texting as the sole form of communication in social media has become increasingly rare.

Despite these reservations, this 2014 Swedish study (Sundqvist & Sylvén) found a significant relationship between online gaming and practising and using English especially for boys.

A 2017 Danish study found a strong relationship between gaming and English acquisition. Signe Jensen examined 107 students and found that for boys playing games with oral and written input performed better on the Peabody Picture Vocabulary Test.

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Tags: cognitive-psychology, learning, language
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# Question
Title: What is the effect of mindfulness practice on cognition?
The following article by ScienceDaily reported on a paper in Psychological Science (Mrazek et al., 2013), claiming that mindfulness training led to improved working memory and GRE scores. These studies always pique my interest, but there is always some hole in it that makes me think "Waait a minute!"

In the case of the present study, my concern is that participants in the experimental condition were required to practice mindfulness in their daily lives, while those in the control nutrition condition were not required to alter their diet. The concern is whether the improvement came from practicing mindfulness, or from practicing **something**, but this is not my area, and it could be that prior research has already accounted for that somehow. Usually I would leave it at that, but this seems well suited for my first CSci.SE question.

My full question is thus: What is the state of the field regarding the effect of practicing meditation on cognition, over and above other forms of practice?

**References**

> Mrazek, M. D., Franklin, M. S., Phillips, D. T., Baird, B., & Schooler, J. W. (2013). Mindfulness Training Improves Working Memory Capacity and GRE Performance While Reducing Mind Wandering. Psychological Science. doi:10.1177/0956797612459659

# Answer
> 13 votes
### General Review of Mindfulness Meditation on Cognitive Performance

There is a review article Chiesa et al (2011) which would provide a good starting point. They review 23 studies looking at the effect of mindfulness meditation on performance on objective cognitive tests.

> Twenty three studies providing measures of attention, memory, executive functions and further miscellaneous measures of cognition were included. Fifteen were controlled or randomized controlled studies and 8 were case–control studies. Overall, reviewed studies suggested that early phases of mindfulness training, which are more concerned with the development of focused attention, could be associated with signiﬁcant improvements in selective and executive attention whereas the following phases, which are characterized by an open monitoring of internal and external stimuli, could be mainly associated with improved unfocused sustained attention abilities. Additionally, MMPs could enhance working memory capacity and some executive functions. However, many of the included studies show methodological limitations and negative results have been reported as well, plausibly reﬂecting differences in study design, study duration and patients' populations. Accordingly, **even though ﬁndings here reviewed provided preliminary evidence suggesting that MMPs could enhance cognitive functions, available evidence should be considered with caution and further high quality studies investigating more standardized mindfulness meditation programs are needed**.

In general I take a very sceptical perspective on the potential capacity of mindfulness meditation to improve cognitive performance. A lot of research shows that IQ is difficult to manipulate, and that much of what we learn is domain specific and is developed by practicing or learning about that particular domain. That said, I would be somewhat more open to the idea that mindfulness meditation might improve capacity for attention or motivation control.

### Review of Mrazek et al

The study used several measures of cognitive performance. Notably verbal GRE and working memory were both used. Significantly greater improvements (pre versus post) were found in the mindfulness condition relative to the control nutrition condition.

I am generally sceptical that mindfulness meditation could lead to a lasting improvement of something as grand as a verbal ability factor. While it is possible that the program was effective, I would expect there is an alternative explanation.

The first issue is the small sample size (albeit the pre-post element of the design is good). There were only 48 participants. There has been ample discussion of issues with small sample experimental studies like this. It is easier for small sample studie to be conducted, and therefore they are more likely to be published when effective. Type 1 error is an explanation. Likewise, without wanting to cast dispersions on any one study, broadly there is greater potential in small sample studies for little analysis decisions to dramatically increase the potential for a Type I error rate to arise (see for example the discussion of the effect of flexibility in data analysis by Simmons et al 2011).

Similarly, even in the absence of any flexible analytic strategies, there still is the publication bias towards statistically significant and interesting findings.

Mindfulness participants appear to have improved verbal GRE accuracy from around 43% to around 53%. This is probably close to a one standard deviation improvement in verbal ability. Even if mindfulness meditation was effective, it seems a little implausible to expect such a large increase given how presumably an equivalent increase might take three or four years of education to achieve with traditional means.

