Massaro D W, Cohen M M
Program in Experimental Psychology, University of California, Santa Cruz 95064, USA.
Acta Psychol (Amst). 1995 Nov;90(1-3):193-209. doi: 10.1016/0001-6918(95)00027-r.
A discrete feature model (DFM) and the fuzzy logical model (FLMP) were formulated to predict the distribution of rating judgments in a pattern recognition task. The distinction was between the spoken vowels /i/ and /I/, as in beet and bit. Subjects were instructed to rate the vowel on a nine-point scale from /i/ to /I/. Two features, the first formant frequency (F1) and the vowel duration, were orthogonally varied: The vowel /i/ has a lower (F1) and a longer duration compared to a somewhat higher (F1) and shorter duration for /I/. The DFM predicts that the separate features are recognized discretely, whereas the FLMP assumes that continuous information is available about each feature. Tests of these models on the observed data indicated that the continuous information assumption of the FLMP gave a significantly better description of the distribution of rating judgments.
构建了一个离散特征模型（DFM）和模糊逻辑模型（FLMP），用于预测模式识别任务中评级判断的分布。区分的是口语元音/i/和/I/，如在单词beet和bit中。受试者被要求在从/i/到/I/的九点量表上对元音进行评级。两个特征，即第一共振峰频率（F1）和元音时长，被正交变化：与/I/相比，元音/i/的F1较低且时长较长，而/I/的F1略高且时长较短。DFM预测单独的特征是离散识别的，而FLMP假设关于每个特征都有连续信息可用。对观测数据进行的这些模型测试表明，FLMP的连续信息假设能显著更好地描述评级判断的分布。
