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Strange synchronized brain pulses on EEG using OpenBCI Cyton board

Strange synchronized brain pulses on EEG using OpenBCI Cyton board



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I recently received an Open BCI headset, and have been experimenting with it. One interesting is my brain gives off very regular pulses. My wife tested the headset too, and she also had these pulses. I'm not sure if this is some artifact of the headset, or if there is something like a 'brain beat' similar to a heart beat.

Here is an example readout from the headset's 8 channels.

The pulse frequency seems to change based on activity. Here is another sample after I've been chatting with some people online. You can see the frequency seems to have doubled, possibly due to increased cognitive load from interaction.

Is there a name for this phenomena, and is there any idea what it is?

One odd thing about the pulses is that they are synchronized across my brain. Here is an image of the 8 electrodes placement on my head, which you can see are spread across the top of my head from the front to the back.

Any idea what can cause the pulses to synchronize like this?

UPDATE: At Bryan Krause suggestion, I looked into whether it is some sort of cardiac pulse. To get ECG data, I attached two skin patches to my biceps and a ground to my elbow. The exact same pattern shows up. I'm guessing it is the electric pulse that drives my heart, but the strange thing is the pulse is much slower than my heart, and does not correspond too closely to my heart rate. For example, at rest I measured 8 beats per pulse. Then I did some exercise to elevate my heart rate. The pulses remained the same frequency but I now had 12 heart beats per pulse.

Here is a shot of the channels. Channel #1 is the ECG data, and rest of the channels are the EEG.


Turns out these pulses are due to software latency in the OpenBCI GUI. One particular widget, the headshot widget, uses a lot of the CPU causing packet delay, and leads to the pulse pattern. Once the headshot widget is turned off, then the pulse artifact disappears.

https://github.com/OpenBCI/OpenBCI_GUI/issues/349


Samsung and Barnes & Noble have come together to work on a new tablet that has finally made its way over to the masses, where this new tablet is known as the Samsung Galaxy Tab 4 NOOK. Before we get . into the nitty gritty and all of the other details, what are some of the key features of the Samsung Galaxy Tab 4 NOOK that might warrant more than just . Zero-emissions hydrogen fuel cells seem like a great idea . At least they do until you realize that isolating the hydrogen that powers them creates a crapload of greenhouse gasses. Now, a Stanford . grad student thinks he's found the answer--and it involves a AAA battery.

Introduction Types of Mistakes Suggestions Resources Table of Contents About Glossary Blog

The notion of “the probability of something” is one of those ideas, like “point” and “time,” that we can’t define exactly, but that are useful nonetheless. The following should give a good working understanding of the concept.

Events

First, some related terminology: The “somethings” that we consider the probabilities of are usually called events. For example, we may talk about the event that the number showing on a die we have rolled is 5 or the event that it will rain tomorrow or the event that someone in a certain group will contract a certain disease within the next five years.

Four Perspectives on Probability

1. Classical (sometimes called “A priori” or “Theoretical”)

This is the perspective on probability that most people first encounter in formal education (although they may encounter the subjective perspective in informal education).

For example, suppose we consider tossing a fair die. There are six possible numbers that could come up (“outcomes”), and, since the die is fair, each one is equally likely to occur. So we say each of these outcomes has probability 1/6. Since the event “an odd number comes up” consists of exactly three of these basic outcomes, we say the probability of “odd” is 3/6, i.e. 1/2.

More generally, if we have a situation (a “random process”) in which there are n equally likely outcomes, and the event A consists of exactly m of these outcomes, we say that the probability of A is m/n. We may write this as “P(A) = m/n” for short.

This perspective has the advantage that it is conceptually simple for many situations. However, it is limited, since many situations do not have finitely many equally likely outcomes. Tossing a weighted die is an example where we have finitely many outcomes, but they are not equally likely. Studying people’s incomes over time would be a situation where we need to consider infinitely many possible outcomes, since there is no way to say what a maximum possible income would be, especially if we are interested in the future.

2. Empirical (sometimes called “A posteriori” or “Frequentist”)

This perspective defines probability via a thought experiment.

To get the idea, suppose that we have a die which we are told is weighted, but we don’t know how it is weighted. We could get a rough idea of the probability of each outcome by tossing the die a large number of times and using the proportion of times that the die gives that outcome to estimate the probability of that outcome.