Even if the finding was replicable, an alternative interpretation would be that the effect was due to some form of effort related factors that varied based on condition (e.g., some form of placebo - effort interaction).

Putting the small sample to one side, this appears to be a well-designed study. That said, based on my prior understanding of mechanisms of cognitive change and the many alternative interpretations mentioned above for the findings, I am not persuaded that mindfulness meditation is effective in changing verbal ability.

### References

* Chiesa, A., Calati, R., & Serretti, A. (2011). Does mindfulness training improve cognitive abilities? A systematic review of neuropsychological findings. Clinical psychology review, 31(3), 449-464. PDF
* Mrazek, M. D., Franklin, M. S., Phillips, D. T., Baird, B., & Schooler, J. W. (2013). Mindfulness Training Improves Working Memory Capacity and GRE Performance While Reducing Mind Wandering. Psychological Science. doi:10.1177/0956797612459659
* Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-Positive Psychology Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 22(11), 1359-1366.

# Answer
> 0 votes
This 2015 Nature Reviews Neuroscience paper summarises the neurological changes underlying the cognitive changes with mindfulness practice.

> Mindfulness practice enhances attention. The anterior cingulate cortex is the region associated with attention in which changes in activity and/or structure in response to mindfulness meditation are most consistently reported.
>
> Mindfulness practice improves emotion regulation and reduces stress. Fronto-limbic networks involved in these processes show various patterns of engagement by mindfulness meditation.
>
> Meditation practice has the potential to affect self-referential processing and improve present-moment awareness. The default mode networks — including the midline prefrontal cortex and posterior cingulate cortex, which support self-awareness — could be altered following mindfulness training.

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Tags: cognitive-psychology, reference-request, attention, working-memory
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# Question
Title: Is there another word for 'affective control'?
In this paper, the authors use the term 'affective control' or the slightly longer form 'affective cognitive control capacity' to mean 'the ability to regulate emotions or manipulate emotional material in the service of task goals'. However wherever I Google 'affective control' I get results that seem to pertain to either Affective Control Theory (which is a much broader theory and seems to be unrelated) or the study in question.

Is there another expression for the ability to manipulate or regulate emotions and emotional material? Are there any domains I can research to learn more? I've heard Emotional Intelligence before (full disclosure though, I haven't read the Daniel Goleman book), but that seems to relate more to learned knowledge - whereas it seems affective control is more related to working memory and capacity to process and manipulate material.

# Answer
> 8 votes
There is a huge body of literature on **emotion regulation**. The main person to look up is James Gross. He's recently published a second edition of the *Handbook of Emotion Regulation* if you'd like a comprehensive review of the field. He also just published a target article in *Psychological Inquiry* about the present status of emotion regulation research and theory (a good starting place, in my opinion). His name will be on the majority of papers published in this area (he collaborates with *a lot* of people).

# Answer
> 1 votes
Emotion regulation as mrt mentioned above is a good term. Affective control seems more task and goal-focused than emotion regulation though.

This definition of emotion regulation outlined by Hoffman et al. 2012 is quite helpful -

> Emotion regulation has been defined as the process by which people influence which emotions they have, when they have them, and how they experience and express these emotions. Based on this conceptualization, an emotion can be regulated at various stages in the process of emotion generation: (1) selection of the situation, (2) modification of the situation, (3) deployment of attention, (4) modification of cognitive appraisal, and (5) modulation of responses.

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Tags: emotion, working-memory
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# Question
Title: Cultural brain hypothesis and gene-culture co-evolution
Recently, Joseph Henrich of UBC has been promoting his cultural brain hypothesis. The goal is to explain a selection pressure behind the development of the human brain and general intelligence. The basic premise is that our brains evolved to be better and better at accurately replicating cultural information (or memes) between generations. A secondary part of his hypothesis is that there was a tight co-evolution between culture and the genes that shape us.

