Antecedent- and consequence-based procedures decrease errors during conditional discrimination training but are not typically guided by error patterns. A framework based in behavioral-choice and signal-detection theory can quantify error patterns due to (1) biases for certain stimuli or locations and (2) discriminability of stimuli within the conditional discrimination. We manipulated levels of disparity between sample (Experiment 1) and comparison (Experiment 2) stimuli by manipulating red saturation using an ABA design with children diagnosed with autism spectrum disorder (ASD). Lower disparities decreased discriminability and biases were observed for some participants during the low-disparity conditions. These findings demonstrate the use of these analyses to identify error patterns during conditional-discrimination performance in a clinically relevant population under laboratory conditions. Further development of this framework could result in the development of technologies for categorizing errors during clinically relevant conditional-discrimination performance with the goal of individualizing interventions to match learner-specific error patterns.

基于先验和结果的程序可减少条件歧视训练中的错误，但通常不受错误模式的指导。基于行为选择和信号检测理论的框架可以量化错误模式，这是由于（1）某些刺激或位置的偏见和（2）可辨别性有条件的歧视内的刺激。我们通过对患有自闭症谱系障碍（ASD）的儿童进行ABA设计来操纵红色饱和度，从而控制了样品（实验1）和比较（实验2）刺激之间的差异水平。较低的视差会降低可分辨性，并且在低视差条件下会观察到一些参与者的偏见。这些发现表明，在实验室条件下，在临床相关人群中，在有条件的歧视行为中使用这些分析可识别错误模式。该框架的进一步发展可能会导致在临床相关的有条件的歧视表现期间对错误进行分类的技术的发展，其目的是使干预措施个性化以匹配特定于学习者的错误模式。