The role of cumulative automatization in logical problem solving: Differences between younger and older adults

This study investigated the role of cumulative automatization in supporting complex logical reasoning, with a specific focus on age-related differences between younger and older adults. Grounded in the cumulative and emerging automatic deficit model, the research explores how the gradual automatization of cognitive subroutines influences higher order problem-solving abilities. Participants (N = 68), divided into two age groups, completed associative learning tasks using Chinese ideograms followed by inductive reasoning problems of increasing complexity. Behavioral data revealed that greater automatization—measured by faster reaction times, fewer errors, and reduced attempts—facilitates more efficient cognitive processing. While older adults showed slower acquisition and higher cognitive load during the learning phases, their performance in complex reasoning tasks aligned with that of younger participants once automatization was achieved. These results suggest that automatization acts as a cognitive buffer, enhancing reasoning efficiency by offloading controlled processes. Findings emphasize the importance of targeting automatization in cognitive training programs, especially in aging populations, and support a dynamic model of interaction between automatic and controlled cognitive mechanisms. This study demonstrated that cumulative automatization of basic cognitive operations facilitates complex logical problem solving across the adult lifespan. Although older adults show slower initial learning, once automatization is achieved, their performance in reasoning tasks matches that of younger adults. These findings highlight the importance of targeting automatization in cognitive training interventions to reduce cognitive load and support reasoning efficiency, with potential implications for promoting healthy cognitive aging. (PsycInfo Database Record (c) 2025 APA, all rights reserved)