Cognitive network enrichment, not degradation, explains the aging mental lexicon and links fluid and crystallized intelligence.

Cognition is a complex system of interacting components. Late-life cognitive decline is often explained as a degradation of the interconnectivity among these components. Evidence from the aging mental lexicon corroborates this interpretation, as older adults produce higher entropy responses in free association tasks, appear to have sparser free association networks, and judge objects to be less similar to one another than younger adults. Here, I demonstrate that all of these effects are produced by a model of cognitive network enrichment, which treats aging as an extension of lifelong learning. By increasing interconnectivity, learning increases competition for activation among potential targets, increasing entropy and reducing targeted activation. The impact of network enrichment is demonstrated using a general prediction error model (Rescorla-Wagner), which learns and enriches a cognitive network representation following lifelong experience with a network of associations in the environment. Sampling from the learned representation to produce behavior reproduces the above effects. A qualitative model comparison shows that various models of degradation fail to capture the above results for entropy and similarity. Both enriched and degraded representations can produce sparsening-free association networks, depending on the specific methodological details of data collection. This underscores the general problem of inferring representation from behavior without considering process. Further, extending cognitive network enrichment more broadly provides a lifelong developmental pathway for overattention to irrelevant stimuli and cognitive slowing-increasing interference, taxing resource limitations, and reducing targeted activation-offering a common cause for rising crystallized intelligence and declining fluid intelligence. (PsycInfo Database Record (c) 2025 APA, all rights reserved).