Reinforcement-learning signals support dynamic adaptive control during language switching
Mingyue Zuo, Huanhuan Liu, Linyan Liu, John W. Schwieter, Yan Jing Wu · 2026
Read Reinforcement-learning signals support dynamic adaptive control during language switching by Mingyue Zuo, Huanhuan Liu, Linyan Liu, John W. Schwieter, Yan Jing Wu (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13620166.txt.
Description
Mingyue Zuo and four colleagues asked how bilingual speakers learn when to switch languages, treating the choice as a lesson in reward, in a 2026 study. Chinese-English bilinguals named pictures in whichever language they liked while feedback paid out with high, medium, or low probability. Surprises in that feedback, called reward prediction errors, were expected to tune the next choice.
Computer modeling showed those surprise signals steadily updated a general switching policy rather than a narrow habit. Brain pattern analysis placed the learned value of the policy in memory-related regions, while the surprise signal itself showed up in a frontal control area. Network models traced three stages: wide exploration early, a hub-centered pattern while the policy paid off, then a tight local circuit for automatic use plus a broad network watching for odd errors. The authors frame language control as value-based learning.
Catalog record
| CREATOR | Mingyue Zuo, Huanhuan Liu, Linyan Liu, John W. Schwieter, Yan Jing Wu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 91411 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |