Since the start of the 21st century, few advances have had as far reaching consequences in science as the widespread adoption of artificial neural networks in fields as diverse as fundamental physics, clinical medicine, and social networking. In behavior analysis, one promising area for the adoption of neural networks involves the analysis of single-case graphs. However, few behavior analysts have any training on the use of these methods, which may limit progress in this area. The purpose of our tutorial is to address this issue by providing a step-by-step description on using artificial neural networks to improve the analysis of single-case graphs. To this end, we trained a new model using simulated data to analyze multiple baseline graphs and compared its outcomes to those of visual analysis on a previously published dataset. In addition to showing that artificial neural networks may outperform visual analysis, the tutorial provides information to facilitate the replication and extension of this line of work to other datasets and designs.
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