Flexibility in valenced reinforcement learning computations across development.
Flexibility in valenced reinforcement learning computations across development.
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DOI：
10.1111/cdev.13791
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发表时间：
2022-09
影响因子：
4.6
通讯作者：
Hartley CA
中科院分区：
文献类型：
--
作者：
Nussenbaum K;Velez JA;Washington BT;Hamling HE;Hartley CA
学习过程中积极和消极结果的最佳整合取决于环境的奖励统计数据。本研究调查了儿童，青少年和成人（N = 142 8 - 25岁，55%女性，42%白色，31%亚洲人，17%混合种族和8%黑人; 2021年收集的数据）在从强化学习时调整其好于预期和差于预期结果的权重的程度。参与者在两种情况下做出选择：一种情况下，积极结果的权重大于消极结果导致更好的表现，另一种情况下则相反。强化学习模型显示，随着年龄的增长，参与者根据环境结构改变了他们的效价偏差。探索性分析显示，随着年龄的增长，上下文相关的灵活性增加。
Optimal integration of positive and negative outcomes during learning varies depending on an environment’s reward statistics. The present study investigated the extent to which children, adolescents, and adults (N = 142 8 – 25 year-olds, 55% female, 42% White, 31% Asian, 17% mixed race, and 8% Black; data collected in 2021) adapt their weighting of better-than-expected and worse-than-expected outcomes when learning from reinforcement. Participants made choices across two contexts: one in which weighting positive outcomes more heavily than negative outcomes led to better performance, and one in which the reverse was true. Reinforcement learning modeling revealed that across age, participants shifted their valence biases in accordance with environmental structure. Exploratory analyses revealed increases in context-dependent flexibility with age.