Research Byte: The #cultural construction of #executivefunction - EF tasks reflect culturally-specific forms of #cognitive development - #cognition #schoolpsychology #schoolpsychologists #neuropsych

The cultural construction of “executive function”.  PNAS:  Research Article:  Psychological and Cognitive Sciences.  This is open access and can be downloaded or read online.  Link here.

Ivan Kroupin , Helen Elizabeth Davis, Emily Burdette, Agustina Bani Cuataf, Vahumburuka Hartley, and Joseph Henrich

Abstract

In cognitive science, the term “executive function” (EF) refers to universal features of the mind. Yet, almost all results described as measuring EF may actually reflect culturally specific cognitive capacities. After all, typical EF measures require forms of decontextualized/arbitrary processing which decades of cross-cultural work indicate develop primarily in “schooled worlds”–industrialized societies with universal schooling. Here, we report comparisons of performance on typical EF tasks by children inside, and wholly outside schooled worlds. Namely, children ages 5 to 18 from a postindustrial context with universal schooling (UK) and their peers in a rural, nonindustrialized context with no exposure to schooling (Kunene region, Namibia/Angola), as well as two samples with intermediate exposure to schooled worlds. In line with extensive previous work on decontextualized/arbitrary processing across such groups, we find skills measured by typical EF tasks do not develop universally: Children from rural groups with limited or no formal schooling show profound, sometimes qualitative, differences in performance compared to their schooled peers and, especially, com-pared to a “typical” schooled-world sample. In sum, some form of latent cognitive control capacities are obviously crucial in all cultural contexts. However, typical EF tasks almost certainly reflect culturally specific forms of cognitive development. This suggests we must decide between using the term EF to describe 1) universal capacities or 2) the culturally specific skill set reflected in performance on typical tasks. Either option warrants revisiting how we understand what has been measured as EF to date, and what we wish to measure going forward.

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Research Byte: A longitudinal study of adolescent-to-young adult #executivefunction development in seven countries - #cognition #selfregulation #schoolpsychology #neuropsychology #developmental

A Longitudinal Study of Adolescent-to-Young Adult Executive Function Development in Seven Countries.  Developmental Science.  Sorry, this is not an open access article you can download

Abstract

Executive functioning (EF) is an important developing self-regulatory process that has implications for academic, social, and emotional outcomes. Most work in EF has focused on childhood, and less has examined the development of EF throughout adolescence and into emerging adulthood. The present study assessed longitudinal trajectories of EF from ages 10 to 21 in a diverse, international sample. 1093 adolescents (50.3% female) from eight locations in seven countries completed computerized EF tasks (Stroop, Tower of London [ToL], Working Memory [WM]) at ages 10, 14, 17, and 21. Latent growth curve models were estimated to understand the average performance at age 10 and the change in performance over time for each task. Meta-analytic techniques were used to assess the heterogeneity in estimates between study sites. On average, EF task performance improved across adolescence into young adulthood with substantial between-site heterogeneity. Additionally, significant individual differences in EF task performance at age 10 and change in EF task performance over time characterized the full sample. EF improves throughout adolescence into young adulthood, making it a potentially important time for intervention to improve self-regulation.

Research Byte: Performance- and report-based measures of #executivefunction as predictors of children’s #academic skills - #neuropsychology #schoolpsychology

DeCamp, C., Alfonso, S. V., & Lonigan, C. J. (2025). Performance- and report-based measures of executive function as predictors of children’s academic skills. Neuropsychology, 39(3), 214–222. https://doi.org/10.1037/neu0000992

