Application of Neuroscience in Corporate Training

Video 1.0 design and deliver effective learning and development initiatives, it is essential to understand how our brains process and retain information. Collins (2015) provides the latest scientific research behind      multiple facets of training and learning, including the design and delivery of face to face, online and virtual learning, and how to create environments conducive to learning, along with how to distinguish between neuromyths and neuroscience.

                                        Video 1. Neuroscience for learning & development? | Stella Collins

There is evidence to suggest that there has been stronger diffusion of learning as the concepts of neuroscience are applied to the planning, production and implementation of organizational learning. Salas, Tannenbaum, Kraiger and Smith-Jentsch (2012) describe learning transfer as the extent to which learning is subsequently applied to the job during training or affects later work performance" (p. 77).

The variables that better predict learning transition in learning in institutions were investigated by a 2005 study by Chiaburu and Marinova. They reiterated previous research finding that the willingness of the individual to attend a training session is predictive of learning transfer (p. 117). It should be expected that those who were inspired to attend a training course will be more curious and more likely to pay attention to the material, in accordance with the concepts of neuroscience. When they had an intention of being good or felt optimistic in their ability, learners were more inspired to undergo the training (p. 118) and set a constructive target of mastering the material (p. 118). It may logically be expected that the anticipation of achievement and superiority would raise optimistic feelings that contribute to the release of dopamine and better focus. Contrary to what could have been expected, support from one's boss did not affect the transfer of skills, while peer support did not affect the transfer of skills (p. 118).

In order to better understand and disaggregate the position of the peer and manager from other organizational and environmental variables that can influence the transition of learning, the authors recommended further study and review. A 2014 research by Homklin, Takahashi and Techakanont with auto staff in Thailand verified the positive effect of peer support, but not supervisor support, on the transfer of learning (p. 126). The authors believe that the findings could be due to the team-based existence of work in the automotive field. Regardless, given the exponential growth in team-based or group practice, the concept of incorporating pre- and post-training peer reinforcement could be a fascinating way to increase learning transfer for those planning and implementing training. This can also be a low to no-cost way for companies to build networks for workers and improve their preparation ROI.

 

Grossman and Salas' 2011 study outlined the variables that facilitate learning transfer, splitting them into trainee traits, training configuration and job climate. They observed that self-efficacy and inspiration contributed to an improved transition of learning, comparable to the Chiaburu and Marinova (2006) report (p. 107). They also found that the perceived usefulness of learner instruction had a positive effect on the transfer of learning (p. 107). Again, one can conclude that they are more likely to pay attention and create new neuronal associations as learners can see the importance of training when they think about how to implement what they understand. Unlike other research (Chiaburu & Marinova, 2006; Homklin, Takahashi, & Techakanont 2014), they found that both peer and supervisor help for the transition of learning positively predicted (p. 108). They indicate that supervisors can help facilitate the transition of learning through setting standards for workers pre-training and setting targets for the application of skills during training (p. 113). Supervisors can also promote the transfer of skills gained through appreciation and input (p. 113). They also noticed that the ability for learners to exercise and gain follow-up on what they had learned back at work contributed to the transition of learning (p. 108). This will be consistent with the spacing and recovery concepts and the belief that learners need to regain the knowledge they have acquired each time they reinforce and strengthen the neural ties and consolidate the learning.

In a one or two-day course, a 2016 research by Mind Gym compared findings for learners who attended a 90-minute Mind Gym session on Impact or covered related content. The Mind Gym session included the concepts of concentration, interest, generation, self-reference and spacing in neuroscience (Mind Gym, 2016). Compared to those who attended a one-day program, those who attended a 90-minute session had better Level two results (knowledge and understanding of influence) (p. 7). Those who attended a two-day program, however, had the greatest increase in their knowledge and comprehension. However, the findings of the 90-minute session were comparable to both the one-day and two-day sessions when it came to the participant's ability to adapt what they had learned (Level 3) (p. 7). Ses effects have potential repercussions for companies that seek a better return on their time invested in workforce preparation and can be repeated for other learning material in other settings.

A 2017 meta-analysis by Lacerenza, Reyes, Marlow, Joseph and Salas looked directly at the variables in leadership growth that determined learning transfer. They observed that systems produced with a needs analysis resulted in better learning transfer than those without a learning analysis (p. 16). The authors assume that programs developed after an analysis of needs would be more relevant to learners. This would be consistent with the self-reference effect and the idea that when learners are able to relate to themselves and existing information, they retain information better (Symons & Johnson,1997). They also observed that voluntary training engagement improved learning transfer (p. 16) in accordance with the theory that interest and enthusiasm contribute to better outcomes of learning. Interestingly, however for services where participation was compulsory, operational effects were higher.

Unlike previous research they found that spaced training was no better for enhancing learning than mass training, but achieved better learning transfer with a more pronounced impact for those programs that spaced weekly sessions (p. 16). For trainers looking to improve the flow of information from programs, this has major consequences. They also observed that the use of instruction, different ways of learning and feedback enhanced the transition of learning (p. 17). Again the hypothesis that novelty raises interest and generation will support this (Davachi et al., 2010, p.3).

Finally given the many variables at play in any business system, it can be difficult to draw a clear connection between neuroscience and the transition of learning in a corporate setting. This include the manager's position, peer engagement, monetary and non-monetary compensation and appreciation, as well as institutional reinforcement for the desired behavioral improvement. Neuroscience is therefore only one of the variables affecting active learning.

 

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