Department of Psychology, University of Exeter, Perry Road, Exeter EX4 4QG, United Kingdom.
Psychol Bull. 2012 Jan;138(1):102-25. doi: 10.1037/a0025715. Epub 2011 Nov 7.
Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus).
分类是认知的基本组成部分之一，分类研究的显著特点是形式建模在多大程度上成为研究的核心和有影响力的组成部分。然而，该领域已经出现了大量的非互补模型，这些模型的相对充分性几乎没有达成共识。迄今为止，评估形式分类模型相对充分性的进展一直受到限制，原因是：（a）形式模型比较考虑的模型和现象数量有限；（b）模型通常没有明确界定其解释范围。模型参数的任意变化也会对每个数据集进行独立拟合，这进一步阻碍了进展。通过回顾文献中的良好实践范例，我们得出结论，当通过基于不可逆转、有序、可穿透成功的数量和比例来比较定义良好的模型（最小灵活性、广度、足够精度、最大简单性和心理焦点原则）来评估相对充分性时，模型比较最有成效。
