Item-specific and relational encoding are effective at reducing the illusion of competence.

Metamemory, or the ability to understand the capacities of one's own memory, is important for learning. To investigate questions surrounding metamemory, researchers commonly have participants make judgments of learning (JOLs) at encoding, in which participants rate their likelihood of recalling the target in a cue-target word pair when shown only the cue at test. However, the associative direction of cue-target pairs can affect the calibration of JOLs. Unlike forward associates (e.g., credit-card), in which JOLs often accurately predict recall, an illusion of competence has been reported for backward associates (e.g., card-credit), symmetrical associates (e.g., salt-pepper), and unrelated cue-target pairs (e.g., artery-bronze) such that JOLs overestimate later recall. The present study evaluates whether the illusion of competence can be reduced when participants apply deep item-specific or relational encoding tasks relative to silent reading. Across two experiments, we show that both item-specific and relational encoding strategies reduce the illusion of competence for backward associates and unrelated pairs while improving the calibration between JOLs and recall. Our findings suggest that these encoding strategies are effective at reducing the illusion of competence, with increased calibration primarily reflecting improved recall. Thus, item-specific and relational encoding strategies primarily affect retrieval processes rather than metacognitive processes that participants engage in at encoding.