Topic: asymmetric learning

Humans are capable of generalising local pairwise relations to transitively infer global relationships between items, states or events. Recent research has shown that asymmetric learning, where beliefs about only the winners (or losers) of local comparisons are updated, is well-suited to allow human and artificial agents to infer relational structures from sparse feedback. However, less is known about how already-acquired knowledge and subsequent learning policies are impacted by experiences that violate the inferred monotonic ordering of items, such as when agents learn that A is greater than