This idea is formalized to define the probability of the event A as
P(A) = the limit as n approaches infinity of m/n,
where n is the number of times the process (e.g., tossing the die) is performed, and m is the number of times the outcome A happens.
(Notice that m and n stand for different things in this definition from what they meant in Perspective 1.)

In other words, imagine tossing the die 100 times, 1000 times, 10,000 times, … . Each time we expect to get a better and better approximation to the true probability of the event A. The mathematical way of describing this is that the true probability is the limit of the approximations, as the number of tosses “approaches infinity” (that just means that the number of tosses gets bigger and bigger indefinitely). Example

This view of probability generalizes the first view: If we indeed have a fair die, we expect that the number we will get from this definition is the same as we will get from the first definition (e.g., P(getting 1) = 1/6 P(getting an odd number) = 1/2). In addition, this second definition also works for cases when outcomes are not equally likely, such as the weighted die. It also works in cases where it doesn’t make sense to talk about the probability of an individual outcome. For example, we may consider randomly picking a positive integer ( 1, 2, 3, … ) and ask, “What is the probability that the number we pick is odd?” Intuitively, the answer should be 1/2, since every other integer (when counted in order) is odd. To apply this definition, we consider randomly picking 100 integers, then 1000 integers, then 10,000 integers, … . Each time we calculate what fraction of these chosen integers are odd. The resulting sequence of fractions should give better and better approximations to 1/2.

However, the empirical perspective does have some disadvantages. First, it involves a thought experiment. In some cases, the experiment could never in practice be carried out more than once. Consider, for example the probability that the Dow Jones average will go up tomorrow. There is only one today and one tomorrow. Going from today to tomorrow is not at all like rolling a die. We can only imagine all possibilities of going from today to a tomorrow (whatever that means). We can’t actually get an approximation.

A second disadvantage of the empirical perspective is that it leaves open the question of how large n has to be before we get a good approximation. The example linked above shows that, as n increases, we may have some wobbling away from the true value, followed by some wobbling back toward it, so it’s not even a steady process.

The empirical view of probability is the one that is used in most statistical inference procedures. These are called frequentist statistics. The frequentist view is what gives credibility to standard estimates based on sampling. For example, if we choose a large enough random sample from a population (for example, if we randomly choose a sample of 1000 students from the population of all 50,000 students enrolled in the university), then the average of some measurement (for example, college expenses) for the sample is a reasonable estimate of the average for the population.

3. Subjective

Subjective probability is an individual person’s measure of belief that an event will occur. With this view of probability, it makes perfectly good sense intuitively to talk about the probability that the Dow Jones average will go up tomorrow. You can quite rationally take your subjective view to agree with the classical or empirical views when they apply, so the subjective perspective can be taken as an expansion of these other views.

However, subjective probability also has its downsides. First, since it is subjective, one person’s probability (e.g., that the Dow Jones will go up tomorrow) may differ from another’s. This is disturbing to many people. Sill, it models the reality that often people do differ in their judgments of probability.

The second downside is that subjective probabilities must obey certain “coherence” (consistency) conditions in order to be workable. For example, if you believe that the probability that the Dow Jones will go up tomorrow is 60%, then to be consistent you cannot believe that the probability that the Dow Jones will do down tomorrow is also 60%. It is easy to fall into subjective probabilities that are not coherent.

The subjective perspective of probability fits well with Bayesian statistics, which are an alternative to the more common frequentist statistical methods. (This website will mainly focus on frequentist statistics.)

4. Axiomatic

This is a unifying perspective. The coherence conditions needed for subjective probability can be proved to hold for the classical and empirical definitions. The axiomatic perspective codifies these coherence conditions, so can be used with any of the above three perspectives.

The axiomatic perspective says that probability is any function (we’ll call it P) from events to numbers satisfying the three conditions (axioms) below. (Just what constitutes events will depend on the situation where probability is being used.)

The three axioms of probability:

  1. 0 ≤ P(E) ≤ 1 for every allowable event E. (In other words, 0 is the smallest allowable probability and 1 is the largest allowable probability).
  2. The certain event has probability 1. (The certain event is the event “some outcome occurs.” For example, in rolling a die, the certain event is “One of 1, 2, 3, 4, 5, 6 comes up.” In considering the stock market, the certain event is “The Dow Jones either goes up or goes down or stays the same.”)
  3. The probability of the union of mutually exclusive events is the sum of the probabilities of the individual events. (Two events are called mutually exclusive if they cannot both occur simultaneously. For example, the events “the die comes up 1” and “the die comes up 4” are mutually exclusive, assuming we are talking about the same toss of the same die. The union of events is the event that at least one of the events occurs. For example, if E is the event “a 1 comes up on the die” and F is the event “an even number comes up on the die,” then the union of E and F is the event “the number that comes up on the die is either 1 or even.”

