The following is a guest post by John Garruto, school psychologist with the Oswego School District and member of the IQs Corner Virtual Community of Scholars. John reviewed the following article and has provided his comments below. [Blog dictator note - John's review is presented "as is" with only a few minor copy edits by the blog dicator
Willingham, D.T. (2007). Critical Thinking: Why Is It So Hard to Teach? American Educator, 31(2), 8-19.
Guilty as charged.Frequently my copy of American Educator makes it to the trash before I have cracked the cover. I have tried to read it a few times, but typically have found that it often did not meet my professional needs or interests. As I was eating breakfast this morning I could not help but note a statue of Rodin’s “The Thinker” and an enticing lead-in on the cover asking the question--“Can Critical Thinking Be Taught?” My curiosity had been substantially piqued, so I took the magazine downstairs to read with my Sunday coffee.
This article was a delight to read. Willingham elaborated on the characteristics of many failed critical thinking programs of the past, given that they only tended to focus on reasoning in and of itself and not without the necessary background knowledge. Examples included Feuerstein’s Instrumental Enrichment, Covington’s Productive Thinking, or de Bono’s Cognitive Research Trust (p. 12). However, the shortcomings of such programs relate to the difficulty in the importance of using a student’s background knowledge to reason out new conclusions.
He gives an interesting example--a story problem relating a marching band with rows of 12, leaving one person behind, then a reconfiguration of columns of 8, with one person behind, and finally rows of 3 with one person behind. The person behind said rows of five would take care of the problem. The problem indicated there were at least 45 musicians on the field, but fewer than 200…how many students were there? Willingham noted that few people answered the question correctly because they tended to focus on marching bands, rather than the math behind the problem. He also indicated that the problem before (one relating to vegetables) could be solved in much of the same way. When students were clued into that fact--more of them answered correctly (by the way-my guess to the answer is 145-the answer would have to be N/5 would have a remainder of zero and N-1 must be divisible by 12, 8, and 3…I found the problem easier to solve once I stopped thinking of it as a story.) He indicates that by looking beyond the surface structure, one can master things so much more easily.
Three thoughts came flooding to my cortex. The first was Horn’s final chapter in CIA2 (see my prior post) particularly related to expertise abilities. His regular examples of playing chess by two different processes (he goes into inductive primarily and then transformed to deductive when expert) can also be conceptualized by looking at things from the surface and deep structure perspectives.
My second thought was how this is related to schema theory and set shifting--something I wish the article had addressed. Typically we use our background knowledge (and we need it) to think critically. However, another important component we need is the ability not to view what we see so rigidly. Sometimes we must alter our cognitive paradigms to reach the answer (consider the above problem).
Finally, I thought about the essence of the article in terms of CHC theory. Clearly, an important component to critical thinking (particularly as it pertains to later secondary and post-secondary education) is not simply Gc-but it is Gc X Gf…or a Gc/Gf interaction. Only by using both ability domains in combination can we see relationships, draw conclusions, and then write or comment using persuasive arguments. It’s an interesting idea for an Aptitude-Treatment Interaction (ATI) research study--I predict that one might need both high Gc and Gf to meaningfully affect a dependent variable we might call “critical thinking”. By the way, for what it’s worth, I used my background knowledge of human cognition and my inductive abilities of overlap to draw conclusions and write this blog entry--it seems Willingham could be right.
I will be careful not to hastily throw out my copy of American Educator so quickly in the future.
Quick FYI email blog post. Aside from the studies main findings, I found the use an AI self-efficacy scale intriguing…a form of self-efficacy that likely will be included in more and more studies…and should be monitored in the increasing volume of AI intervention studies. My interest comes from the inclusion of self-efficacy under self-beliefs, along with motivational achievement orientations and self-regulated learning strategies, in my recent article describing the cognitive-affective-motivation model of learning (CAMML: McGrew, 2022;click hereto view/download).
