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Flexibility in valenced reinforcement learning computations across development
Child Development ( IF 5.661 ) Pub Date : 2022-05-21 , DOI: 10.1111/cdev.13791
Kate Nussenbaum 1 , Juan A Velez 1 , Bradli T Washington 1 , Hannah E Hamling 1 , Catherine A Hartley 1, 2
Affiliation  

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 strengthening of context-dependent flexibility with increasing age.

中文翻译:

跨开发的有效强化学习计算的灵活性

学习过程中积极和消极结果的最佳整合因环境的奖励统计而异。本研究调查了儿童、青少年和成人(N = 142 名 8-25 岁的年轻人,55% 为女性,42% 为白人,31% 为亚裔,17% 为混血儿,8% 为黑人;2021 年收集的数据)在从强化学习中调整好于预期和差于预期的结果的权重。参与者在两种情况下做出选择:一种情况下,积极结果的权重高于消极结果会带来更好的表现,另一种情况正好相反。强化学习模型显示,随着年龄的增长,参与者会根据环境结构改变他们的效价偏差。探索性分析表明,随着年龄的增长,依赖于上下文的灵活性会增强。
更新日期:2022-05-21
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