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Summary: Researchers found in mice that passive exposure, along with active training, can significantly improve the learning process. This study demonstrates how passive exposure to stimuli such as sounds or languages helps the brain form fundamental representations, making active learning more efficient.
The findings, which align with previous research in humans, suggest that combining low-effort passive exposure with active training can lead to faster mastery of new skills, such as learning a musical instrument or a foreign language.
Key facts:
- Mice passively exposed to sounds, in addition to active training, learned to associate sounds with rewards more quickly.
- Artificial neural network simulations indicate that passive exposure creates a fundamental representation of the stimuli in the brain.
- The study’s insights align with human research, suggesting that a combined approach of passive exposure and active training could improve learning of complex skills.
Fountain: University of Oregon
Learning a new skill requires deliberate practice over time, but passive exposure to the topic at hand can help speed up the process, new research in mice from the University of Oregon suggests.
The finding, which builds on previous research in humans, shows how passive exposure can be a valuable tool for learning. It helps explain how watching movies in a foreign language can complement grammar exercises and flashcards, or how listening to recordings of a professional playing piano concertos could help budding musicians improve their own craft.
The study provides additional information about the possible brain mechanisms behind the effect, helping scientists understand why passive exposure is so powerful, said James Murray, a UO neuroscientist who led the study along with UO neuroscientist Santiago Jaramillo. , both part of the Faculty of Arts. and Sciences.
Because it is much easier to study what happens inside the brain of a rodent than that of a human, “studying how both active training and passive exposure affect learning in mice opens up interesting possibilities for investigating the neural mechanisms underlying learning.” interaction between them,” added Murray.
The researchers describe their findings in an article published in the journal. eLife.
To study how mice learn, the researchers trained the animals to reach for a reward at a particular location in response to tones that went up or down in pitch. All mice underwent an active training protocol, in which they received feedback on their performance to see if they had made the correct decision. Some of the mice also received passive exposure, where they listened to the sounds while they were not performing the task.
The researchers showed that mice that were passively exposed to the sounds in addition to being actively trained learned to select the reward location more quickly. It did not seem to matter whether the passive exposure occurred at the beginning of training or was interspersed in small fragments throughout the active training sessions.
Then, to better understand how learning might be occurring in the brain, the researchers trained and tested different artificial neural networks on a simulated version of the learning task. Neural networks, a type of machine learning algorithm, process information in a way that mimics the way the brain processes information.
Artificial neurons represent real neurons, and learning occurs by modifying the strength of the connections between those neurons. They are not a direct replica of the brain, but can be used to generate hypotheses that can then be tested experimentally.
The modeling suggests that passive exposure to a stimulus lays a foundation in the brain, creating a hidden representation of that stimulus that captures its most salient features, like making a pencil outline before diving into a detailed painting. Then, during active learning, the brain links the stimulus to particular behaviors. With passive exposure, the brain is primed to make those connections more quickly.
In the future, the team hopes to record brain activity in mice during a similar learning task, to see if their predictions hold true.
While the research was conducted using a simple task in mice, the researchers suggest that the findings could also have implications for more complex learning in humans. Study co-author Melissa Baese-Berk, a former UO linguist now working at the University of Chicago, has previously published studies showing how passive exposure can help adult humans better learn to understand new speech sounds.
“Together with previous human work by Melissa and colleagues, our results suggest that, in mice and humans, a given performance threshold can be achieved with relatively less effort by combining low-effort passive exposure with active training,” Murray said.
“This information could be useful for humans learning an instrument or a second language, although more work will be needed to better understand how this applies to more complex tasks and how to optimize training programs that combine passive exposure with active training.” “.
About this news about learning and research in neuroscience
Author: Laurel Hamers
Fountain: University of Oregon
Contact: Laurel Hamers – University of Oregon
Image: Image is credited to Neuroscience News.
Original research: Open access.
“Passive exposure to task-relevant stimuli enhances categorization learning” by James Murray et al. eLife
Abstract
Passive exposure to task-relevant stimuli enhances categorization learning
Learning to perform a perceptual decision task is usually achieved through sessions of effortful practice with feedback.
Here, we investigated how passive exposure to task-relevant stimuli, which requires relatively little effort and does not require feedback, influences active learning.
First, we trained mice on a sound categorization task with several schedules that combined passive exposure and active training.
Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interspersed between active sessions.
Next, we train neural network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of the stimuli to improve the separability of the data through unsupervised learning during passive exposure provided the best explanation of the behavioral observations.
Additionally, we found that during interleaved programs, there is greater alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as programs with long periods of passive exposure before. of active training, according to our behavior. observations.
These results provide key information for the design of efficient training programs that combine active learning and passive exposure in both natural and artificial systems.