Algorithmic analysis of relational learning processes in instructional technology: Some implications for basic, translational, and applied research

UMMS Affiliation

Department of Psychiatry; Eunice Kennedy Shriver Center; Implementation Science and Practice Advances Research Center

Publication Date


Document Type



Applied Behavior Analysis | Behavior and Behavior Mechanisms | Educational Psychology | Educational Technology | Mental and Social Health | Psychiatry | Psychiatry and Psychology


A few noteworthy exceptions notwithstanding, quantitative analyses of relational learning are most often simple descriptive measures of study outcomes. For example, studies of stimulus equivalence have made much progress using measures such as percentage consistent with equivalence relations, discrimination ratio, and response latency. Although procedures may have ad hoc variations, they remain fairly similar across studies. Comparison studies of training variables that lead to different outcomes are few. Yet to be developed are tools designed specifically for dynamic and/or parametric analyses of relational learning processes. This paper will focus on recent studies to develop (1) quality computer-based programmed instruction for supporting relational learning in children with autism spectrum disorders and intellectual disabilities and (2) formal algorithms that permit ongoing, dynamic assessment of learner performance and procedure changes to optimize instructional efficacy and efficiency. Because these algorithms have a strong basis in evidence and in theories of stimulus control, they may have utility also for basic and translational research. We present an overview of the research program, details of algorithm features, and summary results that illustrate their possible benefits. It also presents arguments that such algorithm development may encourage parametric research, help in integrating new research findings, and support in-depth quantitative analyses of stimulus control processes in relational learning. Such algorithms may also serve to model control of basic behavioral processes that is important to the design of effective programmed instruction for human learners with and without functional disabilities.


Computerized algorithmic learning supports, Learning by exclusion, Stimulus control, Stimulus equivalence

DOI of Published Version



Behav Processes. 2018 Jul;152:18-25. doi: 10.1016/j.beproc.2018.03.001. Epub 2018 Mar 12. Link to article on publisher's site

Journal/Book/Conference Title

Behavioural processes

Related Resources

Link to Article in PubMed

PubMed ID