Core Hubs Cognitive Training Model

 

The Core Hubs model depicts three overlapping ‘core hub’ functional networks – for fluid intelligence, self- regulation and incubation – distributed between the Executive (Cognitive Control) Network and the Default Mode Network as shown in these Venn diagrams.

 

Core hubs training framework is designed for far transfer training benefits to:

• General intelligence (g), fluid intelligence (Gf) and g Prime (g’) – intelligence related constructs.
• Cognitive control (CC) – an executive function construct, that includes attention focus and flexibility, self-control, habit formation, goal-management and cognitive resilience.

g Prime : The Construct

The model trains g‘ (g Prime), a more extensive construct than general intelligence (g).

g: the construct of general intelligence

g’ (g prime). a broader construct related to general intelligence that incorporates (1) general intelligence (g); (2) rationality (e.g. competence with cognitive biases and reflective critical thinking) 

Note: The prime symbol is generally used to generate more variable names for things which are similar – x′ (prime) means something related to or derived from x.

You can have a very high IQ (g) while not having the cognitive skills to be able to solve basic problems like this one below. 

A bat and a ball cost $1.10 in total. The bat costs a dollar more than the ball. How much does the ball cost? ____ cents.

Getting this right requires a greater level of reflection and rationality than g gives you  – cognitive skills that are part of the extended construct of g’.

 

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15 Brain Training Principles Based on the Core Hubs Training Framework

For each training principle,, the evidence-based Foundations enumerated in Appendix I are noted.

I Executive Processes Brain Training

• 1. Principle of task complexity. G loads more highly on complex tasks. Executive process training will have more far transfer training effects when the cognitive challenges are complex and novel – i.e. more fluid intelligence (Gf) demanding. (Foundation 1)
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• 2. Principle of worst performance improvement. Executive process training will have most impact on improving worst performances (e.g. mistakes, lapses of attention or concentration) rather than best performances. It is these slip-ups during many tasks that largely differentiate lower vs the higher IQ individuals’ cognitive performance (Foundation 1)
  .
• 3. Principle of differential far transfer effects. Executive process brain training  will have marked far transfer effects across all broad abilities measured by subtests of full-scale IQ tests for relatively lower levels of cognitive ability. For higher levels of ability, transfer effects will be more apparent when tasks are more complex and novel – or will depend more on mindware cross-training – see below. (Foundations 1, 6)
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• 4. Principle of multiple bottlenecks and non-additivity of executive processes. Executive processes act as a bottleneck, and they mask individual differences in specific abilities which may only manifest in automated skills. Each process has its own bottleneck, and each process has to be functioning at an appropriate level to perform a cognitive task. Poor cognitive performance (e.g. on IQ tests) is often due to not being able to cope with the executive demands of a task, regardless of any domain-specific knowledge or skill (Gc). (Foundation 1)
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• 5. Principle of executive processing specificity. Executive process brain training that targets (1) strategic long-term memory encoding and retrieval, (2) working memory (WM)  disengagement, (3) WM transformation, (4) WM output gating, and (5) use of problem specific mindware, will more effectively transfer to gains in fluid intelligence (Gf) and g. Executive process training that targets WM input gating, selective attention and WM maintenance will more effectively transfer to gains in cognitive control such as attention focus and flexibility, rapidly instructed task learning (RITL), self-regulation,  and cognitive resilience – see Section II below (Foundation 2).

 

• 6. Principle of cross-training executive processes with mindware. Far transfer effects from brain training across different broad IQ abilities (e.g. Gs, Gv, Gc) benefits from a combination of executive process training and domain specific mindware (strategy) training – e.g. problem solving heuristics or practice in math techniques. Deliberate practice with mindware strategies can result in automatization of those strategies which reduces cognitive load and executive process bottlenecks. (Foundations 1, 3)
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• 7. Principle of cross-training executive processes with domain specific processes. Far transfer effects from brain training across different broad IQ abilities (e.g. Gs, Gv, Gc) benefits from a combination of executive process training and domain specific representational/syntactic training – e.g.  visuospatial visualization training, verbal logical reasoning training, quantitative operation training, etc. (Foundation 1)
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• 8. Principle of meta-awareness and implementation intentions for far transfer. Effective and reliable far transfer requires more than just neuroplasticity training of executive processes. It requires initial meta-awareness of when you are using different executive process functions and the kinds of contexts those can be applied in. Executive training should be effective only if it meaningfully transfers to a real world task where the “executive sub-task has not been “solved” already, and the training increases the chances of solving it. Implementation intentions (specifying the when, where, and how of responses leading to goal attainment) set at higher levels of abstraction derived from reflection can be used as the basis of far transfer between distal contexts. (Foundations 1, 6)

