Visual analysis is a cornerstone of decision-making in Applied Behavior Analysis. Individuals responsible for implementing behavioral interventions and analyzing data are often behavior technicians who may not be provided with the training necessary to be proficient in visual analysis. Therefore, there is a need for an effective and streamlined method to train visual analysis. Previous research has suggested using a decision-making algorithm (DMA) to aid individuals in making decisions about time-series data. The current study further evaluated the effects of a DMA on accurate visual analysis of time-series data. We presented graduate students with time-series graphs, each graph depicting 10 data points which resembled one of the four options depicted in the DMA. The results indicated five of the six participants demonstrated an increase in correct responding when the DMA was introduced. One participant (Participant 4) required an asynchronous feedback session. Correct responding maintained for five of the six participants when the DMA was removed. Following the maintenance probe, high levels of percentage of correct responding maintained for four of the six participants.

中文翻译：

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进一步评估支持时间序列数据可视化分析的决策算法

视觉分析是应用行为分析中决策的基石。负责实施行为干预和分析数据的个人通常是行为技术人员，他们可能没有接受过精通视觉分析所需的培训。因此，需要一种有效且简化的方法来训练视觉分析。先前的研究建议使用决策算法 (DMA) 来帮助个人对时间序列数据做出决策。目前的研究进一步评估了 DMA 对时间序列数据的准确可视化分析的影响。我们向研究生展示了时间序列图，每个图描绘了 10 个数据点，类似于 DMA 中描述的四个选项之一。结果表明，当引入 DMA 时，六名参与者中有五名的正确反应有所增加。一名参与者（参与者 4）需要异步反馈会话。移除 DMA 后，六名参与者中有五名保持了正确的响应。在维护探测之后，六名参与者中的四名保持了高水平的正确响应百分比。