Establishing emergent analogical spatiotemporal relations

Researchers taught 20 college students to match spatiotemporal sequences. First the adults learned 'next' and 'previous' relations between abstract shapes on a screen.

After training, the team tested if the students could solve brand-new analogy problems like 'Shape A is to Shape B as Shape C is to ?' without any extra teaching.

Every adult solved the analogies correctly on the first try. The new performances appeared without direct training, proving true emergent relational learning.

The result repeated in a second experiment with new stimuli, showing the effect is reliable.

Chadwick et al. (2000) used the same equivalence-training logic but focused on vocal responses tracked by speech-recognition software. Both studies show you can build complex verbal skills from simple matching bases.

Becraft et al. (2020) supply the math tool you would need to pool this single-case design with others in a future meta-analysis. Their multilevel-model primer covers exactly this type of two-experiment study.

Cox et al. (2025) move in a different direction. They feed operant data into an AI model to predict the next response, while Pérez-González et al. let the human brain do the predicting through emergent relations.

If you want clients to solve analogies—whether picture schedules, math ratios, or social comparisons—start by teaching simple next/previous relations within sequences. Once those relations are solid, novel analogical problems can emerge without extra drills, saving you teaching time and building flexible thinking.