If we have a fair die, the axioms of probability require that each number comes up with probability 1/6: Since the die is fair, each number comes up with the same probability. Since the outcomes 𔄙 comes up,” 𔄚 comes up,” …𔄨 come up” are mutually exclusive and their union is the certain event, Axiom III says that
P(1 comes up) + P( 2 comes up) + … + P(6 comes up) = P(the certain event),
which is 1 (by Axiom 2). Since all six probabilities on the left are equal, that common probability must be 1/6.

How we change what others think, feel, believe and do
| Menu | Quick | Books | Share | Search | Settings |

What is Thinking? Explanations > Thinking > What is Thinking?Recognizing | Remembering | Reasoning | Imagining | Deciding | So what? Thinking is the ultimate cognitive activity, consciously using our brains to make sense of the world around us and decide how to respond to it. Unconsciously our brains are still ‘thinking’ and this is a part of the cognitive process, but is not what we normally call ‘thinking’. Neurally, thinking is simply about chains of synaptic connections. Thinking as experienced is of ‘thoughts’ and ‘reasoning’ as we seek to connect what we sense with our inner world of understanding, and hence do and say things that will change the outer world.Our ability to think develops naturally in early life. When we interact with others, it becomes directed, for example when we learn values from our parents and knowledge from our teachers. We learn that it is good to think in certain ways and bad to think in other ways. Indeed, to be accepted into a social group, we are expected to think and act in ways that are harmonious with the group culture.RecognizingAt its most basic level, thinking answers the question ‘What’s that?’ As the real-time stream of information from the outer world hits your senses, you have to very quickly identity what it is and what you need to do about it, particularly if it could be a threat.This engages your remarkably powerful pattern recognition system that can recognize a friend standing behind a post. Pattern recognition can fail, which can be embarrassing when we greet strangers as friends, yet a few errors is a small price to pay for the ability to recognize obscured things with a mere glance.RememberingMemory is an annoying thing and we sometimes have to put in extra effort to bring to mind even trivial knowledge. Curiously, we have a lot less problem in naming the things we see as compared to bringing to mind something we already have stored away. The ‘tip of the tongue’ effect happens where we feel we can almost recall something, but it is just out of cognitive reach.Skill in recall can be enhanced significantly by using memory methods that deliberately put more effort into encoding.ReasoningReasoning uses principles of argument to assess facts and causality to determine what actions may lead to what outcomes, and how probable success or failure might be for various strategies and tactics. It typically employs a great deal of ‘if-then’ thinking and hopefully leads to reliable plans, though the future is far from certain, no matter how confident we are. Indeed, we many biases which invade our reasoning and lead us to confidence when perhaps we should not be so certain.A critical element of reasoning is relating, where two elements of knowledge are related in some way. It is often in this connection between things that new understanding is created. This includes relationships such as ‘A is caused by B’, ‘A and B are similar in some way and different in others’, or ‘If A does X then B does Y’.ImaginingAnother factor that distinguishes humanity is our ability to be creative and imagine possible futures. As an extension of reasoning, this becomes less certain but still lets us think about what may happen and how we can influence this. This includes achieving outlandish goals and avoiding potential disasters. Imagining is also a part of art and play where outcomes are not serious but may yet be life-changing.EmotingThe thought process is tied up with emotions, though not always as we wish, especially when the more primitive emotional process overrides the more reasoned thinking, leading us to rash actions that we may later regret. It can be very helpful to pay attention to emotions, both in ourselves and in those we wish to influence. If we can cognitively understand what is going on, then we have a far better chance of avoiding pure emotional reactions and choices.DecidingDeciding is the last step before acting, where we consider various options and choose those that seem to be most advantageous. Even though we may be confident at the moment of decision, there are many well-understood decision errors and traps into which we regularly fall.Decisions are based on an assumption of correct basic data. With false facts or theories, even ‘reasonable choice’ will come to the wrong conclusions. As computer people say, ‘Garbage In, Garbage Out’ (GIGO). This can be a trap when the truth of ‘facts’ cannot be tested. This is one reason why we pay close attention to the trustworthiness of sources. Academic journals, for example, are often trusted because they refuse to publish papers where methods or data seem weak.So what?Thinking makes what we are, yet it is a complex and deeply flawed process, even as we may be quite confident that we are correct in our thoughts, conclusions and consequent actions. Changing minds activity often needs to have a significant effect on how people think, yet it can be harder to change thoughts than we may hope. If we can understand how the other person is thinking, including when their reasoning is strong or weak, then we will have a far greater chance of persuading them.See alsoThe SIFT Model, Memory, Decision errors