Abstract

Objective: Executive function (EF) is thought to be a core component of various cognitive processes. Two common ways to measure EF are through report-based measures that assess EF by collecting informant(s) reports on children’s behaviors and performance-based measures that assess EF through the completion of a task related to EF dimension(s). However, most research reports low associations between these measures. The goal of this study was to determine the unique and overlapping contributions of a report- and a performance-based measure of EF on children’s academic outcomes over time. Method: The sample consisted of 1,152 children (636 boys, 516 girls) who were part of a large-scale preschool intervention study. Children completed measures of academic achievement in kindergarten, first grade, and second grade, and they completed a performance-based measure of EF in kindergarten; teachers reported on children’s EF during the fall of kindergarten. Structural growth modeling was utilized to determine the unique and shared contributions of EF measures on concurrent ability and growth of academic outcomes. Results: Structural growth models indicated that the separate EF measures were both significant predictors of concurrent ability and growth of all academic outcomes, with one exception; the Head–Toes–Knees–Shoulders task was not a significant predictor of growth in math skills. Conclusions: Results of this study suggested that report- and performance-based measures of EF should not be used interchangeably, and these findings have implications for the utility of EF as a risk factor for poor academic achievement.

Research Byte: The structure of adult thinking. A #network approach to #metacognitive processing —#cognition #executivefunction

Author links open overlay panel

Click here to access copy of article

Abstract

Complex cognitive processes have been broadly categorized into three general domains: first-order cognition (i.e., thinking directed to solve problems), metacognition (i.e., thinking about one's thinking during problem-solving), and epistemic cognition (i.e., thinking about the epistemic nature of problems and beliefs about criteria for knowledge justification). Few, if any studies, have empirically examined the conditional dependencies between a large inventory of components simultaneously. This paper aims to contribute the first set of preliminary explorations into the interrelationships between different thinking and reasoning components that represent key aspects of emerging adult cognitive processing using a psychological network approach. In two cross-sectional studies (combined N = 1496), data was collected from undergraduate students enrolled at a large public university. Scrutiny of the networks suggests that thinking dispositions and competency with probability are key bridges between metacognitive abilities and epistemic beliefs. Implications for instruction are discussed.

Educational relevance statement

It remains a perennial aim of all education systems to improve the thinking and reasoning of students. But which complex cognitive processes are worthwhile targets, and how do they fit among the plethora of metacognitive, self-regulatory, and epistemological belief aspects of students? The present set of studies is the first to apply a network approach to a broad array of cognitive components to uncover the central student-level variables that can be targeted with instruction. Based on the findings of the two studies presented, instruction aimed at epistemic dispositions could potentially assist in the development of complex cognition because of their centrality to networks of effective reasoning processes.

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Using Gt distribution parameters to predict executive functions in AHDH: Study consistent with Schneider & McGrew 2018 CHC update chapter

Interesting article consistent with what Joel Schneider and I discussed in our latest CHC Intelligence theory update chapter. Click here for info.

Using inspection time and ex-Gaussian parameters of reaction time to predict executive functions in children with ADHD. Intelligence, 69 (2018) 186–194.

Hilary Galloway-Long, Cynthia Huang-Pollock


A B S T R A C T

Slower and more variable performance in speeded reaction time tasks is a prominent cognitive signature among children with Attention Deficit Hyperactivity Disorder (ADHD), and is often also negatively associated with executive functioning ability. In the current study, we utilize a visual inspection time task and an ex-Gaussian decomposition of the reaction time data from the same task to better understand which of several cognitive subprocesses (i.e., perceptual encoding, decision-making, or fine-motor output) may be responsible for these important relationships. Consistent with previous research, children with ADHD (n = 190; 68 girls) had longer/ slower SD and tau than non-ADHD peers (n = 76; 42 girls), but there were no group differences in inspection time, mu, or sigma. Smaller mu, greater sigma, longer tau, and slower inspection time together predicted worse performance on a latent executive function factor, but only tau partially mediated the relationship between ADHD symptomology and EF. These results suggest that the speed of information accumulation during the decision-making process may be an important mechanism that explains ADHD-related deficits in executive control.

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Assessment Recommendations for Gt (from Schneider & McGrew, 2018)

To be published shortly in:




Tasks measuring Gt are not typically used in clinical settings (except perhaps in CPTs). With the increasing use of low-cost mobile computing devices (i.e., smartphones and iPads/other slate notebook computers), we predict that practical measures of Gt will soon be available for clinical use. Some potential clinical applications are already apparent. We present three examples.