This seems contrary to the more orthodox thinking of that genes shaped the base of humans largely without large-scale culture (when we were small hunter-gatherer tribes and thus culture was minimal), and then recently (on an evolutionary time scale) large-scale culture 'turned on' and there has not been a sufficient time for this to produce large genetic differences. In simplest terms, the co-evolution was minimal and instead we should think of the key players being gene evolution followed by cultural evolution (on different timescales).

To confuse things further, some scientists (like Satoshi Kanazawa of LSE) view most of what we associated with the 'perks' of human brain (such as general intelligence) as mal-adaptive on the individual level. Thus there seems to be a large distinction between the 3 general threads, which raises the question:

**What is the key evidence for the cultural brain hypothesis and gene-culture co-evolution?**

The only evidence I know of potential recent co-evolution of culture and genes is Dediu & Ladd's (2007) suggestion that the split between tonal and atonal languages is related to a recent (~6k years ago) mutation in the ASPM gene. **Is there other evidence of recent co-evolution between genes and large-scale culture?**

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### Related questions

# Answer
> 2 votes
"evidence of recent co-evolution between genes and large-scale culture

This 2018 PLOS Computation Biology simulation study - The Cultural Brain Hypothesis examined the data from animals and humans. It found a relationship between brain size, adaptive knowledge, social learning, group size, and lifespan across the animal kingdom.

> * relationships between brain size, group size, innovation, social learning, mating structures, and the length of the juvenile period that are supported by the existing empirical literature.

The study found evidence of cumulative cultural evolution explaining the brain human sizes.

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Tags: intelligence, evolution, philosophy-of-mind
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# Question
Title: What is the difference between a need, a drive, and a motive?
Going by Hull's drive-reduction theory, as stated in *Psychology*, by Ciccarelli, 4th edition:

> A need is a requirement of some material (such as food or water) that is essential for survival of the organism. When an organism has a need, it leads to a psychological tension as well as a physical arousal that motivates the organism to act in order to fulfill the need and reduce the tension. This tension is called a drive (Hull, 1943).

In the abstract of Mahatoo's (1989) paper, "Motives must be Differentiated from Needs, Drives, Wants: Strategy Implications", the following is stated:

> In a suggested model, motives are suggested as the specific motivational element that directs the consumer′s drive towards a particular response. Thus while needs generate the response tendency, motives determine the specific behavioural action.

What I'm inferring from these two sources of information is that:

1) **Need is a physiological/psychological requirement**

2) **Drive is what propels the organism to eliminate imbalance**

3) **Motive is the course of action the organism takes when faced with a drive.**

An example of point 3 could be the instance where a recuperating substance abuse addict feels the drive to relapse, they may not with the help of proper clinical intervention if their motive is to achieve sobriety.

My question: Am I correct? If not, I would appreciate it if you could post the correction, both for the differences and the example.

# Answer
> 1 votes
The sources and the theory you mentioned are quite old and may be outdated. I learned about drives, needs and motives in a more modern context during my introductory course of Industrial and Organizational (I/O) Psychology. Overall drives are more of a non-human concept that is too simplistic to explain complex human behavior (Landy & Conte, 2013). Nowadays, the definitions below are often used by psychologists.

According to Buchanan and Hyczynski (2013) a **need** is innate and related to survival such as the need for water, oxygen etc. Needs that are triggered by deprivation are known as **drives**.

Buchanan and Hyczynski (2013) use the following description of a **motive**:

> A socially acquired need activated by a desire for fulfilment.

Drives and motives can be contrasted using the information provided by Buchanan and Huczynski (2013): *Drives* 1. are innate 2. have a physiological basis 3. are activated by deprivation 4. are aimed at satiation

*Motives* 1. are learned 2. have a social basis 3. are activated by environment 4. are aimed at stimulation

I know I somewhat glossed over the theory you mentioned but I looked it up and it seems somewhat outdated to me. I hope this more recent information helps answering your question!

Further reading: Landy, F.J. & Conte, J.M. (2013). Work in the 21st century: An introduction to industrial and organizational psychology (6th edition). New York: McGraw-Hill. (Chapter 8) Buchanan, D. & Huczynski, A. (2013). Organizational behavior: An introductory text (8th edition). Essex, UK: Pearson Education. (Chapter 9)

# Answer
> -2 votes
In this context, motive does not refer to an overarching goal, but rather, a more immediate one. Judging from the excerpts you provided, here is an example:

1) My body **needs** nutrients in order to survive. 2) This need results in psychological and physiological discomfort, AKA a **drive**: hunger. 3) As a result of my hunger, I order take-out. My **motive** for doing this is that I want food.

