Ransom Keith J, Perfors Amy, Navarro Daniel J
School of Psychology, University of Adelaide.
Cogn Sci. 2016 Sep;40(7):1775-1796. doi: 10.1111/cogs.12308. Epub 2015 Oct 16.
Everyday reasoning requires more evidence than raw data alone can provide. We explore the idea that people can go beyond this data by reasoning about how the data was sampled. This idea is investigated through an examination of premise non-monotonicity, in which adding premises to a category-based argument weakens rather than strengthens it. Relevance theories explain this phenomenon in terms of people's sensitivity to the relationships among premise items. We show that a Bayesian model of category-based induction taking premise sampling assumptions and category similarity into account complements such theories and yields two important predictions: First, that sensitivity to premise relationships can be violated by inducing a weak sampling assumption; and second, that premise monotonicity should be restored as a result. We test these predictions with an experiment that manipulates people's assumptions in this regard, showing that people draw qualitatively different conclusions in each case.
日常推理需要的证据比单纯的原始数据所能提供的更多。我们探讨了这样一种观点，即人们可以通过思考数据的采样方式来超越这些数据。通过对前提非单调性的考察来研究这一观点，在基于类别的论证中添加前提会削弱而非加强该论证。关联理论从人们对前提项目之间关系的敏感性方面解释了这一现象。我们表明，一个考虑了前提采样假设和类别相似性的基于类别的归纳贝叶斯模型补充了这些理论，并产生了两个重要预测：第一，通过引入一个较弱的采样假设，对前提关系的敏感性可能会被违反；第二，结果应该会恢复前提单调性。我们通过一项实验来检验这些预测，该实验操纵了人们在这方面的假设，结果表明人们在每种情况下都会得出质的不同的结论。
