Relevance of working memory for reinforcement learning in older adults varies with timescale of learning

Irene van de Vijver*, Romain Ligneul

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

In young adults, individual differences in working memory (WM) contribute to reinforcement learning (RL). Age-related RL changes, however, are mostly attributed to decreased reward prediction-error (RPE) signaling. Here, we investigated the contribution of WM to RL in young (18-35) and older (≥65) adults. Because WM supports maintenance across a limited timescale, we only expected a relation between RL and WM with short delays between stimulus repetitions. Our results demonstrated better learning with short than long delays. A week later, however, long-delay associations were remembered better. Computational modeling corroborated that during learning, WM was more engaged by young adults in the short-delay condition than in any other age-condition combination. Crucially, both model-derived and neuropsychological assessments of WM predicted short-delay learning in older adults, who further benefitted from using self-conceived learning strategies. Thus, depending on the timescale of learning, age-related RL changes may not only reflect decreased RPE signaling but also WM decline.

Original languageEnglish
Pages (from-to)654-676
Number of pages23
JournalNeuropsychology, development, and cognition. Section B, Aging, neuropsychology and cognition
Volume27
Issue number5
DOIs
Publication statusPublished - Sept 2020
Externally publishedYes

Keywords

  • Aging
  • reinforcement learning
  • working memory
  • individual differences
  • computational modeling

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