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Samsung and Barnes & Noble have come together to work on a new tablet that has finally made its way over to the masses, where this new tablet is known as the Samsung Galaxy Tab 4 NOOK. Before we get . into the nitty gritty and all of the other details, what are some of the key features of the Samsung Galaxy Tab 4 NOOK that might warrant more than just . Zero-emissions hydrogen fuel cells seem like a great idea . At least they do until you realize that isolating the hydrogen that powers them creates a crapload of greenhouse gasses. Now, a Stanford . grad student thinks he's found the answer--and it involves a AAA battery.

Introduction Types of Mistakes Suggestions Resources Table of Contents About Glossary Blog

The notion of “the probability of something” is one of those ideas, like “point” and “time,” that we can’t define exactly, but that are useful nonetheless. The following should give a good working understanding of the concept.

Events

First, some related terminology: The “somethings” that we consider the probabilities of are usually called events. For example, we may talk about the event that the number showing on a die we have rolled is 5 or the event that it will rain tomorrow or the event that someone in a certain group will contract a certain disease within the next five years.

Four Perspectives on Probability

1. Classical (sometimes called “A priori” or “Theoretical”)

This is the perspective on probability that most people first encounter in formal education (although they may encounter the subjective perspective in informal education).

For example, suppose we consider tossing a fair die. There are six possible numbers that could come up (“outcomes”), and, since the die is fair, each one is equally likely to occur. So we say each of these outcomes has probability 1/6. Since the event “an odd number comes up” consists of exactly three of these basic outcomes, we say the probability of “odd” is 3/6, i.e. 1/2.

More generally, if we have a situation (a “random process”) in which there are n equally likely outcomes, and the event A consists of exactly m of these outcomes, we say that the probability of A is m/n. We may write this as “P(A) = m/n” for short.

This perspective has the advantage that it is conceptually simple for many situations. However, it is limited, since many situations do not have finitely many equally likely outcomes. Tossing a weighted die is an example where we have finitely many outcomes, but they are not equally likely. Studying people’s incomes over time would be a situation where we need to consider infinitely many possible outcomes, since there is no way to say what a maximum possible income would be, especially if we are interested in the future.

2. Empirical (sometimes called “A posteriori” or “Frequentist”)

This perspective defines probability via a thought experiment.

To get the idea, suppose that we have a die which we are told is weighted, but we don’t know how it is weighted. We could get a rough idea of the probability of each outcome by tossing the die a large number of times and using the proportion of times that the die gives that outcome to estimate the probability of that outcome.

This idea is formalized to define the probability of the event A as
P(A) = the limit as n approaches infinity of m/n,
where n is the number of times the process (e.g., tossing the die) is performed, and m is the number of times the outcome A happens.
(Notice that m and n stand for different things in this definition from what they meant in Perspective 1.)

In other words, imagine tossing the die 100 times, 1000 times, 10,000 times, … . Each time we expect to get a better and better approximation to the true probability of the event A. The mathematical way of describing this is that the true probability is the limit of the approximations, as the number of tosses “approaches infinity” (that just means that the number of tosses gets bigger and bigger indefinitely). Example

This view of probability generalizes the first view: If we indeed have a fair die, we expect that the number we will get from this definition is the same as we will get from the first definition (e.g., P(getting 1) = 1/6 P(getting an odd number) = 1/2). In addition, this second definition also works for cases when outcomes are not equally likely, such as the weighted die. It also works in cases where it doesn’t make sense to talk about the probability of an individual outcome. For example, we may consider randomly picking a positive integer ( 1, 2, 3, … ) and ask, “What is the probability that the number we pick is odd?” Intuitively, the answer should be 1/2, since every other integer (when counted in order) is odd. To apply this definition, we consider randomly picking 100 integers, then 1000 integers, then 10,000 integers, … . Each time we calculate what fraction of these chosen integers are odd. The resulting sequence of fractions should give better and better approximations to 1/2.

