Department of Psychology, Princeton University, Princeton, New Jersey 08544, USA; email:
Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA.
Annu Rev Neurosci. 2021 Jul 8;44:253-273. doi: 10.1146/annurev-neuro-092920-120559. Epub 2021 Mar 17.
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.
本篇综述的核心主题是信息选择与学习之间的动态交互。我们针对这一交互提出了一个基本问题：我们如何了解我们的经验中哪些特征值得学习？在人类中，这一过程依赖于注意力和记忆，这两种认知功能共同将世界的表示约束为与目标实现相关的特征。最近的证据表明，注意力和记忆所塑造的表示本身是根据每个任务的经验推断出来的。我们综述了这些证据，并将其置于明确将表示学习描述为统计推断的工作背景下。我们讨论了如何通过基于少量经验来近似置信度，从而将推断扩展到现实世界的决策中。最后，我们强调了这一推断过程对人类在社会环境中进行决策的一些影响。
