Wills A J, Reimers S, Stewart N, Suret M, McLaren I P
Cambridge University, Cambridge, U.K.
Q J Exp Psychol A. 2000 Nov;53(4):983-1011. doi: 10.1080/713755935.
Many theories of learning and memory (e.g., connectionist, associative, rational, exemplar based) produce psychological magnitude terms as output (i.e., numbers representing the momentary level of some subjective property). Many theories assume that these numbers may be translated into choice probabilities via the ratio rule, also known as the choice axiom (Luce, 1959) or the constant-ratio rule (Clarke, 1957). We present two categorization experiments employing artificial, visual, prototype-structured stimuli constructed from 12 symbols positioned on a grid. The ratio rule is shown to be incorrect for these experiments, given the assumption that the magnitude terms for each category are univariate functions of the number of category-appropriate symbols contained in the presented stimulus. A connectionist winner-take-all model of categorical decision (Wills & McLaren, 1997) is shown to account for our data given the same assumption. The central feature underlying the success of this model is the assumption that categorical decisions are based on a Thurstonian choice process (Thurstone, 1927, Case V) whose noise distribution is not double exponential in form.
许多学习与记忆理论(例如,联结主义、联想主义、理性主义、基于范例的理论)都会产生心理量级术语作为输出(即代表某些主观属性瞬间水平的数字)。许多理论假定,这些数字可通过比率法则(也称为选择公理,卢斯,1959 年;或恒定比率法则,克拉克,1957 年)转化为选择概率。我们进行了两项分类实验,采用了由位于网格上的 12 个符号构成的人工视觉原型结构刺激。假设每个类别的量级术语是所呈现刺激中包含的类别适配符号数量的单变量函数,结果表明比率法则在这些实验中是不正确的。给定相同假设,一个关于分类决策的联结主义胜者全得模型(威尔斯和麦克拉伦,1997 年)被证明能够解释我们的数据。该模型成功的核心特征在于假设分类决策基于瑟斯顿选择过程(瑟斯顿,1927 年,案例五),其噪声分布在形式上不是双指数的。