Perception and Action: Ideal Observers and Actors

描述(由申请人提供)：人类在决策或行动的所有方面都可能是随机的情况下做出决定并执行行动。基于感官信息的行动计划有五个组成部分。首先，受试者拥有关于环境状态的先验信息，包括当前位置和速度 附近物体和受试者自己的身体的概率分布，这可以概括为可能的世界状态的概率分布。其次，受试者有关于环境当前状态的感官输入，由于物理和神经噪声，环境状态是不确定的。第三，将这两种信息来源结合起来，以决定预期的行动(按下按钮、手臂或眼睛的运动，或者包括对潜在后续感觉输入的反应的复杂计划)。第四，由于电机噪声，导致的动作可能与预期动作不同。最后，所产生的行动与当前环境的相互作用会导致结果(损失或收益)，并且该结果也可能是不确定的。由于所有这些随机成分，视觉任务和运动规划需要进行相当于在风险下进行决策的计算。在我们最近的工作中，我们已经证明，人类在视觉运动任务中几乎是最优的，因为他们最大化了预期收益，以及在其他人类行为不是最优的情况下。我们建议进行实验，以更好地理解人类在视觉和视觉运动任务中的行为本质。我们经常使用具有实验者指定的奖励/惩罚结构的任务；这种新颖的方法允许我们将行为与最大化预期收益的最优策略进行比较。我们提出以下问题，并提出实验来解决每个问题。(1)在视觉引导的动作中，行为是如何计划的？我们将研究用来编码视觉引导动作的坐标系，以及运动编码如何影响视觉运动系统适应不断变化的条件的能力。(2)人类在具有明确定义的得失的视觉搜索任务中的表现告诉了我们什么关于视觉模式和视觉不确定性的编码？我们将比较人类在视觉搜索任务中的表现与理想观察者模型，后者在收益不对称的情况下最大化预期收益。这些实验的结果将使我们能够区分关于外围视觉信息编码的不同假说。在这两个目标中，我们使用视觉运动表现的模式(同时执行伸展、扫视或按键)来了解视觉刺激、不确定性和视觉引导运动的潜在编码。这些研究将阐明视觉刺激和动作的编码方式，以及视觉如何用于指导行动。 与公共健康相关：拟议的工作通过描述与做出感性决定或使用感觉信息控制运动有关的行为和神经机制，从而使公共健康受益。我们展示了最优的决策和运动计划必须考虑到先验知识、视觉信息的不确定性、运动反应的可变性和动作的后果。各种医疗条件都会影响视觉信息的可靠性(例如，白内障、弱视等)。以及运动输出的质量和对风险(例如帕金森氏病、亨廷顿病、中风)的反应。这项拟议的研究将提高我们对视觉模式和计划动作如何编码的理解，从而优化感知决定或运动计划，从而有助于在感觉输入或运动输出因疾病或其他与健康有关的条件而中断(偏差、增益和/或变异性的变化)时，帮助设计康复计划。

DESCRIPTION (provided by applicant): Humans make decisions and perform actions in situations in which all aspects of the decision or action are potentially stochastic. There are fiv components to the planning of an action based on sensory information. First, the subject has prior information about the state of the environment including the current positions and velocities of nearby objects and of the subject's own body, which can be summarized as a probability distribution across possible world states. Second, the subject has sensory input about the current state of the environment, which is uncertain due to physical and neural noise. Third, these two sources of information are combined to decide on an intended action (button press, arm or eye movement, or a complex plan that includes responses to potential subsequent sensory inputs). Fourth, the resulting action can differ from the intended one due to motor noise. Finally, the interaction of the resulting action with the current environment leads to a consequence (a loss or gain), and this consequence may be uncertain as well. As a result of all these stochastic components, visual tasks and movement planning require a calculation that is equivalent to decision-making under risk. In our recent work, we have demonstrated that humans are nearly optimal in visuomotor tasks in that they maximize expected gain, and other circumstances in which human behavior is suboptimal. We propose experiments to better understand the nature of human behavior in visual and visuomotor tasks. We often use tasks with an experimenter-specified reward/penalty structure; this novel approach allows us to compare behavior with the optimal strategy that maximizes expected gain. We ask the following questions and propose experiments to address each. (1) How is behavior planned in visually guided movements? We will investigate the coordinate systems used to encode visually guided actions and how the encoding of movements affects the ability of the visuomotor system to adapt to changing conditions. (2) What does human performance in visual search tasks with clearly defined gains and losses tell us about the encoding of visual patterns and visual uncertainty? We will compare human performance in visual search tasks to ideal-observer models that maximize expected gain in situations with asymmetric payoffs. The results of these experiments will enable us to distinguish different hypotheses about the encoding of visual information in the periphery. In both aims we use patterns of visuomotor performance (while performing a reach, saccade, or keypress) to learn about the underlying encoding of visual stimuli, uncertainty, and visually guided movement. These studies will shed light on the way in which visual stimuli and movements are encoded, and on how vision is used to guide action. PUBLIC HEALTH RELEVANCE: The proposed work benefits public health by characterizing the behavioral and neural mechanisms that are involved with making perceptual decisions or using sensory information to control movements. We show how optimal decisions and movement plans must take into account prior knowledge, the uncertainty of visual information, the variability of motor response and the consequences of action. A variety of medical conditions can impact both the reliability of visual information (e.g., cataract, amblyopia, etc.) and the quality of motor output and response to risk (e.g., Parkinson's disease, Huntington's disease, stroke). The proposed research will improve our understanding of how visual patterns and planned movements are encoded so as to optimiaze a perceptual decision or movement plan, and thus can serve to help in the design of rehabilitative plans when sensory input or motor output is disrupted (change in bias, gain and/or variability) by disease or other health- related conditions.