Permutation test

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Permutation_test

A permutation test (also called re-randomization test or shuffle test) is an exact statistical hypothesis test making use of the proof by contradiction. A permutation test involves two or more samples. The null hypothesis is that all samples come from the same distribution ${\displaystyle H_0:F=G}$. Under the null hypothesis, the distribution of the test statistic is obtained by calculating all possible values of the test statistic under possible rearrangements of the observed data. Permutation tests are, therefore, a form of resampling.

Permutation tests can be understood as surrogate data testing where the surrogate data under the null hypothesis are obtained through permutations of the original data.

In other words, the method by which treatments are allocated to subjects in an experimental design is mirrored in the analysis of that design. If the labels are exchangeable under the null hypothesis, then the resulting tests yield exact significance levels; see also exchangeability. Confidence intervals can then be derived from the tests. The theory has evolved from the works of Ronald Fisher and E. J. G. Pitman in the 1930s.

Permutation tests should not be confused with randomized tests.

Method

Animation of a permutation test being computed on sets of 4 and 5 random values. The 4 values in red are drawn from one distribution, and the 5 values in blue from another; we'd like to test whether the mean values of the two distributions are different. The hypothesis is that the mean of the first distribution is higher than the mean of the second; the null hypothesis is that both groups of samples are drawn from the same distribution. There are 126 distinct ways to put 4 values into one group and 5 into another (9-choose-4 or 9-choose-5). Of these, one is per the original labeling, and the other 125 are "permutations" that generate the histogram of mean differences ${\displaystyle {\hat{\mu }}_1-{\hat{\mu }}_2}$ shown. The p-value of the hypothesis is estimated as the proportion of permutations that give a difference as large or larger than the difference of means of the original samples. In this example, the null hypothesis cannot be rejected at the p = 5% level.

To illustrate the basic idea of a permutation test, suppose we collect random variables ${\displaystyle X_A}$ and ${\displaystyle X_B}$ for each individual from two groups ${\displaystyle A}$ and ${\displaystyle B}$ whose sample means are ${\displaystyle {\overline{x}}_A}$ and ${\displaystyle {\overline{x}}_B}$, and that we want to know whether ${\displaystyle X_A}$ and ${\displaystyle X_B}$ come from the same distribution. Let ${\displaystyle n_A}$ and ${\displaystyle n_B}$ be the sample size collected from each group. The permutation test is designed to determine whether the observed difference between the sample means is large enough to reject, at some significance level, the null hypothesis H${\displaystyle {}_0}$ that the data drawn from ${\displaystyle A}$ is from the same distribution as the data drawn from ${\displaystyle B}$.

The test proceeds as follows. First, the difference in means between the two samples is calculated: this is the observed value of the test statistic, ${\displaystyle T_{\text{obs}}}$.

Next, the observations of groups ${\displaystyle A}$ and ${\displaystyle B}$ are pooled, and the difference in sample means is calculated and recorded for every possible way of dividing the pooled values into two groups of size ${\displaystyle n_A}$ and ${\displaystyle n_B}$ (i.e., for every permutation of the group labels A and B). The set of these calculated differences is the exact distribution of possible differences (for this sample) under the null hypothesis that group labels are exchangeable (i.e., are randomly assigned).

The one-sided p-value of the test is calculated as the proportion of sampled permutations where the difference in means was greater than ${\displaystyle T_{\text{obs}}}$. The two-sided p-value of the test is calculated as the proportion of sampled permutations where the absolute difference was greater than ${\displaystyle |T_{\text{obs}}|}$. Many implementations of permutation tests require that the observed data itself be counted as one of the permutations so that the permutation p-value will never be zero.

Alternatively, if the only purpose of the test is to reject or not reject the null hypothesis, one could sort the recorded differences, and then observe if ${\displaystyle T_{\text{obs}}}$ is contained within the middle ${\displaystyle (1-\alpha )\times 100}$% of them, for some significance level ${\displaystyle \alpha }$. If it is not, we reject the hypothesis of identical probability curves at the ${\displaystyle \alpha \times 100\mathrm{\%}}$ significance level.

To exploit variance reduction with paired samples the paired permutation test needs to be applied, see paired difference test.

Relation to parametric tests

Permutation tests are a subset of non-parametric statistics. Assuming that our experimental data come from data measured from two treatment groups, the method simply generates the distribution of mean differences under the assumption that the two groups are not distinct in terms of the measured variable. From this, one then uses the observed statistic (${\displaystyle T_{\text{obs}}}$ above) to see to what extent this statistic is special, i.e., the likelihood of observing the magnitude of such a value (or larger) if the treatment labels had simply been randomized after treatment.

In contrast to permutation tests, the distributions underlying many popular "classical" statistical tests, such as the t-test, F-test, z-test, and χ² test, are obtained from theoretical probability distributions. Fisher's exact test is an example of a commonly used parametric test for evaluating the association between two dichotomous variables. When sample sizes are very large, the Pearson's chi-square test will give accurate results. For small samples, the chi-square reference distribution cannot be assumed to give a correct description of the probability distribution of the test statistic, and in this situation the use of Fisher's exact test becomes more appropriate.

Permutation tests exist in many situations where parametric tests do not (e.g., when deriving an optimal test when losses are proportional to the size of an error rather than its square). All simple and many relatively complex parametric tests have a corresponding permutation test version that is defined by using the same test statistic as the parametric test, but obtains the p-value from the sample-specific permutation distribution of that statistic, rather than from the theoretical distribution derived from the parametric assumption. For example, it is possible in this manner to construct a permutation t-test, a permutation ${\chi }^2$ test of association, a permutation version of Aly's test for comparing variances and so on.

The major drawbacks to permutation tests are that they

• Can be computationally intensive and may require "custom" code for difficult-to-calculate statistics. This must be rewritten for every case.
• Are primarily used to provide a p-value. The inversion of the test to get confidence regions/intervals requires even more computation.

Advantages

Permutation tests exist for any test statistic, regardless of whether or not its distribution is known. Thus one is always free to choose the statistic which best discriminates between hypothesis and alternative and which minimizes losses.

