Uploaded June 2026 | Updated September 2026, 2 weeks ago
Vikram Gadagkar (Columbia University)
https://simons.berkeley.edu/talks/vikram-gadagkar-columbia-university-2026-06-10
Topics in Intelligence: World Models and Social Reasoning
Many natural motor skills, like speaking or locomotion, are learned through trial-and-error over development. It has long been hypothesized that dopamine plays a critical role in this process, motivated by artificial learning experiments. Dopamine in the basal ganglia is thought to guide reward-based trial-and-error learning by encoding reward prediction errors, decreasing after worse-than-predicted rewards and increasing after better-than-predicted ones. Similarly, by changing perceived song quality with distorted auditory feedback, our previous work in adult zebra finches showed that dopamine in Area X, the singing-related basal ganglia, encodes performance prediction error: dopamine is suppressed after worse-than-predicted (distorted syllables) and activated after better-than-predicted (undistorted syllables) performance. However, it remains unknown if the learning of natural behaviors, such as developmental vocal learning, occurs through dopamine-based reinforcement. Here we tracked song learning trajectories in juvenile zebra finches and used fiber photometry to monitor concurrent dopamine activity in Area X. We found that dopamine was activated after syllable renditions that were closer to the eventual adult version of the song and suppressed after renditions that were farther away. Furthermore, the relationship between dopamine and song revealed that dopamine predicted the future evolution of song, suggesting that dopamine drives behavior. Finally, dopamine activity was explained by the contrast between the quality of the current rendition against the recent history of renditions, consistent with its hypothesized role of encoding prediction errors. Reinforcement learning algorithms explain learning in reward-based laboratory tasks as well as drive autonomous learning in artificial intelligence. Our results suggest that complex natural behaviors in biological systems can also be learned through dopamine-mediated reinforcement.
Vikram Gadagkar (Columbia University)
https://simons.berkeley.edu/talks/vikram-gadagkar-columbia-university-2026-06-10
Topics in Intelligence: World Models and Social Reasoning
Many natural motor skills, like speaking or locomotion, are learned through trial-and-error over development. It has long been hypothesized that dopamine plays a critical role in this process, motivated by artificial learning experiments. Dopamine in the basal ganglia is thought to guide reward-based trial-and-error learning by encoding reward prediction errors, decreasing after worse-than-predicted rewards and increasing after better-than-predicted ones. Similarly, by changing perceived song quality with distorted auditory feedback, our previous work in adult zebra finches showed that dopamine in Area X, the singing-related basal ganglia, encodes performance prediction error: dopamine is suppressed after worse-than-predicted (distorted syllables) and activated after better-than-predicted (undistorted syllables) performance. However, it remains unknown if the learning of natural behaviors, such as developmental vocal learning, occurs through dopamine-based reinforcement. Here we tracked song learning trajectories in juvenile zebra finches and used fiber photometry to monitor concurrent dopamine activity in Area X. We found that dopamine was activated after syllable renditions that were closer to the eventual adult version of the song and suppressed after renditions that were farther away. Furthermore, the relationship between dopamine and song revealed that dopamine predicted the future evolution of song, suggesting that dopamine drives behavior. Finally, dopamine activity was explained by the contrast between the quality of the current rendition against the recent history of renditions, consistent with its hypothesized role of encoding prediction errors. Reinforcement learning algorithms explain learning in reward-based laboratory tasks as well as drive autonomous learning in artificial intelligence. Our results suggest that complex natural behaviors in biological systems can also be learned through dopamine-mediated reinforcement.
