Principles of Effective Cognitive Training

Let Me Introduce Myself: A Short Bio

My name is Dr. Mark Ashton Smith. I earned my Ph.D. in cognitive psychology and neuroscience from the joint Carnegie Mellon University and Pittsburgh University’s Center for the Neural Basis of Cognition (CNBC) program — a program that was at the forefront of the new emerging discipline of cognitive neuroscience and computational modeling in the early 1990s. For a number of years I was a Lecturer/Assistant Professor in the Department of Psychology at the University of Cambridge. My work now focuses on developing evidence-based apps and coaching interventions for cognitive performance and resilience based on the g Prime Training Framework (see below). In this work I have consulted with medical professionals, directors of academic programs, Tier 1 journalists, Navy Seals, and CEOs. I also provide educational resources to bring cognitive neuroscience and evidence-based cognitive interventions to a wider public — in my Health, Resilience & Performance Lab website.

Core Hubs Training: A Unified Framework For Far Transfer Cognitive Training

Cognitive neuroscientists have been challenged over recent decades to achieve what is called far transfer from brain training such as the dual n-back. Far transfer is when the cognitive training benefits transfer to intelligence and real-world cognitive abilities, not just to performance on the brain training task itself.

Over the past year I and my colleagues have developed an effective brain training framework called the Far Transfer Core Hubs Training Framework — or simplyCore Hubs Training. All the evidence-based principles of effective, far-transfer brain training we’ve determined are derived from this framework.

The Core Hubs framework for brain training is a substantial advance over traditional methods of simply doing 10 hours of computerized working memory training (e.g. dual n-back) or attention training. These research foundations of Core Hubs are summarized in Appendix 1. Their origins in my academic career are summarized in Appendix 2.

Model of the Core Hubs Cognitive Training Framework

Take a close look, and please ask questions via e-mail. This model will be the basis of all subsequent IQ Mindware developments.

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.

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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 the ability to reflect); and (3) cognitive control and flexibility (e.g. the ability to manage personal goals and form or break habits).

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. (Many Harvard and MIT undergrads fail to get this right!)

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’.

The g’ also extends to competence in adaptive, learned behavior as well as successful self-regulation autonomous self-direction and attainment of personal goals.

Brain Training Principles Based on the Core Hubs Training Framework

For each training principle,, the evidence-based Foundations enumerated in Appendix 1 are noted.

Executive Network Hubs Brain Training

  • Principle of specific executive process training. Executive process brain training that targets working memory disengagement and output gating — rather than selective attention and maintenance — will more effectively transfer to fluid intelligence gains. Executive process training that targets working memory input gating and maintenance will more effectively transfer to gans in cognitive control such as attention focus and flexibility, self-control, habit formation, emotion regulation, and cognitive resilience (Foundation 2)
  • Principle of cross-training executive processes with mindware. Far transfer effects from brain training across different broad IQ abilities (e.g. Gs, Gv, Gc) for those with higher levels of cognitive ability requires a combination of executive process training and domain specific mindware (strategy) training — such as practice in techniques for solving math problems. (Foundations 1, 3)
  • Principle of lower cognitive ability far transfer. Executive process brain training will have far transfer effects across all broad abilities measured by subtests of full-scale IQ tests for relatively lower levels of cognitive ability. (Foundations 1, 5)
  • Principle of worst performance improvement. For less complex and novel tasks, 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 task performance that separate the lower vs the higher IQ individuals — for less complex tasks (e.g. reaction time) higher IQ individuals make less slip-ups and mistakes than lower IQ individuals although both low and high IQ individuals have similar best performance levels. (Foundation 1)
  • Principle of task complexity and novelty. For higher levels of cognitive ability, executive process training will have more far transfer training effects when the cognitive challenges in specific domains are complex and novel — i.e. more fluid intelligence demanding. (Foundation 1)
  • Principle of meta-awareness of executive processes and transfer contexts. Effective and reliable far transfer requires more than just exercising executive processes (e.g. with the dual n-back). It requires 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.(Foundation 3)

Self-Regulation Training

  • Principle of self-regulated goal management. Training skills in planning, pursuit and evaluation of personally relevant goals augments far transfer to g’ (g prime). (Foundation 4)
  • Principle of cognitive, motivational and emotional self-regulation. Self-regulation during goal pursuit has three dimensions: 1. Cognitive (e.g. strategic time and effort planning); 2. Motivational (e.g. optimism, resilience, self-efficacy judgments, outcome expectations); 3. Emotional (e.g. taking pleasure in goal attainment or dealing with emotional ego-protection, disappointments or frustrations during training). (Foundation 4)

Creative Incubation Training

  • Principle of goal release. Cognitive health & resilience, but also cognitive performance and intelligence, benefits from appropriate periods of goal release, and disengagement of the Executive Network. This can be trained. (Foundation 5)
  • Principle of defocused incubation. Periods of goal-disengaged daydreaming, spontaneous internal imagery and episodic (autobiographical) and REM sleep (dreaming) can all help with memory consolidation and creative incubation in longer term problem solving and goal satisfaction. (Foundation 5)

APPENDIX 1

5 Foundations of the Core Hubs Cognitive Training Framework

Foundations 1 and 2 are of recent origin having been developed within the last couple of years.

1. Process Overlap Theory (POT) of The General Factor of Intelligence (2016).

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Model

Kovacs & Conway’s Process Overlap Theory of General Intelligence

Research Summary

  • The general intelligence factor (g) measured as correlations among wide ranging cognitive tasks (verbal, visuospatial, numerical/quantitative, sensorimotor, emotional, etc) is explained by overlapping domain general executive processes (involving attention and working memory) based in fronto-parietal core hubs networks with radiating connectivity with all domain specific brain regions.
  • Fluid intelligence (Gf) can be identified with these domain general executive processes (and g) when cognitive challenges are novel and complex.

Foundational 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.

Model

Shipstead & Engle’s 2018 Gf — WMC model

Research Summary

  • 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 in working memory — the ability to remove memory traces of no-longer-relevant ideas or 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 important for learning and applying complex, context-dependent hierarchical rules that are critical in fluid intelligence (Gf).

Foundational 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

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

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

Models

Stanovich’s Tripartite Model of Mind

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Zelato’s Reiterative Reprocessing Model of Higher Cognition

Research Summary​​​​​​​

  • Reflection, or the reprocessing of information, provides a foundation for executive functions.
  • Training reflection and rule use improves executive functions.

Foundational 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 fromhttp://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. Self Regulated Learning (SRL)

Model

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Self-Regulated Learning (SRL) Model

Foundational Research Article

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

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

Models

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Task Positive Network — orange; Default Mode — Blue. From Aboitiz et al (2014)

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Andrews-Hanna et al., 2010

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Compositional Coding.Cole et al., 2013

Foundational Research Articles

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

Executive Processes Flexible Hubs & Compositional Coding Theory
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

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

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

APPENDIX 2

Academic Origins Of The Core Hubs Cognitive Training Framework

The origins of the five foundations of my Core Hubs Training Framework (Appendix 1) 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 (Foundation 5).
  • Professor Jason Chein (bio). 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.
  • Professor Todd Braver (bio) is a fellow 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 5).
  • 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).

This was written by Mark Ashton Smith, Ph.D. and originally published on his excellent newsletter for IQ Mindware.

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