Active learning leads to more neural activation than passive learning. Automaticity involves developing strategic neural connections that reduce the amount of effort that the brain has to expend to activate patterns of neurons.
This post is part of the book The Math Academy Way (Working Draft, Jan 2024). Suggested citation: Skycak, J., advised by Roberts, J. (2024). The Neuroscience of Active Learning and Automaticity. In The Math Academy Way (Working Draft, Jan 2024). https://justinmath.com/the-neuroscience-of-active-learning-and-automaticity/
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Neuroscience of Active Learning
The effects of active learning can be seen quite literally in the brain: in brain imaging studies, active learning consistently leads to more neural activation than passive learning.
For instance, in a study of students actively writing letters versus passively viewing them, the active writing produced higher brain activity in the sensori-motor network and beyond (Kersey & James, 2013):
- “Self-generated production of cursive letters during learning led to the recruitment of a sensori-motor network known to also be active during letter perception and reading, however, passive observation of a letter being formed did not. This finding adds to the growing literature suggesting that self-generated writing is important for setting up reading networks in the developing brain.
…
Further, when we directly compared active to passive learning of cursive letters, greater recruitment of the bilateral insula and claustrum was shown during the perception of actively learned letters than passively learned letters… [This would suggest that] children were better able to phonologically process letters that they learned by writing than those that they learned by observing an experimenter write… [and that] writing practice has led to more similar neural representation between printed letters and those letters learned by writing.”
Not only does active performance produce more physical brain activity than passive viewing, as described above, but researchers have also found that prior active performance can lead to higher brain activity even during passive viewing later on, in a sense “carrying over” to make the passive viewing more active within the brain.
Specifically, Calvo-Merino et al. (2006) demonstrated that when someone views another person performing an action, the viewer experiences higher activation in motor areas if they have frequently performed that action themself:
- “We found greater premotor, parietal, and cerebellar activity when dancers viewed moves from their own motor repertoire, compared to opposite-gender moves that they frequently saw but did not perform.”
The same researchers elaborated more in an earlier paper (Calvo-Merino et al., 2005):
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*“Comparing the brain activity when dancers watched their own dance style versus the other style therefore reveals the influence of motor expertise on action observation.
We found greater bilateral activations in premotor cortex and intraparietal sulcus, right superior parietal lobe and left posterior superior temporal sulcus when expert dancers viewed movements that they had been trained to perform compared to movements they had not.
Our results show that this ‘mirror system’ integrates observed actions of others with an individual’s personal motor repertoire, and suggest that the human brain understands actions by motor simulation.”*
Neuroscience of Automaticity
At a physical level in the brain, automaticity involves developing strategic neural connections that reduce the amount of effort that the brain has to expend to activate patterns of neurons.
Researchers have observed this in functional magnetic resonance imaging (fMRI) brain scans of participants performing tasks with and without automaticity (Shamloo & Helie, 2016). When a participant is at wakeful rest, not focusing on a task that demands their attention, there is a baseline level of activity in a network of connected regions known as the default mode network (DMN). The DMN represents background thinking processes, and people who have developed automaticity can perform tasks without disrupting those processes:
- “The DMN is a network of connected regions that is active when participants are not engaged in an external task and inhibited when focusing on an attentionally demanding task… at the automatic stage (unlike early stages of categorization), participants do not need to disrupt their background thinking process after stimulus presentation: Participants can continue day dreaming, and nonetheless perform the task well.”
When an external task requires lots of focus, it inhibits the DMN: brain activity in the DMN is reduced because the brain has to redirect lots of effort towards supporting activity in task-specific regions. But when the brain develops automaticity on the task, it increases connectivity between the DMN and task-specific regions, and performing the task does not inhibit the DMN as much:
- ”…[S]ome DMN regions are deactivated in initial training but not after automaticity has developed. There is also a significant decrease in DMN deactivation after extensive practice.
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The results show increased functional connectivity with both DMN and non-DMN regions after the development of automaticity, and a decrease in functional connectivity between the medial prefrontal cortex and ventromedial orbitofrontal cortex. Together, these results further support the hypothesis of a strategy shift in automatic categorization and bridge the cognitive and neuroscientific conceptions of automaticity in showing that the reduced need for cognitive resources in automatic processing is accompanied by a disinhibition of the DMN and stronger functional connectivity between DMN and task-related brain regions.”
In other words, automaticity is achieved by the formation of neural connections that promote more efficient neural processing, and the end result is that those connections reduce the amount of effort that the brain has to expend to do the task, thereby freeing up the brain to simultaneously allocate more effort to background thinking processes.
References
Calvo-Merino, B., Glaser, D. E., Grèzes, J., Passingham, R. E., & Haggard, P. (2005). Action observation and acquired motor skills: an FMRI study with expert dancers. Cerebral cortex, 15 (8), 1243-1249.
Calvo-Merino, B., Grèzes, J., Glaser, D. E., Passingham, R. E., & Haggard, P. (2006). Seeing or doing? Influence of visual and motor familiarity in action observation. Current biology, 16 (19), 1905-1910.
Kersey, A. J., & James, K. H. (2013). Brain activation patterns resulting from learning letter forms through active self-production and passive observation in young children. Frontiers in psychology, 4, 567.
Shamloo, F., & Helie, S. (2016). Changes in default mode network as automaticity develops in a categorization task. Behavioural Brain Research, 313, 324-333.
This post is part of the book The Math Academy Way (Working Draft, Jan 2024). Suggested citation: Skycak, J., advised by Roberts, J. (2024). The Neuroscience of Active Learning and Automaticity. In The Math Academy Way (Working Draft, Jan 2024). https://justinmath.com/the-neuroscience-of-active-learning-and-automaticity/
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