Neural and computational mechanisms of motivation and cognitive control

动机和认知控制的神经和计算机制

• 批准号: 10541903
• 负责人: AMITAI SHENHAV
• 金额: $ 39.88万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-02 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10541903
• 关键词: Adult; Alzheimer' s Disease; Anterior; Attention; Behavior; Chronic; Cognitive; Computer Models; Cost Analysis; Costs and Benefits; Cues; Decision Making; Development; Diagnosis; Diagnostic; Diagnostic Factor; Disease; Dissociation; Dorsal; Electroencephalography; Environment; Evaluation; Event-Related Potentials; Feedback; Functional Magnetic Resonance Imaging; Future; General Population; Goals; Impairment; Incentives; Individual; Individual Differences; Investments; Learning; Major Depressive Disorder; Measures; Mental Depression; Mental disorders; Modeling; Motivation; Multivariate Analysis; Outcomes Research; Output; Parkinson Disease; Participant; Pattern; Personal Satisfaction; Persons; Population; Productivity; Prognosis; Psyche structure; Research; Rewards; Risk Assessment; Schizophrenia; Severities; Signal Transduction; Source; Stimulus; Techniques; Testing; Treatment Efficacy; Treatment outcome; Update; Work; career; cingulate cortex; cognitive control; daily functioning; disorder risk; efficacy testing; endophenotype; expectation; experience; functional magnetic resonance imaging/electroencephalography; health goals; improved; indexing; information processing; insight; nervous system disorder; neural; neural circuit; novel; potential biomarker; programs; response

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.

项目总结/摘要 大多数日常任务需要认知控制，但人们在满足控制要求的动机方面存在差异 这些任务所需要的。动机障碍是一种常见的和transdiagnosis的特点， 精神和神经系统疾病-包括严重抑郁症，精神分裂症和阿尔茨海默氏症-严重 损害了患有这些疾病的个体的日常功能和整体健康。不幸的是， 我们知道驱动这些损伤的神经计算机制。我们最近开发了一个 人们如何根据成本评估来决定控制分配的计算模型 控制权预期价值（EVC）模型。我们的模型指出了几个潜在的 动机障碍及其假定的神经基质。这些包括学习激励措施的缺陷， 在预期时发出这些激励信号，和/或在做出决策时适当地利用这些激励措施 关于控制分配。该模型表明，背侧前扣带回（dACC）负责整合 激励信息，以激励水平的认知控制是最值得的。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 控制是达到特定目标所必需的）和（2）达到该目标的预期奖励。 以前的研究主要集中在后者。因此，疗效如何在很大程度上是未知的 学习和预期;它如何与奖励相结合，以指导控制分配;以及在多大程度上 动机障碍是由效能处理的缺陷引起的。我们开发并验证了 一组任务，梳理出奖励和效能对努力分配的独立影响。我们将 让成年参与者在接受EEG或fMRI的同时执行这些任务，以表征 神经计算机制，通过这些机制，预期的奖励和功效（1）被发出信号，（2）被用来 确定努力分配，（3）基于反馈更新，以及（4）推广到新的刺激。我们预测 dACC将整合来自不同额顶叶输入的奖赏和效能信息，以确定 和最有价值的控制类型。这种控制分配将通过dACC的交互来制定 有特定目标的前额叶和皮层下区域我们还预测，奖励和效能选择区域 额纹状体和额顶叶回路的相互作用将指导任务激励的学习和概括。我们 将通过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， 产生具体参与者的估计激励处理和控制分配试验。本研究 将为认知控制动机背后的计算和电路提供关键的新见解。它 因此，它有可能为我们理解评价和激励机制提供更多信息。 一般来说，并提供一条改善诊断和治疗障碍的途径， 流行和transdiagnosis。