Understanding exploration in humans and machines by formalizing the function of curiosity

Rachit Dubey, Thomas L. Griffiths

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

Recent work in machine learning has demonstrated the benefits of providing artificial agents with a sense of curiosity — a form of intrinsic reward that supports exploration. Two strategies have emerged for defining these rewards: favoring novelty and pursuing prediction errors. Psychological theories of curiosity have also emphasized these two factors. We show how these two literatures can be connected by understanding the function of curiosity, which requires thinking about the abstract computational problem that both humans and machines face as they explore their world.

Original languageEnglish (US)
Pages (from-to)118-124
Number of pages7
JournalCurrent Opinion in Behavioral Sciences
Volume35
DOIs
StatePublished - Oct 2020

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Psychiatry and Mental health
  • Behavioral Neuroscience

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