Scurfield BK
Victoria University of Wellington, Wellington, New Zealand
J Math Psychol. 1998 Mar;42(1):5-31. doi: 10.1006/jmps.1997.1183.
This paper presents a generalization of the theory of signal detectability to n-event forced-choice tasks where the evidence can be modelled by an m-dimensional vector. The generalization is based on a nonparametric model that encompasses decision rules for maximizing the proportion of correct decisions. The model assumes that observers identify events by partitioning a decision space of dimension n-1 with a template. Translating the template by varying the decisional bias yields a set of receiver operating characteristic (ROC) surfaces. Following B. K. Scurfield [1996, J. Math. Psych. 40, 253-269], event-discriminability is defined by considering the Shannon entropy of the volumes under the ROC surfaces. The resultant discriminability measure is interpreted with respect to the random vectors assumed to be associated with the decision space and shown to equate with the channel capacity of an observer in a multiple-interval forced-choice task. Copyright 1998 Academic Press.
本文提出了一种将信号检测理论推广到n事件强迫选择任务的方法，其中证据可以用m维向量来建模。这种推广基于一个非参数模型，该模型包含用于最大化正确决策比例的决策规则。该模型假设观察者通过用一个模板划分n - 1维的决策空间来识别事件。通过改变决策偏差来平移模板会产生一组接收者操作特征（ROC）曲面。遵循B. K. 斯库菲尔德[1996，《数学心理学杂志》40，253 - 269]的方法，通过考虑ROC曲面下体积的香农熵来定义事件可辨别性。所得的可辨别性度量是相对于假定与决策空间相关联的随机向量进行解释的，并表明在多区间强迫选择任务中与观察者的信道容量相等。版权所有1998年学术出版社。
