This paper explains training-time out-of-distribution (OOD) detection from a novel view, i.e., interactions between different input variables of deep neural networks (DNNs). Specifically, we provide a unified understanding of the effectiveness of current training-time OOD detection methods, i.e., DNNs trained with these methods all encode more complex interactions for inference than those trained without training-time methods, which contributes to their superior OOD detection performance. We further conduct thorough empirical analyses and verify that complex interactions play a primary role in OOD detection, by developing a simple-yet-efficient method to force the DNN to learn interactions of specific complexities and evaluate the change of OOD detection performances. Besides, we also use interactions to investigate why near-OOD samples are more difficult to distinguish from in-distribution (ID) samples than far-OOD samples, mainly because compared to far-OOD samples, the distribution of interactions in near-OOD samples is more similar to that of ID samples. Moreover, we discover that training-time OOD detection methods can effectively decrease such similarities. The code will be released when the paper is accepted.