The way I conceptualize the distinctions is that it goes need-\> drive -\> motive -\> resulting action.

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Tags: motivation
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# Question
Title: How is the term "affect" different from "emotion"?
I have encountered a vast variety of definitions for affect, each of which contrasts it with the term "emotion" in a whole different way. Here are a few:

* Affect denotes the visible/objective expression of an emotion.
* Affect is the actual underlying subjective feeling an emotion.
* Affect means the initial automatic reaction to a situation before cognitive appraisal helps build-up to the more complex emotions.

Also, given how practically (amongst professionals) both of these terms are often used interchangeably, I wonder if there's a clear non-controversial distinction between "affect" and "emotions".

# Answer
> 3 votes
There is a difference between *affect* and *emotion*, and the first two descriptions you quoted sum up the main way the two words are different

> * Affect denotes the visible/objective expression of an emotion.
> * Affect is the actual underlying subjective feeling \[of\] an emotion.

Wikipedia summarizes that in psychology, the word *affect* is used to describe the **experience** of feeling or emotion. The word also refers sometimes to *affect display*, which is

> a facial, vocal, or gestural behavior that serves as an indicator of affect (APA 2006).

Think of it in the sense that if you have an emotion in play, how do you describe how the emotion **affects** the person **experiencing** the emotion?

Emotions can interfere with how the person reacts to stimuli through *valence* (the "goodness" of the event , which is *positive valence*, or the "badness" of the event, which is negative valence — perceptional or actual), *motivation* or *arousal* (the state of being attentive, alert and awake). This is why *affective disorders* such as *depression* and *anxiety disorders* are not referred to as *emotional disorders*.

## References

APA (2006). VandenBos, Gary R., ed. *APA Dictionary of Psychology* Washington, DC: American Psychological Association.

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Tags: emotion, terminology
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title: Myelin and Myelin Sheath

text:
# Question
Title: Myelin and Myelin Sheath
https://medlineplus.gov/ency/article/002261.htm

Why is Myelin used as a term to mean the Myelin Sheath as opposed to the proper term?

It is apparent to me that Myelin is the substance itself regardless of structure and the Myelin Sheath is the structure around the axis to insulate Electro-Chemically the nervous system composed of this substance.

But Media and Physicians frequently use Myelin instead of Myelin Sheath as opposed to textbooks that make the distinction

# Answer
As Wikipedia points out:

> **Myelin** is a lipid-rich (fatty) substance formed in the central nervous system (CNS) by glial cells called oligodendrocytes, and in the peripheral nervous system (PNS) by Schwann cells.

When referring to the sheath, you are referring to the covering which is made of myelin.

> Myelin sheaths are sleeves of fatty tissue that protect your nerve cells from damage (WebMD).

The thing is that myelin formed in the CNS and PNS is only used in the form of a sheath to protect your nerve cells, so when physicians refer to the myelin, they are referring to the sheath.

With demyelination, the integrity of the sheath is being compromised.
> 4 votes

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Tags: neuroscience, terminology, neural-network, neurophysiology, neuroanatomy
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# Question
Title: Treatment and strategies for procrastination for ADHD
I am keen to know what strategies and treatment approaches works best for procrastination for people with ADHD?

People with ADHD seem to have a different level of complexity from the usual procrastination with neurotypical people. Another thread discussed how people with ADD/ADHD "often heavily discount the importance of an event if it's sufficiently far away from the current time", which does not seem to occur as much with neurotypicals.

ADHD research has increased recently and I was wondering whether there has been any evidence found to be effective with procrastination in this specific population as yet?

The difference between ADHD procrastination and usual procrastination was nicely discussed in this thread - Effectiveness of aversion therapy for procrastination

Usual procrastination is addressed here - What works for procrastination

# Answer
This is an interesting question, and after mostly failing to find recent literature I've become fascinated at why this topic is not more heavily studied. It looks like a field ripe for disruption by new ideas and approaches.

