The Neuroscience of Learning
“In the last few decades, this fixed idea of the brain has been discarded. Instead, we talk about the brain being “plastic”, meaning that the brain changes its own wiring, perhaps almost continuously. Like a piece of silly putty, the brain is moulded and reshaped by the forces of life acting on it. Our wiring grows and develops depending on what we experience—even before birth. As we interact with the world, the world becomes internalized, or mapped, in our brain. The extensive plasticity of the brain continues throughout life.”
James E. Zull (2004)
van Dam (2013), Director and Chief Learning Officer in global talent for Deloitte Touche Tohmatsu Limited, suggests that previously the field of study, training and development, has ‘had its roots in pedagogy, didactics, and instructional design focused on individual education and learning’. He goes on to say that the field of developmental psychology ‘provides us with additional important insights on the integration of the mind and behaviors’, and ‘cognitive neuroscience is the study of mental brain processes and its underlying neural systems’. van Dam (2013) believes the process of training and development needs to take into consideration thinking and behavior that supports the concept of a ‘learning brain’, as this is the process of learning that enables learners to adapt to their ever-changing environments.
Until recently, (1980’s), it was believed that brain structure, once developed during childhood, changed very little. Studies of the brain in the past couple of decades using sophisticated brain imaging tools have allowed researchers to better understand how the brain develops and in particular the process of brain plasticity or neuroplasticity, where, even as adults our brains are able to develop new neural pathways as we learn. This capacity for change in the adult brain requires that learning professionals re-consider their approaches to organisational learning and how it occurs in individuals. A number of innovative, evidence-based findings have been uncovered in recent research, which can affect how organisations should design and employ training interventions. Some of these findings are discussed below.
The AGES Learning Model
Neuroscience research can help with understanding how the brain works, (Rock, 2014). Studying how people recall information is an aspect of learning that can be explored in a research lab. Rock (2014) suggests that there is now very good research showing that recall of ideas correlates to activation, during an encoding task, of a brain region called the hippocampus. He suggests that whether or not we remember something later closely links to how active the hippocampus is while we are learning. Rock (2014) refers to research conducted at the the NeuroLeadership Institute conducted by leading memory neuroscientists to review lab findings from across the globe on what makes the hippocampus activate. He suggests they found a surprisingly robust pattern that can affect designing learning interventions.
Rock (2014) suggests the research points to four must-haves to embed new ideas:
- ‘Attention has to be very high; multitasking dramatically reduces recall. The chemical processes to encode memories only activate when we’re very focused,
- People need to Generate their own mental maps around new ideas. They can’t just watch or listen; effort is central,
- Emotions need to be high; learners only remember things they feel strongly about,
- People grow their memories, so Spacing out learning is critical’.
These four elements, attention, generation, emotion and spacing, form the Neuroscience Institute AGES model. Rock (2014), believes that high AGES are necessary for people to recall ideas, but when AGES domains are low, idea recall will be poor. Rock (2014), further advocates that designing learning programs must begin with a focus on the brain, take each of the AGES points into account, and then getting creative on how to execute the learning intervention.
Prior Knowledge, a catalyst to learning new knowledge
Researchers stress the continued importance of being able to learn from previous experiences and share this knowledge in and between interventions, in order to avoid repetition of mistakes and redundancy of information (Prencipe & Tell, 2001). Cohen and Levinthal (1990) suggest that there is an increased likelihood of sharing between individuals taking place when ‘a foundation of prior relevant knowledge exists’.
There are two categories of knowledge: tacit knowledge and explicit knowledge (Nonaka & Takeuchi, 1995).Tacit knowledge is an individual type of knowledge, acquired through experience (Augier, Shari, and Vendelo, 2001). Willoughby & Wood (1994, p. 140) proposed that ‘elaborative interrogation enhances learning by supporting the integration of new information with existing prior knowledge’.
The processing of similarities and differences among ‘to-be-learned facts’, accounts for findings that elaborative-interrogation effects are often larger when elaborations are precise rather than imprecise, when prior knowledge is higher rather than lower, consistent with research showing that pre-existing knowledge enhances memory by facilitating distinctive processing (Rawson & Van Overschelde, 2008).
Learning requires Active Engagement by the individual learner
In order for neural changes to take place in the brain, it is necessary for the learning process to stimulate the brain. Research suggests that active engagement is a prerequisite for changes in the brain (Rock, Tang & Dixon, 2009). A learner brain will not activate while sitting listening and watching a boring PowerPoint presentation and as a result very little learning will take place. In order to activate learning and stimulate active engagement the process has to include training initiatives that include facilitation, simulation, games, and role-play, van Dam, (2013).
