旧的策略,新的环境:在社交媒体上加强学习
Georgia Turner1, Amanda M Ferguson1, Tanay Katiyar2
1MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.
Biological psychiatry
|December 26, 2024
概括
人类的社会策略可能无法有效地转移到社交媒体. 强化学习 (RL) 可以识别在线行为和数字环境之间的不匹配,影响心理健康.
科学领域:
- 计算神经科学是一种计算神经科学.
- 社会心理学 社会心理学
- 人与计算机的互动.
背景情况:
- 社交媒体深刻地改变了社会互动和行为.
- 现有的人类社会策略是为离线环境而发展的,在线可能不有效.
- 了解这些动态对于解决社交媒体对幸福感的影响至关重要.
研究的目的:
- 应用强化学习 (RL) 的计算框架来分析人类在社交媒体上的社会行为.
- 在数字社会背景中识别行为环境不匹配.
- 了解社交媒体负担如何与RL流程相互作用.
主要方法:
- 利用强化学习 (RL) 框架,通过最大化长期回报来模式学习.
- 分析RL阶段:奖励更新,主观成本估值和行动选择.
- 检查社交媒体功能 (负担能力) 如何与RL偏见相互作用.
主要成果:
- 社交媒体的负担能力可以利用先前存在的RL偏见,导致不理想的行为.
- 算法个性化可能与确认偏差等认知偏差相互作用.
- 当在线环境中应用离线演变的偏见时,可能会出现不匹配.
结论:
- 该RL框架提供了一种精确的方法来诊断社交媒体上的行为环境不匹配.
- 了解这些互动是理解数字环境如何塑造人类行为的关键.
- 这种方法可以为研究社交媒体对发育和心理健康的影响提供信息.
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