Gregory, Nettelbeck, and Wilson (2009) demonstrated that initial level of and rate of changes in inspection time might serve as an important biomarker of aging. Briefly, a biomarker for the aging process “is a biological parameter, like blood pressure or visual acuity that measures a basic biological process of ageing and predicts later functional capabilities more effectively than can chronological age . . . a valid biomarker should predict a range of important age-related outcomes including cognitive functioning, everyday independence and mortality, in that order of salience” (p. 999). In a small sample of elderly individuals, initial inspection time level and rate of slowing (over repeated testing) was related to cognitive functioning and everyday competence. Repeated, relatively low-cost assessment of adults' inspection times might serve a useful function in cognitive aging research and serve as a routine measure (much like blood pressure) to detect possible early signs of cognitive decline.

Researchers have demonstrated how to harness the typical non-normal distributions of RT as a potential aid in diagnosis of certain clinical disorders. Most RT response distributions are not normally distributed in the classic sense. They are virtually always positively skewed, with most RTs falling at the faster end of the distribution. These distributions are called ex-Gaussian, which is a mathematical combination of Gaussian and exponential distributions. It can be characterized by the mean (m), the standard deviation (s),and an exponential function (t) that reflects the mean and standard deviation exponential component (Balota & Yap, 2011). (Don't worry; one does not need to under-stand this statistics-as-a-second-language brief description to appreciate the potential application.) The important finding is that “individuals carry with them their own characteristic RT distributions that are relatively stable over time” (p. 162). Thus, given the ease an efficiency with which RT tests could be repeatedly administered to individu-als (via smart devices and portable computers), it would be possible to readily obtain each person's RT distribution signature. Of most importance is the finding that all three RT distribution parameters are relatively stable, and t is very stable (e.g., test–retest correlations in the high .80s to low .90s). Furthermore, there is a robust relation between t and working memory performance that is consistent with the worst-performance rule (WPR) discovered in the intelligence literature. The WPR states that on repeated trial testing on cognitive tasks, the trials where a person does poorest (worst) are better predictors of intelligence than the best-performance trials (Coyle, 2003). It has been demonstrated, in keeping with the WPR, that the portion of each person's RT distribution representing the slowest RTs is strongly related to fluid intelligence and working memory.

In the not-too-distant future, assessment personal armed with portable smart devices or computers could test an individual repeatedly over time with RT paradigms. Then, via magical software or app algorithms, a person's RT distribution signature could be obtained (and compared against the normative distribution) to gain insights into the person's general intelligence, Gf, or working memory over time. This could have im-portant applications in monitoring of age-related cognitive changes, responses to medication for attention-deficit/hyperactivity disorder (ADHD) or other disorders, the effectiveness of brain fitness programs, and so forth. Finally, using the same general RT paradigms and metrics, research has indicated that it may be possible to differentiate children with ADHD from typically developing children (Kofler et al., 2013) and children with ADHD from those with dyslexia (Gooch, Snowling, & Hulme, 2012), based on the RT variability—not the mean level of performance. It is also possible that RT variability might simply be a general marker for a number of underlying neurocognitive disorders.

We have the technology. We have the capability to build portable, low-cost assessment technology based on Gt assessment paradigms. With more efficient and better assessments than before, build it . . . and they (assessment professionals) will come.


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The Role of Executive Functions in Reading Comprehension - excellent overview of major models of reading comprehension

The Role of Executive Functions in Reading Comprehension. Article or link.

Reese Butterfuss and Panayiota Kendeou

ABSTRACT

Our goal in this paper is to understand the extent to which, and under what conditions, executive functions (EFs) play a role in reading comprehension processes. We begin with a brief review of core components of EF (inhibition, shifting, and updating) and reading comprehension. We then discuss the status of EFs in process models of reading comprehension. Next, we review and synthesize empirical evidence in the extant literature for the involvement of core components of EF in reading comprehension processes under different reading conditions and across different populations. In conclusion, we propose that EFs may help explain complex interactions between the reader, the text, and the discourse situation, and call for both existing and future models of reading comprehension to include EFs as explicit components.

