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.
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.
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