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策并执行行动。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，包括附近物体和受试者自身的当前位置和速度;这些信息几乎肯定是不完整的，并且可以概括为可能的世界状态的概率分布。其次，受试者具有关于环境当前状态的感觉输入，并且由于物理和神经噪声源，该输入也将是不确定的。第三，受试者结合这两个信息来源，并决定一个预期的动作（按钮按压，达到运动，眼球运动，或更复杂的运动计划，包括对潜在的后续感官输入的反应）。第四，由于电机噪声，产生的动作可能与预期的动作不同。最后，所产生的动作与当前环境的相互作用导致主体的结果（损失或收益），并且该结果也可以是随机的。由于所有这些随机成分，视觉任务和运动规划都需要计算，这相当于风险下的决策所需的计算。在我们最近的工作中，我们已经描绘了人类在视觉运动任务中接近最佳的情况，因为他们最大化了预期收益，以及人类行为次优的其他情况。我们提出的实验，以更好地了解人类行为的性质，在视觉和视觉运动任务。我们继续使用具有实验者指定的奖励/惩罚结构的任务，以便我们可以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）哪些方面的任务不确定性估计以及人类观察员和最佳使用选择运动计划？我们将确定人类是否最佳计划下的风险运动的感觉输入和/或运动输出的噪音通过各种手段。(2)在视觉运动任务的不同组成部分发生变化时，运动规划的灵活性如何？我们将测量视觉运动任务中的学习进展，其中先验概率，运动结果或回报是不确定的，并随时间变化。(3)在日常生活中，需要对视觉目标进行检测、辨别和搜索，以引导对这些目标的行动，作为获得后期奖励的手段。在这里，我们要问的是，当涉及到明确定义的收益和损失时，人类在典型的视觉任务中是否是最佳的。我们将确定人类在视觉检测，歧视和搜索任务的性能是否是最佳的人类性能进行比较理想的观察者模型，最大限度地提高预期收益的情况下，不对称的回报。公共卫生相关性拟议的工作有利于公共卫生的特点，涉及作出知觉决定或使用感官信息来控制运动的神经机制。我们展示了最佳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未知或不断变化的回报。各种医学状况可以影响视觉信息的可靠性（例如，白内障、弱视等）以及电动机输出的质量（例如，帕金森氏病，中风）。拟议的研究将提高我们对最佳感知决策或运动计划的理解，因此可以帮助设计康复计划，当疾病或其他健康相关条件破坏感觉输入或运动输出时（偏差，增益和/或可变性的变化）。

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 five 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; this information is almost certainly incomplete, and can be summarized as a probability distribution across possible world states. Second, the subject has sensory input about the current state of the environment, and that input will also be uncertain due to physical and neural noise sources. Third, the subject combines these two sources of information and decides on an intended action (button press, reaching movement, eye movement, or a more complex movement 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) for the subject, and this consequence may be random as well. As a result of all these stochastic components, both visual tasks and movement planning require a calculation that is equivalent to that required for decision-making under risk. In our recent work, we have delineated situations in which humans are nearly optimal in visuo-motor 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 visuo-motor tasks. We continue to use tasks with an experimenter-specified reward/penalty structure so that we may compare behavior with the optimal strategy that maximizes expected gain. We ask the following questions and propose experiments to address each: (1) What aspects of task uncertainty are estimated well by human observers and used optimally to select a movement plan? We will determine whether humans optimally plan movements under risk as sensory input and/or motor output is made noisier by a variety of means. (2) How flexible is movement planning in response to changes in different components of a visuo-motor task? We will measure the progress of learning in visuo-motor tasks in which prior probabilities, motor outcome or payoff are uncertain and changing over time. (3) In daily life, detection, discrimination and search for visual targets are required to guide action toward those targets as a means of obtaining later rewards. Here, we ask if humans are optimal in typical visual tasks when clearly defined gains and losses are involved. We will determine whether human performance in visual detection, discrimination and search tasks is optimal by comparing human performance to ideal-observer models that maximize expected gain in situations with asymmetric payoffs. PUBLIC HEALTH RELEVANCE The proposed work benefits public health by characterizing the 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 unknown or changing payoffs. A variety of medical conditions can impact both the reliability of visual information (e.g., cataract, amblyopia, etc.) and the quality of motor output (e.g., Parkinson's disease, stroke). The proposed research will improve our understanding of what is meant by an optimal 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.