Rock et al. (2009) propose that ‘the neural basis of engagement of an individual can be defined by considering the average levels of activation of the brain’s reward and self-regulation circuitry when that individual thinks about or participates in their work’. An employee who has a high level of engagement at work would experience high levels of activation of their reward and self-regulation circuitry. These individuals would have good levels of dopamine in their executive attention or self-regulation networks and reward circuitry, and only moderate levels of activation of the threat circuitry, (Limbic system, amygdala, and hippocampus). Under these conditions, dopamine is sent directly into associated regions of the brain, positively affecting a wide range of cognitive and emotional functions through increasing brain resources and functional connectivity (Tang & Posner, 2009; Tang et al, 2009).
Saks (2006) points out that there is currently no globally recognised definition of engagement, but that in his opinion there is increasing acceptance in the academic community that engagement is a psychological state and is “a distinct and unique construct consisting of cognitive, emotional and behavioural components…associated with individual job performance”.
Burton et.al. (2015) believe that the definition by Saks helps to establish two important principles:
- ‘That engagement is individual and personal – organisations cannot build engagement at group level, interventions must rather be focused on engaging every individual within the group.
- That engaged people make an emotional, cognitive and physical commitment to their work – engaged people commit to their work on more than one level’.
They go on further to suggest that, ‘unless the focus of that engagement is aligned with organisational intent and adds value, the engagement may actually be detrimental to organisational interests’.
There is a significant emotional base to the learning process
Davichi, Keifer, Rock, D., & Rock, L. (2010) suggest that ‘Learning happens in many complex layers, with emotion being one of the more important regulators of learning and memory formation’. They quote Jensen (2005), who identified a 0.9 correlation between the vividness of a memory, and the emotionality of the original event. They suggest two ways in which they believe memory is enhanced:
- ‘Emotional content is thought to grab the attention of the individual, and, hence, help to focus attention on the emotional event or stimulus.
- Emotion activates the amygdala, which signals the hippocampus that a particular event is salient, and, increases the effectiveness of encoding’.
Learners are inclined, (motivated), to engage in situations with an emotionally positive bias and avoid those with an emotionally negative bias. Learning program designers need to design learning interventions that tap into emotions. This can be achieved by, for example, asking learners to share work experiences that have been difficult for them can engage individual emotions, or asking learners to present some of the learning material to the class.
Use learning techniques that enhance memory formation
Dunlosky, Rawson, Marsh, Nathan, & Willingham (2013) explored the efficacy of the following 10 learning techniques that could be used to improve learner recall across a wide variety of content domains:
- ‘Elaborative interrogation Generating an explanation for why an explicitly stated fact or concept is true
- Self-explanation Explaining how new information is related to known information, or explaining steps taken during problem solving
- Summarization Writing summaries (of various lengths) of to-be-learned texts
- Highlighting/underlining Marking potentially important portions of to-be-learned materials while reading
- Keyword mnemonic Using keywords and mental imagery to associate verbal materials
- Imagery for text Attempting to form mental images of text materials while reading or listening
- Rereading Restudying text material again after an initial reading
- Practice testing Self-testing or taking practice tests over to-be-learned material
- Distributed practice Implementing a schedule of practice that spreads out study activities over time
- Interleaved practice Implementing a schedule of practice that mixes different kinds of problems, or a schedule study that mixes different kinds of material, within a single study session’.
Practice testing and distributed practice received high utility assessments as Dunlosky et al. (2013) felt the practices benefited learners of different ages and abilities and boost learners’ performance across many criterion tasks.
Dunlosky et al. (2013) found that elaborative interrogation, self-explanation, and interleaved practice received moderate utility assessments. They believe there is insufficient evidence available to support confidence in assigning a higher utility although showed enough promise for them to recommend their use in appropriate situations.
Five techniques received a low utility assessment from the team: summarization, highlighting, the keyword mnemonic, imagery use for text learning, and rereading. Dunlosky et al. (2013) rated these techniques as low utility for a number of reasons. Summarization and imagery produced limited benefits, and they recommend further research to fully explore their overall effectiveness. They found learners struggled to implement keyword mnemonic in some contexts, with short retention intervals. Dunlosky et al. (2013) report that the majority of learners report rereading and highlighting, even though these techniques do not consistently boost learners’ performance. They recommend learners use other more beneficial techniques such as practice testing instead of rereading.
Dunlosky et al. (2013) suggest that the learning techniques described are not a ‘panacea for improving achievement for all learners’, and will only benefit only learners who are motivated and capable of using them. When used properly, Dunlosky et al. (2013) recommend that learners should be encouraged to more consistently, (and explicitly), use high utility learning techniques as they engage in learning interventions.
