Signal Detection-Based Analyses of Stimulus Control

项目3将侧重于刺激控制发展中刺激变量和动机变量之间尚未完全了解的相互作用。利用辨别学习的经典范式，我们的研究将调查变量和变量交互作用，这些变量和变量交互作用可能有助于解释智障人士经常观察到的学习结果缓慢和/或不一致的原因。虽然这种缺陷在历史上被归因于注意力不集中、参加的范围过广和/或范围过窄、和/或缺乏动力，但我们的假设是，按照传统标准，长期歧视学习失败可能发生在那些既专注又动机良好的个人身上。我们认为，当教师/实验者编程时，从学习者的角度来看，无法区分的意外情况会导致长期失败。拟议的项目的灵感来自于我们实验室的大量发现和当前对学习过程的定量理论分析，这些分析植根于信号检测理论(SDT)。这种量化方法在残疾研究中几乎没有被研究过，但我们认为它们至少有两个显著的优势。首先，它们提供了一种高度结构化的、经过经验验证的方法，用于分析程序化或有事件的决策要素的可区分度。其次，有有效的量化技术来评估学位 学习失败是由于刺激和/或强化变量造成的偏差的结果。总之，我们相信，基于SDT的分析的这两个特点将有助于阐明在分析智障人士的学习过程中存在的某些长期问题。我们还认为，基于定量SDT的方法在支持此类个人更有效的教学互动方面具有天然的优势。为了展示这些优势，我们将使用另一种量化方法，即百分位数排序法。通过将基于SDT的分析与百分位数方法相结合， 我们的目标是为有效利用计算机管理目前用于残疾人教育干预的歧视学习程序奠定基础。

Project 3 will focus on as yet incompletely understood interactions between stimulus variables and motivational variables in the development of stimulus control. Making use of classic paradigms in discrimination learning, our research will investigate variables and variable interactions that may help to account for the slow and/or inconsistent learning outcomes that are frequently observed in persons with intellectual disabilities. Whereas such deficits have historically been attributed to inattention, overly broad and/or narrow attending, and/or lack of motivation, our hypothesis is that protracted discrimination learning failures can occur in individuals who would be classified as both attentive and well-motivated by traditional standards. We propose that protracted failures result when teachers/experimenters program contingencies that are indiscriminable from the perspective of the learner. The proposed project is inspired by numerous findings from our laboratories and by current quantitative theoretical analyses of learning processes that have their roots in signal detection theory (SDT). Such quantitative approaches are virtually unstudied in disability research, but we believe that they have at least two significant advantages. First, they offer a highly structured, empirically verified method for analyzing the degree to which cdtical elements of programmed contingencies are discriminable. Second, there are effective quantitative techniques for assessing the degree to which learning failures are the result of biases due to stimulus and/or reinforcer variables. Together, we believe that these two features of SDT-based analyses will help to shed light on certain longstanding problems in the analysis of learning processes of individuals with intellectual disabilities. We suggest also that a quantitative SDT-based methodology has natural advantages for supporting more effective teaching interactions for such individuals. To demonstrate these advantages, we will employ another quantitative method, the percentile-scheduling approach. By combining SDT-based analyses with percentile approaches, our goal is to establish a foundation for effective computer-based management of discrimination learning procedures of the type that are now used in educational intervention for persons with disabilities.