Good news…and open access article available at link below.👍
The expanding role of artificial intelligence (AI) in education raises important questions about how AI-supported learning may foster higher-order thinking and creative talent development. Guided by social cognitive theory, the current research examined how AI self-efficacy predicts creativity among second language (L2) learners through the mediating roles of AI literacy and critical thinking disposition. Two substudies were conducted. Study 1 (N= 72) tested a simple mediation model and demonstrated that AI self-efficacy positively predicted creativity both directly and indirectly through AI literacy. Study 2 (N= 135) extended these findings by incorporating critical thinking disposition and by using another measure of creativity. Results showed that AI self-efficacy positively predicted creativity, and this relationship was mediated independently by AI literacy and critical thinking disposition, as well as sequentially through both factors. The current study provides empirical evidence for pathways linking AI self-efficacy, AI literacy, critical thinking disposition, and creativity in AI-supported L2 learning. It highlights the importance of reflective and critical use of AI tools in language education.
This study examined the relationship between creative self-beliefs and critical thinking disposition using a network analysis approach. The sample comprised 672 final-year undergraduates who completed the Short Scale of Creative Self (SSCS) and the Critical Thinking Disposition Scale (CTDS). A regularized partial correlation network estimated via EBICglasso revealed that the two domains were largely organized into distinct but weakly connected communities. Although cross-construct associations were generally small, bridge centrality analyses identified specific items—particularly those reflecting openness to new ideas and perceived capacity to cope with complex situations—as key connectors between the two systems. Classical centrality indices further indicated that creative personal identity constituted the structural core of the creative self-beliefs network, whereas reflective self-monitoring emerged as central within critical thinking disposition. Community detection analysis further supported a two-community structure consistent with partial segregation between constructs. Overall, the findings suggest that creative self-beliefs and critical thinking disposition function as relatively differentiated yet selectively integrated systems. These results highlight the importance of targeting specific bridge processes when designing educational interventions aimed at fostering both creative and critical thinking in higher education.
Pardon typos and spelling errors-Message may be sent from iPhone and I've always had spelling problems :)
Daniel Kahneman on the Trap of 'Thinking That We Know'
The National Academy of Sciences did a great service to science early this week by holding a conference on "The Science of Science Communication." A centerpiece of the two-day meeting was a lecture titled "Thinking That We Know," delivered by Daniel Kahneman, the extraordinary behavioral scientist who was awarded a Nobel Prize in economics despite never having taken an economics class.
The talk is extraordinary for the clarity (and humor) with which he repeatedly illustrates the powerful ways in which the mind filters and shapes what we call information. He discusses how this relates to the challenge of communicating science in a way that might stick.
Please carve out the time to watch his slide-free, but image-rich, talk. It's a shorthand route to some of the insights described in Kahneman's remarkable book, "Thinking, Fast and Slow" (I'm a third of the way through).
Here's the video of the talk (which is "below the fold" because it's set up to play automatically):
. As I noted via Twitter during the meeting, this talk and many other engaging presentations at the event illustrate the importance of adding a fresh facet to the popular notion that today's citizens, and particularly students, would do well to improve their capacity for critical thinking:
"Critical thinking has to include assessing one's own thinking."
[Note. Original post on March 6, 2017 has now been updated (March 7, 2017) to include reference to research in Spain]
The CHC taxonomy is officially a
globetrotter with a large bank of frequent flier miles. An indicator of the increasing prominence
and spread of the CHC taxonomy is reflected in the globalization of CHC
assessment activities in countries beyond the United States. Several examples, which are not exhaustive,
are summarized below.
