Artificial neural networks reveal individual differences in metacognitive monitoring of memory.

Previous work supports an age-specific impairment for recognition memory of pairs of words and other stimuli. The present study tested the generalization of an associative deficit across word, name, and nonword stimulus types in younger and older adults. Participants completed associative and item memory tests in one of three stimulus conditions and made metacognitive ratings of perceptions of self-efficacy, task success ("postdictions"), strategy success, task effort, difficulty, fatigue, and stamina. Surprisingly, no support was found for an age-related associative deficit on any of the stimulus types. We analyzed our data further using a multilayer perceptron artificial neural network. The network was trained to classify individuals as younger or older and its hidden unit activities were examined to identify data patterns that distinguished younger from older participants. Analysis of hidden unit activities revealed that the network was able to correctly classify by identifying three different clusters of participants, with two qualitatively different groups of older individuals. One cluster of older individuals found the tasks to be relatively easy, they believed they had performed well, and their beliefs were accurate. The other cluster of older individuals found the tasks to be difficult, believed they were performing relatively poorly, yet their beliefs did not map accurately onto their performance. Crucially, data from the associative task were more useful for neural networks to discriminate between younger and older adults than data from the item task. This work underscores the importance of considering both individual and age differences as well as metacognitive responses in the context of associative memory paradigms.

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.