Quantyfying stimulus, response, and numerical biases.

虽然人们无法对比例做出直观的判断（有关评论，请参见Rothman & Kiviniemi，1999）已被证明会导致对健康相关问题的不良判断（Kaplan，哈梅尔，& Schimmel，1985），相对较少的研究已经针对理解的认知表征， 比例。目前的提案将通过以下方式填补这一空白：（1）提供关于内部 比例的认知表征模型的生成 类似于整数的认知表征模型。目前的提案还 讨论了经验在数字偏差形成和维持中的作用。学习如何和 为什么这些符号被误解将导致技术，以减少这些偏见，这反过来又将导致 更有效的数学教学方法所提出的方法结合了实证研究， 数值统计程序、探索性分析和计算建模。特别是 拟议的研究将评估三种数字格式和三种刺激的距离效应， 数量是固有的，评估与六种刺激的跨模态匹配相关的偏差，并使用 数值统计模型来估计数值偏差的参数。一旦函数形式 数字偏差的识别和描述，我将研究经验的形成和 保持这些偏见。

Although peoples inability to make intuitive judgments about proportions (for a review, see Rothman & Kiviniemi, 1999) has been shown to lead to poor judgments about health related issues (Kaplan, Hammel, & Schimmel, 1985), relatively little research has been directed at understanding the cognitive representation of proportions. The current proposal will fill this gap by (1) providing extensive data on the internal representation of proportions, and (2) generating models of the cognitive representation of proportions similar to the models that exist for the cognitive representation of integers. The current proposal also addresses the role of experience in the formation and maintenance of numerical biases. Learning how and why these symbols are misinterpreted will lead to techniques to reduce these biases, which in turn will lead to more effective methods of teaching mathematics. The proposed approach combines empirical research, numerical statistical procedures, exploratory analysis, and computational modeling. In particular, the proposed research will assess the distance effect for three numerical formats and three stimuli in which quantity is inherent, assess the biases associated with cross-modality matching for the six stimuli, and use numerical statistical models to estimate the parameters of the numerical biases. Once the functional form of numerical biases are identified and described, I will study the role of experience in the formation and maintanance of these biases.