Optimal and Adaptive Learning Models for Nondeterministic Tasks

9310347 Daniel Friedman这项研究考察了人们在不确定任务（如医疗诊断）中是如何学习的。学习这样的任务是困难的，因为偶尔一个决策者可能会因为错误的原因而正确，或者（就像一个有不确定图表的专家医生）因为正确的原因而错误。在实验环境中，我们将改变任务的复杂性、所需决策的类型和学习环境，以观察人们何时能够有效地学习，何时不能。这类任务的一个例子是医疗决策。医生在检查病历时，会在心里把每个单独症状的信息结合起来，然后做出诊断。医生的诊断能力随着时间的推移而提高，因为他们对每种症状的信息量以及如何最好地结合这些信息有了更多的了解。通常，图表信息是不确定的，所以即使是最好的医生有时也会做出错误的诊断。这类任务经常出现在其他决策领域。经济学家和其他一些社会科学家使用的均衡模型实际上是假设瞬时学习的，这项研究的结果应该有助于显示这些模型可能准确或不准确的地方。结果也应该是直接有用的认知科学家和决策者（包括医疗诊断医师）谁希望改善学习过程中的不确定性任务。＊＊＊

9310347 Daniel Friedman This research examines how people learn in nondeterministic tasks (such as medical diagnosis). Learning is difficult such tasks because occasionally a decision maker can be right for the wrong reason or (like an expert doctor with an inconclusive chart) wrong for the right reason. In experimental settings we will vary the complexity of the task, the type of decision required, and the learning environment in order to see when people are able learn effectively and when they do not. An example of the type of task would be medical decision making. A doctor examining a medical chart mentally combines the information from each separate symptom and makes a diagnosis. Doctors' diagnostic abilities increase over time as they learn more about the informativeness of each symptom and how best to combine the information. Often the chart information is inconclusive, so even the best doctor sometimes makes an incorrect diagnosis. This kind of task appears frequently in other decision domains. Economists and some other social scientists use equilibrium models that in effect assume instantaneous learning, and results of this study should be useful in showing where these models are likely to be accurate or inaccurate. The results also should be directly useful to cognitive scientists and decision makers (including medical diagnosticians) who wish to improve the learning process in nondeterministic tasks. ***