Keywords Executive functions . Reading comprehension . Discourse processes

This article includes an excellent summary of the major models of reading comprehension. This post includes that select material.

The Status of Executive Functions in Models of Reading Comprehension

Reading comprehension is one of the most complex and important cognitive activities humans perform (Kendeou et al. 2016). Given its importance and complexity, researchers have sought to understand reading comprehension via the development and specification of a multitude of models and frameworks that account for various processes and mechanisms of reading.

Generally, reading comprehension refers to the construction of a mental representation of what the text is about (Kintsch and V an Dijk 1978). Although most models of reading comprehension converge on this general idea, the processes and assumptions by which readers construct such representations differ across models and frameworks. It is also important to note that a unified, comprehensive model of reading comprehension has yet to be established. McNamara and Magliano (2009) reviewed and compared one set of models, which are concerned primarily with the construction of the mental representation during reading: The Construction-Integration Model (Kintsch 1988), the Structure-Building Framework (Gernsbacher 1991), the Resonance Model (Albrecht and O'Brien 1993), the Event-Indexing Model (Zwaan et al. 1995), the Causal Network Model (Trabasso et al. 1989), the Constructionist Theory (Graesser et al. 1994), and the Landscape Model (van den Broek et al. 1999). In this review, we investigate the status of EFs in each of these models.

Among this set of models, the Construction-Integration (CI) model (Kintsch 1988) is perhaps the most comprehensive, and it is considered the best approximation to a true theory of reading comprehension (Kendeou and O'Brien 2017). According to the CI model, comprehension is the result of two processes, construction and integration. Construction refers to the activation of information in the text and background knowledge. There are four potential sources of activation: the current text input, the prior sentence, background knowledge, and prior text. As this information is activated, it is connected into a network of concepts. Integration refers to the continuous spread of activation within this network until activation settles. Activation sources from the construction process are iteratively integrated, and only those concepts that are connected to many others are maintained in the network. At the completion of reading, the result is a complete network or a mental representation of what the text is about. This mental representation has been termed the situation model. Even though the initial model makes no explicit reference to EFs, in a subsequent revision, Kintsch (1998) included a suppression mechanism in the CI model by adopting inhibitory links. Specifically, the CI model relies on links between information units to promote an appropriate representation of a text and inhibit inappropriate representations. In this context, facilitatory links connect related information units, and inhibitory (or negative) links connect conflicting or inappropriate information units. Inhibitory links serve to suppress or inhibit inappropriate representations (Kintsch 1998).

The Structure-Building Framework (Gernsbacher 1991) describes comprehension as the result of three processes. The first process, laying a foundation, involves using initial information from a text to lay the groundwork for a mental representation to be constructed. The second process, mapping, involves mapping information from the text onto that foundation to create structures. The third process, shifting, involves a shift to begin building a new structure when readers are unable to map information onto an existing structure. Irrelevant information that does not cohere with a current structure is suppressed. Thus, within the Structure-Building Framework, the suppression mechanism attempts to account for individual differences in comprehension ability. Specifically, the model posits that if incoming information is related to the current structure, then activation of that information is enhanced, resulting in its incorporation into the current structure. When information is not related to the current structure, then activation to that information is suppressed, or, alternatively, readers may shift and use that information to begin building a new structure. The suppression mechanism is the result of readers' ability to inhibit irrelevant information. This ability moderates reading comprehension in that skilled readers have a strong suppression mechanism and can therefore suppress irrelevant information, whereas less-skilled readers lack a strong suppression mechanism. As a result, less-skilled comprehenders' poor suppression ability may lead them to shift too often, which impairs comprehension because more information is competing for limited resources.

The Resonance Model (Myers and O'Brien 1998) attempts to account for factors that influence the activation of information during comprehension, particularly information that is no longer active in working memory. The model emphasizes automatic, memory-based retrieval mechanisms as fundamental assumptions. Specifically, the model assumes that information in working memory serves as a signal to all of memory, which activates information that resonates with the signal. Elements resonate as a function of the number of features that overlap with the contents of working memory. Even though the model has not explicitly incorporated any EFs, O'Brien et al. (1995) found that suppression was involved in processes relevant to the Resonance Model. Specifically, O'Brien et al. found that when an anaphoric phrase reactivated more than one potential antecedent from the text, the selected target antecedent was strengthened in long-term memory, whereas potential, but non-target, antecedents that interfered with the target antecedent were suppressed.

The Event-Indexing Model (Zwaan et al. 1995) was developed as an attempt to account more fully for processes involved with situation model construction of narrative texts. It operates under the assumption that readers monitor and establish coherence along five dimensions of continuity, and thus situation model construction: time, space, causality, motivation, and agents. Thus, within the event-indexing model, EFs such as shifting attention from one dimension to another as well as updating the construction of the situation model account for individual differences in comprehension ability. For example, Bohn-Gettler et al. (2011) found that there are developmental differences in children's ability to monitor the shifts in each of these dimensions.

The Causal Network Model (Trabasso et al. 1989) accounts for how readers generate causal inferences and represent causality during reading. Causal inferences are at the core of building a coherent representation of a story. Narrative elements can be categorized as either settings, events, goals, attempts, outcomes, or reactions. Also, there are assumed to be four types of causal relations: enabling, psychological, motivational, and physical. The model also provided a discourse analysis tool, Causal Network Analysis, to identify the causal structure that underlies story constituents. Overall, the model accounts for the importance of causal relations in memory for the text, but makes no assumptions about specific EFs. The Constructionist theory (Graesser et al. 1994) attempts to account for factors that predict inference generation during reading. The theory emphasizes the role of top-down, strategic processes in the construction of meaning, what has been termed Bsearch after meaning.^ Three assumptions define search after meaning. The first is the reader goal assumption, which suggests that readers construct meaning in accordance with their reading goals. The second is the coherence assumption, which suggests that readers construct meaning at both local and global levels. The third is the explanation assumption, which suggests that readers are driven to construct meaning that explains events they read. Even though the theory makes no concrete assumptions about EFs, it is reasonable to assume that shifting attention likely exerts an influence on the top-down, strategic processes that govern search after meaning.

Lastly, the Landscape Model (van den Broek et al. 1999) simulates the fluctuation of concept activation during reading. The Landscape Model is similar to the CI Model in that it assumes the same four sources of activation. The model also includes two important mechanisms, cohort activation and coherence-based retrieval. Cohort activation assumes that when a concept is activated, all other concepts that are also activated become associated with it (McClelland and Rumelhart 1985). Coherence-based retrieval assumes that the activation of text elements is in accordance with the readers' standards of coherence. In turn, standards of coherence refer to readers' implicit or explicit criteria for comprehension. Even though the Landscape Model makes no concrete assumptions about EFs, it is reasonable to assume that shifting likely exerts an influence on readers' standards of coherence, directing attention to information that aligns with readers' standards.



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What about executive functions and CHC theory? New research for discussion

What about executive functions and CHC theory: New research for discussion from Kevin McGrew

Research Byte: Executive functioning and working memory deficits in kingergarten are predictive of reading and math deficits in first grade

 

Nice study.  For WJ III/WJ IV users, the measure of working memory (Gwm), which was the most predictive variable of first grade reading and math, was Numbers Reversed.

 

Contemporary Educational Psychology

Available online 7 March 2016

In Press, Accepted Manuscript — Note to users

Executive functioning deficits increase kindergarten children's risk for reading and mathematics difficulties in first grade

• Paul L. Morgan, Ph.D.¹,
• Hui Li, Ph.D.¹,
• George Farkas, Ph.D.²,
• Michael Cook, M.S.¹,
• Wik Hung Pun, M.S.¹,
• Marianne M. Hillemeier, Ph.D., M.P.H.¹

• ¹ The Pennsylvania State University
• ² University of California, Irvine

Available online 7 March 2016

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doi:10.1016/j.cedpsych.2016.01.004

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Highlights

• Executive functioning deficits in kindergarten uniquely predict reading and mathematics difficulties in first grade

