Testing the structure of human cognitive ability using evidence obtained from the impact of brain lesions over abilities

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. •Focal cortical lesions lead to local, not global, deficits.•Measurement models to explain the positive manifold are causal models with unique predictions going beyond model fit statistics.•Correlated factor, network, process sampling, mutualism, investment models, make causal predictions inconsistent with lesion evidence.•Hierarchical and bifactor models are consistent with the pattern of lesion effects, as well as possibly one form of bonds sampling models.•Future models and explanations of the positive manifold have to accommodate focal lesions leading to local not global deficits.