Artificial intelligence for the measurement of vocal stereotypy.

Both researchers and practitioners often rely on direct observation to measure and monitor behavior. When these behaviors are too complex or numerous to be measured in vivo, relying on direct observation using human observers increases the amount of resources required to conduct research and to monitor the effects of interventions in practice. To address this issue, we conducted a proof of concept examining whether artificial intelligence could measure vocal stereotypy in individuals with autism. More specifically, we used an artificial neural network with over 1,500 minutes of audio data from 8 different individuals to train and test models to measure vocal stereotypy. Our results showed that the artificial neural network performed adequately (i.e., session-by-session correlation near or above .80 with a human observer) in measuring engagement in vocal stereotypy for 6 of 8 participants. Additional research is needed to further improve the generalizability of the approach.

Using a Visual Structured Criterion for the Analysis of Alternating-Treatment Designs.

Although visual inspection remains common in the analysis of single-case designs, the lack of agreement between raters is an issue that may seriously compromise its validity. Thus, the purpose of our study was to develop and examine the properties of a simple structured criterion to supplement the visual analysis of alternating-treatment designs. To this end, we generated simulated data sets with varying number of points, number of conditions, effect sizes, and autocorrelations, and then measured Type I error rates and power produced by the visual structured criterion (VSC) and permutation analyses. We also validated the results for Type I error rates using nonsimulated data. Overall, our results indicate that using the VSC as a supplement for the analysis of systematically alternating-treatment designs with at least five points per condition generally provides adequate control over Type I error rates and sufficient power to detect most behavior changes.

Using Single-Case Designs in Practical Settings: Is Within-Subject Replication Always Necessary?

Behavior analysts have widely adopted and embraced within-subject replication through the use of reversal and multielement designs. However, the withdrawal of treatment, which is central to these designs, may not be desirable, feasible, or even ethical in practical settings. To examine this issue, we extracted 501 ABAB graphs from theses and dissertations to examine to what extent we would have reached correct or incorrect conclusions if we had based our analysis on the initial AB component only. In our first experiment, we examined the proportion of datasets for which the results of the first AB component matched the results of the subsequent phase reversals. In our second experiment, we calculated three effect size estimates for the same datasets to examine whether these measures could predict the relevance of conducting a within-subject replication. Our analyses indicated that the initial effects were successfully replicated at least once in approximately 85% of the cases and that effect size may predict the probability of within-subject replication. Overall, our results support the rather controversial proposition that it may be possible to set threshold values of effect size above which conducting a replication could be considered unnecessary. That said, more research is needed to confirm and examine the generalizability of these results prior to recommending changes in practice.