Retrieval-based inference in the acquired equivalence paradigm

Generalization is fundamental to cognition. In acquired equivalence, two stimuli that share a common association become treated as equivalent, with information acquired about one stimulus generalizing to the other. Acquired equivalence has been thought to rely on integrating related memories as they are encoded, resulting in fast spontaneous generalization, but other studies suggested effortful on-demand recombination of initially separate memories at retrieval. Here, we tested whether the tendency to separate vs. integrate related information may depend on a methodological detail of a traditional acquired equivalence paradigm.Human participants underwent feedback-based learning of overlapping face-scene associations, choosing a correct scene for a face from two options on each trial. Foil (incorrect) scenes were controlled for half of the participants to ensure that they can only learn from corrective feedback. The other half had foils selected randomly on each trial, allowing statistical learning of face-scene co-occurrence to supplement feedback-based learning. We hypothesized that the opportunity for statistical learning would boost learning and generalization and facilitate memory integration.The opportunity for statistical learning increased associative learning and generalization. However, rather than integrated memories, generalization was increased through learning during test.The results indicate that the tendency for generalization in the acquired equivalence is rather small, with no evidence for integrative encoding irrespective of group. The results inform current debates regarding encoding-based vs. retrieval-based mechanisms of generalization. They also highlight how methodological details may alter performance and the involvement of cognitive processes that underlie it.