When Learning becomes SCRAP Learning
The adult brain changes following the acquisition of new skills, however, the brains of learners who do not have the opportunity to use their new skills will undergo reversals in brain changes as they lose skills that are not used. The result is that training initiatives are less effective when learners are not encouraged or allowed to apply their new skills in the workplace.
Brinkerhoff (2014) refers to this phenomenon as Scrap Learning. He estimates that only 9 percent of learners actually apply what they learn with positive results. In order to mitigate the risk of wasted learning, Mattox (2011, recommends that organisational managers meet with the learner prior to the learning intervention in order to set learning and performance expectations, and create a joint action plan aligned with business goals. Post training, the manager needs to review the action plan with the learner and determine if it still aligns with what was taught.
The manager must supervise and provide meaningful praise and feedback when the learner applies training on the job. This will reinforce success and correct mistakes affording the manager the opportunity to seek projects, events, or situations where the learner can hone their new skills in the organisational context. By participating in the training process pre- and post-event, managers can ensure that employees retain and apply more of what they learn (Mattox, 2011).
Multitasking – A Learning Distraction
Human brains are not wired for multitasking because most of us can only apply our full conscious attention to one stimulus at a time. Mihaly Csikszentmihalyi (Ted Talks Video) has found that the human nervous system can only input 110 bits per second of information, which allows learners to pay attention to just one stream of information, (a single conversation is typically 60 bits per second), at a time with very little room for distractions.
Working memory, the part of the brain that allows us to focus our attention on a task such as reading, continues to interact with long-term memory where learners retrieve and store specific information. Learners who try to conduct two tasks at the same time must switch between the different tasks and there is a resulting overload between working memory and long-term memory, resulting in lost time.
Davis et al. (2014) point out that ‘those who think they are good at multitasking have been shown to be the worst at it’. They refer to work by Ophir, Nass, & Wagner, (2009), who have shown that ‘people who report multitasking more often actually multitask worse than those who report multitasking less often’. Ophir et al. (2009) go on to suggest that multitaskers may believe they are more efficient, but research has demonstrated that although learners believe they are optimising their time while multitasking during activities such as reading, texting, typing, listening, and speaking at the same time, they are in fact deceiving themselves.
The impact of Cognitive Bias on Learning
Liebermann, Rock, Halvorson, & Cox (2015) suggest that biases are ‘unconscious drivers that influence how we see the world’. Cognitive biases affect learning and decision making by using previous knowledge to inform new decisions. The problem with this ability occurs when individual bias clouds new information inhibiting the consideration of a broad range of options when making an important decision or taking on new information. Negative thinking, cognitive biases and faulty assumptions can distort thinking, emotions and behavior leading to depression, post-traumatic stress disorder and other psychological disorders.
Cognitive biases are trickier to detect than negative emotions as they exert unconscious influences that are difficult to detect. Cognitive biases play tricks with the mind and lead to irrational thinking, such as, overestimating the likelihood of an event happening if it is associated with an emotional event.
Liebermann et al. (2015) believe that biases are a significant issue in organizations. They point out the need for business leaders, front-line staff, and, organizations as a whole to identify and then mitigate biases based on the underlying issues associated with five bias categories they refer to as SEEDS;
- ‘Similarity – self-interest and self-sustaining motives often conflict with an objective perception of others, the world, and ourselves.
- Expedience – mental shortcuts help us make quick and efficient decisions, but may be based on incorrect judgments
- Experience – the implicit belief that our perceptions and beliefs are objectively true.
- Distance – assigning greater value to those things that we perceive to be closer to us, simply because they are close.
- Safety – negative information tends to be motivating than positive information influencing our decisions’.
Liebermann et al. (2015) acknowledge that the SEEDS Model is new and requires more research, but suggest that it is intended to be a tool for learning managers in ‘reducing the unhelpful biases that are at the heart of many organizational challenges today’.
Significant progress has been made over the past couple of decades in understanding the cognitive neuroscience of learning. It is imperative however, that learning professionals educate themselves on the various aspects of educational neuroscience and avoid unproven untested historical ideas and learning myths that add no value to learning interventions and/or result in scrap learning. In order to derive the best possible value from learning interventions, organisations and their learning professionals will need to develop a fundamental knowledge of the brain and understanding of neuroscience research discoveries, which enhance learning and memory embedding of new skills in learners. This approach will ensure innovative and creative design of learning programs that will enhance the value add to both individual on organisation.
- Burton, C. & Buchan, L. (2015). ‘Engagement and Wellbeing: An Integrated Model’, retrieved from’ www.designed4success.co.uk, on 12 July 2016.