I'm not convinced that procrastination is any different in those diagnosed with ADHD than in neurotypicals. As with many psychiatric diagnoses, behaviors are described in the DSM but mechanisms are usually unknown. Without knowing any mechanism (hand-waving a probable weakness of connection between prefrontal areas and motor areas, but no idea what may weaken it) it's hard to say whether an unknown mechanism in ADHD differs from an unknown mechanism in everyone else, or even that the distinction between ADHD and normal has clear boundaries. I'd really appreciate the education of being shown evidence I'm wrong here, but I've seen heated debates over defining ADHD, and Stack Exchange doesn't do extended back-and-forth discussions.

The slope of the curve representing what I've known as "delay discounting" (also called "time preference") was described by Green and Myerson and has been associated with ADHD, drug abuse and gambling, and lower socioeconomic status. But I don't see this as a mechanism, just another way to quantify the behavior.

The nature of procrastination by Piers Steel and Longitudinal study of procrastination, performance, stress, and health by Roy Baumeister seem to be the most relevant (highly cited) descriptions of procrastination, and both are over a decade old. Some intervention articles I've found are ADHD outcomes for children treated in community based pediatric settings by Jeffrey Epstein (not that one), et al and Academic interventions for academic procrastination by Zacks and Hen, but I can't find anything on interventions that seems to be highly cited and well-accepted.

I disclose that I often procrastinate on StackExchange. I'm currently reading and writing about procrastination rather than working on analyses I need done asap. :(
> 5 votes

# Answer
The problem with this question is that procrastination has many components and is not an explicit diagnostic category of ADHD. As the result, finding evidence for a phenomenon that is poorly described and measured plus not part of a diagnostic or treatment criteria is very difficult. Having said that, ADHD as a concept and a disease construct is still helpful and there are increasing studies examining ADHD and its associated phenomenologies.

A 2016 systematic review of cognitive-behavioural therapy by Jensen et al found CBT to be effective for ADHD. The study only found two studies that fitted their selection criteria and analysed their intervention in details. One study had two sessions focused on procrastination.

> 11 -Consequential Thinking - Application of Skills to Procrastination
>
> 13- Constructive Planning - Application of Skills to Procrastination

So in a round about way CBT is helpful for procrastination in ADHD.
> 2 votes

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Tags: attention, adhd
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# Question
Title: Constraints on dataset size for AI-assisted connectome reconstruction
From an exchange with Dr. Seung, an expert on connectomics, I learned that one of his former PhD students Viren Jain is leading the Connectomic effort at Google using supervised learning methods. Now, supervised deep learning methods usually require large amounts of data to succeed. (I know this based on experience in industry as well as the deep learning literature.) However, due to the time and effort of human annotation the datasets for circuit reconstruction are really small:

*It takes about six months for humans to annotate 1 cubic millimeter of brain volume.*

In fact, Dr. Seung informed me that the following dataset from a circuit reconstruction competition in 2016 which is less than five gigabytes in total is basically state of the art.

Given that augmenting training data with synthetic data is frequently used for computer vision problems, I am led to the following questions:

1. Might it theoretically be possible to augment human-annotated training data with morphologically similar synthetic data?
2. What methods in the near future might allow us to significantly increase the size of human-annotated training data?

## References:

1. Michał Januszewski, Jörgen Kornfeld, Peter H. Li, Art Pope, Tim Blakely, Larry Lindsey, Jeremy Maitin-Shepard, Mike Tyka, Winfried Denk & Viren Jain. High-precision automated reconstruction of neurons with flood-filling networks. Nature. 2018.
2. Luis Perez and Jason Wang. The Effectiveness of Data Augmentation in Image Classification using Deep Learning. Arxiv. 2017.
3. Alberto Bailoni, Constantin Pape, Steffen Wolf, Thorsten Beier, Anna Kreshuk, Fred A. Hamprecht. A Generalized Framework for Agglomerative Clustering of Signed Graphs applied to Instance Segmentation. Arxiv. 2019.
4. EyeWire: mapping neurons. https://science.eyewire.org/science-mapping-neurons Accessed 21 August 2019.

