Researchers have found that passive exposure, paired with active training, can significantly improve the learning process in mice. This study shows how passive exposure to stimuli such as sounds or languages helps the brain to build foundational representations, making active learning more effective. These findings are consistent with previous research in humans and 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:The mice exposed to sounds passively, in addition to active training, learned to associate sounds with rewards more quickly.Simulations of artificial neural networks indicate that passive exposure creates a foundational representation of stimuli in the brain.The study’s insights align with human research, suggesting a combined approach of passive exposure and active training could enhance complex skill learning.Source: University of OregonLearning a new skill requires deliberate practice over time, but passive exposure to the subject matter at hand can help expedite the process, according to new research conducted at the University of Oregon in mice. This finding, which builds upon past research in humans, demonstrates the value of passive exposure as a tool for learning. It helps elucidate how watching movies in a foreign language might complement grammar drills and vocabulary flashcards, or how listening to recordings of professional piano concertos could assist budding musicians in honing their craft. With passive exposure, the brain is primed to make those connections more quickly. Credit: Neuroscience NewsThe study offers further insight into the potential brain mechanisms underpinning the effect, assisting scientists in understanding why passive exposure is so potent, said James Murray, a UO neuroscientist who led the study alongside fellow UO neuroscientist Santiago Jaramillo, both part of the College of Arts and Sciences.Because it’s much easier to study what’s happening inside the brain of a rodent than a human, “studying how both active training and passive exposure affect learning in mice opens up exciting possibilities for investigating the neural mechanisms underlying the interplay between them,” Murray added.The researchers describe their findings in a paper published in the journal eLife.To study how mice learn, researchers trained the animals to reach for a reward in a particular spot in response to tones that slid up or down in pitch. All of the mice were subjected to an active training protocol, in which they received feedback on their performance. Some of the mice also received passive exposure, hearing the sounds while not actively engaged in the task. The mice who were passively exposed to the sounds in addition to being actively trained learned how to select the reward location more rapidly, the researchers demonstrated. The timing of passive exposure, whether at the beginning of training or interspersed in small segments throughout the active training sessions, did not seem to significantly affect the outcome.Then, to gain a better understanding of how learning might occur in the brain, the researchers trained and tested different artificial neural networks on a simulated version of the learning task. Neural networks, a form of machine learning algorithm, process information in a manner that mirrors the brain’s information processing. Artificial neurons represent real neurons, and learning occurs through modifying the strengths of the connections between those neurons. While not a direct replica of the brain, they can be used to formulate hypotheses that can be experimentally tested.The modeling indicates that passive exposure to a stimulus establishes the groundwork in the brain, creating a concealed representation of that stimulus, capturing its most prominent features, akin to making a pencil outline before creating a detailed painting. During active learning, the brain links the stimulus to specific behaviors, and with passive exposure, the brain is prepared to make those connections more rapidly.In the future, the team aspires to record brain activity in mice during a similar learning task to verify their predictions.While the research was conducted using a simple task in mice, the findings may also have implications for more complex learning in humans, the researchers propose. Study co-author Melissa Baese-Berk, a former UO linguist now at the University of Chicago, has previously published studies demonstrating how passive exposure can aid adult humans in better understanding new speech sounds.“Alongside the previous work on humans from Melissa and her collaborators, our results suggest that, in mice and in humans, a given performance threshold can be achieved with relatively less effort by combining low-effort passive exposure with active training,” Murray said.“This insight could be beneficial for humans learning an instrument or a second language, although further investigation will be necessary to better comprehend how this applies to more complex tasks and how to optimize training schedules that integrate passive exposure with active training.”About this learning and neuroscience research newsAuthor: Laurel Hamers
Source: University of Oregon
Contact: Laurel Hamers – University of Oregon
Image: The image is credited to Neuroscience NewsOriginal Research: Open access.
“Passive exposure to task-relevant stimuli enhances categorization learning” by James Murray et al. eLifeAbstractPassive exposure to task-relevant stimuli enhances categorization learningLearning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback.Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning.First, we trained mice in a sound-categorization task with various schedules combining 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 interleaved between active sessions.We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations.We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations.These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.
