Enriched Learning: Behavior, Brain, and Computation
By Brian Mathias, TRENDS IN COGNITIVE SCIENCES, January 2023, Vol.27, Iss.1
As discussed extensively in our sister-publication Trends, lifetime learning for all employees is becoming a competitive advantage for more and more companies because it increases both productivity and retention.
So, it’s more important than ever that training and development be effective.
Many educational approaches assume that integrating complementary sensory and motor information into the learning experience can enhance learning.
For example, gestures help in learning new vocabulary in foreign language classes.
Recent research published in the journal Trends in Cognitive Sciences, summarize these methods using the term “multimodal enrichment.”
This means enrichment with multiple senses and movement.
Numerous current scientific studies prove that multimodal enrichment can enhance learning outcomes.
Experiments in classrooms show similar results.
In this case, the two researchers compare these findings with cognitive, neuroscience, and computational theories of multimodal enrichment.
Recent neuroscience research has found that the positive effects of enriched learning are associated with response in brain regions that serve perception and motor function.
For example, hearing a recently learned foreign language word, may elicit activity in motor brain regions if the word was associated with the performance of a congruent gesture during learning.
These brain responses are causal to the benefits of multimodal enrichment for learning outcome. Computer algorithms confirm this hypothesis.
The brain is optimized for learning with all the senses and with movement.
Brain structures for perception and motor skills work together to promote this type of learning.
A deeper understanding of the brain’s learning mechanisms will facilitate the development of optimal learning strategies in the future.
The literature reviewed contributes to an understanding of why several long-used learning strategies, such as parts of the Montessori method, are effective.
They also provide clear clues as to why some approaches are not as effective.
Recently uncovered neuroscientific mechanisms may inspire the updating of cognitive and computational theories of learning, providing new hypotheses about learning.
It’s likely that such an interdisciplinary and evidence-based approach will lead to the optimization of learning and teaching strategies in the future, for both humans and artificial systems.
