Here we examine three classes of models regarding the structure of human cognition: common cause models, sampling/network models, and interconnected models. That disparate models can accommodate one of the most globally replicated psychological phenomena—namely, the positive manifold—is an extension of underdetermination of theory by data. Statistical fit indices are an insufficient and sometimes intractable method of demarcating between the theories; strict tests and further evidence should be brought to bear on understanding the potential causes of the positive manifold. The cognitive impact of focal cortical lesions allows testing the necessary causal connections predicted by competing models. This evidence shows focal cortical lesions lead to local, not global (across all abilities), deficits. Only models that can accommodate a deficit in a given ability without effects on other covarying abilities can accommodate focal lesion evidence. After studying how different models pass this test, we suggest bifactor models (class: common cause models) and bond models (class: sampling models) are best supported. In short, competing psychometric models can be informed when their implied causal connections and predictions are tested.

在这里，我们检查关于人类认知结构的三类模型：共因模型、采样/网络模型和互连模型。完全不同的模型可以适应全球复制最多的心理现象之一——即正流形——是数据对理论的不确定性的延伸。统计拟合指数是一种不充分且有时难以区分理论的方法；应该进行严格的测试和进一步的证据，以了解阳性歧管的潜在原因。局灶性皮层病变的认知影响允许测试竞争模型预测的必要因果关系。该证据表明，局灶性皮层病变会导致局部而非整体（所有能力）缺陷。不影响其他共变能力可以容纳局灶性病变证据。在研究了不同模型如何通过此测试后，我们建议最好支持双因子模型（类别：共因模型）和键模型（类别：采样模型）。简而言之，当测试其隐含的因果关系和预测时，可以告知竞争的心理测量模型。