Permutation tests can be used for analyzing unbalanced designs and for combining dependent tests on mixtures of categorical, ordinal, and metric data (Pesarin, 2001). They can also be used to analyze qualitative data that has been quantitized (i.e., turned into numbers). Permutation tests may be ideal for analyzing quantitized data that do not satisfy statistical assumptions underlying traditional parametric tests (e.g., t-tests, ANOVA), see PERMANOVA.

Before the 1980s, the burden of creating the reference distribution was overwhelming except for data sets with small sample sizes.

Since the 1980s, the confluence of relatively inexpensive fast computers and the development of new sophisticated path algorithms applicable in special situations made the application of permutation test methods practical for a wide range of problems. It also initiated the addition of exact-test options in the main statistical software packages and the appearance of specialized software for performing a wide range of uni- and multi-variable exact tests and computing test-based "exact" confidence intervals.

Limitations

An important assumption behind a permutation test is that the observations are exchangeable under the null hypothesis. An important consequence of this assumption is that tests of difference in location (like a permutation t-test) require equal variance under the normality assumption. In this respect, the classic permutation t-test shares the same weakness as the classical Student's t-test (the Behrens–Fisher problem). This can be addressed in the same way the classic t-test has been extended to handle unequal variances: by employing the Welch statistic with Satterthwaite adjustment to the degrees of freedom. A third alternative in this situation is to use a bootstrap-based test. Statistician Phillip Good explains the difference between permutation tests and bootstrap tests the following way: "Permutations test hypotheses concerning distributions; bootstraps test hypotheses concerning parameters. As a result, the bootstrap entails less-stringent assumptions." Bootstrap tests are not exact. In some cases, a permutation test based on a properly studentized statistic can be asymptotically exact even when the exchangeability assumption is violated. Bootstrap-based tests can test with the null hypothesis ${\displaystyle H_0:F\ne G}$ and, therefore, are suited for performing equivalence testing.

Monte Carlo testing

An asymptotically equivalent permutation test can be created when there are too many possible orderings of the data to allow complete enumeration in a convenient manner. This is done by generating the reference distribution by Monte Carlo sampling, which takes a small (relative to the total number of permutations) random sample of the possible replicates. The realization that this could be applied to any permutation test on any dataset was an important breakthrough in the area of applied statistics. The earliest known references to this approach are Eden and Yates (1933) and Dwass (1957). This type of permutation test is known under various names: approximate permutation test, Monte Carlo permutation tests or random permutation tests.

After ${\displaystyle N}$ random permutations, it is possible to obtain a confidence interval for the p-value based on the Binomial distribution, see Binomial proportion confidence interval. For example, if after ${\displaystyle N=10000}$ random permutations the p-value is estimated to be ${\displaystyle \hat{p}=0.05}$, then a 99% confidence interval for the true ${\displaystyle p}$ (the one that would result from trying all possible permutations) is ${\displaystyle \left[\hat{p}-z\sqrt{\frac{0.05(1-0.05)}{10000}},\hat{p}+z\sqrt{\frac{0.05(1-0.05)}{10000}}\right]=[0.045,0.055]}$.

On the other hand, the purpose of estimating the p-value is most often to decide whether ${\displaystyle p\le \alpha }$, where ${\displaystyle \alpha }$ is the threshold at which the null hypothesis will be rejected (typically ${\displaystyle \alpha =0.05}$). In the example above, the confidence interval only tells us that there is roughly a 50% chance that the p-value is smaller than 0.05, i.e. it is completely unclear whether the null hypothesis should be rejected at a level ${\displaystyle \alpha =0.05}$.

If it is only important to know whether ${\displaystyle p\le \alpha }$ for a given ${\displaystyle \alpha }$, it is logical to continue simulating until the statement ${\displaystyle p\le \alpha }$ can be established to be true or false with a very low probability of error. Given a bound ${\displaystyle ϵ}$ on the admissible probability of error (the probability of finding that ${\displaystyle \hat{p}>\alpha }$ when in fact ${\displaystyle p\le \alpha }$ or vice versa), the question of how many permutations to generate can be seen as the question of when to stop generating permutations, based on the outcomes of the simulations so far, in order to guarantee that the conclusion (which is either ${\displaystyle p\le \alpha }$ or ${\displaystyle p>\alpha }$) is correct with probability at least as large as ${\displaystyle 1-ϵ}$. (${\displaystyle ϵ}$ will typically be chosen to be extremely small, e.g. 1/1000.) Stopping rules to achieve this have been developed which can be incorporated with minimal additional computational cost. In fact, depending on the true underlying p-value it will often be found that the number of simulations required is remarkably small (e.g. as low as 5 and often not larger than 100) before a decision can be reached with virtual certainty.

Absent-mindedness

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Absent-mindedness

In the field of psychology, absent-mindedness is a mental state wherein a person is forgetfully inattentive. It is the opposite mental state of mindfulness.

Absentmindedness is often caused by things such as boredom, sleepiness, rumination, distraction, or preoccupation with one's own internal monologue. When experiencing absent-mindedness, people exhibit signs of memory lapses and weak recollection of recent events.

Absent-mindedness can usually be a result of a variety of other conditions often diagnosed by clinicians, such as attention deficit hyperactivity disorder and depression. In addition to absent-mindedness leading to an array of consequences affecting daily life, it can have more severe, long-term problems.

Conceptualization

Absent-mindedness seemingly consists of lapses of concentration or "zoning out". This can result in lapses of short or long-term memory, depending on when the person in question was in a state of absent-mindedness. Absent-mindedness also relates directly to lapses in attention. Schachter and Dodsen of the Harvard Psychology department say, that in the context of memory, "absent-mindedness entails inattentive or shallow processing that contributes to weak memories of ongoing events or forgetting to do things in the future".

Causes

Though absent-mindedness is a frequent occurrence, there has been little progress made on what the direct causes of absent-mindedness are. However, it tends to co-occur with ill health, preoccupation, and distraction.

The condition has three potential causes:

1. a low level of attention ("blanking" or "zoning out");
2. intense attention to a single object of focus (hyperfocus) that makes a person oblivious to events around them; or
3. unwarranted distraction of attention from the object of focus by irrelevant thoughts or environmental events.

