ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2020
Intact Reinforcement Learning But Impaired Attentional Control During Multidimensional Probabilistic Learning in Older Adults.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence and psychosocial functioning in psychosis: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Mistakes matter, age doesn't: task outcome modulates implicit motor adaptation similarly in young and older adults.GeroScience · 2026Article
- How working memory and reinforcement learning interact when avoiding punishment and pursuing reward concurrently.Journal of experimental psychology. General · 2025Article
- Intact reinforcement learning in healthy ageing.Experimental brain research · 2025Article
- Computational markers show specific deficits for dyslexia and ADHD in complex learning settings.NPJ science of learning · 2025Article
- Schemas, reinforcement learning and the medial prefrontal cortex.Nature reviews. Neuroscience · 2025Review
- Contributions of Attention to Learning in Multidimensional Reward Environments.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Neural correlates of reduced sensitivity to information about uncertainty during valuation in older adults: An fNIRS study.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Material hardship, not household income, predicts impaired punishment learning: a computational reinforcement learning perspective.Frontiers in psychology · 2025Article
- Adaptive empathic response selection is sensitive to multiple dimensions of social interaction.Communications psychology · 2024Article
- The Arrival of the Metaverse in Neurorehabilitation: Fact, Fake or Vision?Biomedicines · 2022Review
- Rumination Derails Reinforcement Learning with Possible Implications for Ineffective Behavior.Clinical psychological science : a journal of the Association for Psychological Science · 2022Article
- Advances in modeling learning and decision-making in neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2022Review
- What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience.Current opinion in behavioral sciences · 2021Article
- Ageing is associated with disrupted reinforcement learning whilst learning to help others is preserved.Nature communications · 2021Article
- The Role of Executive Function in Shaping Reinforcement Learning.Current opinion in behavioral sciences · 2021Article
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3 authors.
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Abstract
To efficiently learn optimal behavior in complex environments, humans rely on an interplay of learning and attention. Healthy aging has been shown to independently affect both of these functions. Here, we investigate how reinforcement learning and selective attention interact during learning from trial and error across age groups. We acquired behavioral and fMRI data from older and younger adults (male and female) performing two probabilistic learning tasks with varying attention demands. Although learning in the unidimensional task did not differ across age groups, older adults performed worse than younger adults in the multidimensional task, which required high levels of selective attention. Computational modeling showed that choices of older adults are better predicted by reinforcement learning than Bayesian inference, and that older adults rely more on reinforcement learning-based predictions than younger adults. Conversely, a higher proportion of younger adults' choices was predicted by a computationally demanding Bayesian approach. In line with the behavioral findings, we observed no group differences in reinforcement-learning related fMRI activation. Specifically, prediction-error activation in the nucleus accumbens was similar across age groups, and numerically higher in older adults. However, activation in the default mode was less suppressed in older adults in for higher attentional task demands, and the level of suppression correlated with behavioral performance. Our results indicate that healthy aging does not significantly impair simple reinforcement learning. However, in complex environments, older adults rely more heavily on suboptimal reinforcement-learning strategies supported by the ventral striatum, whereas younger adults use attention processes supported by cortical networks.
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