However, the empirical perspective does have some disadvantages. First, it involves a thought experiment. In some cases, the experiment could never in practice be carried out more than once. Consider, for example the probability that the Dow Jones average will go up tomorrow. There is only one today and one tomorrow. Going from today to tomorrow is not at all like rolling a die. We can only imagine all possibilities of going from today to a tomorrow (whatever that means). We can’t actually get an approximation.

A second disadvantage of the empirical perspective is that it leaves open the question of how large n has to be before we get a good approximation. The example linked above shows that, as n increases, we may have some wobbling away from the true value, followed by some wobbling back toward it, so it’s not even a steady process.

The empirical view of probability is the one that is used in most statistical inference procedures. These are called frequentist statistics. The frequentist view is what gives credibility to standard estimates based on sampling. For example, if we choose a large enough random sample from a population (for example, if we randomly choose a sample of 1000 students from the population of all 50,000 students enrolled in the university), then the average of some measurement (for example, college expenses) for the sample is a reasonable estimate of the average for the population.

3. Subjective

Subjective probability is an individual person’s measure of belief that an event will occur. With this view of probability, it makes perfectly good sense intuitively to talk about the probability that the Dow Jones average will go up tomorrow. You can quite rationally take your subjective view to agree with the classical or empirical views when they apply, so the subjective perspective can be taken as an expansion of these other views.

However, subjective probability also has its downsides. First, since it is subjective, one person’s probability (e.g., that the Dow Jones will go up tomorrow) may differ from another’s. This is disturbing to many people. Sill, it models the reality that often people do differ in their judgments of probability.

The second downside is that subjective probabilities must obey certain “coherence” (consistency) conditions in order to be workable. For example, if you believe that the probability that the Dow Jones will go up tomorrow is 60%, then to be consistent you cannot believe that the probability that the Dow Jones will do down tomorrow is also 60%. It is easy to fall into subjective probabilities that are not coherent.

The subjective perspective of probability fits well with Bayesian statistics, which are an alternative to the more common frequentist statistical methods. (This website will mainly focus on frequentist statistics.)

4. Axiomatic

This is a unifying perspective. The coherence conditions needed for subjective probability can be proved to hold for the classical and empirical definitions. The axiomatic perspective codifies these coherence conditions, so can be used with any of the above three perspectives.

The axiomatic perspective says that probability is any function (we’ll call it P) from events to numbers satisfying the three conditions (axioms) below. (Just what constitutes events will depend on the situation where probability is being used.)

The three axioms of probability:

  1. 0 ≤ P(E) ≤ 1 for every allowable event E. (In other words, 0 is the smallest allowable probability and 1 is the largest allowable probability).
  2. The certain event has probability 1. (The certain event is the event “some outcome occurs.” For example, in rolling a die, the certain event is “One of 1, 2, 3, 4, 5, 6 comes up.” In considering the stock market, the certain event is “The Dow Jones either goes up or goes down or stays the same.”)
  3. The probability of the union of mutually exclusive events is the sum of the probabilities of the individual events. (Two events are called mutually exclusive if they cannot both occur simultaneously. For example, the events “the die comes up 1” and “the die comes up 4” are mutually exclusive, assuming we are talking about the same toss of the same die. The union of events is the event that at least one of the events occurs. For example, if E is the event “a 1 comes up on the die” and F is the event “an even number comes up on the die,” then the union of E and F is the event “the number that comes up on the die is either 1 or even.”

If we have a fair die, the axioms of probability require that each number comes up with probability 1/6: Since the die is fair, each number comes up with the same probability. Since the outcomes 𔄙 comes up,” 𔄚 comes up,” …𔄨 come up” are mutually exclusive and their union is the certain event, Axiom III says that
P(1 comes up) + P( 2 comes up) + … + P(6 comes up) = P(the certain event),
which is 1 (by Axiom 2). Since all six probabilities on the left are equal, that common probability must be 1/6.

