Neural and computational mechanisms of motivation and cognitive control

项目摘要/摘要 大多数日常任务需要认知控制，但人们满足控制需求的动机各不相同 这些任务都是必需的。动机障碍是一种常见的、跨诊断的特征 精神和神经障碍-包括严重的抑郁症、精神分裂症和阿尔茨海默氏症-严重 损害患有这些疾病的个人的日常功能和整体福祉。不幸的是，几乎没有 已知驱动这些损伤的神经计算机制。我们最近开发了一种 人们如何根据成本评估做出控制权分配决策的计算模型 和收益(控制的期望值[EVC]模型)。我们的模型指出了几个潜在的来源 动机障碍及其可能的神经基础。其中包括缺乏对激励机制的了解， 在预期时发出激励信号，和/或在决策时正确利用这些激励 关于控制权分配的问题。该模型提示背侧前扣带回(DACC)负责整合 激励信息才能激发认知控制的水平，这是最值得的。我们的模型进一步 指出了控制激励的两个可分离的组成部分：(1)控制的预期效果(程度 达到特定目标所需的控制)和(2)达到该目标的预期回报。 以往的研究主要集中在后一部分。因此，很大程度上还不清楚疗效如何。 学习和预期；它如何与奖励相结合以指导控制权分配；以及在多大程度上 动机障碍是由效能加工过程中的缺陷引起的。我们已经开发并验证了 梳理奖励和效能对努力分配的独立影响的一组任务。我们会 让成年参与者在接受脑电或功能磁共振检查的同时执行这些任务，以表征 预期奖赏和功效的神经计算机制：(1)发出信号，(2)用于 确定努力分配，(3)基于反馈进行更新，以及(4)推广到新的刺激。我们预测 DACC将整合来自不同额顶输入的奖励和疗效信息，以确定金额 以及最有价值的控制类型。这种控制分配将通过dACC的交互来实施 具有特定于目标的前额叶和皮质下区域。我们还预测，奖赏和疗效选择性区域 额纹状体和额顶叶回路的相互作用将指导学习和任务激励的推广。我们 我将使用基于模型的行为和神经活动分析来测试这些预测，使用我们的EVC模型来 为试验中的激励处理和对照分配生成特定于参与者的估计。这项研究 将为认知控制动机背后的计算和回路提供关键的新见解。它 因此有可能让我们对评价和激励机制有更多的了解 通常，并提供一条途径，以改善对既有 流行的和跨诊断的。

PROJECT SUMMARY/ABSTRACT Most daily tasks demand cognitive control, but people vary in their motivation to meet the control demands required of those tasks. Motivational impairments are a common and transdiagnostic feature of a wide range of psychiatric and neurological disorders—including major depression, schizophrenia, and Alzheimer’s—severely compromising the daily functioning and overall wellbeing of individuals with these disorders. Unfortunately, little is known about the neurocomputational mechanisms that drive these impairments. We recently developed a computational model of how people make decisions about control allocation based on an evaluation of the costs and benefits (the Expected Value of Control [EVC] model). Our model points to several potential sources of motivational impairments and their putative neural substrates. These include deficits in learning about incentives, signaling those incentives when expected, and/or properly utilizing those incentives when making decisions about control allocation. The model suggests that dorsal anterior cingulate (dACC) is responsible for integrating incentive information in order to motivate the level of cognitive control that is most worthwhile. Our model further points to two dissociable components of the incentives for control: (1) the expected efficacy of control (the extent to which control is necessary to reach a particular goal) and (2) the expected reward for reaching that goal. Previous research has primarily focused on the latter component. It is therefore largely unknown how efficacy is learned and anticipated; how it is integrated with reward to guide control allocation; and to what extent motivational impairments are caused by deficits in the processing of efficacy. We have developed and validated a set of tasks that tease apart the independent influences of reward and efficacy on effort allocation. We will have adult participants perform these tasks while undergoing EEG or fMRI, to characterize the neurocomputational mechanisms by which expected reward and efficacy are (1) signaled, (2) utilized to determine effort allocation, (3) updated based on feedback, and (4) generalized to novel stimuli. We predict that dACC will integrate reward and efficacy information from separate frontoparietal inputs, to determine the amount and type of control that is most worthwhile. This control allocation will be enacted through dACC’s interactions with goal-specific prefrontal and subcortical regions. We also predict that reward- and efficacy-selective regions of frontostriatal and frontoparietal circuits will interact to guide learning and generalization of task incentives. We will test these predictions with model-based analyses of behavior and neural activity, using our EVC model to generate participant-specific estimates of incentive processing and control allocation across trials. This research will offer critical new insight into the computations and circuits underlying the motivation of cognitive control. It therefore has the potential to inform our understanding of the mechanisms of evaluation and motivation more generally, and to provide a path towards improving diagnosis and treatment for impairments that are both prevalent and transdiagnostic.