 

II Self-Regulation Training

Self-regulation =df: the ability to direct one’s attention, thoughts, moods, and behavior in line with one’s personal goals. Note that all flexible, adaptive goal-pursuit depends on the executive processes described above. Lack of self-regulation can result in demotivation, loss of performance, burnout and mental health issues.

• 9. Principle of goal setting. Training skills in goal setting (goal intentions) & pursuit, evaluation of personally relevant goals (e.g. future-self visualization and pursuit) benefits both far transfer to g’ (g prime) and cognitive health & resilience. (Foundations 4, 5)
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• 10. Principle of implementation intentions for goal pursuit and habit formation. Effective self-goal pursuit and habit-formation requires use of implementation intentions as well as goal intentions.  These delegate the control of goal-directed responses to anticipated situational cues, which when actually encountered can elicit these responses automatically. By forming implementation intentions, people can strategically switch from conscious and effortful control of their goal-directed behaviors (i.e., the effortful deliberations described above) to being automatically controlled by selected situational cues.
  ..
• 11. Principle of goal management. Training outcomes benefit from engaging in 3 phases of task strategy: (1) preparatory phase (task definition, goal setting/planning, motivation induction); (2) performance phase (goal striving & tracking, performance feedback, metacognitive self-monitoring, self-efficacy perceptions, persistence, self-control, environmental structuring, coaching); (3) appraisal phase (performance feedback, self-evaluation, emotional reactions, causal attribution, anticipatory task adaptation). (Foundation 4)
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• 12. Principle of appraisal/framing, motivational and emotional strategies for self-regulation. Appraisal strategies include adopting self-efficacy and growth mindsets for self-regulation, as well as ways of reinterpreting setbacks and failures through cognitive reappraisal. Motivational and emotional strategies include optimism and evaluative conditioning to mentally link goal pursuit with desired affective experiences. (Foundation 4).
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• 13. Principle of goal release. Cognitive health, resilience and performance benefits from the ability to release goals when it is appropriate. Inability to release goals can result in burnout and mental health issues. (Foundations 4, 6)
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• 14. Principle of rest & downtime.  Regular periods of waking rest and adequate sleep  promote cognitive health, resilience and performance and.are needed to optimize training gains and the learning process. (Foundations 4, 5)
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• 15. Principle of core hubs network switching. Some practices (e.g. meditation) and strategies (e.g. deliberate goal disengagement) may help train efficiency in adaptively switching between core hub networks. This helps training outcomes and benefits g‘. (Foundations 4, 6)
  .

 

III Creative Incubation Training

 

• 16. Principle of defocused incubation. Periods of goal-disengaged distraction, daydreaming, spontaneous internal imagery, non-directed meditation & episodic thought can all help with memory consolidation and facilitate incubation processes for creative thought. (Foundations 5, 6)
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• 17. Principle of REM and non-REM incubation. Dreaming enhances the integration of non-associated information for creative problem solving as well as consolidating and strengthening newly formed memories, integrating them into existing knowledge.  Interleaving of REM and non-REM sleep may help with divergent thinking and the formation and restructuring of complex knowledge frameworks facilitating creative thought (Foundations 5, 6).

 

IV. Social and Environmental Bootstrapping

• 18. Multiplier Effects. Small initial IQ differences (e.g. 5 IQ points) can magnify quickly over time through ongoing IQ-environment feedback loops into very large IQ differences. Anything (environmental or genetic) that makes someone better at something improves skills and ability that improve the social and educational environment that improves ability and so on through a positive feedback loop. (Foundation 7).
  