Absent-mindedness is also noticed as a common characteristic of personalities with schizoid personality disorder.

Consequences

Lapses of attention are clearly a part of everyone's life. Some are merely inconvenient, such as missing a familiar turn-off on the highway, while some are extremely serious, such as failures of attention that cause accidents, injury, or loss of life. Sometimes, lapses of attention can lead to a significant impact on personal behaviour, which can influence an individual's pursuit of goals. Beyond the obvious costs of accidents arising from lapses in attention, there are lost time; efficiency; personal productivity; and quality of life. These can also occur in the lapse and recapture of awareness and attention to everyday tasks. Individuals for whom intervals between lapses are very short are typically viewed as impaired. Given the prevalence of attentional failures in everyday life, and the ubiquitous and sometimes disastrous consequences of such failures, it is rather surprising that relatively little work has been done to directly measure individual differences in everyday errors arising from propensities for failures of attention. Absent-mindedness can also lead to bad grades at school, boredom, and depression.

Absent-mindedness in popular culture

The absent-minded professor is a stock character often depicted in fictional works, usually as a talented academic whose focus on academic matters leads them to ignore or forget their surroundings. This stereotypical view can be traced back as far as the philosopher Thales, who it is said, "walked at night with his eyes focused on the heavens and, as a result, fell down a well". One classic example of this is in the Disney film The Absent-Minded Professor made in 1963 and based on the short story "A Situation of Gravity", by Samuel W. Taylor. Two examples of this character portrayed in more modern media include doctor Emmett Brown from Back to the Future and Professor Farnsworth of Futurama.

In literature, "The Absent-Minded Beggar" is a poem by Rudyard Kipling, written in 1899, and was directed at the absent–mindedness of the population of Great Britain in ignoring the plight of their troops in the Boer War. The poem illustrated the fact that soldiers who could not return to their previous jobs needed support, and the need to raise money to support the fighting troops. The poem was also set to music by Gilbert & Sullivan and a campaign raised to support the British troops, especially on their departure and return, and the sick and wounded. Franz Kafka also wrote "Absent-minded Window-gazing", one of his short-story titles from Betrachtung.

Other characters include:

• Amberglas from The Seven Towers by Patricia Wrede.
• Alicja, a character from Joanna Chmielewska's novels. Alicja is described by the author as "the epitome of absent-mindedness".
• Caboose, from the web series Red vs. Blue by RoosterTeeth.
• Hay Lin is an absent-minded character from the Italian comic book series W.I.T.C.H., later made into an animated Disney production.
• Professor Calculus in The Adventures of Tintin comics.

Measurement and treatment

Absent-mindedness can be avoided or fixed in several ways. Although it can not be accomplished through medical procedures, it can be accomplished through psychological treatments. Some examples include: altering work schedules to make them shorter, having frequent rest periods and utilizing a drowsy-operator warning device.

Absent-mindedness and its related topics are often measured in scales developed in studies to survey boredom and attention levels. For instance, the Attention-Related Cognitive Errors Scale (ARCES) reflects errors in performance that result from attention lapses. Another scale, called the Mindful Attention Awareness Scale (MAAS) measures the ability to maintain a reasonable level of attention in everyday life. The Boredom Proneness Scale (BPS) measures the level of boredom in relation to the attention level of the subject.

Mistakes and related phenomena

Absent-mindedness can lead to automatic behaviors or automatisms. Additionally, absent-minded actions can involve behavioral mistakes. A phenomenon called Attention-Lapse Induced Alienation occurs when a person makes a mistake while absent-minded. The person then attributes the mistake to their hand rather than their self, because they were not paying attention.

Another related topic to absent-mindedness is daydreaming. It may be beneficial to differentiate between these two topics. Daydreaming can be viewed as a coping or defense mechanism. As opposed to inattentiveness, daydreaming is a way for emotions to be explored and even expressed through fantasy. It may even bring attention to previously experienced problems or circumstances. It is also a way to bring about creativity.

Mind-wandering

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Mind-wandering 

Mind-wandering is loosely defined as thoughts that are not produced from the current task. Mind-wandering consists of thoughts that are task-unrelated and stimulus-independent. This can be in the form of three different subtypes: positive constructive daydreaming, guilty fear of failure, and poor attentional control.

In general, a folk explanation of mind-wandering could be described as the experience of thoughts not remaining on a single topic for a long period of time, particularly when people are engaged in an attention-demanding task.

One context in which mind-wandering often occurs is driving. This is because driving under optimal conditions becomes an almost automatic activity that can require minimal use of the task positive network, the brain network that is active when one is engaged in an attention-demanding activity. In situations where vigilance is low, people do not remember what happened in the surrounding environment because they are preoccupied with their thoughts. This is known as the decoupling hypothesis.

Studies using event-related potentials (ERPs) have quantified the extent that mind-wandering reduces the cortical processing of the external environment. When thoughts are unrelated to the task at hand, the brain processes both task-relevant and unrelated sensory information in a less detailed manner.

Mind-wandering appears to be a stable trait of people and a transient state. Studies have linked performance problems in the laboratory and in daily life. Mind-wandering has been associated with possible car accidents. Mind-wandering is also intimately linked to states of affect. Studies indicate that task-unrelated thoughts are common in people with low or depressed mood. Mind-wandering also occurs when a person is intoxicated via the consumption of alcohol.

Studies have demonstrated a prospective bias to spontaneous thought because individuals tend to engage in more future than past related thoughts during mind-wandering. The default mode network is thought to be involved in mind-wandering and internally directed thought, although recent work has challenged this assumption.

History

The history of mind-wandering research dates back to 18th century England. British philosophers struggled to determine whether mind-wandering occurred in the mind or if an outside source caused it. In 1921, Varendonck published The Psychology of Day-Dreams, in which he traced his "'trains of thoughts' to identify their origins, most often irrelevant external influences".

Wallas (1926) considered mind-wandering as an important aspect of his second stage of creative thought – incubation. It was not until the 1960s that the first documented studies were conducted on mind-wandering. John Antrobus and Jerome L. Singer developed a questionnaire and discussed the experience of mind-wandering.

