Optimal metacognitive control of memory recall.

Abstract

Most of us have experienced moments when we could not recall some piece of information but felt that it was just out of reach. Research in metamemory has established that such judgments are often accurate; but what adaptive purpose do they serve? Here, we present an optimal model of how metacognitive monitoring (feeling of knowing) could dynamically inform metacognitive control of memory (the direction of retrieval efforts). In two experiments, we find that, consistent with the optimal model, people report having a stronger memory for targets they are likely to recall and direct their search efforts accordingly, cutting off the search when it is unlikely to succeed and prioritizing the search for stronger memories. Our results suggest that metamemory is indeed adaptive and motivate the development of process-level theories that account for the dynamic interplay between monitoring and control.

Top-down task goals induce the retrieval state

“…If anything, we might expect greater retrieval state evidence for high compared to low confidence hits, whereby stronger representations produce greater retrieval state engagement. Instead, we find greater retrieval state evidence for low compared to high confidence hits which may reflect differences in memory search or evidence accumulation across the two conditions (Balsdon et al, 2020; Callaway et al, 2023; Lee, Daunizeau, & Pezzulo, 2023). Together, these results are consistent with the interpretation that the retrieval state is engaged via top-down demands in service of the attempt to access an internally stored representation.…”

“…Alternatively, to the extent that the retrieval state tracks attention or control required to access a representation, there should be more retrieval state evidence during low compared to high confidence hits. The logic is that less evidence should require more internal attention to support a decision, possibly by prolonging the search or evidence accumulation process (Callaway, Griffiths, Norman, & Zhang, 2023).…”