We are joined today by Ivan Kairatov, a biopharma expert whose groundbreaking research is rewriting our fundamental understanding of how we learn. For over a century, we’ve believed that repetition is the key to learning, but his work reveals that timing is the far more critical ingredient. Our conversation will explore how the spacing of rewards impacts the brain’s dopamine system, offering profound insights into everything from effective study habits and treating addiction to the future of artificial intelligence.
For over a century, we’ve linked learning to repetition, like Pavlov’s dog. Your findings suggest timing is the key factor. Could you elaborate on how the interval between rewards impacts the brain’s learning process and what this means for the “practice makes perfect” model?
It’s a fascinating shift in perspective. We’ve all been raised on this idea that more practice equals better learning, but our work shows it’s not that simple. The brain is far more sophisticated; it’s not just counting repetitions. What really matters is the duration between those learning experiences. When cue-reward pairings happen very close together, the brain seems to learn less from each individual instance. It’s almost as if it devalues the information because it’s so frequent. This tells us that the old model of “practice makes perfect” needs an update. It’s more a case of “timing is everything.”
Your research compared mice receiving rewards minutes apart versus seconds apart. What was happening at a dopaminergic level that allowed the group with far fewer rewards to learn just as quickly? Can you walk us through the brain’s response in each of those scenarios?
This was the most striking part of the study. We had two groups of mice. One group had trials spaced just 30 to 60 seconds apart, receiving a constant stream of rewards. The other group had their trials spaced five to 10 minutes apart, or even more. The first group received a massive number of rewards, but the mice in the second group, who got up to 20 times fewer rewards, learned the association just as quickly. When we looked at their brains, we saw that when the rewards were spaced further apart, their dopamine systems responded much more robustly to the cue. Their brains began releasing dopamine in anticipation of the reward much sooner, with far fewer repetitions needed to solidify that connection.
Many students cram for exams, assuming more repetition in a short period is effective. Based on your findings about spaced rewards, what specific advice would you offer on structuring study sessions? How can they leverage timing to learn more efficiently with less overall effort?
The student who crams is behaving just like the mice in our rapid-trial group. They’re bombarding their brain with information in a short window, but the brain learns less from each pass. My advice would be to embrace the spacing. Instead of one eight-hour marathon session, break it up into several shorter sessions spread across a few days or even the whole semester. This spacing allows the brain to properly consolidate the learning from each experience. It feels less frantic, and our research suggests it’s profoundly more effective, allowing you to learn the same amount, or even more, with significantly less total time spent hitting the books.
Considering that smoking provides intermittent rewards, how does this timing reinforce the addiction? Conversely, how might a constant-delivery system like a nicotine patch disrupt the brain’s cue-reward association, and what are the clinical implications for treating other addictive behaviors?
Addiction is a powerful form of associative learning, and smoking is a perfect example of intermittent reinforcement. The rewards aren’t constant; they are linked to specific cues—the smell of a cigarette, the end of a meal, a coffee break. This irregular timing makes the brain’s dopamine response to those cues incredibly strong. A nicotine patch works on a completely different principle. It delivers nicotine constantly, a slow and steady stream. This completely disrupts that crucial cue-reward association. Because there’s no spike of reward linked to a specific action, the brain’s powerful connection between the cue and the dopamine hit begins to weaken, which can blunt the urge to smoke and make it easier to quit.
Current AI models often learn slowly through massive repetition. How could a new model based on your findings, which prioritizes the timing of experiences over their sheer volume, accelerate machine learning? What would be the first practical step in implementing such a system?
This is an incredibly exciting frontier. Today’s AI systems are built on the old model of learning, requiring billions of data points and countless tiny refinements. They learn, but it’s a brute-force method. A new model based on our findings would be designed to weigh experiences differently based on when they occur. Instead of treating every data point equally, it would give more weight to experiences that are spaced out. The first practical step would be to build an algorithm that doesn’t just process a stream of data but also analyzes the temporal relationship between inputs. This could allow a machine to learn a lot more from fewer, more strategically timed, experiences. It’s a way of teaching AI to learn more like a human brain, which is still far more efficient than any machine we’ve built.
What is your forecast for artificial intelligence?
I believe the future of AI lies in making it more efficient and intuitive, moving away from brute-force data processing and toward models that mirror the elegance of biological learning. Our research highlights a key principle—the importance of timing—that is largely overlooked in current systems. By incorporating concepts like spaced learning, we can create AI that learns faster, from less data, and perhaps with a more nuanced understanding of context. The ultimate goal isn’t just to build more powerful machines, but to build smarter ones that can learn and adapt with the same remarkable efficiency that nature has already perfected in our own brains.