This questionnaire, known as the Imaginal Processes Inventory (IPI), provides a trait measure of mind-wandering and it assesses the experience on three dimensions: how vivid the person's thoughts are, how many of those thoughts are guilt- or fear-based, and how deep into the thought a person goes. As technology continues to develop, psychologists are starting to use functional magnetic resonance imaging to observe mind-wandering in the brain and reduce psychologists' reliance on verbal reports.

Research methods

Jonathan Smallwood and colleagues popularized the study of mind-wandering using thought sampling and questionnaires. Mind-wandering is studied using experience sampling either online or retrospectively. One common paradigm within which to study mind-wandering is the SART (sustained attention to response task).

In a SART task there are two categories of words. One of the categories are the target words. In each block of the task a word appears for about 300 ms, there will be a pause and then another word. When a target word appears the participant hits a designated key. About 60% of the time after a target word a thought probe will appear to gauge whether thoughts were on task. If participants were not engaged in the task they were experiencing task-unrelated thoughts (TUTs), signifying mind-wandering.

Another task to judge TUTs is the experience sampling method (ESM). Participants carry around a personal digital assistant (PDA) that signals several times a day. At the signal a questionnaire is provided. The questionnaire questions vary but can include: (a) whether or not their minds had wandered at the time of the (b) what state of control they had over their thoughts and (c) about the content of their thoughts.

Questions about context are also asked to measure the level of attention necessary for the task. One process used was to give participants something to focus on and then at different times ask them what they were thinking about. Those who were not thinking about what was given to them were considered "wandering". Another process was to have participants keep a diary of their mind-wandering. Participants are asked to write a brief description of their mind-wandering and the time in which it happened. These methodologies are improvements on past methods that were inconclusive.

Neuroscience

Mind-wandering is important in understanding how the brain produces what William James called the train of thought and the stream of consciousness. This aspect of mind-wandering research is focused on understanding how the brain generates the spontaneous and relatively unconstrained thoughts that are experienced when the mind wanders.

One candidate neural mechanism for generating this aspect of experience is a network of regions in the medial frontal and medial parietal cortex known as the default network. This network of regions is highly active even when participants are resting with their eyes closed suggesting a role in generating spontaneous internal thoughts. One relatively controversial result is that periods of mind-wandering are associated with increased activation in both the default and executive system a result that implies that mind-wandering may often be goal oriented.

It is commonly assumed that the default mode network is known to be involved during mind-wandering. The default mode network is active when a person is not focused on the outside world and the brain is at wakeful rest because experiences such as mind-wandering and daydreaming are common in this state.

It is also active when the individual is thinking about others, thinking about themselves, remembering the past, and planning for the future. However, recent studies show that signals in the default mode network provide information regarding patterns of detailed experience in active tasks states. This data suggests that the relationship between the default mode network and mind-wandering remains a matter of conjecture.

In addition to neural models, computational models of consciousness based on Bernard Baars' Global Workspace theory suggest that mind-wandering, or "spontaneous thought" may involve competition between internally and externally generated activities attempting to gain access to a limited capacity central network.

Individual differences

There are individual differences in some aspects of mind-wandering between older and younger adults. Although older adults reported less mind-wandering, these older participants showed the same amount of mind-wandering as younger adults. There were also differences in how participants responded to an error.

After an error, older adults took longer to return focus back to the task when compared with younger adults. It is possible that older adults reflect more about an error due to conscientiousness. Research has shown that older adults tend to be more conscientious than young adults. Personality can also affect mind-wandering.

People that are more conscientious are less prone to mind-wandering. Being more conscientious allows people to stay focused on the task better which causes fewer instances of mind-wandering. Differences in mind-wandering between young and older adults may be limited because of this personality difference.

Mental disorders such as ADHD (attention deficit hyperactivity disorder) are linked to mind-wandering. Seli et al. (2015) found that spontaneous mind-wandering, the uncontrolled or unwarranted shifting of attention, is a characteristic of those who have ADHD. However, they note that deliberate mind-wandering, or the purposeful shifting of one's attention to different stimuli, is not a consistent characteristic of having ADHD.

Franklin et al. (2016) arrived at similar conclusions; they had college students take multiple psychological evaluations that gauge ADHD symptom strength. Then, they had the students read a portion of a general science textbook. At various times and at random intervals throughout their reading, participants were prompted to answer a question that asked if their attention was either on task, slightly on task, slightly off task, or off task prior to the interruption.

In addition, they were asked if they were aware, unaware, or neither aware nor unaware of their thoughts as they read. Lastly, they were tasked to press the space bar if they ever caught themselves mind-wandering. For a week after these assessments, the students answered follow-up questions that also gauged mind-wandering and awareness.

This study's results revealed that students with higher ADHD symptomology showed less task-oriented control than those with lower ADHD symptomology. Additionally, those with lower ADHD symptomology were more likely to engage in useful or deliberate mind-wandering and were more aware of their inattention. One of the strengths of this study is that it was performed in both lab and daily-life situations, giving it broad application.

Mind-wandering in and of itself is not necessarily indicative of attention deficiencies. Studies show that humans typically spend 25-50% of their time thinking about thoughts irrelevant to their current situations.

In many disorders it is the regulation of the overall amount of mind-wandering that is disturbed, leading to increased distractibility when performing tasks. Additionally, the contents of mind-wandering is changed; thoughts can be more negative and past-oriented, particularly unstable or self-centered.

Working memory

See also: Working memory § Attention

Recent research has studied the relationship between mind-wandering and working memory capacity. Working memory capacity represents personal skill to have a good command of individual's mind. This relationship requires more research to understand how they influence one another. It is possible that mind-wandering causes lower performance on working memory capacity tasks or that lower working memory capacity causes more instances of mind-wandering.

Only the second of these has actually been proven. Reports of task-unrelated thoughts are less frequent when performing tasks that do not demand continuous use of working memory than tasks which do. Moreover, individual difference studies demonstrate that when tasks are non-demanding, high levels of working memory capacity are associated with more frequent reports of task-unrelated thinking especially when it is focused on the future. By contrast, when performing tasks that demand continuous attention, high levels of working memory capacity are associated with fewer reports of task-unrelated thoughts.

