Relational generalization through memory integration: building knowledge beyond direct experience
Abstract
Memory allows us to remember the past but also form complex knowledge combining information obtained across time. As a key example, relational generalization helps us infer new information by connecting related information acquired separately (AB & BC, therefore AC). Associative inference across arbitrary associations of visual objects has been the most fruitful paradigm, but how it relates to real-world knowledge acquisition is unclear. A more naturalistic paradigm is self-derivation of knowledge, assessing the ability to integrate two related, real-world facts to infer a novel fact. However, because of the rooting in verbal material and semantic memory, it is unclear to what degree the two paradigms assess a common underlying process. Here we tested the degree to which an individual's self-derivation performance can be predicted from their general verbal retrieval abilities and a standard associative inference task with arbitrary object pairs. Results showed that associative inference and verbal fluency separately positively predicted self-derivation of knowledge. Associative inference predicted self-derivation even after controlling for individual differences in associative memory strength. Finally, we found that resistance to interference in the associative inference paradigm may serve as an alternative measure of memory integration. These findings demonstrate that self-derivation of knowledge and associative inference are underpinned by the same process of memory…
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