Qian Lechen, Burrell Mark, Hennig Jay A, Matias Sara, Murthy Venkatesh N, Gershman Samuel J, Uchida Naoshige
Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
Center for Brain Science, Harvard University, Cambridge, MA, USA.
Nat Neurosci. 2025 Mar 18. doi: 10.1038/s41593-025-01915-4.
Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. In the present study, we examined the dopamine activity in the ventral striatum-a signal implicated in associative learning-in a Pavlovian contingency degradation task in mice. We show that both anticipatory licking and dopamine responses to a conditioned stimulus decreased when additional rewards were delivered uncued, but remained unchanged if additional rewards were cued. These results conflict with contingency-based accounts using a traditional definition of contingency or a new causal learning model (ANCCR), but can be explained by temporal difference (TD) learning models equipped with an appropriate intertrial interval state representation. Recurrent neural networks trained within a TD framework develop state representations akin to our best 'handcrafted' model. Our findings suggest that the TD error can be a measure that describes both contingency and dopaminergic activity.
关联性学习依赖于偶然性，即一个刺激预测一个结果的程度。尽管其很重要，但将偶然性与行为联系起来的神经机制仍不清楚。在本研究中，我们在小鼠的经典条件性偶然性退化任务中，检测了腹侧纹状体中的多巴胺活性——一种与关联性学习有关的信号。我们发现，当额外奖励在无提示的情况下发放时，对条件刺激的预期舔舐和多巴胺反应均降低，但如果额外奖励有提示，则保持不变。这些结果与使用传统偶然性定义的基于偶然性的解释或新的因果学习模型（ANCCR）相冲突，但可以由配备适当试验间隔状态表征的时间差（TD）学习模型来解释。在TD框架内训练的循环神经网络会形成类似于我们最佳“手工制作”模型的状态表征。我们的研究结果表明，TD误差可以作为一种描述偶然性和多巴胺能活性的指标。
