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New research reveals that the brain\u2019s dopamine system is tuned to prioritize the time between rewards rather than the sheer number of repetitions, upending a century of learning theory. Credit: Neuroscience News<\/p>\nSummary: For over a century, the cornerstone of psychology has been the Pavlovian idea that we learn through repetition\u2014the more a bell rings before food, the stronger the association. However, a groundbreaking study is upending this 100-year-old assumption.<\/p>\n
Researchers discovered that the brain actually learns more efficiently when rewards are rare and spaced far apart. Rather than \u201cpractice makes perfect,\u201d the brain\u2019s dopamine system prioritizes the timing between events. This discovery suggests that our neural circuitry is designed to extract maximum information from infrequent experiences, providing a new biological explanation for why \u201ccramming\u201d for exams fails while spaced-out learning succeeds.<\/p>\n
Key Facts<\/p>\n
The Timing Rule: The brain determines how much to learn based on the time between cue-reward pairings, rather than the total number of repetitions.Dopamine Acceleration: When rewards are spaced further apart, the brain requires significantly fewer repetitions before it begins releasing dopamine in anticipation of the reward.Sparse Learning Efficiency: Mice that received rewards only 10% of the time learned at the same rate\u2014or faster\u2014than those who received rewards 20 times more frequently.The \u201cCramming\u201d Effect: When experiences happen too close together, the brain \u201cdownregulates\u201d its learning, explaining why frequent, repetitive exposure can lead to diminishing returns in memory.AI Implications: This discovery could lead to faster artificial intelligence. Current AI requires billions of data points to learn, but a model based on this \u201csparse learning\u201d theory could learn more quickly from fewer experiences.<\/p>\n
Source: UCSF<\/p>\n
More than a century ago, Pavlov trained his dog to associate the sound of a bell with food. Ever since, scientists assumed the dog learned this through repetition: The more times the dog heard the bell and then got fed, the better it learned that the sound meant food would soon follow.<\/p>\n
Now, scientists at UC San Francisco are upending this 100-year-old assumption about associative learning. The new theory asserts that it depends less on how many times something happens and more on how much time passes between rewards.<\/p>\n
New research reveals that the brain\u2019s dopamine system is tuned to prioritize the time between rewards rather than the sheer number of repetitions, upending a century of learning theory. Credit: Neuroscience News<\/p>\n
\u201cIt turns out that the time between these cue-reward pairings helps the brain determine how much to learn from that experience,\u201d said\u00a0Vijay Mohan K. Namboobidiri, PhD, an associate professor of Neurology and senior author of\u00a0the study, published Feb. 12 in\u00a0Nature Neuroscience.<\/p>\n
When the experiences happen closer together, the brain learns less from each instance, Namboodiri said, adding that this could explain why students who cram for exams don\u2019t do as well as those who studied throughout the semester.<\/p>\n
Learning the cues<\/p>\n
Scientists have traditionally thought of associative learning as a process of trial and error. Once the brain has detected that certain cues might lead to rewards, it begins to predict them. Scientists have postulated that at first the brain only releases dopamine when a reward like tasty food arrives.\u00a0<\/p>\n
But if the reward arrives often enough, the brain begins to anticipate it with a release of dopamine as soon as it gets the cue. The dopamine hit refines the brain\u2019s prediction, the theory goes, strengthening the link with the cue if the reward arrives \u2014 or weakening it if the reward fails to appear.\u00a0<\/p>\n
Namboodiri and postdoctoral scholar\u00a0Dennis Burke, PhD, trained mice to associate a brief sound with getting sugar-sweetened water, varying the time between trials. They spaced the trials 30 to 60 seconds apart for some of the mice, and five to 10 minutes apart, or more, for others. The result was that the mice whose trials were closer together received many more rewards than those who trials were spaced farther apart in the same amount of time.\u00a0<\/p>\n
If associative learning depended only on repetition, the mice with more trials should have learned faster. Instead, the mice that got very few rewards learned the same amount as those that got 20 times more trials over the same amount of time.\u00a0<\/p>\n
\u201cWhat this tells us is that associative learning is less \u2018practice makes perfect\u2019 and more \u2018timing is everything,\u2019\u201d said Burke, the first author of the study.\u00a0<\/p>\n
Namboodiri and Burke then looked at what dopamine was doing in the mouse brain.\u00a0<\/p>\n
When the rewards were spaced further apart, the mice needed fewer repetitions before their brains began to respond to the sound with dopamine.<\/p>\n
Then, the researchers tried a different variation. They repeatedly played the sound \u2014 spacing the cues 60 seconds apart \u2014 but only gave the mice sugar water 10% of the time. These mice needed far fewer rewards before they began releasing dopamine after the cue, regardless of whether it was followed by a reward.\u00a0<\/p>\n
More rapid learning<\/p>\n
The findings could shift the way we look at learning and addiction. Smoking, for example, is intermittent and can involve cues \u2014 like the sight or smell of cigarettes \u2014 that increase the urge to smoke. Because a nicotine patch delivers nicotine constantly, it may disrupt the brain\u2019s association between nicotine and the resulting dopamine reward, blunting the urge to smoke and making it easier to quit.\u00a0<\/p>\n
Next, Namboodiri plans to investigate how his new theory could speed up artificial intelligence. Current AI systems learn quite slowly, because they are based on the prevailing model of associative learning, making small refinements after every interaction between billions of data points.\u00a0<\/p>\n
\u201cA model that borrows from what we\u2019ve discovered could potentially learn more quickly from fewer experiences,\u201d Namboodiri said. \u201cFor the moment, though, our brains can learn a lot faster than our machines and this study helps explain why.\u201d<\/p>\n
Authors:\u00a0Additional authors on the study include Annie Taylor, Huijeong Jeong, SeulAh Lee, Leo Zsembik, Brenda Wu, Joseph Floeder, Gautam Naik, and Ritchie Chan, all of UCSF.<\/p>\n
Funding:\u00a0This work was supported by the National Institutes of Health (grants R00MH118422, R01MH129582, F32DA060044). the National Science Foundation, the Klingenstein-Simons Fellowship, the David and Lucile Packard Foundation, and Shurl and Kay Curci Foundation.<\/p>\n
Key Questions Answered:Q: Does this mean I should stop practicing things every day?<\/p>\n
A: Not necessarily, but it means \u201cspacing\u201d is more important than \u201cgrinding.\u201d If you\u2019re trying to learn a new language or instrument, your brain will actually absorb more from three 20-minute sessions spread throughout the day than one solid hour of repetition.<\/p>\n
Q: Why would the brain prefer rare events over common ones?<\/p>\n
A: From an evolutionary standpoint, rare rewards (like finding a hidden fruit tree) are more \u201cinformative\u201d than common ones. If something happens all the time, the brain treats it as background noise. If it\u2019s rare, the brain pays extra attention to the timing to make sure it doesn\u2019t miss the next opportunity.<\/p>\n
Q: How does this link to addiction?<\/p>\n
A: It explains why intermittent rewards (like gambling or social media notifications) are so addictive. Because the rewards are unpredictable and spaced out, the brain\u2019s dopamine system remains highly sensitive and \u201clearns\u201d the habit much more deeply than if the reward was constant.<\/p>\n
Editorial Notes:This article was edited by a Neuroscience News editor.Journal paper reviewed in full.Additional context added by our staff.About this learning and neuroscience research<\/p>\n
Author: Laura Kurtzman<\/a>
Source: UCSF<\/a>
Contact: Laura Kurtzman \u2013 UCSF
Image: The image is credited to Neuroscience News<\/p>\n