- Csikszentmihalyi, M. ‘Positive Psychology’, TedTalks video viewed on 12 June 2015.
- Cohen, W. M. and Levinthal, D. (1990). Absorptive capacity: a new perspective on learning and innovation. Administrative Science Quarterly, 35: 128–152.
- Davichi, L., Keifer, T., Rock, D., & Rock, L. (2010). ‘Learning that lasts through AGES’, NeuroLeadership Journal, Issue 3, 2010.
- Davis. J, Balda. M, Rock. D, McGinniss. P, and Davichi. L, “The Science of Making Learning Stick: An Update to the AGES Model”, Neuroleadership Journal, Volume 5, August 2014.
- Dunlosky, J, Rawson, R.A. Marsh, E.J. Nathan, M.J. Willingham, D.T.(2013), ‘Improving Students’ Learning With Effective Learning Techniques: Promising Directions From Cognitive and Educational Psychology’, Psychological Science in the Public Interest 14(1) 4–58
- Hunt, R. R. (2006). The concept of distinctiveness in memory research. In R. R. Hunt & J. B. Worthen (Eds.), Distinctiveness and memory (pp. 3–25). New York, NY: Oxford University Press.
- Ikujiro Nonaka and Hirotaka Takeuchi (1995) The Knowledge-Creating Company. How Japanese Companies Create the Dynamics of Innovation Oxford University Press, New York
- Josh Davis, j. Balda, M. Rock, R. McGinniss, P, & Davachi, L. ‘The Science of Making Learning Stick: An Update to the AGES Model’, NeuroLeadership Journal Volume Five | August 2014
- Lieberman, M.D. Rock, D. Halvorson, H.G. & Cox, C. ‘Breaking Bias Updated : The Seeds Model’, NeuroLeadership JOURNAL VOLUME SIX | NOVEMBER 2015
- MATTOX, J.R. II, (2011), “Scrap Learning and Manager Engagement”, Chief Learning Officer, Business Inrtelligence.
- Mie Augier, Syed Z. Shariq, Morten Thanning Vendelø, (2001) “Understanding context: its emergence, transformation and role in tacit knowledge sharing”, Journal of Knowledge Management, Vol. 5 Iss: 2, pp.125 – 137
- Ophir, E. Nass, C. & Wagner, A. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106, (7), 15583-15587.
- Prencipe, A. & Tell, F. (2001). Inter-project learning: processes and outcomes of knowledge codification in project-based firms, Research Policy, 30(9), pp.1373-1394.
- Rawson, K. A. & Van Overschelde, J. P. (2008). How does knowledge promote memory? The distinctiveness theory of skilled memory. Journal of Memory and Language, 58, 646–668.
- Rock, D. ‘The AGES model of learning – the Brain at Work’, September 2014
- Rock, Dr. D Tang Dr. Y & Dixon, P. ‘Neuroscience of engagement’, NeuroLeadership journal, issue TWO, 2009
- Saks, A.M. (2006) Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21, 600-619.
- Tang Y.Y. Ma, Y. Fan, Y. Feng H, Wang, J. Feng, S. Lu, Q.Hu, B. Lin, Y. Li J, Zhang Y, Wang Y, Zhou L, & Fan, M. (2009) Central and autonomic nervous system interaction is altered by short term meditation. Proceedings of the National Academy of Sciences, US A, 106, 8865–70.
- Tang, Y.Y. & Posner, M.I. (2009) Attention training and attention state training. Trends in Cognitive Sciences, 13, 222–227.
- The Maritz Institute White Paper, Ronni Hendel-Giller in collaboration with Cindy Hollenbach, David Marshall,
- Kathy Oughton, Tamra Pickthorn, Mark Schilling and Giulietta Versiglia, “The Neuroscience of Learning: A New Paradigm for Corporate Education”, May 2010.
- The Truth about Personality – retrieved from, https://www.youtube.com/watch?v=qr0eJ9cPPPM, on 4 April 2014.
- van Dam. N. ‘Inside the Learning Brain’, – Monday, April 08, 2013, retrieved from ttps://www.td.org/Publications/Magazines/TD/TD-Archive/2013/04/Inside-the-Learning-Brain, on 11 July 2016
- Wang, Z. David, P. Srivastava, J. Powers, S. Brady, C. D’Angelo, J. & Moreland, J. (2012). Behavioral performance and visual attention in communication multitasking: A comparison between instant messaging and online voice chat. Computers in Human Behavior, 28, (3), 968-975.
- Willoughby, T. & Wood, E. (1994). Elaborative interrogation examined at encoding and retrieval. Learning and Instruction, 4, 139–149.
Next – Part 7/7 The Role of Management