Multi-step inference can be improved across the lifespan with individualized memory interventions

Effective goal-directed decision-making relies on both memory and planning–processes that are each known to decline with age. We tested the hypothesis that these declines stem from a common mechanism by focusing on mnemonic discrimination, a measure of memory precision that shows unique vulnerability to age-related decline. We used a latent learning task that measures the ability to learn and make judgments about multi-step associations among interconnected stimuli, assessing performance across the adult lifespan. In Experiment 1, we examined relationships between judgment performance and mnemonic discrimination ability. In Experiment 2, we tested whether a learning schedule designed to reduce memory interference (by temporally separating overlapping object pairs) could improve performance, particularly for individuals with weaker memory abilities. We also implemented an artificial neural network simulation varying training sequence and the network’s representational capacity to model performance. Across the lifespan, both young and older adults showed evidence of successful latent learning and inference, but variability in judgment performance was explained by mnemonic discrimination ability. In Experiment 2, mnemonic discrimination interacted with training condition: intermixed training benefited those with high memory precision, whereas blocked training benefited those with low memory precision. The neural network simulation reproduced these patterns. These findings suggest that age-related declines in complex judgments may stem from declines in mnemonic discrimination. Importantly, they demonstrate that individualized, memory-based training interventions can improve learning and reasoning processes that support goal-directed planning, offering a promising approach to preserve decision-making abilities across the lifespan.