Statistical learning (SL) is the ability to generate predictions based on probabilistic dependencies in the environment, an ability that is present throughout life. The effect of aging on SL is still unclear. Here, we explore statistical learning in healthy adults (40 younger and 40 older). The novel paradigm tracks learning trajectories and shows age-related differences in overall performance, yet similarities in learning rates. Bayesian models reveal further differences between younger and older adults in dealing with uncertainty in this probabilistic SL task. We test computational models of 3 different learning strategies: (a) Win-Stay, Lose-Shift, (b) Delta Rule Learning, (c) Information Weights to explore whether they capture age-related differences in performance and learning in the present task. A likely candidate mechanism emerges in the form of age-dependent differences in information weights, in which young adults more readily change their behavior, but also show disproportionally strong reactions toward erroneous predictions. With lower but more balanced information weights, older adults show slower behavioral adaptation but eventually arrive at more stable and accurate representations of the underlying transitional probability matrix. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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年龄相关的统计学习轨迹揭示了信息权重的差异。

统计学习（SL）是根据环境中的概率依赖性生成预测的能力，这是一生中存在的能力。老化对SL的影响仍不清楚。在这里，我们探索健康成年人（40岁以下和40岁以上）中的统计学习。新颖的范例跟踪学习轨迹，并显示出与年龄相关的整体表现差异，但学习率却相似。贝叶斯模型揭示了在处理这种概率性SL任务的不确定性时，年轻人和老年人之间的进一步差异。我们测试了3种不同学习策略的计算模型：（a）胜任，输局，（b）Delta规则学习，（c）信息权重，以探索它们是否捕获了本任务中与年龄相关的性能和学习差异。可能的候选机制以信息权重的年龄相关差异形式出现，其中年轻人更容易改变其行为，但对错误的预测也表现出不成比例的强烈反应。信息权重较低但平衡性更高，老年人的行为适应性较慢，但最终它们可以更稳定，更准确地表示潜在的过渡概率矩阵。（PsycInfo数据库记录（c）2020 APA，保留所有权利）。老年人表现出较慢的行为适应能力，但最终获得了潜在的过渡概率矩阵的更稳定和准确的表示。（PsycInfo数据库记录（c）2020 APA，保留所有权利）。老年人表现出较慢的行为适应能力，但最终获得了潜在的过渡概率矩阵的更稳定和准确的表示。（PsycInfo数据库记录（c）2020 APA，保留所有权利）。