TY - JOUR
T1 - Exploration
T2 - from machines to humans
AU - Fox, Lior
AU - Dan, Ohad
AU - Elber-Dorozko, Lotem
AU - Loewenstein, Yonatan
N1 - Publisher Copyright:
© 2020
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85092328381&partnerID=8YFLogxK
U2 - 10.1016/j.cobeha.2020.08.004
DO - 10.1016/j.cobeha.2020.08.004
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AN - SCOPUS:85092328381
SN - 2352-1546
VL - 35
SP - 104
EP - 111
JO - Current Opinion in Behavioral Sciences
JF - Current Opinion in Behavioral Sciences
ER -