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: 10362532
• 负责人: AMITAI SHENHAV
• 金额: $ 39.83万
• 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/10362532
• 关键词: Adult; Alzheimer' s Disease; Anterior; Attention; Behavior; Chronic; Cognitive; Computer Models; Costs and Benefits; Cues; Decision Making; Development; Diagnosis; Disease; Dorsal; Electroencephalography; Environment; Evaluation; Event-Related Potentials; Feedback; Functional Magnetic Resonance Imaging; Future; General Population; Goals; Impairment; Incentives; Individual; Individual Differences; Investments; Lead; 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; base; career; cingulate cortex; cognitive control; daily functioning; disorder risk; efficacy testing; expectation; experience; health goals; improved; indexing; information processing; insight; nervous system disorder; neural circuit; novel; potential biomarker; programs; relating to nervous system; 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/文摘