 

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APPENDIX I

6 Foundations of the Core Hubs Cognitive Training Framework

 

1. Process Overlap Theory: A unified account of g.

Model

Research Summary

• g as a statistical construct has  increasing explanatory power at lower levels of cognitive ability or higher levels of task complexity.
• g  is the result, rather than the cause, of the correlations between group factors is not itself explanatory.The general intelligence factor (g) measured as correlations among wide ranging cognitive tests (verbal, visuospatial, numerical/quantitative, sensorimotor, emotional, etc) does not refer to any underlying psychological process or neural mechanism.  
• g is explained (mechanistically) by overlapping domain general executive processes across all CHC broad ability domains – subserved by frontoparietal core hubs networks with radiating connectivity with domain specific brain regions – such as those dedicated to verbal processing and visuospatial processing.
• g is virtually identical with Gf at a statistical factor level. Fluid intelligence (Gf) can be identified with domain general executive processes when cognitive challenges are complex and novel.
• Worst performance predicts g-loaded measures better than best performance. The difference between the correlations with best and worst performance is larger on tests that are more complex and more g loaded: Indices of the worst performance on complex tests reveal strong correlations with g.  
• The processes sampled by different mental test items are not additive. Each process has its own limitations, and each process has to be functioning at an appropriate level to arrive at a correct answer to a mental test item.

Key Research Article

Kovacs, K., & Conway, A. R. A. (2016). Process Overlap Theory: A Unified Account of the General Factor of Intelligence. Psychological Inquiry, 27(3), 151–177. https://doi.org/10.1080/1047840X.2016.1153946

 

2. The Maintenance, Disengagement & Output Gating Accounts of the Fluid Intelligence (Gf) -Working Memory Capacity (WMC) Correlation  

Models

 

 

 

Research Summary

• According to Shipstead & Engle there are two main working memory executive processes: (1) maintenance of content in working memory – the ability to temporarily hold ideas or information in your ‘mental workspace’ in the face of distractions or concurrent tasks; and (2) disengaging from content/rules in working memory – the ability to remove memory traces of no-longer-relevant information. These two WM functions have to cooperate in both working memory capacity (WMC) and fluid intelligence (Gf) tasks.WMC tasks depend relatively more on the maintenance function while Gf tasks depend relatively more the on the disengagement function – e.g. clearing from your mind any hypotheses that you have thought through but ruled out as you think through a problem.
• Output gating mechanisms in prefrontal cortex are critical for learning and applying complex, context-dependent hierarchical rules – processes that are central to Gf.
• Updating has the highest correlation with Gf and transformation (rather than retrieval or substitution) has the strongest impact on WM updating performance.
• Secondary (long term) memory abilities are needed in order to bring task-relevant information into the focus of attention so that it can be combined with the current contents of the focus. Like capacity and attention control, secondary memory abilities are critical for higher-order cognitive functioning to ensure that information that could not be actively maintained can nonetheless be accessed rapidly.

Key Research Articles

Badre, D. (2008). Cognitive control, hierarchy, and the rostro–caudal organization of the frontal lobes. Trends in Cognitive Sciences, 12(5), 193–200. https://doi.org/10.1016/j.tics.2008.02.004

Ecker, U. K. H., Lewandowsky, S., Oberauer, K., & Chee, A. E. H. (2010). The components of working memory updating: an experimental decomposition and individual differences. Journal of Experimental Psychology. Learning, Memory, and Cognition, 36(1), 170–189. https://doi.org/10.1037/a0017891

Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., DeFries, J. C., & Hewitt, J. K. (2006). Not All Executive Functions Are Related to Intelligence. Psychological Science, 17(2), 172–179. https://doi.org/10.1111/j.1467-9280.2006.01681.x

Shipstead, Z., Harrison, T. L., & Engle, R. W. (2016). Working Memory Capacity and Fluid Intelligence: Maintenance and Disengagement. Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 11(6), 771–799. https://doi.org/10.1177/1745691616650647

