Computational models of stimulus equivalence: An intersection for the study of symbolic behavior
Free computer models can rehearse equivalence-class training for you—use them to preview what might work in therapy.
01Research in Context
What this study did
Tovar and colleagues scanned every computer model that tries to copy how stimulus-equivalence classes grow.
They grouped the models by how they learn: some act like neurons, others act like rules, and some act like both.
The paper is a map, not a lab study—no new data, just a tour of the free software you can download today.
What they found
The authors found twelve working programs.
All of them can build A-B and B-C trained links and then spit out untrained A-C choices—emergent relations in silicon.
Most models run on ordinary laptops and need only a few lines of code to tweak.
How this fits with other research
Hughes et al. (2014) asked if pigeons can do equivalence.
Tovar et al. (2023) answer back: “Yes, and here are the circuits that mimic them.”
The new review does not kill the bird work; it gives it a wiring diagram.
Kulubekova et al. (2013) showed a virtual rat that shifts preference when reinforcement rates change.
Tovar widens the lens from simple choice to full-blown symbolic classes, showing the same modeling trick works for both.
Barnes-Holmes et al. (2018) argued that derived relations are the engine of human language.
The 2023 catalog hands you the toolbox to test that claim without human subjects—run the model, watch language bloom.
Why it matters
If a client struggles to form equivalence classes, you can now test tweaks in a model before you burn session time.
Load free software, swap pictures for spoken words, change reinforcement size, and see which version sparks emergent relations fastest.
Take the winning setup straight to the table—no extra trials on the kid.
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02At a glance
03Original abstract
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.
Journal of the Experimental Analysis of Behavior, 2023 · doi:10.1002/jeab.829