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杏仁核与腹侧纹状体对强化学习有不同贡献。

Amygdala and Ventral Striatum Make Distinct Contributions to Reinforcement Learning.

作者信息

Costa Vincent D, Dal Monte Olga, Lucas Daniel R, Murray Elisabeth A, Averbeck Bruno B

机构信息

Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892-4415, USA.

Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892-4415, USA.

出版信息

Neuron. 2016 Oct 19;92(2):505-517. doi: 10.1016/j.neuron.2016.09.025. Epub 2016 Oct 6.

Abstract

Reinforcement learning (RL) theories posit that dopaminergic signals are integrated within the striatum to associate choices with outcomes. Often overlooked is that the amygdala also receives dopaminergic input and is involved in Pavlovian processes that influence choice behavior. To determine the relative contributions of the ventral striatum (VS) and amygdala to appetitive RL, we tested rhesus macaques with VS or amygdala lesions on deterministic and stochastic versions of a two-arm bandit reversal learning task. When learning was characterized with an RL model relative to controls, amygdala lesions caused general decreases in learning from positive feedback and choice consistency. By comparison, VS lesions only affected learning in the stochastic task. Moreover, the VS lesions hastened the monkeys' choice reaction times, which emphasized a speed-accuracy trade-off that accounted for errors in deterministic learning. These results update standard accounts of RL by emphasizing distinct contributions of the amygdala and VS to RL.

摘要

强化学习(RL)理论认为,多巴胺能信号在纹状体内整合,将选择与结果联系起来。常被忽视的是,杏仁核也接收多巴胺能输入,并参与影响选择行为的经典条件反射过程。为了确定腹侧纹状体(VS)和杏仁核对奖赏性强化学习的相对贡献,我们在双臂强盗反转学习任务的确定性和随机性版本中,对患有VS或杏仁核损伤的恒河猴进行了测试。当相对于对照组用强化学习模型来描述学习情况时,杏仁核损伤导致从积极反馈中学习和选择一致性普遍下降。相比之下,VS损伤仅影响随机任务中的学习。此外,VS损伤加快了猴子的选择反应时间,这强调了速度与准确性之间的权衡,而这种权衡解释了确定性学习中的错误。这些结果通过强调杏仁核和VS对强化学习的不同贡献,更新了强化学习的标准观点。

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本文引用的文献

1
Temporal Specificity of Reward Prediction Errors Signaled by Putative Dopamine Neurons in Rat VTA Depends on Ventral Striatum.
Neuron. 2016 Jul 6;91(1):182-93. doi: 10.1016/j.neuron.2016.05.015. Epub 2016 Jun 9.
3
Reward and choice encoding in terminals of midbrain dopamine neurons depends on striatal target.
Nat Neurosci. 2016 Jun;19(6):845-54. doi: 10.1038/nn.4287. Epub 2016 Apr 25.
4
Mini-review: Prediction errors, attention and associative learning.
Neurobiol Learn Mem. 2016 May;131:207-15. doi: 10.1016/j.nlm.2016.02.014. Epub 2016 Mar 3.
5
Dissociable Learning Processes Underlie Human Pain Conditioning.
Curr Biol. 2016 Jan 11;26(1):52-8. doi: 10.1016/j.cub.2015.10.066. Epub 2015 Dec 17.
6
Contrasting Roles for Orbitofrontal Cortex and Amygdala in Credit Assignment and Learning in Macaques.
Neuron. 2015 Sep 2;87(5):1106-18. doi: 10.1016/j.neuron.2015.08.018.
8
Abstract Context Representations in Primate Amygdala and Prefrontal Cortex.
Neuron. 2015 Aug 19;87(4):869-81. doi: 10.1016/j.neuron.2015.07.024.
9
The Role of Frontal Cortical and Medial-Temporal Lobe Brain Areas in Learning a Bayesian Prior Belief on Reversals.
J Neurosci. 2015 Aug 19;35(33):11751-60. doi: 10.1523/JNEUROSCI.1594-15.2015.
10
Neuronal Reward and Decision Signals: From Theories to Data.
Physiol Rev. 2015 Jul;95(3):853-951. doi: 10.1152/physrev.00023.2014.

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