# Answer
# Improving labeling

Increasing the amount of data that humans can annotate in a reasonable amount of time (your second question), one essentially needs to develop better labeling tools. Typically, this can either involve **nicer interfaces** or training ML algorithms to **propose labels** which humans can then more easily confirm or improve.

I recently attended a talk by Moritz Helmstaedter in which he presented work in both these directions. If I remember their approach correctly, they first had people trace axons and dendrites in serial electron microscope sections. Based on these traces, they then found the membrane around these axons/dendrites using an algorithm, which gave them a proposal for the segmentation. For the tracing part, they developed a nicer interface (see this paper): instead of showing only slices along the cardinal directions to their labelers, they developed a "flight mode" in which the labeler can see the data from an ego-centric view in the current direction of the neurite they are tracing. This gave them a 4- to 13-fold speedup.

# Synthetic data

Concerning your first question, I'm not aware of any work on synthetic datasets in this area. I imagine generating one would be quite difficult, since cell morphology is complex. In order to generate synthetic EM images, you'd essentially need a detailed description of the cell morphologies in a cube of cortical tissue - and we usually get these descriptions from EM data. Alternatively, you could try to use deep learning (autoencoders or generative adversarial networks) to produce such synthetic data, but this recent review concluded that these will most likely not give you the quality you would need for training, since

> the artificial images would likely either fail in subtle ways to be truly realistic or would fail to capture the full diversity of real-world data.

For now it seems the field will have to aim for better / faster human annotations instead of synthetic data.
> 1 votes

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Tags: theoretical-neuroscience, experimental-neuroscience, connectionism
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# Question
Title: How does Emotional Freedom Technique (EFT) work?
There is mounting evidence that EFT, a form of therapy which combines cognitive, exposure therapy along with acupuncture point stimulation is effective -
EFT for PTSD question

What are the biological evidence for the efficacy for EFT?

# Answer
> 1 votes
There are a couple of studies that seem to outline some possible mechanisms for how EFT works.

A 2012 study of salivary cortisol of 83 subjects found that a significant decrease after one hour of clinical EFT treatment.

> decrease in cortisol level (-24.39%; SE, 2.62) compared with the decrease observed in the SI (-14.25%; SE, 2.61) and NT (-14.44%; SE, 2.67) groups (p \< 0.03)

In a 2018 randomised study of 16 subjects with PTSD having done 10 clinical EFT treatment found genetic changes.

> Significant differences (P \< .05) were found for 6 genes—chemokine receptor 3 (CXCR3), interleukin 18 (IL-18), interleukin 10 receptor beta, tumor necrosis factor alpha–induced protein 6, leukocyte-endothelial cell adhesion molecule 1 (selectin L), and interferon-induced transmembrane protein 1. These target genes are generally known to be involved in the regulation of cellular immunity and inflammation and are associated with stress.

One of the mechanisms EFT is thought to work is through acupuncture stimulation. This review article describes 2009 study of 48 subjects treated with acupuncture found -

> decreased activity in limbic and paralimbic regions including the medial prefrontal, medial parietal and medial temporal lobes, along with increased activity in the sensorimotor cortices and select paralimbic structures.

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Tags: cognitive-psychology, neuroscience, therapy, emotional-freedom-technique
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# Question
Title: Evidence for EFT (Emotional Freedom technique) for PTSD
What is the evidence for Emotional Freedom Technique or EFT for PTSD?

Emotional Freedom Technique or EFT is a form of therapy for PTSD. EFT combines the cognitive components from CBT and exposure therapy and combines it with stimulation of eight acupuncture points.

From Church, D. et al. (2018):

> A typical sequence in the treatment of PTSD might be to have the client vividly recall details of a traumatizing event (exposure) while pairing the memory with emotionally neutral statements (cognitive reframing). To these cognitive and exposure elements, EFT adds the stimulation of a pre-established set of eight acupuncture points (acupoints) by tapping on them with the fingertips, a form of acupressure.

