Contributions of associative learning to age and individual differences in fluid intelligence

According to the cognitive cascade hypothesis, age-related slowing results in decreased working memory, which in turn affects higher-order cognition. Because recent studies show complex associative learning correlates highly with fluid intelligence, the present study examined the role of complex associative learning in cognitive cascade models of data from adults aged 30–80 years. Path analyses revealed that an extended cascade model, in which associative learning mediated the relation between working memory and fluid intelligence, provided an adequate fit to the data. Moreover, an alternative extended cascade model, one with an additional path from speed to fluid intelligence and separate learning and secondary memory components, provided an excellent fit. These findings establish a role for complex associative learning in the extended cognitive cascade underlying age and individual differences in fluid intelligence.

► A cognitive cascade model with learning predicts gF better than one that doesn't.
► Both the learning and secondary memory mediated the relation between WM an gF.
► Speed also played an important role in predicting gF.
► These findings indicate that learning underlies age and individual differences in gF.