In this paper our goal is to undertake a systematic assessment of the first, most widely known, and simplest computational model of metaphor comprehension, the predication model developed by Kintsch (Cognitive Science, 25(2), 173–202, 2000). 622 metaphors of the form “x is a y” were selected from a much larger set generated randomly. The metaphors were judged for quality using best/worst judgments, which asks judges to pick the best and worst metaphor from among four presented metaphors. The metaphors and their judgments have been publicly released. We modeled the judgments by extending Kintsch’s predication model (2000) by systematically walking through the parameter space of that model. Our model successfully differentiated metaphors rated as good (> 1.5z) from metaphors rated as bad (< −1.5z; Cohen’s d = 0.72) and was able to successfully classify good metaphors with an accuracy of 82.9%. However, it achieved a true negative rate below chance at 36.3% and had a resultantly low kappa of 0.037. The model could not distinguish unselected random metaphors from those selected by humans as having metaphorical potential. In a follow-up study we showed that the model’s quality estimates reliably predict metaphor decision times, with better metaphors being judged more quickly than worse metaphors.

在本文中，我们的目标是对隐喻理解的第一个、最广为人知和最简单的计算模型进行系统评估，即由 Kintsch 开发的预测模型 ( Cognitive Science, 25 (2), 173–202, 2000)。从随机生成的更大的集合中选择了 622 个“x is ay”形式的隐喻。使用最佳/最差判断来判断隐喻的质量，这要求评委从四个呈现的隐喻中挑选最好和最差的隐喻。隐喻和他们的判断已经公开发布。我们通过系统地遍历该模型的参数空间来扩展 Kintsch 的预测模型 (2000)，从而对判断进行建模。我们的模型成功地将被评为好 (> 1.5z) 的隐喻与被评为差 (< -1.5z; Cohen'sd  = 0.72) 并且能够以 82.9% 的准确率成功地对好的隐喻进行分类。然而，它实现了低于机会的真负率，为 36.3%，因此 kappa 为 0.037。该模型无法区分未选择的随机隐喻与人类选择的具有隐喻潜力的隐喻。在后续研究中，我们表明模型的质量估计可靠地预测了隐喻决策时间，更好的隐喻比糟糕的隐喻被判断得更快。