Shipstead, Z., & Engle, R. W. (2018, January). Mechanisms of Working Memory Capacity and Fluid Intelligence and Their Common Dependence on Executive Attention. https://doi.org/10.1017/9781316817049.019

Unger, K., Ackerman, L., Chatham, C. H., Amso, D., & Badre, D. (2016). Working memory gating mechanisms explain developmental change in rule-guided behavior. Cognition, 155, 8–22. https://doi.org/10.1016/j.cognition.2016.05.020

Unsworth, N., Fukuda, K., Awh, E., & Vogel, E. K. (2014). Working Memory and Fluid Intelligence: Capacity, Attention Control, and Secondary Memory Retrieval. Cognitive Psychology, 71, 1–26. https://doi.org/10.1016/j.cogpsych.2014.01.003

 

3. The Tripartite (3 System) and Iterative Reprocessing (IR) Models of Mind

Models

 

 

Research Summary

• Reflection, or the reprocessing of information, provides a foundation for executive function development. For example, the reflective reprocessing of information prior to responding, provides a foundation for the control of attention – flexibly, over time, and selectively.
• Goal-directed modulation of attention is typically verbally mediated and involves the formulation and maintenance in working memory of explicit action-oriented rules.
• The development of EF is made possible, in part, by increases in the efficiency of reflective reprocessing which allow for increases in the hierarchical complexity of the rules that can be used to characterize problems and select context-appropriate rules for responding.
• The is separate neural system in the PFC for metacognition and functions of the PFC in metacognition are dissociable into a metacognitive monitoring and a metacognitive control subsystem.
• There are many individual differences in rational thinking that are not correlated with (or only weakly correlated with) individual differences in intelligence – because intelligence and rationality are different constructs.
• According to Stanovich’s Tripartite Theory of Mind, cognition can be categorized as: Type 1 automatic processing – the autonomous mind; and Type 2 controlled processing, subdivided into the algorithmic mind and the reflective mind.
• IQ tests and other cognitive aptitude ‘optimal performance’ measures are those in which  task interpretation is determined externally and the person performing the task is instructed to maximize performance. These measures capture the processing efficiency of the algorithmic mind.
• By contrast, measures of critical or rational thinking are often assessed under typical (not optimal), open-ended performance conditions.are measures of the reflective mind—they assess in part goal prioritization and epistemic regulation. They also measure individual differences in thinking dispositions, variation in goal
  management, epistemic values, and epistemic self-regulation – e.g. tendency to collect information before making up one’s mind, to seek various points of view before coming to a conclusion,  to think extensively about a problem before responding, to calibrate the degree of strength of one’s opinion to the degree of evidence available, the to think about future consequences before taking action, the tendency to explicitly weigh pros and cons, etc.
• Rationality requires the proper functioning of both the reflective and the algorithmic mind. In contrast, intelligence tests index the computational power of the algorithmic mind.
• In dealing with cognitive biases that are independent of IQ level, the autonomous mind can be overridden by algorithmic-level mechanisms; but override itself is initiated by higher level goal states and epistemic thinking dispositions of the reflective mind.

Key Research Articles

Stanovich, K. E. (2009). Distinguishing the reflective, algorithmic, and autonomous minds: Is it time for a tri-process theory? Oxford University Press. Retrieved from http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780199230167.001.0001/acprof-9780199230167-chapter-3

Zelazo, P. D. (2015). Executive function: Reflection, iterative reprocessing, complexity, and the developing brain. Developmental Review, 38, 55–68. https://doi.org/10.1016/j.dr.2015.07.001

 

4. Adaptive Self Regulation & Self-Regulated Learning (SRL)

There is overlap here with Foundation 3, but the emphasis in Foundation 4 is not on  task–demands but on self-referential monitoring, regulation and evaluation.