How we change what others think, feel, believe and do
| Menu | Quick | Books | Share | Search | Settings |

What is Thinking? Explanations > Thinking > What is Thinking?Recognizing | Remembering | Reasoning | Imagining | Deciding | So what? Thinking is the ultimate cognitive activity, consciously using our brains to make sense of the world around us and decide how to respond to it. Unconsciously our brains are still ‘thinking’ and this is a part of the cognitive process, but is not what we normally call ‘thinking’. Neurally, thinking is simply about chains of synaptic connections. Thinking as experienced is of ‘thoughts’ and ‘reasoning’ as we seek to connect what we sense with our inner world of understanding, and hence do and say things that will change the outer world.Our ability to think develops naturally in early life. When we interact with others, it becomes directed, for example when we learn values from our parents and knowledge from our teachers. We learn that it is good to think in certain ways and bad to think in other ways. Indeed, to be accepted into a social group, we are expected to think and act in ways that are harmonious with the group culture.RecognizingAt its most basic level, thinking answers the question ‘What’s that?’ As the real-time stream of information from the outer world hits your senses, you have to very quickly identity what it is and what you need to do about it, particularly if it could be a threat.This engages your remarkably powerful pattern recognition system that can recognize a friend standing behind a post. Pattern recognition can fail, which can be embarrassing when we greet strangers as friends, yet a few errors is a small price to pay for the ability to recognize obscured things with a mere glance.RememberingMemory is an annoying thing and we sometimes have to put in extra effort to bring to mind even trivial knowledge. Curiously, we have a lot less problem in naming the things we see as compared to bringing to mind something we already have stored away. The ‘tip of the tongue’ effect happens where we feel we can almost recall something, but it is just out of cognitive reach.Skill in recall can be enhanced significantly by using memory methods that deliberately put more effort into encoding.ReasoningReasoning uses principles of argument to assess facts and causality to determine what actions may lead to what outcomes, and how probable success or failure might be for various strategies and tactics. It typically employs a great deal of ‘if-then’ thinking and hopefully leads to reliable plans, though the future is far from certain, no matter how confident we are. Indeed, we many biases which invade our reasoning and lead us to confidence when perhaps we should not be so certain.A critical element of reasoning is relating, where two elements of knowledge are related in some way. It is often in this connection between things that new understanding is created. This includes relationships such as ‘A is caused by B’, ‘A and B are similar in some way and different in others’, or ‘If A does X then B does Y’.ImaginingAnother factor that distinguishes humanity is our ability to be creative and imagine possible futures. As an extension of reasoning, this becomes less certain but still lets us think about what may happen and how we can influence this. This includes achieving outlandish goals and avoiding potential disasters. Imagining is also a part of art and play where outcomes are not serious but may yet be life-changing.EmotingThe thought process is tied up with emotions, though not always as we wish, especially when the more primitive emotional process overrides the more reasoned thinking, leading us to rash actions that we may later regret. It can be very helpful to pay attention to emotions, both in ourselves and in those we wish to influence. If we can cognitively understand what is going on, then we have a far better chance of avoiding pure emotional reactions and choices.DecidingDeciding is the last step before acting, where we consider various options and choose those that seem to be most advantageous. Even though we may be confident at the moment of decision, there are many well-understood decision errors and traps into which we regularly fall.Decisions are based on an assumption of correct basic data. With false facts or theories, even ‘reasonable choice’ will come to the wrong conclusions. As computer people say, ‘Garbage In, Garbage Out’ (GIGO). This can be a trap when the truth of ‘facts’ cannot be tested. This is one reason why we pay close attention to the trustworthiness of sources. Academic journals, for example, are often trusted because they refuse to publish papers where methods or data seem weak.So what?Thinking makes what we are, yet it is a complex and deeply flawed process, even as we may be quite confident that we are correct in our thoughts, conclusions and consequent actions. Changing minds activity often needs to have a significant effect on how people think, yet it can be harder to change thoughts than we may hope. If we can understand how the other person is thinking, including when their reasoning is strong or weak, then we will have a far greater chance of persuading them.See alsoThe SIFT Model, Memory, Decision errors

You can buy books here
More Kindle books:
And the big
paperback book
Look inside
Please help and share:
Quick links
Disciplines* Argument
* Brand management
* Change Management
* Coaching
* Communication
* Counseling
* Game Design
* Human Resources
* Job-finding
* Leadership
* Marketing
* Politics
* Propaganda
* Rhetoric
* Negotiation
* Psychoanalysis
* Sales
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