School of Biological and Chemical Sciences.
Department of Psychology, Queen's University Belfast.
J Exp Psychol Gen. 2014 Dec;143(6):2082-102. doi: 10.1037/a0037653. Epub 2014 Sep 8.
Across a range of domains in psychology different theories assume different mental representations of knowledge. For example, in the literature on category-based inductive reasoning, certain theories (e.g., Rogers & McClelland, 2004; Sloutsky & Fisher, 2008) assume that the knowledge upon which inductive inferences are based is associative, whereas others (e.g., Heit & Rubinstein, 1994; Kemp & Tenenbaum, 2009; Osherson, Smith, Wilkie, López, & Shafir, 1990) assume that knowledge is structured. In this article we investigate whether associative and structured knowledge underlie inductive reasoning to different degrees under different processing conditions. We develop a measure of knowledge about the degree of association between categories and show that it dissociates from measures of structured knowledge. In Experiment 1 participants rated the strength of inductive arguments whose categories were either taxonomically or causally related. A measure of associative strength predicted reasoning when people had to respond fast, whereas causal and taxonomic knowledge explained inference strength when people responded slowly. In Experiment 2, we also manipulated whether the causal link between the categories was predictive or diagnostic. Participants preferred predictive to diagnostic arguments except when they responded under cognitive load. In Experiment 3, using an open-ended induction paradigm, people generated and evaluated their own conclusion categories. Inductive strength was predicted by associative strength under heavy cognitive load, whereas an index of structured knowledge was more predictive of inductive strength under minimal cognitive load. Together these results suggest that associative and structured models of reasoning apply best under different processing conditions and that the application of structured knowledge in reasoning is often effortful.
在心理学的多个领域中，不同的理论假设了知识的不同心理表征。例如，在基于类别归纳推理的文献中，某些理论（如 Rogers 和 McClelland，2004；Sloutsky 和 Fisher，2008）假设基于归纳推理的知识是联想的，而其他理论（如 Heit 和 Rubinstein，1994；Kemp 和 Tenenbaum，2009；Osherson、Smith、Wilkie、López 和 Shafir，1990）则假设知识是结构化的。在本文中，我们研究了在不同的处理条件下，联想和结构化知识是否在不同程度上为归纳推理提供基础。我们开发了一种衡量类别之间关联程度的知识的度量方法，并表明它与结构化知识的度量方法不同。在实验 1 中，参与者对类别之间具有分类或因果关系的归纳论点的强度进行了评分。当人们需要快速反应时，联想强度的衡量标准可以预测推理，而当人们反应较慢时，因果和分类知识可以解释推理强度。在实验 2 中，我们还操纵了类别之间的因果关系是预测性的还是诊断性的。当参与者在认知负荷下做出反应时，他们更喜欢预测性的论点而不是诊断性的论点。在实验 3 中，我们使用了一种开放式归纳推理范式，参与者生成并评估自己的结论类别。在认知负荷较重的情况下，联想强度可以预测归纳强度，而在认知负荷较轻的情况下，结构化知识的指标则更能预测归纳强度。这些结果表明，联想和结构化的推理模型在不同的处理条件下应用效果最佳，并且推理中结构化知识的应用往往需要付出努力。