The influence of CHC theory, primarily
via university assessment training in the use of the CHC-based Batería III Woodcock-Munoz(BAT III; Muñoz-Sandoval, Woodcock,
McGrew, Mather, N. (2005a, 2005b), is prominent in Spanish speaking countries
south of the US border. This includes
training, research or clinical use of the BAT III in Cuba, Mexico, Chile, Costa
Rica, Panama, and Guatemala.[1] Farther south, researchers in Brazil were
early adopters of the CHC taxonomy as a guide for intelligence test development
(Primi, 2003; Wechsler & de Cassia Nakano, 2016). For example, Wechsler and colleagues
(Wechsler & Schelini, 2006; Wechsler Nunes, Schelini, Pasian, Homsi,
Moretti, & Anache, 2010; Wechsler, Vendramini, & Schelini, 2007)
completed several studies in an attempt to adapt the CHC-based WJ III to
Brazil. More recently, Wechsler,
Vendramini, Schelini, Lourenconi, de Souza and Bundim (2014) developed the Brazilian Adult Intelligence Battery (BAIB),
which although only measuring Gf and Gc, is grounded in CHC theory. Other Brazilian researchers have focused on
the nature and measurement of Gf (Primi, Maria Ferrã, Almeida, 2010; Primi,
2014) with their research clearly couched in the context of the CHC model. Even broader in scope, I (Kevin McGrew)
together with Dr. Joel Schneider consulted with researchers from the Brazilian Ayrton Senna Institute (from
2016 to 2017) on the use of the CHC model as the key cognitive ability
framework for developing measures of critical thinking and creativity as per
the Organization for Economic Cooperationand Development (OECD, 2016) efforts to develop 21st century skills in
students.
CHC influences are also present north
of the US border in Canada. The
CHC-based WJ III has been used by practitioners in Canada based on a
US-Canadian matched sampled comparison study (Ford, Swart, Negreiros, Lacroix
& McGrew, 2010). The WJ IV is also
sold and used in Canada. Additionally, a
school-based group administered CHC test (Insight;
Beal, 2011) measuring Gf, Gc, Gv, Ga, Gwm, Glr, Gs, CDS (Gt) is available in
Canada. CHC theory and testing has a
prominent place in school psychology assessment courses in several major
Canadian universities (e.g., University of British Columbia; University of
Alberta).[2]
One of first systematic global CHC test
development outreach project was efforts, led by Richard Woodcock and the Woodcock-Munoz Foundation, to provide
Eastern European countries with cost-effective briefer versions of the
CHC-based Woodcock-Johnson Tests of Cognitive
Ability—Third Edition. The WJ III-IE (international editions)
projects started in the early 2000’s and continued until approximately
2015. WJ III-IE norming efforts occurred in the Czech Republic, Hungary,
Latvia, Romania, and Slovakia. Other
European efforts include the development of the Austrian-developed computerized
Intelligence
Structure Battery (INSBAT; Arendasy, Hornket, Sommer, Wagner-Menghin,
Gittler, Hausler, Bongnar, & Wenzl, 2012) that measures six broad CHC
abilities (Gf, Gq, Gc, Gwm, Gv, Glr).
The spread of CHC theory has also reached France and Spain. French researchers have
analyzed French versions of the various Wechsler scales from the perspective of
the CHC framework (e.g., see Golay, Reverte, Rossier J, Favez N and Lecerf,
2013; also, Lecerf, Rossier, Favez, Revert and Coleaux, 2010). In Spain, researchers in computer science education have used the CHC taxonomy to analyze the components of the Computational Thinking Test (CTt; Roman-Gonzalez, Perez-Gonzalez, Jimenez-Fernandez, 2016). German intelligence research has also been
influenced by the CHC model (e.g., see Baghaei & Tabatabaee, 2015) as best
illustrated by its incorporation in the popular German-based Berlin Intelligence Structure (BIS)
program of research literature (Beauducel, Brocke & Liepmann, 2001; SÜß
& Beauducel, 2015; Vock, Preckel, Holling, 201X), Additionally, the Wuerzburger Psychologische Kurz-Diagnostik (WUEP-KD), a
neuropsychological battery used in German speaking countries, is grounded in
the CHC model (Ottensmeier, Zimolong, Wolff, Ehrich, Galley, von Hoff , Kuehl
and Rutkowski, 2015).
Additional emerging CHC outposts in
northern Europe include the Netherlands and Belgium. Hurksa and Bakker (2016) reviewed the
influence of CHC theory, as well as the neuropsychological PASS theory, in an
article providing a historical overview of intelligence testing efforts in the
Netherlands. A strong indicator of the
growing interest in CHC theory was a CHC theory and assessment conference (New angles on intelligence! A closer look on
the CHC–model) at Thomas More University, Antwerp Belgium, in February
2015. Faculty at Thomas More University
have developed a CHC assessment battery (CoVaT-CHC)
for children in Flanders that measures the CHC domains of Gf, Gc, Gv, Gwm, and
Gs.
