The role of uncertainty for motor learning and adaptation

 描述(申请人提供)：运动康复正在慢慢过渡到一种证据和理论驱动的操作模式，在这种模式下，成功的治疗是由对大脑和身体如何工作的理解指导的。作为这一过渡的一部分，模型的开发非常重要。在模型领域，贝叶斯和最优控制模型在过去十年或二十年中特别有影响力。关于大脑(“贝叶斯大脑”)中统计效率处理的模型一直很有影响力，并假设大脑天生就能解决统计问题。其他通常不太受欢迎的模型认为，大脑只是从反复试验中学习如何以统计上有效的方式行事。对于行为结果的解释，这种差异是至关重要的。这个问题的答案有望为理论、电生理学和康复提供信息。在这里，我们建议使用人类受试者的实验和模型构建来量化学习在处理不确定性方面的作用。我们将过度训练的任务与新的任务进行比较，以询问人类是否具有统计上有效的整合能力。我们将成年人与儿童进行比较，以询问是否有必要获得解决这些任务的适当经验。为了分析不确定性的神经表征，我们进行了泛化实验。最后，我们构建了通用学习系统的模型，并与现有的贝叶斯模型进行了比较，并在大型数据库上进行了测试。在整个实验中，我们将把量化不确定性的两种选择强制选择范例与允许估计其对行为影响的感觉运动整合实验并列在一起。计划中的实验将为人类行为如何在重要任务的统计意义上变得如此高效这一问题提供一个微妙的答案。本项目要研究的三个目标是：目标1：确定大脑是否需要学习如何整合不确定的感觉运动信息。我们会问，为了让人类有效地组合多个不确定的信息，学习是否是必要的。虽然目前的许多理论隐含地假设有效的整合不需要学习，但我们的初步数据表明并非如此。学习不确定性很重要，因为它无处不在，而且会影响学习。目标2：确定概率分布的表示和推广是否是硬连线的。我们会问，不确定性的泛化是固定的，还是可以学习的。这一点很重要，因为跨任务的泛化是运动康复的主要目标之一。目标3：构建并测试学习如何整合不确定信息的新模型。我们将基于深度学习的思想构建模型，这些模型可以解释大脑中统计上有效的处理过程，而不需要内置它。这些模型将与“硬连线”的贝叶斯模型和一个大型的行为数据库进行测试。通过建立强大的模型，对当前和潜在的行为做出准确的预测，将允许更有效和更有针对性的运动康复。

 DESCRIPTION (provided by applicant): Movement rehabilitation is slowly transitioning towards an evidence and theory driven mode of operating where successful treatments are guided by an understanding of how the brain and the body work. As part of this transition, the development of models is very important. In the space of models, Bayesian and optimal control models have been particularly influential over the last decade or two. Models about statistically efficient processing in the brain ("Bayesian brain") have been influential and assume that the brain is hardwired for solving statistical problems. Other, generally less popular, models assume that the brain just learns to act in a statistically efficient way from trial and error. For the interpretation of behavioral results this difference is crucial. An answer to this question promise to inform theories, electrophysiology and rehabilitation. Here we propose to use experiments with human subjects and model building to quantify the role of learning for the processing of uncertainty. We compare over trained tasks with novel tasks to ask if humans are hardwired for statistically efficient integration. We compare adults with children to ask if the appropriate experience solving these tasks is necessary. We perform generalization experiments to analyze the neural representation of uncertainty. And lastly, we build models of general learning systems for comparison with existing Bayesian models and test them on large databases. Across the experiments we will juxtapose two-alternative-forced-choice paradigms that quantify uncertainty with sensorimotor integration experiments that allow estimating its effect on behavior. The planned experiments will provide a nuanced answer to the question of how human behavior becomes so efficient in a statistical sense for important tasks. The three aims to be investigated in this project are: Aim 1: Determine if the brain needs to learn how to integrate uncertain sensorimotor information. We will ask if learning is necessary to allow humans to efficiently combine multiple pieces of uncertain information. While many current theories implicitly assume that efficient integration requires no learning our preliminary data suggests otherwise. Studying uncertainty is important as it is ubiquitous and affects learning. Aim 2: Determine if the representation of probability distributions and thus generalization is hardwired. We will ask if th generalization of uncertainty is hardwired or can be learned. This is important because generalization across tasks is one of the prime objectives of movement rehabilitation. Aim 3: Construct and test new models that learn how to integrate uncertain information. We will construct models, based on the idea of deep learning, that can explain statistically efficient processing in the brain without requiring it to be built in. The models will be tested against "hardwired" Bayesian models and a large database of behaviors. By building strong models that make accurate predictions about current and potential behavior would allow for more efficient and targeted movement rehabilitation.