For the several decades, researchers studied human-avatar interactions using a virtual reality (VR). However, speculation on the interaction between a human's recognition memory and interaction types/methods has not enough considered yet. In the current study, we designed a VR interaction paradigm with two different types of human-avatar interaction including initiating and responding interactions, and we also included two interaction methods including a head-gazing and a hand-pointing. The result indicated that there are significant differences in the recognition memory between the initiating and responding interactions. However, we couldn't find any significant effects of interaction methods in the current study. These results suggest that the human-avatar interaction may have similar patterns with the human-human interaction in the recognition memory, and methodological advances also required.

SUMMARY: We want to evaluate the performance of two FDR-based multiple testing procedures by Benjamini and Hochberg (1995, J. R. Stat. Soc. Ser. B, 57, 289-300) and Storey (2002, J. R. Stat. Soc. Ser. B, 64, 479-498) in analyzing real microarray data. These procedures commonly require independence or weak dependence of the test statistics. However, expression levels of different genes from each array are usually correlated due to coexpressing genes and various sources of errors from experiment-specific and subject-specific conditions that are not adjusted for in data analysis. Because of high dimensionality of microarray data, it is usually impossible to check whether the weak dependence condition is met for a given dataset or not. We propose to generate a large number of test statistics from a simulation model which has asymptotically (in terms of the number of arrays) the same correlation structure as the test statistics that will be calculated from the given data and to investigate how accurately the FDR-based testing procedures control the FDR on the simulated data. Our approach is to directly check the performance of these procedures for a given dataset, rather than to check the weak dependency requirement. We illustrate the proposed method with real microarray datasets, one where the clinical endpoint is disease group and another where it is survival.