Evidence map›Paper›PMID 41196344›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2025

Dynamic changes of dopamine neuron activity and plasticity at different stages of negative reinforcement learning.

Qiangqiang Cheng, Wenqing Liu, Li Yao, Shuyuan Xu, Chunling Wei, Qiaohua Zheng, Meilin Wu, Jing Han, Zhiqiang Liu, Wei Ren and 1 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Qiangqiang Cheng *School of Psychology, Shaanxi Normal University, Xi'an 710062, China.
Wenqing Liu *Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.
Li YaoKey Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.
Shuyuan XuKey Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.
Chunling WeiKey Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.
Qiaohua ZhengKey Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.
Meilin WuKey Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.
Jing HanKey Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.ORCID 0000-0001-8705-5095
Zhiqiang LiuKey Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.
Wei RenFaculty of Education, Shaanxi Normal University, Xi'an 710062, China.
Zongpeng SunSchool of Psychology, Shaanxi Normal University, Xi'an 710062, China.ORCID 0000-0002-6145-6250

Funding

China Postdoctoral Science Foundation (China Postdoctoral Foundation Project) 2022M712004MOE | Humanities and Social Science Fund of Ministry of Education of China (Humanities and Social Sciences Fund, Ministry of Education) 22XJC880005MOE | Humanities and Social Science Fund of Ministry of Education of China (Humanities and Social Sciences Fund, Ministry of Education) 24JYC190034MOE | Humanities and Social Sciences Youth Foundation, Ministry of Education (Humanities and Social Sciences Youth Foundation of Ministry of Education of China) 22XJC880011| Natural Science Basic Research Program of Shaanxi Province () 2024JC-YBQN-0225PhD Start-up Foundation of Shanxi Medical University 1110011181
6 · The paper itself

Abstract

Research indicates that midbrain dopaminergic neurons encode reward prediction error (RPE) signals involved in positive reinforcement learning. However, studies on dopamine's role in negative reinforcement learning (NRL) are scarce. Learning to escape aversive stimuli is vital for survival and may differ significantly from positive reinforcement in behavior and neural mechanisms. This study employs footshocks as aversive stimuli to investigate neural activity, synaptic transmission, and intrinsic excitability in a NRL paradigm using fiber photometry and ex vivo electrophysiology. Results show that inescapable footshocks initially increase activity in substantia nigra pars compacta (SNc) dopaminergic neurons, which later shifts to reflect shock termination as exposure increases. Electrophysiological observations reveal increased intrinsic excitability and excitatory synaptic transmission in SNc neurons, with decreased inhibitory transmission. After mice learn to escape the shock by nose-poking, dopaminergic activity shifts from shock termination to shock onset. Furthermore, inhibitory input increases, while excitatory input decreases after learning, with intrinsic excitability returning to baseline levels. This indicates that SNc dopaminergic neurons exhibit RPE-like signals in response to aversive stimuli, with their intrinsic excitability adjusting according to expectations of shock termination. These findings enhance our understanding of RPE encoding in negative reinforcement learning and may inform therapeutic strategies for disorders caused by environmental factors such as aversive stimuli.

Indexed as

Dopaminergic NeuronsLearningNeuronal PlasticityReinforcement, PsychologyAnimalsDopamineMaleMiceMice, Inbred C57BLPars CompactaRewardSynaptic TransmissionDopaminedopaminergic neuronsnegative reinforcement learningneural plasticityreward prediction error

Identifiers

PMID41196344
PMCPMC12625994

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.