• Executive functioning deficits more strongly predict mathematics difficulties than reading difficulties, although these deficits predict both types of difficulties

• Working memory deficits more strongly predict mathematics and reading difficulties than cognitive flexibility deficits

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Abstract

Whether executive functioning deficits result in children experiencing learning difficulties is presently unclear. Yet evidence for these hypothesized causal relations has many implications for early intervention design and delivery. We used a multi-year panel design, multiple criterion and predictor variable measures, extensive statistical control for potential confounds including autoregressive prior histories of both reading and mathematics difficulties, and additional epidemiological methods to preliminarily examine these hypothesized relations. Results from multivariate logistic regression analyses of a nationally representative and longitudinal sample of 18,080 children (i.e., the Early Childhood Longitudinal Study-Kindergarten Cohort of 2011, or ECLS-K: 2011) indicated that working memory and, separately, cognitive flexibility deficits uniquely increased kindergarten children's risk of experiencing reading as well as mathematics difficulties in first grade. The risks associated with working memory deficits were particularly strong. Experimentally-evaluated, multi-component interventions designed to help young children with reading or mathematics difficulties may also need to remediate early deficits in executive function, particularly in working memory.

Keywords

• Executive functioning;
• working memory;
• cognitive flexibility;
• learning difficulties;
• longitudinal

Two more Go (general olfactory ability domain) research articles to file under Go in CHC taxonomy of human abilities

Longitudinal changes in odor identification performance and neuropsychological measures in aging individuals.

Wehling, Eike I.; Wollschlaeger, Daniel; Nordin, Steven; Lundervold, Astri J.

Neuropsychology, Vol 30(1), Jan 2016, 87-97. http://dx.doi.org.ezp1.lib.umn.edu/10.1037/neu0000212

Abstract

1. Objective: To examine changes in odor identification performance and cognitive measures in healthy aging individuals. While cross-sectional studies reveal associations between odor identification and measures of episodic memory, processing speed, and executive function, longitudinal studies so far have been ambiguous with regard to demonstrating that odor identification may be predictive of decline in cognitive function. Method: One hundred and 7 healthy aging individuals (average age 60.2 years, 71% women) were assessed with an odor identification test and nonolfactory cognitive measures of verbal episodic memory, mental processing speed, executive function, and language 3 times, covering a period of 6.5 years. Results: The cross-sectional results revealed odor identification performance to be associated with age, measures of verbal episodic memory, and processing speed. Using linear mixed models, the longitudinal analyses revealed age-associated decline in all measures. Controlling for retest effects, the analyses demonstrated that gender was a significant predictor for episodic memory and mental processing speed. Odor identification performance was further shown to be a significant predictor for episodic verbal memory. Conclusion: This study shows age-related decline in odor identification as well as nonolfactory cognitive measures. The finding showing that odor identification is a significant predictor for verbal episodic memory is of great clinical interest as odor identification has been suggested as a sensitive measure of incipient pathologic cognitive decline. (PsycINFO Database Record (c) 2015 APA, all rights reserved)

Olfactory identification and its relationship to executive functions, memory, and disability one year after severe traumatic brain injury.

Sigurdardottir, Solrun; Andelic, Nada; Skandsen, Toril; Anke, Audny; Roe, Cecilie; Holthe, Oyvor Oistensen; Wehling, Eike

Neuropsychology, Vol 30(1), Jan 2016, 98-108. http://dx.doi.org.ezp1.lib.umn.edu/10.1037/neu0000206

