Decision Augmentation Theory: toward a Model of Anomalous Mental Phenomena

Decision augmentation theory (DAT) holds that humans integrate information obtained by anomalous cognition into the usual decision process. The result is that, to a statistical degree, such decisions are biased toward volitional outcomes. We introduce our model and show that the domain over which it is applicable is within a few standard deviations from chance. We contrast the theory's experimental consequences with those of models that treat anomalous effects as due to a force. We derive mathematical expressions for DAT and force-like models using two distributions, normal and binomial. DAT is testable both retrospectively and prospectively, and we provide statistical power curves to assist in the experimental design of such tests. We show that the experimental consequences of our theory are different from those of force-like models except for one special case.