Centre for Translational Data Science, University of Sydney, Sydney, NSW 2006, Australia.
Data61, Commonwealth Scientific and Industrial Research Organisation, Sydney, NSW 2015, Australia.
J Neurosci. 2022 Apr 27;42(17):3636-3647. doi: 10.1523/JNEUROSCI.1079-21.2022. Epub 2022 Mar 16.
From an associative perspective the acquisition of new goal-directed actions requires the encoding of specific action-outcome (AO) associations and, therefore, sensitivity to the validity of an action as a predictor of a specific outcome relative to other events. Although competitive architectures have been proposed within associative learning theory to achieve this kind of identity-based selection, whether and how these architectures are implemented by the brain is still a matter of conjecture. To investigate this issue, we trained human participants to encode various AO associations while undergoing functional neuroimaging (fMRI). We then degraded one AO contingency by increasing the probability of the outcome in the absence of its associated action while keeping other AO contingencies intact. We found that this treatment selectively reduced performance of the degraded action. Furthermore, when a signal predicted the unpaired outcome, performance of the action was restored, suggesting that the degradation effect reflects competition between the action and the context for prediction of the specific outcome. We used a Kalman filter to model the contribution of different causal variables to AO learning and found that activity in the medial prefrontal cortex (mPFC) and the dorsal anterior cingulate cortex (dACC) tracked changes in the association of the action and context, respectively, with regard to the specific outcome. Furthermore, we found the mPFC participated in a network with the striatum and posterior parietal cortex to segregate the influence of the various competing predictors to establish specific AO associations. Humans and other animals learn the consequences of their actions, allowing them to control their environment in a goal-directed manner. Nevertheless, it is unknown how we parse environmental causes from the effects of our own actions to establish these specific action-outcome (AO) relationships. Here, we show that the brain learns the causal structure of the environment by segregating the unique influence of actions from other causes in the medial prefrontal and anterior cingulate cortices and, through a network of structures, including the caudate nucleus and posterior parietal cortex, establishes the distinct causal relationships from which specific AO associations are formed.
从联想的角度来看,新的目标导向动作的获取需要对特定的动作-结果 (AO) 关联进行编码,因此,需要对动作作为特定结果的预测相对于其他事件的有效性敏感。尽管在联想学习理论中已经提出了竞争架构来实现这种基于身份的选择,但这些架构是否以及如何被大脑实现仍然是一个推测的问题。为了研究这个问题,我们在进行功能神经影像学 (fMRI) 的同时,训练人类参与者来编码各种 AO 关联。然后,我们通过增加在没有相关动作的情况下结果出现的概率来降低一个 AO 关联的有效性,同时保持其他 AO 关联不变。我们发现这种处理选择性地降低了降级动作的性能。此外,当一个信号预测了未配对的结果时,动作的表现得到了恢复,这表明这种降级效应反映了动作和上下文之间对特定结果的预测之间的竞争。我们使用卡尔曼滤波器来模拟不同因果变量对 AO 学习的贡献,发现前额内侧皮质 (mPFC) 和背侧前扣带皮质 (dACC) 的活动分别跟踪了动作和上下文与特定结果的关联变化。此外,我们发现 mPFC 与纹状体和后顶叶皮层一起参与了一个网络,以分离各种竞争预测因素的影响,从而建立特定的 AO 关联。人类和其他动物学习他们行为的后果,使他们能够以目标导向的方式控制自己的环境。然而,我们不知道如何将环境的原因与自己行为的影响区分开来,以建立这些特定的动作-结果 (AO) 关系。在这里,我们表明大脑通过在前额内侧和前扣带皮质中分离动作的独特影响和其他原因,以及通过包括尾状核和后顶叶皮层在内的结构网络,来学习环境的因果结构,从而建立从特定 AO 关联形成的独特因果关系。