Simultaneous learning of multiple sensorimotor transformations

同时学习多种感觉运动转换

• 批准号: 274922074
• 负责人: Professor Dr. Mathias Hegele
• 金额: --
• 依托单位: Arbeitsbereich Experimentelle Sensomotorik
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/274922074?language=en
• 关键词: Simultaneous; learning; multiple; sensorimotor; transformations

Technological advances continuously lead to the development of modern tools that pose new demands on the sensorimotor capabilities of humans in work settings as well as in public and private spaces. Of particular interest, at least from a perspective of sensorimotor learning, is a novel class of movements that are characterized by the spatial separation and mechanical decoupling of movement execution and observation. These movements are typically found in the use of electronic tools. A prototypical exemplar is the computer mouse. With this tool, an electronic device mediates the transformation of hand movements into motions of the cursor on the computer monitor. In everyday life, humans encounter a wide variety of such electronic tools, whose characteristics strongly differ and most of the time, must be learned from scratch in the course of deliberate practice. Once learned, humans effortlessly switch between various tool transformations, for example, in driving a car, when the experienced driver is able to instantaneously switch from engaging a gear in the horizontal plane to turning the steering wheel in the frontoparallel plane. Thus, the use of modern tools requires an adaptive system that is highly flexible and able to represent multiple tool transformations at the same time. In the present project, I seek to understand how humans can learn multiple tool transformations at the same time. To this end, I investigate explicit and implicit components of human multilearning. In four work packages, I seek to understand (a) how the processing of different kinds of performance errors by the central nervous system drives explicit and implicit learning, (b) the role of the cerebellum, which has traditionally been associated with implicit learning, for explicit, strategy-driven learning, (c) if and how additional cognitive tasks affect explicit and implicit components of learning multiple transformations at the same time, and finally, (d) if it is possible to identify common principles of information processing in human multilearning, with which we can bridge the gap between cognitive psychology and movement science in order to contribute to the overarching goal of the project, the formulation of a novel, more comprehensive theory of human multitasking (in my project: human multilearning).

技术进步不断导致现代工具的发展，对人类在工作环境以及公共和私人空间中的感觉运动能力提出了新的要求。特别感兴趣的是，至少从感觉运动学习的角度来看，一类新的运动，其特征在于运动执行和观察的空间分离和机械解耦。这些动作通常在使用电子工具时发现。 一个典型的例子是计算机鼠标。利用该工具，电子设备将手的运动转换为计算机监视器上的光标的运动。在日常生活中，人类会遇到各种各样的电子工具，它们的特性差异很大，大多数时候，必须在刻意练习的过程中从头开始学习。一旦学会了，人类就可以毫不费力地在各种工具转换之间切换，例如，在驾驶汽车时，当有经验的驾驶员能够从在水平面中接合齿轮瞬间切换到在前平行平面中转动方向盘时。因此，现代工具的使用需要一个高度灵活的自适应系统，能够同时表示多个工具转换。在本项目中，我试图了解人类如何同时学习多种工具转换。为此，我调查人类多元学习的外显和内隐成分。在四个工作包中，我试图理解（a）中枢神经系统对不同类型的表现错误的处理如何驱动外显和内隐学习，（B）小脑的作用，它传统上与内隐学习有关，对于外显，策略驱动的学习，（c）额外的认知任务是否以及如何影响同时学习多种转换的外显和内隐成分，最后，（d）是否有可能确定人类多重学习中信息处理的共同原则，通过这些原则，我们可以弥合认知心理学和运动科学之间的差距，以促进该项目的总体目标，即制定一个新颖的，更全面的人类多重任务理论（在我的项目中：人类多重学习）。