Gebreegziabher et al. [24] argued that counterfactual generation that follows the principles of VT allowed the introduction of discriminatory variance for the model to learn on.

Building on methods proposed in PaTAT [24], Mocha first generates human-readable neuro-symbolic pattern rules from partially labeled text data for classification.

These theories have proven insightful for understanding how humans grasp and compare concepts, shaping the development of human-AI collaboration systems for sensemaking [29], hypothesis testing [2], as well as model training [24].

Both systems enabled users to quickly identify variations and patterns within the data and support exploration and hypothesis testing.

The last two prior works also combine Variation Theory (VT) and SAT together, as we did (i.e., a corollary of SAT referred to as Analogical Transfer/Learning Theory).

In line with previous work, Mocha aims to support a user's efforts in the disambiguation of concepts through structural comparisons of counterfactual data in the context of machine teaching.

Most previous research in counterfactual generation has focused on the model side by either generating counterfactuals to improve the model's performance or explaining its behaviors post hoc.

While SAT-based rendering supported human sensemaking in both Gero et al. [29] and Mocha, we also show that the combination of VT and SAT support the model's learning.

This finding is consistent with previous work that supports users' sense-making of text, e.g., by modulating text saliency. Specifically, Gu et al. [32] and Gero et al. [29] both found improved reading efficiency and comprehension with saliency-modulating text renderings.