Credit assignment in movement planning and movement execution

运动计划和运动执行中的学分分配

• 批准号: RGPIN-2018-05161
• 负责人: Blohm, Gunnar
• 金额: $ 3.42万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=645633
• 关键词: Credit; assignment; movement; planning; execution

How we plan and execute movements is one of the fundamental questions in neuroscience. Inaccuracies in either process generally result in movement errors that provide insight into underlying brain processes. E.g. when planning reaches with the head tilted to one shoulder, observed head-roll-dependent reach errors correlate with brain under-estimated head angles required to counter-rotate tilted visual information on the retina. Similarly, one can interpret systematic biases in reach endpoints under altered arm weight (e.g. while carrying a full grocery bag in the elbow) as a consequence of the brain mis-estimating the altered arm dynamics. The problem is, these interpretations rely on the assumption that planning and control are functionally separate and separable. Given the large overlap of brain areas for planning and control, it is questionable whether this assumption is justified; but this could never be explicitly tested because a key missing piece in the field is a model that seamlessly integrates motor planning and movement execution to disambiguate the origin of movement errors. Models can link functional mechanisms in the brain to behaviour. Without an integrated model, we run the risk of erroneously crediting errors to the wrong underlying mechanism, slowing progress in understanding the brain. It is thus crucial to address this problem and examine the potential cause of movement errors in the light of a combined movement planning and execution model to either confirm or correct current interpretations. ******Assigning movement errors to specific processes requires an integrated characterization of the potential causes and their interactions. To achieve this, we need a new model combining known movement planning features with modern motor control theories. Planning processes include converting sensory signals to different coordinate frames and integrating complementary sources of information. Optimal feedback control best captures movement execution, relying on the optimal estimation of body state, an internal model of the body dynamics and the use of noisy sensory feedback. Our lab has expertise in both planning and control; we thus uniquely qualified to develop a planning & execution model and fill this gap. Our hypothesis is that some (but not all) of the previously observed planning / control errors could also result from the other process. For example, head-roll-dependent movement errors could arise from mis-estimations in online sensory feedback, instead of errors in motor planning as thought today. Our strategy will be to examine how inaccuracies in different components of the combined model translate into specific, distinctive movement error pattern. We will then design new behavioural, imaging and stimulation experiments that disambiguate alternative error sources. This multi-disciplinary project will provide an ideal training environment; HQP will lead all aims.

我们如何计划和执行动作是神经科学的基本问题之一。这两个过程中的任何一个不准确通常都会导致运动错误，从而提供对潜在大脑过程的了解。例如，当头部倾斜到一个肩膀时，观察到的头部滚动依赖的到达误差与大脑低估的头部角度有关，这需要反向旋转视网膜上倾斜的视觉信息。同样地，我们可以解释在手臂重量改变的情况下到达终点的系统性偏差（例如，当肘部携带一个装满杂货的袋子时），这是大脑错误估计改变的手臂动力学的结果。问题是，这些解释依赖于这样的假设：计划和控制在功能上是分离的、可分离的。考虑到规划和控制的大脑区域有很大的重叠，这种假设是否合理是值得怀疑的；但这永远无法被明确地测试，因为该领域的一个关键缺失部分是一个无缝集成运动规划和运动执行的模型，以消除运动错误的起源。模型可以将大脑的功能机制与行为联系起来。如果没有一个完整的模型，我们就有可能错误地将错误归因于错误的潜在机制，从而减缓对大脑的理解。因此，解决这个问题和检查运动错误的潜在原因是至关重要的，结合运动计划和执行模型来确认或纠正当前的解释。******将运动误差分配给特定过程需要对潜在原因及其相互作用进行综合表征。为了实现这一目标，我们需要一个新的模型，将已知的运动规划特征与现代运动控制理论相结合。规划过程包括将感官信号转换为不同的坐标框架和整合互补的信息源。最优反馈控制最好地捕捉运动执行，依赖于身体状态的最佳估计，身体动力学的内部模型和使用噪声感官反馈。我们的实验室在计划和控制方面都有专长；因此，我们有独特的资格来开发一个计划和执行模式，填补这一空白。我们的假设是，之前观察到的一些（但不是全部）计划/控制错误也可能是由其他过程引起的。例如，依赖于头部滚动的运动错误可能源于在线感官反馈的错误估计，而不是今天认为的运动规划错误。我们的策略将是研究组合模型的不同组成部分的不准确性如何转化为特定的、独特的运动误差模式。然后，我们将设计新的行为、成像和刺激实验，以消除其他错误来源的歧义。这个多学科项目将提供一个理想的培训环境；HQP将引领所有目标。