Models

 

Research Summary

• SRL is a powerful framework to anchor crucial variables that improve learning.
• Three SRL phases can be identified: (a) preparatory / forethought (e.g. task analysis, planning, goal setting/activation; (b) performance (e.g. monitoring and controlling progress of performance) and (c) appraisal / self-reflection (e.g. reflection and adaptive anticipation of future performances).
• The most powerful predictors of learning outcomes—goal setting, persistence, effort, and (particularly) self-efficacy. In one study, these four constructs accounted for 17% of the variance in learning after controlling for cognitive ability and pre-training knowledge.
• Targeting motivational and emotional aspects of learning, such as self-efficacy and goal setting, boost effort regulation and result in better higher educational outcomes.
• Metacognition is understood as a gateway to self-regulating learning – through monitoring, evaluating and regulating.
• Context sensitivity is an important variable to SRL – e..g  learning benefits from awareness of transfer context; interpretation of the context activates different goal pathways, and previous experiences may affect the different roles that learners adopt.
• Automaticity is an important aspect in the majority of the models. Goal activation can be both deliberate/controlled but also automatic, triggered directly by environmental cues, outside the awareness of the individual. Mindware/past knowledge can also be automatically activated.
• Automatisation occurs when a strategy can be executed without metacognitive monitoring, and where there can be a focus on feedback from outcomes rather than processes. Beneficial automatisation takes practice.
• Some automatic reactions or strategies  (both cognitive and emotional- motivational)  may be detrimental to learning, and require metacognitive monitoring and executive processes to override.
• SRL is a cornerstone for team-based forms of regulation such as co-regulation and shared regulation. SRL interventions may be particularly effective when the amount of group (team) work increases.
• Activating self-regulatory processes such as having a promotion focus have been connected to the human capacity for creativity—the ability to generate ideas, insights, and solutions. This focus involves being oriented toward opportunities and accomplishing aspirational  goals and engaging in approach-related
  behaviors toward positive end states. A prevention focus (which are anti-goal averse) that leads to activation (fear, effort to avoid an anti-goal) can also result in creativity; when prevention goals are successfully regulated (relief, fulfilled prevention goals), there can be loss of creativity.
• Sleep deprivation can impair attention, vigilance and working memory. It can also impair other functions, such as memory consolidation and decision-making.
• Mental work can result in cognitive fatigue, impaired performance and poorer self-regulation.
• Goal disengagement forms an essential aspect of effective self-regulation, mental health and well-being.
• During  wakeful rest minds wander while engaging the Default Mode Network & this can benefit memory, learning & creative problem solving.

Key Research Articles

Alhola, P., & Polo-Kantola, P. (2007). Sleep deprivation: Impact on cognitive performance. Neuropsychiatric Disease and Treatment, 3(5), 553–567.

Baas, M., De Dreu, C. K. W., & Nijstad, B. A. (2011). When prevention promotes creativity: The role of mood, regulatory focus, and regulatory closure. Journal of Personality and Social Psychology, 100(5), 794–809. https://doi.org/10.1037/a0022981

Evans, D. R., Boggero, I. A., & Segerstrom, S. C. (2015). The nature of self-regulatory fatigue and “ego depletion”: Lessons from physical fatigue. Personality and Social Psychology Review : An Official Journal of the Society for Personality and Social Psychology, Inc. https://doi.org/10.1177/1088868315597841

Gollwitzer, P. M. (1999). Implementation Intentions. American Psychologist, 54(7), 493-503.

Immordino-Yang, M. H., Christodoulou, J. A., & Singh, V. (2012). Rest Is Not Idleness: Implications of the Brain’s Default Mode for Human Development and Education. Perspectives on Psychological Science, 7(4), 352–364. https://doi.org/10.1177/1745691612447308

Mrazek, A. J., Ihm, E. D., Molden, D. C., Mrazek, M. D., Zedelius, C. M., & Schooler, J. W. (2018). Expanding minds: Growth mindsets of self-regulation and the influences on effort and perseverance. Journal of Experimental Social Psychology, 79, 164–180. https://doi.org/10.1016/j.jesp.2018.07.003

Panadero, E. (2017). A Review of Self-regulated Learning: Six Models and Four Directions for Research. Frontiers in Psychology, 8. https://doi.org/10.3389/fpsyg.2017.00422

Wrosch, C., Scheier, M. F., Carver, C. S., & Schulz, R. (2003). The importance of goal disengagement in adaptive self-regulation: When giving up is beneficial. Self and Identity, 2, 1-20. https://doi.org/10.1080/15298860309021

Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In Handbook of metacognition in education (pp. 299–315). New York, NY, US: Routledge/Taylor & Francis Group.