Transported via the Chunnel to the
United Kingdom and Northern Ireland, the CHC flame has been lit, but has not yet resulted in
significant CHC test development. In the
1990’s the WJ III author team was consulted to develop Irish norms for the WJ
III. One of the WJ III authors (Fred
Schrank) visited and consulted at several universities in Ireland (University
College Dublin, in particular) and continues to do so regarding the WJ IV (Fred
Schrank, personal communication, March 2, 2017). The CHC theory is now the dominant cognitive
taxonomy taught in psychology departments (Trevor James, personal
communication, March 3, 2017).
Traveling to the Middle East, known CHC
activities have been occurring in Jordan and Turkey. Under the direction of Bashir Abu-Hamour (Abu-Hamour,
2014; Abu-Hamour, Hmouz, Mattar & Muhaidat, 2012;), the CHC-based WJ III
has received considerable attention and the WJ IV was recently translated,
adapted, and nationally normed in Jordan (Abu-Hamour & Al-Hmouz,
2017). In Turkey, the first national
intelligence test (Anatolu-Sak
Intelligence Scale; ASIS) was developed between 2015-2017. Although the ASIS composite scores are not
couched in the CHC nomenclature, the theories listed as the foundation for the
Turkish ASIS are general intelligence, CHC and PASS. Additionally, I (Kevin McGrew) worked with
two universities in 2016 in the preparation of government sponsored grant
proposals for additional national intelligence test development in Turkey, both
that proposed to use the CHC taxonomy.
Pivoting toward Asia and the world
“down under” reveals major CHC test development efforts. Since the publication of the CHC-based WJ III
several key universities and an Australian publisher have delved deep into CHC
theory and assessment. Psychological
Assessments Australia (PAA) has translated, adapted, and normed the
CHC-based WJ III and WJ IV in Australia and New Zealand. The Melbourne area has been a particular
flash point for CHC training and research.
Neuropsychologist and researcher Stephen Bowden and his students at the
University of Melbourne have produced a series of multiple sample confirmatory
factor analysis studies with markers of CHC abilities to investigate the
constructs measured by neuropsychological tests measures. The University of Monash, initially under the
direction of John Roodenburg, and subsequently by his students, placed the CHC
model at the core of their assessment course sequence and have influenced in
the infusion of the CHC framework into the assessment practices of Australian
psychologists (James, Jacobs, Roodenburg, 2015).
Finally, one of the most ambitious CHC
test development projects has been occurring in Indonesia since 2013. Sponsored and directed by the Yayasan Dharma Bermakna Foundation (YDB), a nationally normed (over 4000 individuals) individually
administered CHC-based battery of tests for school age children (ages 5-18) is,
at the time of this writing, nearing completion. The AJT Cognitive Assessment Test (AJT-CAT) will be one of the most
comprehensive individually administered tests of cognitive abilities in the
world. The AJT-CAT currently consists of 27 individual cognitive tests
designed to measure 21 different narrow CHC abilities (and two psychomotor
tests to screen for motor difficulties) and preliminary confirmatory factor
analysis indicated that the battery measure eight broad CHC cognitive domains
(Gf, Gc, Gv, Gwm, Ga, Gs, Gl, Gr) and the Gp motor domain.[3]
[1] Thanks to Dr.
Todd Fletcher for providing this information.
[2] Thanks to Laurie
Ford and Damien Cormier for this information.
[3] Kevin McGrew has served as the CHC and applied psychometric expert
consultant on this project and helped complete these preliminary structural
analyses.
Man has always known that the brain is the center of human behavior. Early attempts at understanding which locations in the brain controlled different functions were non-scientific and included such practices as phrenology. This pseudoscience believed that by feeling the bumps of a persons head it was possible to draw conclusions about specific brain functions and traits of the person.
(double click on any image to enlarge)
Eventually brain science revealed that different regions of the brain where specialized for different specific cognitive processes (but it was not related to the phrenological brain bump maps). This has been called the modular or functional specialization view of the brain, which is grounded in the conclusion that different brain areas acted more-or-less as independent mechanisms for completing specific cognitive functions.
