Department of Communicative Disorders and Sciences, University at Buffalo, 122 Cary Hall, Buffalo, NY, 14214, USA.
Indiana University, Bloomington, IN, USA.
Psychon Bull Rev. 2019 Feb;26(1):103-126. doi: 10.3758/s13423-018-1501-2.
To account for natural variability in cognitive processing, it is standard practice to optimize a model's parameters by fitting it to behavioral data. Although most language-related theories acknowledge a large role for experience in language processing, variability reflecting that knowledge is usually ignored when evaluating a model's fit to representative data. We fit language-based behavioral data using experiential optimization, a method that optimizes the materials that a model is given while retaining the learning and processing mechanisms of standard practice. Rather than using default materials, experiential optimization selects the optimal linguistic sources to create a memory representation that maximizes task performance. We demonstrate performance on multiple benchmark tasks by optimizing the experience on which a model's representation is based.
为了说明认知处理中的自然变化，通过将模型拟合到行为数据上来优化模型参数是标准做法。尽管大多数与语言相关的理论都承认经验在语言处理中起着重要作用，但在评估模型对代表性数据的拟合程度时，通常会忽略反映这些知识的可变性。我们使用基于经验的优化来拟合基于语言的行为数据，这是一种在保留标准实践中的学习和处理机制的同时优化模型所获得材料的方法。基于经验的优化不是使用默认材料，而是选择最佳语言来源来创建一个记忆表示，以最大限度地提高任务绩效。我们通过优化模型表示所基于的经验来展示多个基准任务上的性能。
