Exploration: from machines to humans

Lior Fox, Ohad Dan, Lotem Elber-Dorozko, Yonatan Loewenstein

Research output: Contribution to journalReview articlepeer-review

Abstract

Consider a wildlife photographer that has just entered a rainforest that she has never visited. Looking for a good spot for animal photos, she can spend all her time in the first hideout that she found, slowly learning which animals visit that spot. Alternatively, she can consider other locations, which are potentially better but might also be worse. To identify these better locations she needs to leave her hideout and walk further into the forest, thus missing the opportunity to learn more about the qualities of her first hideout. How should she explore the forest? How does she explore it? Here we describe the computational principles and algorithms underlying exploration in the field of Machine Learning and discuss their relevance to human behavior.

Original languageAmerican English
Pages (from-to)104-111
Number of pages8
JournalCurrent Opinion in Behavioral Sciences
Volume35
DOIs
StatePublished - Oct 2020

All Science Journal Classification (ASJC) codes

  • Psychiatry and Mental health
  • Cognitive Neuroscience
  • Behavioral Neuroscience

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