Adapting Systems Factorial Technology to Model Selection:Applications to Perception and Classification

模型是科学家用来理解数据、探索新想法和产生新研究途径的工具。有些模型是物理的或生物的，比如太阳系的原子模型或生物学家可信的果蝇模型。但许多科学模型采用一组方程或算法的形式，这些方程或算法可以作为计算机程序来实现。这样的计算模型在决定相互竞争的理论和观点时非常有用，因为它们对应的计算机程序可以相互竞争，看看哪一个能更好地解释数据。问题是，通常不太容易确定什么是“更好”的账户。一个模型可能很好地捕获一个数据集，但提供的洞察很少超出该特定数据集。由于科学理论通常关注的是一般原理，而不是特定的数据集，因此，拥有对模型与给定数据集的拟合更敏感的方法是有用的。在美国国家科学基金会（National Science Foundation）的支持下，托马斯博士将开发一种方法，这种方法可以广泛地用于根据模型的形式和功能对模型进行相互检验，而不仅仅是根据数据进行检验。这些方法将在人类模式识别理论的背景下发展，这是一个足够普遍的主题，与行为和社会科学的一系列领域相关。从听一个单词到驾驶汽车再到投资，探测和识别世界模式的能力是人类活动的核心。这个主题的重要性使得许多研究人员制定了许多计算模型，这些模型捕捉了关于如何在大脑和思想中识别模式的理论。因此，这个主题领域为方法的发展提供了一个合适的试验场，这些方法将有助于识别值得和不值得追求的理论。

Models are tools that scientists use to make sense of data, explore new ideas, and generate new avenues of research. Some models are physical or biological, like the solar system model of the atom or the biologist's trusty fruit fly. But many scientific models take the form of a set of equations or algorithms that can be implemented as computer programs. Such computational models can be extremely useful in deciding between competing theories and ideas, because their corresponding computer programs can be pitted against each other to see which one provides a better account of the data. The problem is that often it is not so easy to determine what a "better" account is. A model might capture one data set very well, but provide little insight beyond that particular data set. Since scientific theories are usually concerned with general principles rather particular sets of data, it is useful to have methods that are sensitive to more than just the fit of a model to a given data set.With support of the National Science Foundation, Dr. Thomas will develop methods that can be used broadly to test models against one another on the basis of model form and function, rather than just fit to data. These methods will be developed in the context of theories of human pattern recognition, a topic that is general enough to be relevant to range of areas in the behavioral and social sciences. From hearing a word to driving a car to making an investment, the ability to detect and recognize patterns in the world is central to human activity. The importance of this topic has led many researchers to formulate many computational models that capture theories about how patterns are recognized in the brain and mind. Therefore this topic domain provides an apt testing ground for the development of methods that will help to identify theories that are and are not worth pursuing as evidence comes to light.