<sub> Church, D., Stapleton, P., Mollon, P., Feinstein, D., Boath, E., Mackay, D., & Sims, R. (2018, December). Guidelines for the Treatment of PTSD Using Clinical EFT (Emotional Freedom Techniques). In Healthcare (Vol. 6, No. 4, p. 146). Multidisciplinary Digital Publishing Institute. </sub>

# Answer
> 1 votes
The meta-analysis on anxiety referred to by Church, D. et al. (2018) was performed by Sebastian & Nelms, (2017) was published in a questionable journal.

This critique by Ferrara found that

> four studies suffered from two fatal flaws: they had a small number of participants, and EFT was compared to waitlist controls that did nothing. These studies really tell us nothing about the effectiveness of EFT and they do not validate the theory behind it.
>
> Small, biased studies without comparison groups from one group of researchers with a financial interest in EFT have shown the treatment is associated with improved PTSD symptoms. However, when it has been compared to CBT, there are no differences between the approaches.

https://healthybutsmart.com/emotional-freedom-technique/

<sub> Church, D., Stapleton, P., Mollon, P., Feinstein, D., Boath, E., Mackay, D., & Sims, R. (2018, December). Guidelines for the Treatment of PTSD Using Clinical EFT (Emotional Freedom Techniques). In Healthcare (Vol. 6, No. 4, p. 146). Multidisciplinary Digital Publishing Institute. </sub>

<sub> Sebastian, B., & Nelms, J. (2017). The Effectiveness of Emotional Freedom Techniques in the Treatment of Posttraumatic Stress Disorder: A Meta-Analysis. EXPLORE, 13(1), 16–25. </sub>

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Tags: therapy, ptsd
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title: Good neuroscience research blogs

text:
# Question
Title: Good neuroscience research blogs
In the field of AI research there are really good research blogs that regularly analyse methods and review papers. Examples that come to mind are:

1. BAIR blog: The Berkeley AI Research blog
2. Distill: an AI journal devoted to clear explanations

Might there be something similar for neuroscience? To qualify what I mean by 'good' the blog would have to cover:

* methods for experimental, computational and theoretical neuroscience
* comprehensive reviews of basic neuroscience research

Three neuroscience blogs that come to mind are:

1. Harvard Neuro Blog: articles from graduate students and postdocs working on basic research in the neurosciences at Harvard University
2. Neuwrite West Blog: a blog to make neuroscience accessible to anyone curious about the brain
3. Niko Kriegeskorte's blog: the blog of the visual scientist Niko Kriegeskorte

So far, I am unaware of an online journal like Distill for neuroscience research.

**Note:** As per the suggestion of Arnon Weinberg I decided to qualify what exactly I mean by 'good' and provided examples for concreteness.

# Answer
> 2 votes
Also check: https://bitsandbrains.io/ https://www.talyarkoni.org/blog/ calling it a blog is too much but sometimes we share stuff: kordinglab.com

But also, there is maybe a bit more of a culture of publishing thoughts in papers in the field.

# Answer
> 2 votes
I would also like to plug

Markus Meister’s blog: https://markusmeister.com

Jeremy Borniger’s blog and journal club: https://jeremyborniger.com/new-blog

The Spike on Medium curated by Mark Humphries and curated by others; probably the closest to what you are looking for: https://medium.com/the-spike

Inscopix’s corporate blog: https://media.inscopix.com

Off the Convex Path which features ML and computational neuroscience: https://www.offconvex.org

And Richard Gao’s blog: http://www.rdgao.com/blog/

None of them are “one-stop-shops” like some machine learning blogs but they all offer interesting perspectives and reviews across computational and systems neuroscience.

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Tags: neuroscience, theoretical-neuroscience, experimental-neuroscience
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# Question
Title: Why does a neuron choose to connect to another?
I have been reading about neuron creation, guidance cues and all sorts of highly complex mechanisms used to allow one neuron axon to extend or connect - but to what end?

*Why* does *one* neuron end up connecting to another? *(instead of all the others)*

# Answer
> 8 votes
Donald Hebb's postulate only applies when two neurons are already connected. It seems you are asking more specifically, 'when two neurons are not already connected and they want to connect, how and why?' Correct?

In this case, we do not know. StrangeLoop mentions it is due to the location of the neurons and the spreading of activation. Yes these might be the case but evidence is extremely limited.

