Perception and Action: Ideal Observers and Actors

感知与行动：理想的观察者和行动者

• 批准号: 7474253
• 负责人: MICHAEL S LANDY
• 金额: $ 31.87万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-08-01 至 2011-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7474253
• 关键词: Accounting; Address; Amblyopia; Auditory; Bayesian Method; Beer; Behavior; Cataract; Choice Behavior; Color; Complex; Condition; Cues; Daily; Decision Making; Decision Theory; Depth; Detection; Discrimination; Disease; Environment; Eye Movements; Face; Goals; Human; Human Characteristics; Knowledge; Learning; Life; Location; Measures; Medical; Methods; Modality; Modeling; Motor; Motor output; Movement; Noise; Outcome; Parkinson Disease; Perception; Performance; Pliability; Positioning Attribute; Probability; Procedures; Public Health; Range; Rehabilitation therapy; Relative (related person); Research; Rewards; Risk; Sensory; Source; Specific qualifier value; Speed; Stroke; Structure; Testing; Time; Uncertainty; Vision; Visual; Weight; Work; Yang; base; concept; design; haptics; improved; loss of function; neuromechanism; relating to nervous system; research study; response; theories; visual information; visual motor; visual search; visual-vestibular

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.

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规划有五个组成部分。首先，受试者拥有有关环境状态的先验信息，包括附近物体以及受试者自己身体的当前位置和速度；这些信息几乎肯定是不完整的，可以概括为可能的世界状态的概率分布。其次，受试者对当前环境状态有感觉输入，并且由于物理和神经噪声源，该输入也将是不确定的。第三，受试者结合这两种信息源并决定预期的动作（按钮按下、伸手运动、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，由于电机噪音，最终的动作可能与预期的不同。最后，所产生的动作与当前环境的相互作用会给主体带来结果（损失或增益），并且该结果也可能是随机的。由于所有这些随机成分，视觉任务和运动计划都需要进行与风险下决策所需的计算相当的计算。在我们最近的工作中，我们描述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。我们提出实验来更好地理解人类在视觉和视觉运动任务中行为的本质。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佳策略进行比较。我们提出以下问题并提出实验来解决每个问题：（1）任务不确定性的哪些方面可以被人类观察者很好地估计并最佳地用于选择运动计划？我们将确定人类是否能在风险下最优地计划运动，因为通过各种方式使感觉输入和/或运动输出变得更加嘈杂。 （2）针对视觉运动任务不同组成部分的变化，运动计划的灵活性如何？我们将测量视觉运动任务中的学习进度，其中先验概率、运动结果或回报是不确定的并且随着时间的推移而变化。 (3)在日常生活中，需要对视觉目标进行检测、辨别和搜索，以引导针对这些目标的行动，作为获得后续奖励的手段。在这里，我们询问当涉及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。我们将通过将人类表现与理想观察者模型进行比较来确定人类在视觉检测、辨别和搜索任务中的表现是否最佳，该模型在不对称回报的情况下最大化预期收益。公共健康相关性拟议的工作通过描述与做出感知决策或使用感官信息控制运动有关的神经机制来有益于公共健康。我们展示了最佳决策和运动计划必须如何考虑先验知识、视觉信息的不确定性、运动反应的可变性以及未知或变化的回报。各种医疗状况都会影响视觉信息的可靠性（例如白内障、弱视等）和运动输出的质量（例如帕金森病、中风）。拟议的研究将提高我们对最佳感知决策或运动计划含义的理解，因此可以在感觉输入或运动输出因疾病或其他健康相关状况而中断（偏差、增益和/或变异性的变化）时帮助设计康复计划。