One of the most exciting developments in contemporary neuroscience is the recognition that the human brain processes information via different brain circuits or loops which at a higher level can be studied as large scale brain networks. Although the modular view still provides important brain insights, the accumulating evidence suggests that it has serious limitations and might in fact be misleading (Bresslor and Menon, 2010). One of the best summaries of this cutting edge research is that by Bresslor and Menon.
Large scale brain network research suggests that congitive functioning is the result of interactions or communication between different brain systems distributed throughout the brain. That is, when performing a particular task, just one isolated brain area is not working alone. Instead, different areas of the brain, often far apart from each other within the geogrpahic space of the brain, are communicating through a fast-paced sychronized set of brain signals. These networks can be considered preferred pathways for sending signals back and forth to perform a specific set of cognitive or motor behaviors.
To understand preferred neural pathways, think of walking on a college campus where there are paved sidewalks connecting different buildings that house specialized knowledge and activities. If you have spent anytime on a college campus, one typically finds foot-worn short cuts in the grass that are the preferred (and more efficient) means by which most people move between building A and B. The combined set of frequently used paved and unpaved pathways are the most efficient or preferred pathways for moving efficiently between buildings. The human brain has developed preferred communication pathays that link together different brain circuits or loops in order to quickly and efficiently complete specific tasks.
According to Bresslor and Menon (2010), “a large-scale functional network can therefore be defined as a collection of interconnected brain areas that interact to perform circumscribed functions.” More importantly, component brain areas in these large-scale brain networks perform different roles. Some act as controllers or task switchers that coordinate, direct and synchronize the involvement of other brain networks. Other brain networks handle the flow of sensory or motor information and engage in concious manipulation of the information in the form of “thinking.”
As illustrated in the figure above, neuroscientists have identified a number of core brain network nodes or circuits. The important new insight is that these various nodes or circuits are integrated together into a grander set of higher-level core functional brain networks. Three important core networks are receiving considerable attention in explaining human beavhior.
Major functional brain networks
The default mode (DMN) or default brain network (shown in blue) is what your brain does when not engaged in specific tasks. It is the busy or active part of your brain when you are mentally passive. According to Bresslor and Brennon the “DMN is seen to collectively comprise an integrated system for autobiographical, self-monitoring and social cognitive functions.” It has also been characterized as responsible for REST (rapid episodic spontaneous thinking). In other words, this is the spontaneous mind wandering and internal self-talk and thinking we engage in when not working on a specific task or, when completing a task that is so automatized (e.g., driving a car) that our mind starts to wander and generate spontaneous thoughts. As I have discussed previously (at IM-HOME blog), the default network is responsible for the unquiet or noisy mind. And, it is likely that people differ in amount of spontaneous mind wandering (which can be both positive creative thinking or distracting thoughts), with some having a very unquiet mind that is hard to turn off, while others can turn off the inner thought generation and self-talk and display tremendous self-focus or controlled attention to perform a cognitively or motorically demanding task. A very interesting discussion of the serendipitous discovery and explanation of the default brain network is in the following soon to be published scientific article.
The salience network (shown in yellow) is a controllor or network switcher. It monitors information from within (internal input) and from the external world arounding us, which is constantly bombarding us with information. Think of the salience network as the air traffic controllor of the brain. Its job is to scan all information bombarding us from the outside world and also that from within our own brains. This controller decides which information is most urgent, task relevant, and which should receive priority in the que of sending brain signals to areas of the brain for processing. This controlling network must suppress either the default or executive networks depending on the task at hand. It must supress one, and activiate the other. Needless to say, this decision making and distribution of information must require exquisite and efficienct neural timing as regulated by the brain clock(s).
Finally, the central-executive network (CEN; shown in red) “is engaged in higher-order cognitive and attentional control.” In other words, when you must engage your concious brain to work on a problem, place information in your working memory as you think, focus your attention on a task or problem, etc., you are “thinking” and must focus your controlled attention. As I understand this research, the salience or controller network is a multi-switching mechanism that is constantly initiating dynamic switching between the REST (sponatenous and often creative unquie mind wandering) and thinking networks to best match the current demands you are facing.
