Annual Review of Neuroscience ( IF 12.1 ) Pub Date : 2021-07-08 , DOI: 10.1146/annurev-neuro-092920-120559 Angela Radulescu 1, 2 , Yeon Soon Shin 2 , Yael Niv 1, 2
The central theme of this review is the dynamic interaction between information selection and learning. We pose a fundamental question about this interaction: How do we learn what features of our experiences are worth learning about? In humans, this process depends on attention and memory, two cognitive functions that together constrain representations of the world to features that are relevant for goal attainment. Recent evidence suggests that the representations shaped by attention and memory are themselves inferred from experience with each task. We review this evidence and place it in the context of work that has explicitly characterized representation learning as statistical inference. We discuss how inference can be scaled to real-world decisions by approximating beliefs based on a small number of experiences. Finally, we highlight some implications of this inference process for human decision-making in social environments.
中文翻译:
人类表征学习
这篇综述的中心主题是信息选择和学习之间的动态互动。我们提出了一个关于这种互动的基本问题:我们如何了解我们经验的哪些特征值得学习?在人类中,这个过程取决于注意力和记忆力,这两种认知功能共同将世界的表征限制为与目标实现相关的特征。最近的证据表明,注意力和记忆形成的表征本身是从每项任务的经验中推断出来的。我们回顾了这一证据,并将其置于将表征学习明确描述为统计推断的工作背景中。我们讨论了如何通过基于少量经验的近似信念来将推理扩展到现实世界的决策。最后,