Abstract

1. Objective: To explore the frequency of posttraumatic olfactory (dys)function 1 year after severe traumatic brain injury (TBI) and determine whether there is a relationship between olfactory identification and neuropsychological test performance, injury severity and TBI-related disability. Method: A population-based multicenter study including 129 individuals with severe TBI (99 males; 16 to 85 years of age) that could accomplish neuropsychological examinations. Olfactory (dys)function (anosmia, hyposmia, normosmia) was assessed by the University of Pennsylvania Smell Identification Test (UPSIT) or the Brief Smell Identification Test (B-SIT). Three tests of the Delis-Kaplan Executive Function System (D-KEFS) were used to assess processing speed, verbal fluency, inhibition and set-shifting, and the California Verbal Learning Test-II was used to examine verbal memory. The Glasgow Outcome Scale-Extended (GOSE) was used to measure disability level. Results: Employing 2 different smell tests in 2 equal-sized subsamples, the UPSIT sample (n = 65) classified 34% with anosmia and 52% with hyposmia, while the B-SIT sample (n = 64) classified 20% with anosmia and 9% with hyposmia. Individuals classified with anosmia by the B-SIT showed significantly lower scores for set-shifting, category switching fluency and delayed verbal memory compared to hyposmia and normosmia groups. Only the B-SIT scores were significantly correlated with neuropsychological performance and GOSE scores. Brain injury severity (Rotterdam CT score) and subarachnoid hemorrhage were related to anosmia. Individuals classified with anosmia demonstrated similar disability as those with hyposmia/normosmia. Conclusions: Different measures of olfaction may yield different estimates of anosmia. Nevertheless, around 1 third of individuals with severe TBI suffered from anosmia, which may also indicate poorer cognitive outcome. (PsycINFO Database Record (c) 2015 APA, all rights reserved)

Executive function of "peacekeeping" has a role in fluid intelligence (Gf)

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Brain networks and fine tuning the networks: An OBG post

[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.

Research byte: Excellent description of similarities and differences of three primary models of working memory

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Tuesday, February 24, 2015

11:18 AM

Research byte: Good brief article linking contemporary creativity (Glr) and CHC model research

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Tuesday, February 24, 2015

7:45 PM

Review of computer-based cog training for ADHD: A mixed bag

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Klingberg on working memory dev/trng, P-FIT model, neural/temporal efficiency, bran networks and cognitive development

Excellent article by Klingberg (2014) (copy with annnotated comments and links to other research) that brings together important constructs of working memory (Gwm), working memory training, brain networks and synchronization, white matter mattters, neuroal and temporal processing efficiency, and maturation and training effects on children's cognitive development.
The article does a good job of "connecting the dots" from many different programs of research.

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Mind wandering: Annual Review of Psychology review

A nice, concise review of the mind wandering research is now available in the Annual Review of Psychology. Click on images to enlarge.

Previous posts on mind wandering can be found at the Brain Clock blog at this link.







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ADHD: And even MORE evidence suggestive of a brain network connectivity disorder

And more evidence for ADHD as being related to poor brain network connectivity. (click here for more posts) Click on images to enlarge.






And, again, this extant research is consistent with the three-level hypothesized explanation of the impact of certain brain training programs on controlled attention (click here for special white paper as well as on-line PPT modules and keynote video presentation of this model).




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The external/internal-directed cognition (EDC/IDC) framework

I just skimmed the article below. I like the way it uses the terms external/internal-directed (ECD/ICD) cognition framework to discuss the differences and relations between the activities of the default brain network and the executive control networks (click here for excellent article explaining these two networks)

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I resonate to this EDC/IDC framework as it is relevant to my white paper on improving attentional control (via IM training--although the paper, IMHO, is more about how different brain training programs may work). That hypothesized model is in the figure above, and can be found at the MindHub.



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Working memory training (n-back task) improves fluid intelligence (Gf) 3-4 IQ points

Interesting meta-analysis suggestingthat working memory training, via the n-back task, over a relatively short period of time, can improve fluid intelligence/reasoning (Gf). Conservative estimate of 3-4 Gf IQ points improvement. Click on images to enlarge.









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School readiness = self regulation learning competence?

Excellent review article on the relationship between the development of self-regulated learning strategies/competence and school readiness. Having worked in the schools as a school psychologist for 12 years, I would like to have a buck for every time a kindergarten teacher described children who where struggling in terms of self-regulation--although they did not use that term. A must ready for anyone working with preschool and early elementary students and stuff.

Click on images to enlarge. For more on self-regulated learning as per the Model of Academic Competence and Motivation (MACM), click here.










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