According to Bresslor and Melon, not only is this large scale brain network helping us better understand normal cognitive and motor behavior, it is providing insights into clinical disorders of the brain. Poor synchronization between the three major brain networks has been implicated in Alzheimer’s, schizophrenia, autism, the manic phase of biploar and Parkinson’s (Bresslor and Melon, 2010), disorders that have all been linked to a brain or neural timing (i.e, the brain clock or clocks). I also believe that ADHD would be implicated. If the synchronized milli-second based communicaiton between and within these large networks is compromised, and if the network traffic controller (the salience network) is disrupted in particular, efficient and normal cognition or motor behavior can be compromised.
I find this emerging research fascinating. I believe it provides a viable working hypothesis to explain why different brain fitness or training neurotechnologies have shown promise in improving cognitive function in working memory, ADHD, and other clinical disorders. It is my current hypothesis that various brain training technologies may focus on different psychological constructs (e.g., working memory; planning; focus or controlled attention), but their effectiveness may all be directly or indirectly facilitating the sychronization between the major brain networks. More specifically, by strengthing the ability to invoke the salience or controller network, a person can learn to supress, inhibit or silence the REST-producing default brain network more efficiently, long enough to exert more controlled attention or focus when invoking the thinking central executive network. Collectively these brain fitness techonologies may all improving the use of those abilities called executive function, or what I have called the personal brain manager. Those technologies that focus on rhythm or brain timing are those I find most fascinating. For example, the recent example of the use of melodic intonation therapy with Congresswoman Gabby Giffords (she suffered serious brain trauma due to a gun shot) demonstrates how rhythm-based brain timing therapies may help repair destroyed preferred and efficient neural pathways or, develop new pathways, much like the development of a new foot worn pathway in the grass on a college campus if a preferred pathway is disrupted by a new building, temporary work or rennovation, or some other destruction of a preferred and efficient network of movement path.
To understand the beauty of the synchronized brain, it is best to see the patterns of brain network connections in action. Below is a video called the “Meditating Mind.” I urge you to view the video for a number of reasons.
A number of observations should be clear. First, during the first part of the video the brain is seen as active even during a resting state. This is visual evidence of the silent private dialouge (REST) of the default mode or network of the brain. Next, the video mentions the rhythm of increased and decreased neural activation as the brain responds to no visual information or presentation of a video. The changes in color and sound demonstrate the rich rhthymic sychronization of large and different parts of the brain, depending on whether the brain is engaged in a passive or active cognitive task. The beauty of the rapidly changing and spreading communication should make it obvious that efficient rhythmic synchronization of timing of brain signals to and from different networks or ciruits is critical to efficient brain functioning.
Finally, the contrast between the same brain under normal conditions and when engaged in a form of meditation is striking. Clearly when this person’s brain is mediating, the brain is responding with a change in rates and frequency of brain network activation and synchrony. As I described in my personal IM-HOME based experience post, mastering Interactive Metronome (IM) therapy requires “becoming one with the tone”…which sounds similar to the language of those who engage in various forms of meditation. Could it be that the rhythmic demans of IM, which require an individual to “lock on” to the auditory tone and stay in that synchronized, rhythmic and repetitive state for as long as possible, might be similar to the underlying mechanics of some forms of meditation, which also seek to suppress irrelevant and distracting thoughts and eventually “let the mind go"---posibbly to follow a specific train of thought with complete and distraction free focus.
Yes…this is speculation. I am trying to connect research-based and personal experience dots. It is exciting. My IM-HOME based induce personal focus experience makes sense from the perspective of the function and interaction between the three major large scale brain networks.
[This is an OBG (oldie but goodie) post first posted December 16, 2011 at the Brain Clock blog]
Man has always known that the brain is the center of human behavior. Early attempts at understanding which locations in the brain controlled different functions were non-scientific and included such practices as phrenology. This pseudoscience believed that by feeling the bumps of a persons head it was possible to draw conclusions about specific brain functions and traits of the person.
