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Blockchain technology has completely changed the area of cryptocurrency with a Peer-to-Peer system named Bitcoin. It can provide a distributed, transparent and highly confidential database by recording immutable transactions. Currently, the technique has obtained great research interest on other areas, including the Internet of vehicles (IoVs). In order to solve some centralized problems and improve the architecture of the IoVs, the blockchain technology is utilized to build a decentralized and secure vehicular environment. In this survey, we aim to construct a comprehensive analysis on the applications of blockchain in the IoV. This paper starts with the introduction of the IoVs and the blockchain. Additionally, some existing surveys on the blockchain enabled IoVs are reviewed. Besides, the combination of the blockchain technology and the IoVs is analyzed from seven aspects to describe how the blockchain is implemented in the IoVs. Finally, the future research directions related to the integration are highlighted.

Evidence from multiple studies conducted in the past few decades converges on the conclusion that numerical properties can be associated with specific directions in space. Such spatial-numerical associations (SNAs), as a signature of elementary number processing, seem to be a likely correlate of math skills. Nevertheless, almost three decades of research on the spatial-numerical association of response codes (SNARC) effect, the hallmark of SNAs, has not provided conclusive results on whether there is a relation with math skills. Here, going beyond reviewing the existing literature on the topic, we try to answer a more fundamental question about why the SNARC effect should (and should not) be related to math skills. We propose a multiroute model framework for a SNARC-math skills relationship. We conclude that the relationship is not straightforward and that several other factors should be considered, which under certain circumstances or in certain groups can cause effects of opposite directions. The model can account for conflicting results, and thus may be helpful for deriving predictions in future studies.