Together these data are consistent with the claim that working memory capacity helps sustain a train of thought whether it is generated in response to a perceptual event or is self-generated by the individual. Therefore, under certain circumstances, the experience of mind-wandering is supported by working memory resources. Working memory capacity variation in individuals has been proven to be a good predictor of the natural tendency for mind-wandering to occur during cognitively demanding tasks and various activities in daily life.

Mind-wandering sometimes occurs as a result of saccades, which are the movements of one's eyes to different visual stimuli. In an antisaccade task, for example, subjects with higher working memory capacity scores resisted looking at the flashing visual cue better than participants with lower working memory capacity. Higher working memory capacity is associated with fewer saccades toward environmental cues.

Mind-wandering has been shown to be related to goal orientation; people with higher working memory capacity keep their goals more accessible than those who have lower working memory capacity, thus allowing these goals to better guide their behavior and keep them on task.

Another study compared differences in speed of processing information between people of different ages. The task they used was a go/no go task where participants responded if a white arrow moved in a specific direction but did not respond if the arrow moved in the other direction or was a different color. In this task, children and young adults showed similar speed of processing but older adults were significantly slower.

Speed of processing information affects how much information can be processed in working memory.  People with faster speed of processing can encode information into memory better than people that have slower speed of processing. This can lead to memory of more items because more things can be encoded.

Retention

Mind-wandering affects retention where working memory capacity is directly related to reading comprehension levels. Participants with lower working memory capacity perform worse on comprehension-based tests.

When investigating how mind-wandering affects retention of information, experiments are conducted where participants are asked a variety of questions about factual information, or deducible information while reading a detective novel. Participants are also asked about the state of their mind before the questions are asked.

Throughout the reading itself, the author provides important cues to identify the villain, known as inference critical episodes (ICEs). The questions are asked randomly and before critical episodes are reached. It was found that episodes of mind-wandering, especially early on in the text led to decreased identification of the villain and worse results on both factual and deducible questions.

Therefore, when mind-wandering occurs during reading, the text is not processed well enough to remember key information about the story. Furthermore, both the timing and the frequency of mind-wandering helps determine how much information is retained from the narrative.

Reading comprehension

Reading comprehension must also be investigated in terms of text difficulty. To assess this, researchers provide an easy and hard version of a reading task. During this task, participants are interrupted and asked whether their thoughts at the time of interruption had been related or unrelated to the task. What is found is that mind-wandering has a negative effect on text comprehension in more difficult readings.

This supports the executive-resource hypothesis which describes that both task related and task-unrelated thoughts (TUT) compete for executive function resources. Therefore, when the primary task is difficult, little resources are available for mind-wandering, whereas when the task is simple, the possibility for mind-wandering is abundant because it takes little executive control to focus on simple tasks.

However, mind-wandering tends to occur more frequently in harder readings as opposed to easier readings. Therefore, it is possible that similar to retention, mind-wandering increases when readers have difficulty constructing a model of the story.

Happiness

As part of his doctoral research at Harvard University, Matthew Killingsworth used an iPhone app that captured a user's feelings in real time. The tool alerts the user at random times and asks: "How are you feeling right now?" and "What are you doing right now?" Killingsworth and Gilbert's analysis suggested that mind-wandering was much more typical in daily activities than in laboratory settings.

They also describe that people were less happy when their minds were wandering than when they were otherwise occupied. This effect was somewhat counteracted by people's tendency to mind-wander to happy topics, but unhappy mind-wandering was more likely to be rated as more unpleasant than other activities.

The authors note that unhappy moods can also cause mind-wandering, but the time-lags between mind-wandering and mood suggests that mind-wandering itself can also lead to negative moods. Furthermore, research suggests that regardless of working memory capacity, subjects participating in mind-wandering experiments report more mind-wandering when bored, stressed, or unhappy.

Executive functions

Executive functions (EFs) are cognitive processes that make a person pay attention or concentrate on a task. Three executive functions that relate to memory are inhibiting, updating and shifting. Inhibiting controls a person's attention and thoughts when distractions are abundant. Updating reviews old information and replaces it with new information in the working memory. Shifting controls the ability to go between multiple tasks. All three EFs have a relationship to mind-wandering.

Executive functions have roles in attention problems, attention control, thought control, and working memory capacity. Attention problems relate to behavioral problems such as inattention, impulsivity and hyperactivity. These behaviors make staying on task difficult leading to more mind-wandering. Higher inhibiting and updating abilities correlates to lower levels of attention problems in adolescence.

The inhibiting executive function controls attention and thought. The failure of cognitive inhibition is a direct cause of mind-wandering. Mind-wandering is also connected to working memory capacity (WMC). People with higher WMC mind-wander less on high concentration tasks no matter their boredom levels. People with low WMC are better at staying on task for low concentration tasks, but once the task increases in difficulty they had a hard time keeping their thoughts focused on task.

Updating takes place in the working memory, therefore those with low WMC have a lower updating executive function ability. That means a low performing updating executive function can be an indicator of high mind-wandering. Working memory relies on executive functions, with mind-wandering as an indicator of their failure. Task-unrelated thoughts (TUTs) are empirical behavioral manifestations of mind-wandering in a person. The longer a task is performed the more TUTs reported. Mind-wandering is an indication of an executive control failure that is characterized by TUTs.

Metacognition serves to correct the wandering mind, suppressing spontaneous thoughts and bringing attention back to more "worthwhile" tasks.

Fidgeting

Paul Seli and colleagues have shown that spontaneous mind-wandering is associated with increased fidgeting; by contrast, interest, attention and visual engagement lead to Non-Instrumental Movement Inhibition. One possible application for this phenomenon is that detection of non-instrumental movements may be an indicator of attention or boredom in computer aided learning.

Traditionally teachers and students have viewed fidgeting as a sign of diminished attention, which is summarized by the statement, “Concentration of consciousness, and concentration of movements, diffusion of ideas and diffusion of movements go together.” However, James Farley and colleagues have proposed that fidgeting is not only an indicator of spontaneous mind-wandering, but is also a subconscious attempt to increase arousal in order to improve attention and thus reduce mind-wandering.