(double click on any image to enlarge)
Eventually brain science revealed that different regions of the brain where specialized for different specific cognitive processes (but it was not related to the phrenological brain bump maps). This has been called the modular or functional specialization view of the brain, which is grounded in the conclusion that different brain areas acted more-or-less as independent mechanisms for completing specific cognitive functions.
One of the most exciting developments in contemporary neuroscience is the recognition that the human brain processes information via different brain circuits or loops which at a higher level can be studied as large scale brain networks. Although the modular view still provides important brain insights, the accumulating evidence suggests that it has serious limitations and might in fact be misleading (Bresslor and Menon, 2010). One of the best summaries of this cutting edge research is that by Bresslor and Menon.
Large scale brain network research suggests that cognitive functioning is the result of interactions or communication between different brain systems distributed throughout the brain. That is, when performing a particular task, just one isolated brain area is not working alone. Instead, different areas of the brain, often far apart from each other within the geographic space of the brain, are communicating through a fast-paced synchronized set of brain signals. These networks can be considered preferred pathways for sending signals back and forth to perform a specific set of cognitive or motor behaviors.
To understand preferred neural pathways, think of walking on a college campus where there are paved sidewalks connecting different buildings that house specialized knowledge and activities. If you have spent anytime on a college campus, one typically finds foot-worn short cuts in the grass that are the preferred (and more efficient) means by which most people move between building A and B. The combined set of frequently used paved and unpaved pathways are the most efficient or preferred pathways for moving efficiently between buildings. The human brain has developed preferred communication pathways that link together different brain circuits or loops in order to quickly and efficiently complete specific tasks.
According to Bresslor and Menon (2010), “a large-scale functional network can therefore be defined as a collection of interconnected brain areas that interact to perform circumscribed functions.” More importantly, component brain areas in these large-scale brain networks perform different roles. Some act as controllers or task switchers that coordinate, direct and synchronize the involvement of other brain networks. Other brain networks handle the flow of sensory or motor information and engage in conscious manipulation of the information in the form of “thinking.”
As illustrated in the figure above, neuroscientists have identified a number of core brain network nodes or circuits. The important new insight is that these various nodes or circuits are integrated together into a grander set of higher-level core functional brain networks. Three important core networks are receiving considerable attention in explaining human behavior.
Major functional brain networks
The default mode (DMN) or default brain network (shown in blue) is what your brain does when not engaged in specific tasks. It is the busy or active part of your brain when you are mentally passive. According to Bresslor and Brennon the “DMN is seen to collectively comprise an integrated system for autobiographical, self-monitoring and social cognitive functions.” It has also been characterized as responsible for REST (rapid episodic spontaneous thinking). In other words, this is the spontaneous mind wandering and internal self-talk and thinking we engage in when not working on a specific task or, when completing a task that is so automatized (e.g., driving a car) that our mind starts to wander and generate spontaneous thoughts. As I have discussed previously (at IM-HOME blog), the default network is responsible for the unquiet or noisy mind. And, it is likely that people differ in amount of spontaneous mind wandering (which can be both positive creative thinking or distracting thoughts), with some having a very unquiet mind that is hard to turn off, while others can turn off the inner thought generation and self-talk and display tremendous self-focus or controlled attention to perform a cognitively or motorically demanding task. A very interesting discussion of the serendipitous discovery and explanation of the default brain network is in the following soon to be published scientific article.
The salience network (shown in yellow) is a controller or network switcher. It monitors information from within (internal input) and from the external world arounding us, which is constantly bombarding us with information. Think of the salience network as the air traffic controller of the brain. Its job is to scan all information bombarding us from the outside world and also that from within our own brains. This controller decides which information is most urgent, task relevant, and which should receive priority in the que of sending brain signals to areas of the brain for processing. This controlling network must suppress either the default or executive networks depending on the task at hand. It must suppress one, and activate the other. Needless to say, this decision making and distribution of information must require exquisite and efficient neural timing as regulated by the brain clock(s).
