Pitt Mark A, Kim Woojae, Navarro Daniel J, Myung Jay I
Department of Psychology.
Psychol Rev. 2006 Jan;113(1):57-83. doi: 10.1037/0033-295X.113.1.57.
To model behavior, scientists need to know how models behave. This means learning what other behaviors a model can produce besides the one generated by participants in an experiment. This is a difficult problem because of the complexity of psychological models (e.g., their many parameters) and because the behavioral precision of models (e.g., interval-scale performance) often mismatches their testable precision in experiments, where qualitative, ordinal predictions are the norm. Parameter space partitioning is a solution that evaluates model performance at a qualitative level. There exists a partition on the model's parameter space that divides it into regions that correspond to each data pattern. Three application examples demonstrate its potential and versatility for studying the global behavior of psychological models.
为了对行为进行建模，科学家需要了解模型的行为方式。这意味着要了解模型除了在实验中由参与者产生的那种行为之外，还能产生哪些其他行为。这是一个难题，原因在于心理模型的复杂性（例如，其众多参数），还因为模型的行为精度（例如，区间尺度性能）往往与其在实验中的可测试精度不匹配，在实验中定性、有序预测才是常态。参数空间划分是一种在定性层面评估模型性能的解决方案。在模型的参数空间上存在一种划分，它将参数空间划分为与每个数据模式相对应的区域。三个应用示例展示了其在研究心理模型全局行为方面的潜力和通用性。