 

5. Unconscious Cognitive Work & Creative Incubation

Research Summary

• Periods of distraction allowing for mental set-shifting and exposure to cues that contribute to creative problem solving or decision making.
• It is not merely the absence of conscious, goal-focused thought that drives creatively, but during an incubation period, unconscious processes can do work and contribute to creative thinking.
• Memory replay mechanisms in non-REM can abstract rules from instances of learned information, while replay in REM may promote novel associations. Iterative interleaving of REM and non-REM sleep may help with divergent thinking and the formation and restructuring of complex knowledge frameworks facilitating creative thought.

Key Research Articles

Cai, D. J., Mednick, S. A., Harrison, E. M., Kanady, J. C., & Mednick, S. C. (2009). REM, not incubation, improves creativity by priming associative networks. Proceedings of the National Academy of Sciences of the United States of America, 106(25), 10130–10134. https://doi.org/10.1073/pnas.0900271106

Ritter, S. M., & Dijksterhuis, A. (2014). Creativity—the unconscious foundations of the incubation period. Frontiers in Human Neuroscience, 8. https://doi.org/10.3389/fnhum.2014.00215

 

6. Flexible Hubs Accounts of The Cognitive Control (Task Positive) & Default  Mode (Task Negative) Network Organization Of The Brain

Models

 

 

 

Research Summary

• The Executive / Task Positive network (also called the cognitive control network (CCN)) includes portions of lateral prefrontal cortex (LPFC), posterior parietal cortex, anterior insula cortex and medial prefrontal cortex. It decomposes into three distinct graph-theoretic networks: Fronto-Parietal (FPN), Dorsal Attention (DAN), and Cingulo-Opercular (CON) networks. These networks subserve executive processes and fluid intelligence (Gf).
• The Default Mode Network (DMN) comprises two subsystems that interact with a common core: The midline core (posterior cingulate and anterior medial prefrontal cortex) is active when during self-relevant activity regardless of temporal context, and shares functional properties of both subsystems. It’s activity correlates with personal significance, introspection about one’s own mental states, and evoked emotion  A dorsal medial prefrontal cortex subsystem (dMPFC, temporo-parietal junction, lateral temporal cortex and temporal pole) This is active when participants reflect on their present mental states, particularly affective states – and when participants infer the mental states of other people (social cognition). A medial temporal lobe subsystem (ventral MPFC, posterior inferior parietal lobule, retrosplenial cortex, parahippocampal cortex, and hippocampal formation. This network becomes active during recall of the past (autobiographical memory) and imagination of the future, and when when decisions involve constructing a mental scene based on memory.
• The two subsystems interact when individuals are left to spontaneous thought -.typically freely wandering past recollection or day-dreaming.
• There is overlap between the DMN and the FPN during experimentally- directed tasks during autobiographical planning tasks.
• Core hubs and global variable connectivity. Brain regions of both the FPN network and the midline core of the DMN  act as ‘hubs’ that flexibly shift their functional connectivity patterns with multiple brain networks across a wide variety of processing tasks. Both are less well integrated on local scale but relatively connected to other functional systems. This flexibly adapts processing to a wide range of task sets.
• The flexible hub theory (building on guided activation theories) predicts that frontoparietal regions are hubs and their functional connections are flexible across task contexts. Frontoparietal regions are distributed cognitive control/multiple demand networks. 
• The FPN is for adaptive task control.and is most active during the implementation of novel and non-­routine tasks (i.e. those putting demands on fluid intelligence). 
• The FPN implements compositional coding  for rapid instructed task learning (RITL). This depends on a systematic relationship between connectivity patterns and task rules/operations allowing established representations to be re-used in novel contexts, allowing transfer of skills and knowledge across tasks. Compositional coding via frontoparietal flexible hubs can be interpreted as the neural basis of Gollwitzer’s implementation intentions for self-regulation and goal-pursuit. Compositional coding set at higher levels of abstraction (e.g. through reflection in Zelato’s Iterative Reprocessing Model of Higher Cognition) can be interpreted as the basis of far transfer between distal contexts.
• There is a positive correlation between creative performance and grey matter volume in medial temporal lobe subsystem of the DMN.
• Functional connectivity among the DMN core regions remains consistent across deep sleep and REM sleep states, but connectivity among DMN subsystems differs for REM sleep with greater functional connectivity across the entire DMN..