Finally, the central-executive network (CEN; shown in red) “is engaged in higher-order cognitive and attentional control.” In other words, when you must engage your conscious brain to work on a problem, place information in your working memory as you think, focus your attention on a task or problem, etc., you are “thinking” and must focus your controlled attention. As I understand this research, the salience or controller network is a multi-switching mechanism that is constantly initiating dynamic switching between the REST (sponatenous and often creative unique mind wandering) and thinking networks to best match the current demands you are facing.
According to Bresslor and Melon, not only is this large scale brain network helping us better understand normal cognitive and motor behavior, it is providing insights into clinical disorders of the brain. Poor synchronization between the three major brain networks has been implicated in Alzheimer’s, schizophrenia, autism, the manic phase of bipolar and Parkinson’s (Bresslor and Melon, 2010), disorders that have all been linked to a brain or neural timing (i.e, the brain clock or clocks). I also believe that ADHD would be implicated. If the synchronized millisecond based communication between and within these large networks is compromised, and if the network traffic controller (the salience network) is disrupted in particular, efficient and normal cognition or motor behavior can be compromised.
I find this emerging research fascinating. I believe it provides a viable working hypothesis to explain why different brain fitness or training neurotechnologies have shown promise in improving cognitive function in working memory, ADHD, and other clinical disorders. It is my current hypothesis that various brain training technologies may focus on different psychological constructs (e.g., working memory; planning; focus or controlled attention), but their effectiveness may all be directly or indirectly facilitating the sychronization between the major brain networks. More specifically, by strengthening the ability to invoke the salience or controller network, a person can learn to suppress, inhibit or silence the REST-producing default brain network more efficiently, long enough to exert more controlled attention or focus when invoking the thinking central executive network. Collectively these brain fitness technologies may all improving the use of those abilities called executive function, or what I have called the personal brain manager. Those technologies that focus on rhythm or brain timing are those I find most fascinating. For example, the recent example of the use of melodic intonation therapy with Congresswoman Gabby Giffords (she suffered serious brain trauma due to a gun shot) demonstrates how rhythm-based brain timing therapies may help repair destroyed preferred and efficient neural pathways or, develop new pathways, much like the development of a new foot worn pathway in the grass on a college campus if a preferred pathway is disrupted by a new building, temporary work or rennovation, or some other destruction of a preferred and efficient network of movement path.
To understand the beauty of the synchronized brain, it is best to see the patterns of brain network connections in action. Below is a video called the “Meditating Mind.” I urge you to view the video for a number of reasons.
A number of observations should be clear. First, during the first part of the video the brain is seen as active even during a resting state. This is visual evidence of the silent private dialogue (REST) of the default mode or network of the brain. Next, the video mentions the rhythm of increased and decreased neural activation as the brain responds to no visual information or presentation of a video. The changes in color and sound demonstrate the rich rhythmic synchronization of large and different parts of the brain, depending on whether the brain is engaged in a passive or active cognitive task. The beauty of the rapidly changing and spreading communication should make it obvious that efficient rhythmic synchronization of timing of brain signals to and from different networks or circuits is critical to efficient brain functioning.
Finally, the contrast between the same brain under normal conditions and when engaged in a form of meditation is striking. Clearly when this person’s brain is mediating, the brain is responding with a change in rates and frequency of brain network activation and synchrony. As I described in my personal IM-HOME based experience post, mastering Interactive Metronome (IM) therapy requires “becoming one with the tone”…which sounds similar to the language of those who engage in various forms of meditation. Could it be that the rhythmic demans of IM, which require an individual to “lock on” to the auditory tone and stay in that synchronized, rhythmic and repetitive state for as long as possible, might be similar to the underlying mechanics of some forms of meditation, which also seek to suppress irrelevant and distracting thoughts and eventually “let the mind go"---posibsly to follow a specific train of thought with complete and distraction free focus.
Yes…this is speculation. I am trying to connect research-based and personal experience dots. It is exciting. My IM-HOME based induce personal focus experience makes sense from the perspective of the function and interaction between the three major large scale brain networks.
The following is a guest post by John Garruto, school psychologist with the Oswego School District and member of the IQs Corner Virtual Community of Scholars. John reviewed the following article and has provided his comments below. [Blog dictator note - John's review is presented "as is" with only a few minor copy edits by the blog dicator
