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Tuning in to brainwave rhythms speeds learning in adults

rhythms speeds

Summary: Connecting to a person’s brainwave cycle before they perform a learning task can dramatically improve the rate at which cognitive skills improve.

Source: University of Cambridge

Scientists have shown for the first time that briefly listening to a person’s brainwave cycle before they perform a learning task dramatically increases the rate at which cognitive skills improve.

rhythms speeds

According to the team behind the study, calibrating information delivery rates to match our brain’s natural rhythm increases our ability to absorb and adapt to new information.

Researchers at the University of Cambridge say these techniques could help us retain ‘neuroplasticity’ much later in life and advance lifelong learning.

“Each brain has its own natural rhythm, generated by the oscillation of neurons working together,” said Professor Zoe Kourtzi, lead author of the study from Cambridge’s Department of Psychology.

“We simulated these fluctuations to get the brain in tune with itself – and in the best state to thrive.”

“The plasticity of our brain is the ability to restructure and learn new things, continually building on previous patterns of neural interactions.

By harnessing brainwave rhythms, it may be possible to enhance flexible learning throughout life, from infancy through adulthood,” Kourtzi said.

The results, published in the journal Cerebral cortexwill be explored as part of the Center for Lifelong Learning and Individualized Cognition:

A Research Collaboration between Cambridge and Nanyang Technological University (NTU), Singapore.

Neuroscientists used electroencephalography — or EEG — sensors strapped to the head to measure electrical activity in the brains of 80 study participants and sample brainwave rhythms.

The team took alpha wave readings. The mid-range of the brainwave spectrum, this wave frequency tends to dominate when we are awake and relaxed.

Alpha waves oscillate between eight and twelve hertz: a complete cycle every 85 to 125 milliseconds. However, each person has their own maximum alpha frequency within this range.

Scientists used these readings to create an optical “pulse”: a white square that shimmers against a dark background at the same rate as each person’s individual alpha wave.

Participants were given a personalized 1.5-second pulse dose to get their brains to work at their natural rhythm – a technique called “training” – before being presented with a tricky and fast-paced cognitive task: trying to identify shapes specific in a barrage of visual clutter.

A brain wave cycle consists of a peak and a trough. Some participants received pulses corresponding to the peak of their waves, others to the trough, while others received random or mispaced rhythms (a little faster or slower).

Each participant repeated more than 800 variations of the cognitive task, and neuroscientists measured how quickly people improved.

The learning rate of those stuck in the right rhythm was at least three times faster than for all other groups. When the participants returned the next day to perform another set of tasks, those who had learned much faster under training had maintained their higher level of performance.

“It was exciting to find out the specific conditions you need to get that awesome boost in learning,” said first author Dr Elizabeth Michael, now at Cambridge’s Cognitive and Brain Sciences Unit.

“The intervention itself is very simple, just a brief flicker on a screen, but when we hit the right frequency plus the right phase alignment, it seems to have a strong and lasting effect.”

Above all, the drive pulses should ring with the trough of a brain wave. Scientists believe this is the point in a cycle where neurons are in a “high receptivity” state.

“We feel like we’re constantly paying attention to the world, but in fact our brain takes quick snapshots and then our neurons communicate with each other to string the information together,”

said co-author Professor Victoria Leong, from NTU and the Cambridge Department of Paediatrics. .

“Our hypothesis is that by matching the delivery of information to the optimal phase of a brain wave, we maximize information capture because this is when our neurons are at their peak of excitability. ”

Previous work from Leong’s Baby-LINC lab shows that the brainwaves of mothers and babies will synchronize when they communicate.

Leong thinks the mechanism in this latest study is so effective because it mirrors the way we learn as infants.

“We are harnessing a mechanism that allows our brain to align itself with temporal stimuli from our environment, specifically communication cues like speech, gaze, and gestures that are naturally exchanged during parent-baby interactions,” Leong said.

This shows a person in an EEG cap
The brainwave experiment set up in the Adaptive Brain Lab, led by Professor Zoe Kourtzi, within the Department of Psychology at the University of Cambridge. Credit: University of Cambridge

“When adults talk to young children, they adopt child-directed speech – a form of slow, exaggerated speech.

This study suggests that child-directed speech may be a spontaneous way to match rhythm and train children’s slower brain waves to support learning.

The researchers say that although the new study tested visual perception, these mechanisms are likely to be “general domain”

: applying to a wide range of tasks and situations, including auditory learning.

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They argue that the potential applications of brainwave entrainment may sound like science fiction, but are increasingly realizable.

“While our study used complex EEG machines, now there are simple headband systems that allow you to measure brain frequencies quite easily,” Kourtzi said.

“Children now do a lot of their learning in front of screens. One can imagine using brainwave rhythms to improve certain aspects of learning for children who struggle in regular classrooms, perhaps due to attention deficits.

Other early applications of brainwave entrainment to boost learning could involve training in professions where quick learning and quick decision-making are essential, such as pilots or surgeons.

“Virtual reality simulations have become an integral part of training in many professions,” Kourtzi said.

“Implementing impulses that synchronize with brainwaves in these virtual environments could give new learners an edge or help those retraining later in life.”

About this learning research news

Author: Fred Lewsey
Source: University of Cambridge
Contact: Fred Lewsey – University of Cambridge
Picture: Image is credited to Cambridge University

Original research: Free access.
“Learning at the pace of your brain: individualized training stimulates perceptual decision learning” by Zoe Kourtzi et al. Cerebral cortex


Learn at the pace of your brain: individualized training stimulates learning for perceptual decisions

Training is known to improve our ability to make decisions when interacting in complex environments. However, individuals vary in their ability to learn new tasks and acquire new skills in different contexts. Here, we test whether this variability in learning ability is related to individual brain oscillatory states.

We use a visual flicker paradigm to train individuals at their own brain rhythm (i.e. peak alpha frequency) as measured by resting-state electroencephalography (EEG). We demonstrate that this individual frequency-matching brain training results in faster learning in a visual identification task (i.e.

Additionally, we show that learning is specific to the phase relationship between the entraining flicker and the target visual stimulus. EEG during training showed that individualized alpha training increases alpha power, induces phase alignment in the pre-stimulus period, and results in shorter latency of early visual evoked potentials, suggesting that training brain facilitates early visual processing to support better perceptual decisions.

These results suggest that individualized brain training can stimulate perceptual learning by altering gain control mechanisms in the visual cortex, indicating a key role for individual neural oscillatory states in learning and brain plasticity.

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