权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

An investigation into the neurobehavioral interactions between sensory- and reward-prediction errors during motor learning

运动学习过程中感觉预测错误和奖励预测错误之间神经行为相互作用的调查

基本信息

批准号：
10283091
负责人：
Olivia Ann Kim
金额：
$ 6.6万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-02-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10283091
关键词：
Activities of Daily Living Affect Area Awareness Basal Ganglia Behavior Behavioral Behavioral Model Brain Categories Cerebellar Diseases Cerebellum Complement Computer Models Conscious Data Decision Theory Dependence Disease Dissociation Educational process of instructing Event Functional Magnetic Resonance Imaging Functional disorder Future Goals Hybrids Impairment Individual Investigation Joints Knowledge Learning Light Link Measures Mediating Modeling Motor Motor output Movement Nature Neurodegenerative Disorders Neuropsychology Outcome Output Parkinson Disease Patients Performance Process Protocols documentation Psychological reinforcement Rehabilitation therapy Reporting Research Rewards Role Route Sensory Signal Transduction Spinocerebellar Ataxias Support System System Task Performances Testing Variant Work adaptive learning base behavior influence data modeling design evidence base experience experimental study improved insight learning network motor behavior motor impairment motor learning neurobehavioral neuroimaging novel programs relating to nervous system response sensory feedback visual motor

项目摘要

PROJECT SUMMARY When we experience a prediction error, a difference between what was expected and what was observed, the brain uses information conveyed by the error to guide adaptive learning that improves future accuracy. The proposed research focuses on the mechanisms by which concurrently available prediction errors inform changes in our motor behaviors. Prior work divided prediction errors into two categories, according to the conditions that cause them and the learning that they induce. Reward-prediction errors (RPE) arise when task performance is better or worse than expected, and cause learning that affects action selection. Sensory- prediction errors (SPE), on the other hand, occur when there is a mismatch between a movement’s expected and observed sensory feedback, and drive adaptation of that particular movement. Prior work has linked RPEs with the basal ganglia and SPEs with the cerebellum, leading to the idea that the two kinds of error operate on distinct networks to control distinct learning processes. Indeed, SPE (but not RPE) causes implicit adaptation beneath the level of conscious awareness, while RPE (but not SPE) causes explicit learning, suggesting that different neural systems use the different error signals to support separate learning processes. However, recent work has revealed that joint SPE and RPE produce different effects on implicit and explicit learning than either in isolation, suggesting that SPE-based and RPE-based learning networks interact. These effects may be mediated by a direct interaction between SPE and RPE signals, with presentation of one error affecting the neural teaching signal that normally drives learning in response to the other. Alternately, the interaction may be indirect, with parallel learning in SPE- and RPE-based systems converging on a common neural target to influence behavioral output. To understand the mechanism underlying these interactions, I will present different prediction errors during visuomotor reach adaptation (VMR) and measure their independent and joint effects on behavior and fMRI BOLD signals. Neuropsychological investigations will complement these studies and provide insight into the functional interactions between errors and their supporting systems during disease. These studies, by focusing on the neural basis for interactions between errors and the systems that process them, will provide valuable insight into the events and signals that support adaptive motor learning. My findings may also provide insight for the design of rehabilitation protocols based on new knowledge about the capacity for different error signals to drive learning in systems affected or spared by disease.

项目总结