It may be the case that correlated activation of two neurons instigates the formation of a synapse between them. It may also be the case that synapses form completely randomly and plasticity takes advantage of any new synapses (don't forget anti-hebbian). It may also be a combination of both. We do not know.

We further do not know concretely what happens in the reverse; for example, what causes a synapse to be eliminated, is it activity driven or a stochastic process?

Some recent evidence showed that spine generation and elimination are related to 'learning' and 'unlearning'. This suggests that activity driven plasticity can form/eliminate synapses, but my previous reference shows strong evidence that formation/elimination are stochastic -- hence we do not know!

Another thing which is not clear from your question, as James mentions, is at what age you are considering. Early development of synapses is more well understood and it may be the case that during early development a group of mechanisms determine why/where/how a synapse forms/eliminates and during adult another set of mechanisms are in charge.

For example, in the early post-natal visual system, it is found that cells have responses with similar properties as adult; however, later on in development visual experience is required to maintain these properties. Furthermore, these changes must occur within a critical period for activity-dependent plasticity.

# Answer
> 8 votes
Donald Hebb originally formulated what would later come to be known as spike-time dependent plasticity by famously stating "neurons that fire together wire together". In actuality, the firing has to be sequential (not simultaneous) and causal: if a neuron A fires and causes B to consequently fire, the synaptic strength between them increases. This is how the brain implements learning and memory. \[1\] This principle fits with the idea that the brain essentially learns causal relationships that exist in the world, as formalized in the Bayesian brain hypothesis \[2\].

\[1\] Markram et al., "A history of spike-timing-dependent plasticity": http://infoscience.epfl.ch/record/168812/files/fnsyn-03-00004.pdf

\[2\] Clark et al., "Whatever next? Predictive brains, situated agents, and the future of cognitive science": http://www.fil.ion.ucl.ac.uk/~karl/Whatever%20next.pdf

# Answer
> 1 votes
From a developmental perspective, this question is partially addressed in the pages of *Mechanisms for Connectome Development* by Marcus Kaiser. Spatial considerations are an important constraint on the probability of a connection between pairs of neurons in two ways:

1. The first factor is the neuronal density which he explains very clearly:

> ...as a higher number of neurons per volume make it more likely to hit a potential target neuron. Say that for each volume element (e.g., 1 μm3) there is a probability $p$ that the space contains a neuron and a probability $q = 1 − p$ that the space is empty. Hitting another neuron three volume elements away from the starting point given a (certain) direction means two times passing through empty volume elements and one time (the last growth step) entering a volume element that contains a neuron (Figure 2B): the probability to hit another neuron after n steps or passed volume elements is $P(X = n) = qn−1 \> \cdot p$. The exponential decay of the probability to encounter another neuron as a function of distance between two neurons (or $n$ steps) means that it is more likely to hit a nearby neuron than hitting a neuron that is far away after failing to encounter many nearby neurons. Imagine going through a crowded room in a straight line. You are more likely to bump into someone early on than only passing through an empty space and reaching a person who is farther away. Therefore, basic considerations for axon growth can already account for the observed exponential decay of connection probability with distance.

2. The degree of curvature during axonal growth is another consideration as a random walk in space would actually increase the probability of revisiting spaces that don't contain a neuron. But, there may be reasons for changing course:

> While axons normally grow in a straight line, their target neurons might often be off-course leading to the need to change the growth trajectory. Causes for changing direction could be physical obstacles that either block the path or lead to adhesion of the axon. Alternatively, concentration gradients can lead an axon towards a target. In this way, axonal growth cones detect the concentration of a molecule and follow a gradient of increasing concentration towards the source of that molecule \[22\]. However, the concentration of a molecule decays exponentially with the square of the distance between source and growth cone. Given the diffusion constant in vivo \[23\], the estimated maximum distance across which a growth cone can detect molecules emitted from a target neuron is 1 cm \[24\].

And what about axons that are more than a centimeter in length? Dr. Kaiser addresses this case as well. In fact, the whole paper is an enjoyable read and covers many related questions.

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Tags: neuroscience, theoretical-neuroscience
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