Machine learning techniques benefit science and industry primarily insofar as they enable data analysts to better understand their data, make valid conclusions, and gain insight into their domain of investigation. Studies in anthropocentric data analysis seek to understand how human judgment is applied in the data analysis process and to develop methods that provide explicit opportunities for human interaction and insight. We present three studies of human visual reasoning exploring the extent to which novice and expert subjects are able to judge the quality of and understand cluster analysis and dimensionality reduction stimuli. We then investigate whether humans are able to learn how cluster analysis algorithms function simply through interacting with them rapidly across diverse data sets. Finally we show how multicore processing on CPUs and GPUs can move human/algorithm interactions closer to real-time.