Teaching Symbolic Relations in Down Syndrome Through Equivalence-Based Instruction: A Case Study

Tovar, Ángel Eugenio; Torres-Chávez, Álvaro

doi:10.5514/rmac.v47.i2.81165

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…Moreover, this has resulted in a solid development of applied studies with benefits in clinical and educational contexts. The paradigm has been used for teaching word-object mappings, basic language, writing, and naming skills in children (Stromer et al, 1992); complex concepts to college students including inferential statistics and statistical interactions (Fields, Travis, et al, 2009;, classes of logical fallacies (Gallant et al, 2021), and biological concepts such as brainbehavior relations ; developing clinical interventions in adults (Guinther & Dougher, 2015), with particular relevance in therapies such as acceptance and commitment therapy (Tarbox et al, 2020); and training communication abilities, relational responding, and generalization skills in children with neurodevelopmental disorders (Arnall et al, 2021;Gale & Stewart, 2020;Tovar & Torres-Ch avez, 2021).…”

Section: Stimulus Equivalencementioning

confidence: 99%

Computational models of stimulus equivalence: An intersection for the study of symbolic behavior

Tovar

Torres-Chávez

Mofrad

et al. 2023

J Exper Analysis Behavior

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Stimulus equivalence is a central paradigm in the analysis of symbolic behavior, language, and cognition. It describes emergent relations between stimuli that were not explicitly trained and cannot be explained by primary stimulus generalization. In recent years, researchers have developed computational models to simulate the learning of equivalence relations. These models have been used to address primary theoretical and methodological issues in this field, such as exploring the underlying mechanisms that explain emergent equivalence relations and analyzing the effects of training and testing protocols on equivalence outcomes. Nonetheless, although these models build upon general learning principles, their operation is usually obscure for nonmodelers, and in the field of stimulus equivalence computational models have been developed with a variety of approaches, architectures, and algorithms that make it difficult to understand the scope and contributions of these tools. In this paper, we present the state of the art in computational modeling of stimulus equivalence. We seek to provide concise and accessible descriptions of the models' functioning and operation, highlight their main theoretical and methodological contributions, identify the existing software available for researchers to run experiments, and suggest future directions in the emergent field of computational modeling of stimulus equivalence.

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