Human Representation Learning

Angela Radulescu, Yeon Soon Shin, Yael Niv

Research output: Contribution to journalReview articlepeer-review

24 Scopus citations


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 aboutα 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.

Original languageEnglish (US)
Pages (from-to)253-273
Number of pages21
JournalAnnual Review of Neuroscience
StatePublished - Jul 8 2021

All Science Journal Classification (ASJC) codes

  • General Neuroscience


  • learning selective attention
  • memory
  • representation learning


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