Natural behaviour is learned through dopamine-mediated reinforcement

Jonathan Kasdin, Alison Duffy, Nathan Nadler, Arnav Raha, Adrienne L. Fairhall, Kimberly L. Stachenfeld and Vikram Gadagkar ()
Additional contact information
Jonathan Kasdin: Columbia University
Alison Duffy: University of Washington
Nathan Nadler: Columbia University
Arnav Raha: Columbia University
Adrienne L. Fairhall: University of Washington
Kimberly L. Stachenfeld: Columbia University
Vikram Gadagkar: Columbia University

Nature, 2025, vol. 641, issue 8063, 699-706

Abstract: Abstract Many natural motor skills, such as speaking or locomotion, are acquired through a process of trial-and-error learning over the course of development. It has long been hypothesized, motivated by observations in artificial learning experiments, that dopamine has a crucial role in this process. Dopamine in the basal ganglia is thought to guide reward-based trial-and-error learning by encoding reward prediction errors1, decreasing after worse-than-predicted reward outcomes and increasing after better-than-predicted ones. Our previous work in adult zebra finches—in which we changed the perceived song quality with distorted auditory feedback—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) performance2. However, it remains unknown whether the learning of natural behaviours, such as developmental vocal learning, occurs through dopamine-based reinforcement. Here we tracked song learning trajectories in juvenile zebra finches and used fibre photometry3 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, compared with recent renditions, and suppressed after renditions that were further away. Furthermore, the relationship between dopamine and song fluctuations revealed that dopamine predicted the future evolution of song, suggesting that dopamine drives behaviour. Finally, dopamine activity was explained by the contrast between the quality of the current rendition and the recent history of renditions—consistent with dopamine’s hypothesized role in encoding prediction errors in an actor–critic reinforcement-learning model4,5. Reinforcement-learning algorithms6 have emerged as a powerful class of model to explain learning in reward-based laboratory tasks, as well as for driving autonomous learning in artificial intelligence7. Our results suggest that complex natural behaviours in biological systems can also be acquired through dopamine-mediated reinforcement learning.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41586-025-08729-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:641:y:2025:i:8063:d:10.1038_s41586-025-08729-1

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-025-08729-1

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().