Default mode network

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Default_mode_network

Default mode network
fMRI scan showing regions of the default mode network; the dorsal medial prefrontal cortex, the posterior cingulate cortex, the precuneus and the angular gyrus

Default mode network connectivity. This image shows main regions of the default mode network (yellow) and connectivity between the regions color-coded by structural traversing direction (xyz → rgb).

In neuroscience, the default mode network (DMN), also known as the default network, default state network, or anatomically the medial frontoparietal network (M-FPN), is a large-scale brain network primarily composed of the dorsal medial prefrontal cortex, posterior cingulate cortex, precuneus and angular gyrus. It is best known for being active when a person is not focused on the outside world and the brain is at wakeful rest, such as during daydreaming and mind-wandering. It can also be active during detailed thoughts related to external task performance. Other times that the DMN is active include when the individual is thinking about others, thinking about themselves, remembering the past, and planning for the future. The DMN creates a coherent "internal narrative" control to the construction of a sense of self.

The DMN was originally noticed to be deactivated in certain goal-oriented tasks and was sometimes referred to as the task-negative network, in contrast with the task-positive network. This nomenclature is now widely considered misleading, because the network can be active in internal goal-oriented and conceptual cognitive tasks. The DMN has been shown to be negatively correlated with other networks in the brain such as attention networks.

Evidence has pointed to disruptions in the DMN of people with Alzheimer's disease and autism spectrum disorder. Psilocybin produces the largest changes in areas of the DMN associated with neuropsychiatric disorders.

History

Hans Berger, the inventor of the electroencephalogram, was the first to propose the idea that the brain is constantly busy. In a series of papers published in 1929, he showed that the electrical oscillations detected by his device do not cease even when the subject is at rest. However, his ideas were not taken seriously, and a general perception formed among neurologists that only when a focused activity is performed does the brain (or a part of the brain) become active.

But in the 1950s, Louis Sokoloff and his colleagues noticed that metabolism in the brain stayed the same when a person went from a resting state to performing effortful math problems, suggesting active metabolism in the brain must also be happening during rest. In the 1970s, David H. Ingvar and colleagues observed blood flow in the front part of the brain became the highest when a person is at rest. Around the same time, intrinsic oscillatory behavior in vertebrate neurons was observed in cerebellar Purkinje cells, inferior olivary nucleus and thalamus.

In the 1990s, with the advent of positron emission tomography (PET) scans, researchers began to notice that when a person is involved in perception, language, and attention tasks, the same brain areas become less active compared to passive rest, and labeled these areas as becoming "deactivated".

In 1995, Bharat Biswal, a graduate student at the Medical College of Wisconsin in Milwaukee, discovered that the human sensorimotor system displayed "resting-state connectivity," exhibiting synchronicity in functional magnetic resonance imaging (fMRI) scans while not engaged in any task.

Later, experiments by neurologist Marcus E. Raichle's lab at Washington University School of Medicine and other groups showed that the brain's energy consumption is increased by less than 5% of its baseline energy consumption while performing a focused mental task. These experiments showed that the brain is constantly active with a high level of activity even when the person is not engaged in focused mental work. Research thereafter focused on finding the regions responsible for this constant background activity level.

Raichle coined the term "default mode" in 2001 to describe resting state brain function; the concept rapidly became a central theme in neuroscience. Around this time the idea was developed that this network of brain areas is involved in internally directed thoughts and is suspended during specific goal-directed behaviors. In 2003, Greicius and colleagues examined resting state fMRI scans and looked at how correlated different sections in the brain are to each other. Their correlation maps highlighted the same areas already identified by the other researchers. This was important because it demonstrated a convergence of methods all leading to the same areas being involved in the DMN. Since then other networks have been identified, such as visual, auditory, and attention networks. Some of them are often anti-correlated with the default mode network.

Until the mid-2000s, researchers labeled the default mode network as the "task-negative network" because it was deactivated when participants had to perform external goal-directed tasks. DMN was thought to only be active during passive rest and inactive during tasks. However, more recent studies have demonstrated the DMN to be active in certain internal goal-directed tasks such as social working memory and autobiographical tasks.

Around 2007, the number of papers referencing the default mode network skyrocketed. In all years prior to 2007, there were 12 papers published that referenced "default mode network" or "default network" in the title; however, between 2007 and 2014 the number increased to 1,384 papers. One reason for the increase in papers was the robust effect of finding the DMN with resting-state scans and independent component analysis (ICA). Another reason was that the DMN could be measured with short and effortless resting-state scans, meaning they could be performed on any population including young children, clinical populations, and nonhuman primates. A third reason was that the role of the DMN had been expanded to more than just a passive brain network.

Anatomy

Graphs of the dynamic development of correlations between brain networks. (A) In children the regions are largely local and are organized by their physical location; the frontal regions are highlighted in light blue. (B) In adults the networks become highly correlated despite their physical distance; the default network is highlighted in light red. This result is now believed to have been confounded by artifactual processes attributable to the tendency of younger subjects to move more during image acquisition, which preferentially inflates estimates of connectivity between physically proximal regions (Power 2012, Satterthwaite 2012).

The default mode network is an interconnected and anatomically defined set of brain regions. The network can be separated into hubs and subsections:

Functional hubs: Information regarding the self

• Posterior cingulate cortex (PCC) & precuneus: Combines bottom-up (not controlled) attention with information from memory and perception. The ventral (lower) part of PCC activates in all tasks which involve the DMN including those related to the self, related to others, remembering the past, thinking about the future, and processing concepts plus spatial navigation. The dorsal (upper) part of PCC involves involuntary awareness and arousal. The precuneus is involved in visual, sensorimotor, and attentional information.
• Medial prefrontal cortex (mPFC): Decisions about self-processing such as personal information, autobiographical memories, future goals and events, and decision making regarding those personally very close such as family. The ventral (lower) part is involved in positive emotional information and internally valued reward.
• Angular gyrus: Connects perception, attention, spatial cognition, and action and helps with parts of recall of episodic memories.