Key Research Articles

General Review

Mark Ashton Smith, Ph.D. HRP Lab Academic Blog. (n.d.). Retrieved September 12, 2018, from https://www.hrplab.org/

Cognitive Control and Default Mode Brain Networks

Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673–9678. https://doi.org/10.1073/pnas.0504136102

Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences of the United States of America, 105(34), 12569–12574. https://doi.org/10.1073/pnas.0800005105

Flexible Hubs & Compositional Coding Theory

Cole, M. W., Braver, T. S., & Meiran, N. (2017). The task novelty paradox: Flexible control of inflexible neural pathways during rapid instructed task learning. Neuroscience and biobehavioral reviews, 81(Pt A), 4-15.

Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16(9), 1348–1355. http://doi.org/10.1038/nn.3470

Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-Anatomic Fractionation of the Brain’s Default Network. Neuron, 65(4), 550–562. https://doi.org/10.1016/j.neuron.2010.02.005

Vatansever, D., Menon, D. K., Manktelow, A. E., Sahakian, B. J., & Stamatakis, E. A. (2015). Default Mode Dynamics for Global Functional Integration. Journal of Neuroscience, 35(46), 15254–15262. https://doi.org/10.1523/JNEUROSCI.2135-15.2015

Default Mode Network Hubs: Self Regulation

Pan, J., Zhan, L., Hu, C., Yang, J., Wang, C., Gu, L., … Wu, X. (2018). Emotion Regulation and Complex Brain Networks: Association Between Expressive Suppression and Efficiency in the Fronto-Parietal Network and Default-Mode Network. Frontiers in Human Neuroscience, 12. https://doi.org/10.3389/fnhum.2018.00070

Vatansever, D., Menon, D. K., Manktelow, A. E., Sahakian, B. J., & Stamatakis, E. A. (2015). Default Mode Dynamics for Global Functional Integration. Journal of Neuroscience, 35(46), 15254–15262. https://doi.org/10.1523/JNEUROSCI.2135-15.2015

Default Mode Network Hubs: Creative Incubation

Koike, T., Kan, S., Misaki, M., & Miyauchi, S. (2011). Connectivity pattern changes in default-mode network with deep non-REM and REM sleep. Neuroscience Research, 69(4), 322–330. https://doi.org/10.1016/j.neures.2010.12.018

Executive and DMN Switching

Luders, E., Kurth, F., Mayer, E. A., Toga, A. W., Narr, K. L., & Gaser, C. (2012). The Unique Brain Anatomy of Meditation Practitioners: Alterations in Cortical Gyrification. Frontiers in Human Neuroscience, 6. https://doi.org/10.3389/fnhum.2012.00034

Sridharan, D., Levitin, D. J., & Menon, V. (2008). A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proceedings of the National Academy of Sciences of the United States of America, 105(34), 12569–12574. https://doi.org/10.1073/pnas.0800005105

 

7. The Heredity and Phenotype-Environment Interactions of IQ

Models

 

