Cognitive Network Enrichment, Not Degradation, Explains the Aging Mental Lexicon and Links Fluid and Crystallized Intelligence.

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Title: Cognitive Network Enrichment, Not Degradation, Explains the Aging Mental Lexicon and Links Fluid and Crystallized Intelligence.
Authors: Hills, Thomas T.1 (AUTHOR) t.t.hills@warwick.ac.uk
Source: Psychological Review. Mar2026, Vol. 133 Issue 2, p488-506. 19p.
Subject Terms: *Crystallized intelligence, *Associative learning, *Cognitive development, *Cognition, Cognitive aging, Executive function, Cognition disorders
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of Psychological Review is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Psychological Review is the property of American Psychological Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Mar2026
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