Dorsal medial subsystem: Thinking about others

• Functional hubs: PCC, mPFC, and angular gyrus
• Dorsal medial prefrontal cortex (dmPFC): Involved in social directed thought such as determining or inferring the purpose of others' actions
• Temporoparietal junction (TPJ): Reflects on beliefs about others, also known as theory of mind
• Lateral temporal cortex: Retrieval of social semantic and conceptual knowledge
• Anterior temporal pole: Abstract conceptual information particularly social in nature

Medial temporal subsystem: Autobiographical memory and future simulations

• Functional hubs: PCC, mPFC, and angular gyrus
• Hippocampus (HF+): Formation of new memories as well as remembering the past and imagining the future
• Parahippocampus (PHC): Spatial and scene recognition and simulation
• Retrosplenial cortex (RSC): Spatial navigation
• Posterior inferior parietal lobe (pIPL): Junction of auditory, visual, and somatosensory information and attention

The default mode network is most commonly defined with resting state data by putting a seed in the posterior cingulate cortex and examining which other brain areas most correlate with this area. The DMN can also be defined by the areas deactivated during external directed tasks compared to rest. Independent component analysis (ICA) robustly finds the DMN for individuals and across groups, and has become the standard tool for mapping the default network.

It has been shown that the default mode network exhibits the highest overlap in its structural and functional connectivity, which suggests that the structural architecture of the brain may be built in such a way that this particular network is activated by default. Recent evidence from a population brain-imaging study of 10,000 UK Biobank participants further suggests that each DMN node can be decomposed into subregions with complementary structural and functional properties. It has been a widespread practice in DMN research to treat its constituent nodes to be functionally homogeneous, but the distinction between subnodes within each major DMN node has mostly been neglected. However, the close proximity of subnodes that propagate hippocampal space-time outputs and subnodes that describe the global network architecture may enable default functions, such as autobiographical recall or internally-orientated thinking.

In the infant's brain, there is limited evidence of the default network, but default network connectivity is more consistent in children aged 9–12 years, suggesting that the default network undergoes developmental change.

Functional connectivity analysis in monkeys shows a similar network of regions to the default mode network seen in humans. The PCC is also a key hub in monkeys; however, the mPFC is smaller and less well connected to other brain regions, largely because human's mPFC is much larger and well developed.

Diffusion MRI imaging shows white matter tracts connecting different areas of the DMN together. The structural connections found from diffusion MRI imaging and the functional correlations from resting state fMRI show the highest level of overlap and agreement within the DMN areas. This provides evidence that neurons in the DMN regions are linked to each other through large tracts of axons and this causes activity in these areas to be correlated with one another. From the point of view of effective connectivity, many studies have attempted to shed some light using dynamic causal modeling, with inconsistent results. However, directionality from the medial prefrontal cortex towards the posterior cingulate gyrus seems confirmed in multiple studies, and the inconsistent results appear to be related to small sample size analysis.

Function

The default mode network is thought to be involved in several different functions:

It is potentially the neurological basis for the self:

• Autobiographical information: Memories of collection of events and facts about one's self
• Self-reference: Referring to traits and descriptions of one's self
• Emotion of one's self: Reflecting about one's own emotional state

Thinking about others:

• Theory of mind: Thinking about the thoughts of others and what they might or might not know
• Emotions of others: Understanding the emotions of other people and empathizing with their feelings
• Moral reasoning: Determining a just and an unjust result of an action
• Social evaluations: Good-bad attitude judgements about social concepts
• Social categories: Reflecting on important social characteristics and status of a group
• Social isolation: A perceived lack of social interaction

Remembering the past and thinking about the future:

• Remembering the past: Recalling events that happened in the past
• Imagining the future: Envisioning events that might happen in the future
• Episodic memory: Detailed memory related to specific events in time
• Story comprehension: Understanding and remembering a narrative
• Replay: Consolidating recently acquired memory traces

The default mode network is active during passive rest and mind-wandering which usually involves thinking about others, thinking about one's self, remembering the past, and envisioning the future rather than the task being performed. Recent work, however, has challenged a specific mapping between the default mode network and mind-wandering, given that the system is important in maintaining detailed representations of task information during working memory encoding. Electrocorticography studies (which involve placing electrodes on the surface of a subject's cerebral cortex) have shown the default mode network becomes activated within a fraction of a second after participants finish a task. Additionally, during attention demanding tasks, sufficient deactivation of the default mode network at the time of memory encoding has been shown to result in more successful long-term memory consolidation.

Studies have shown that when people watch a movie, listen to a story, or read a story, their DMNs are highly correlated with each other. DMNs are not correlated if the stories are scrambled or are in a language the person does not understand, suggesting that the network is highly involved in the comprehension and the subsequent memory formation of that story. The DMN is shown to even be correlated if the same story is presented to different people in different languages, further suggesting the DMN is truly involved in the comprehension aspect of the story and not the auditory or language aspect.

The default mode network is deactivated during some external goal-oriented tasks such as visual attention or cognitive working memory tasks. However, with internal goal-oriented tasks, such as social working memory or autobiographical tasks, the DMN is positively activated with the task and correlates with other networks such as the network involved in executive function. Regions of the DMN are also activated during cognitively demanding tasks that require higher-order conceptual representations. The DMN shows higher activation when behavioral responses are stable, and this activation is independent of self-reported mind wandering. Meditation, which involves focusing the mind on breathing and relaxation, is associated with reduced activity of the DMN.

Tsoukalas (2017) links theory of mind to immobilization, and suggests that the default network is activated by the immobilization inherent in the testing procedure (the patient is strapped supine on a stretcher and inserted by a narrow tunnel into a massive metallic structure). This procedure creates a sense of entrapment and, not surprisingly, the most commonly reported side-effect is claustrophobia.

Gabrielle et al. (2019) suggests that the DMN is related to the perception of beauty, in which the network becomes activated in a generalized way to aesthetically moving domains such as artworks, landscapes, and architecture. This would explain a deep inner feeling of pleasure related to aesthetics, interconnected with the sense of personal identity, due to the network functions related to the self.