Research Summary

• Experience-dependent plasticity of brain and behavior subsists into late adulthood.
• Flexibility refers to the capacity for variations in behavioral repertoire that do not require reorganization of brain structures and connections. Plasticity refers to changes in behavior that do require neuroplastic, structural change. Mismatches between cognitive supply and demand need to be prolonged to overcome the inertia of plasticity and to push the system away from its current dynamic equilibrium.
• Older brains accumulate an increasingly large behavioral repertoire (‘models’ of the world), and plastic reorganization of the brain is metabolically costly. Thus the brains of healthy older adults are less likelyto react to environmental challenges with a plastic response than the brains of children and adolescents.
• Phenotypic behavioral traits are a continuous – year on year – product of the reciprocal causation of environment and phenotype.
• The reciprocal causation of phenotypic IQ and environment could mask, multiply, and average environmental effects, so that relatively small environmental influences could produce large changes in IQ. This can explain how changes in environment produce the huge IQ gains that have been observed (e.g. the Flynn effect or through educational gains).
• Some educational or training programs more than a year old influence current IQ only because of their effect on past IQ and the effect of past IQ on today’s environment!  Adult IQ is influenced mainly by adult environment.
• Training programs are more likely to produce long-term IQ gains if they teach how to replicate outside the program the kinds of cognitively demanding experiences that produce IQ gains while in the program and motivate them to persist in that replication long after they have left the program.

Key Research Articles

Clouston, S. A. P., Kuh, D., Herd, P., Elliott, J., Richards, M., & Hofer, S. M. (2012). Benefits of educational attainment on adult fluid cognition: international evidence from three birth cohorts. International Journal of Epidemiology, 41(6), 1729–1736. https://doi.org/10.1093/ije/dys148

Dickens, W. T., & Flynn, J. R. (2001). Heritability estimates versus large environmental effects: the IQ paradox resolved. Psychological Review, 108(2), 346–369.

Lindenberger, U. (2014). Human cognitive aging: Corriger la fortune?. Science, 346(6209), 572. https://doi.org/10.1126/science.1254403

Lövdén, M., Bäckman, L., Lindenberger, U., Schaefer, S., & Schmiedek, F. (2010). A theoretical framework for the study of adult cognitive plasticity. Psychological Bulletin, 136(4), 659–676. https://doi.org/10.1037/a0020080

Lövdén, M., Wenger, E., Mårtensson, J., Lindenberger, U., & Bäckman, L. (2013). Structural brain plasticity in adult learning and development. Neuroscience and Biobehavioral Reviews, 37(9 Pt B), 2296–2310. https://doi.org/10.1016/j.neubiorev.2013.02.014

Trahan, L. H., Stuebing, K. K., Fletcher, J. M., & Hiscock, M. (2014). The Flynn effect: a meta-analysis. Psychological Bulletin, 140(5), 1332–1360. https://doi.org/10.1037/a0037173

Kuzawa, C. W., Chugani, H. T., Grossman, L. I., Lipovich, L., Muzik, O., Hof, P. R., … Lange, N. (2014). Metabolic costs and evolutionary implications of human brain development. Proceedings of the National Academy of Sciences, 111(36), 13010. https://doi.org/10.1073/pnas.1323099111

 

 

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APPENDIX II

Academic Origins of the Core Hubs Cognitive Training Framework

The origins of the six foundations of  Core Hubs Cognitive Training Framework (Appendix I) can be found early in my academic career:

• My Ph.D. advisor was Professor Walter Schneider (bio) a world authority since the 1970s on working memory and controlled vs automatic processes (Foundations 1,2 and 3).
• Prof. Jonathan Schooler was one of my Ph.D. committee members (bio). He is a world authority on the functioning of the Default Mode Network (Foundations 5, 6), and researches the growth mindset for self-regulation (Foundation 4).
• Professor Jason Chein (bio) was a fellow grad student. Research in his lab investigates how the development and training of working memory and cognitive control impacts the landscape of one’s cognitive abilities, including executive functioning, learning, problem solving and decision making. (Foundations 2, 6)
• Professor Todd Braver (bio) is another graduate from the Center for the Neural Basis in Cognition (CNBC) Program. Todd is a leading authority on working memory, the link between working memory and fluid intelligence, and Flexible Hubs prefrontal cortex (executive processes) theory (Foundations 1, 2 and 6).
• Professor Randall O’Reilly (bio) is a fellow graduate from the Center for the Neural Basis of Cognition (CNBC) Program. He is a leading authority on computational models of working memory, including models of input and output gating (which gated dual n-back training is based on). (Foundations 2, 6)