A discrimination analysis of training-structure effects on stimulus equivalence outcomes.
Run many-to-one (comparison-as-node) training if you want the best shot at building stimulus equivalence classes.
01Research in Context
What this study did
McConkey et al. (1999) looked at how the shape of equivalence training changes what people learn.
They used graph theory to map every link in four common training set-ups.
The goal was to see which structure gives learners all the simple discriminations they need.
What they found
Only the comparison-as-node, or many-to-one, layout contains every simple discrimination.
Other layouts skip some.
Missing links can block the new relations from emerging later.
How this fits with other research
O'mara (1991) built the counting tool R et al. used, so the new paper is a direct follow-up.
Murphy et al. (2014) later showed that overtraining successive trials to 500 lifts equivalence scores to 85%.
Their result backs the core idea: more practice on the simple discriminations that R et al. flagged equals stronger classes.
Ayres‐Pereira et al. (2025) pushed this further.
They gave adults near-identical pictures and found that only side-by-side comparison displays produced perfect equivalence.
Again, the comparison view—the same node R et al. highlighted—was the key.
Why it matters
When you plan conditional-discrimination programs, choose many-to-one training.
Put the new target as the comparison that gets paired with many samples.
This single switch gives the learner every simple discrimination in the set and raises the odds that untaught relations will pop out later.
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02At a glance
03Original abstract
Experiments designed to establish stimulus equivalence classes frequently produce differential outcomes that may be attributable to training structure, defined as the order and arrangement of baseline conditional discrimination training trials. Several possible explanations for these differences have been suggested. Here we develop a hypothesis based on an analysis of the simple simultaneous and successive discriminations embedded in conditional discrimination training and testing within each of the training structures that are typically used in stimulus equivalence experiments. Our analysis shows that only the comparison-as-node (many-to-one) structure presents all the simple discriminations in training that are subsequently required for consistently positive outcomes on all tests for the properties of equivalence. The sample-as-node (one-to-many) training structure does not present all the simple discriminations required for positive outcomes on either the symmetry or combined transitivity and symmetry (equivalence) tests. The linear-series training structure presents all the simple discriminations required for consistently positive outcomes on tests for symmetry, but not for symmetry and transitivity combined (equivalence) or transitivity alone. Further, the difference in the number of simple discriminations presented in comparison-as-node training versus the other training structures is larger when the intended class size is greater than three or the number of classes is larger than two. We discuss the relevance of this analysis to interpretations of stimulus equivalence research, as well as some methodological and theoretical implications.
Journal of the experimental analysis of behavior, 1999 · doi:10.1901/jeab.1999.72-117