Clinical significance

The default mode network has been hypothesized to be relevant to disorders including Alzheimer's disease, autism, schizophrenia, major depressive disorder (MDD), chronic pain, post-traumatic stress disorder (PTSD) and others. In particular, the DMN has also been reported to show overlapping yet distinct neural activity patterns across different mental health conditions, such as when directly comparing attention deficit hyperactivity disorder (ADHD) and autism.

People with Alzheimer's disease show a reduction in glucose (energy use) within the areas of the default mode network. These reductions start off as slight decreases in patients with mild symptoms and continue to large reductions in those with severe symptoms. Surprisingly, disruptions in the DMN begin even before individuals show signs of Alzheimer's disease. Plots of the peptide amyloid-beta, which is thought to cause Alzheimer's disease, show the buildup of the peptide is within the DMN. This prompted Randy Buckner and colleagues to propose the high metabolic rate from continuous activation of DMN causes more amyloid-beta peptide to accumulate in these DMN areas. These amyloid-beta peptides disrupt the DMN and because the DMN is heavily involved in memory formation and retrieval, this disruption leads to the symptoms of Alzheimer's disease.

DMN is thought to be disrupted in individuals with autism spectrum disorder. These individuals are impaired in social interaction and communication which are tasks central to this network. Studies have shown worse connections between areas of the DMN in individuals with autism, especially between the mPFC (involved in thinking about the self and others) and the PCC (the central core of the DMN). The more severe the autism, the less connected these areas are to each other. It is not clear if this is a cause or a result of autism, or if a third factor is causing both (confounding).

Although it is not clear whether the DMN connectivity is increased or decreased in psychotic bipolar disorder and schizophrenia, several genes correlated with altered DMN connectivity are also risk genes for mood and psychosis disorders.

Rumination, one of the main symptoms of major depressive disorder, is associated with increased DMN connectivity and dominance over other networks during rest. Such DMN hyperconnectivity has been observed in first-episode depression and chronic pain. Altered DMN connectivity may change the way a person perceives events and their social and moral reasoning, thus increasing their susceptibility to depressive symptoms.

Lower connectivity between brain regions was found across the default network in people who have experienced long-term trauma, such as childhood abuse or neglect, and is associated with dysfunctional attachment patterns. Among people experiencing PTSD, lower activation was found in the posterior cingulate gyrus compared to controls, and severe PTSD was characterized by lower connectivity within the DMN.

Adults and children with ADHD show reduced anticorrelation between the DMN and other brain networks. The cause may be a lag in brain maturation. More generally, competing activation between the DMN and other networks during memory encoding may result in poor long-term memory consolidation, which is a symptom of not only ADHD but also depression, anxiety, autism, and schizophrenia.

Modulation

The default mode network (DMN) may be modulated by the following interventions and processes:

• Acupuncture – Deactivation of the limbic brain areas and the DMN. It has been suggested that this is due to the pain response.
• Antidepressants – Abnormalities in DMN connectivity are reduced following treatment with antidepressant medications in PTSD.
• Attention Training Technique - Research shows that even a single session of Attention Training Technique changes functional connectivity of the DMN.
• Deep brain stimulation – Alterations in brain activity with deep brain stimulation may be used to balance resting state networks.
• Meditation – Structural changes in areas of the DMN such as the temporoparietal junction, posterior cingulate cortex, and precuneus have been found in meditation practitioners. There is reduced activation and reduced functional connectivity of the DMN in long-term practitioners. Various forms of nondirective meditation, including Transcendental Meditation and Acem Meditation, have been found to activate the DMN.
• Physical Activity and Exercise – Physical Activity, and more likely Aerobic Training, may alter the DMN. In addition, sports experts are showing networks differences, notably of the DMN.
• Psychedelic drugs – Reduced blood flow to the PCC and mPFC was observed under the administration of psilocybin. These two areas are considered to be the main nodes of the DMN. One study on the effects of LSD demonstrated that the drug desynchronizes brain activity within the DMN; the activity of the brain regions that constitute the DMN becomes less correlated.
• Psychotherapy – In PTSD, the abnormalities in the default mode network normalize in individuals who respond to psychotherapy interventions.
• Sleep deprivation – Functional connectivity between nodes of the DMN in their resting-state is usually strong, but sleep deprivation results in a decrease in connectivity within the DMN. Recent studies suggest a decrease in connectivity between the DMN and the task-positive network as a result of sleep loss.
• Sleeping and resting wakefulness
  • Onset of sleep – Increase in connectivity between the DMN and the task-positive network.
  • REM sleep – Possible increase in connectivity between nodes of the DMN.
  • Resting wakefulness – Functional connectivity between nodes of the DMN is strong.
  • Stage N2 of NREM sleep – Decrease in connectivity between the posterior cingulate cortex and medial prefrontal cortex.
  • Stage N3 of NREM sleep – Further decrease in connectivity between the PCC and MPFC.

Criticism

Some have argued the brain areas in the default mode network only show up together because of the vascular coupling of large arteries and veins in the brain near these areas, not because these areas are actually functionally connected to each other. Support for this argument comes from studies that show changing in breathing alters oxygen levels in the blood which in turn affects DMN the most. These studies however do not explain why the DMN can also be identified using PET scans by measuring glucose metabolism which is independent of vascular coupling and in electrocorticography studies measuring electrical activity on the surface of the brain, and in MEG by measuring magnetic fields associated with electrophysiological brain activity that bypasses the hemodynamic response.

The idea of a "default network" is not universally accepted. In 2007 the concept of the default mode was criticized as not being useful for understanding brain function, on the grounds that a simpler hypothesis is that a resting brain actually does more processing than a brain doing certain "demanding" tasks, and that there is no special significance to the intrinsic activity of the resting brain.

Nomenclature

The default mode network has also been called the language network, semantic system, or limbic network. Even though the dichotomy is misleading, the term task-negative network is still sometimes used to contrast it against other more externally-oriented brain networks.

In 2019, Uddin et al. proposed that medial frontoparietal network (M-FPN) be used as a standard anatomical name for this network.