کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7285447 | 1474094 | 2018 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Deconstructing the human algorithms for exploration
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موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
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چکیده انگلیسی
The dilemma between information gathering (exploration) and reward seeking (exploitation) is a fundamental problem for reinforcement learning agents. How humans resolve this dilemma is still an open question, because experiments have provided equivocal evidence about the underlying algorithms used by humans. We show that two families of algorithms can be distinguished in terms of how uncertainty affects exploration. Algorithms based on uncertainty bonuses predict a change in response bias as a function of uncertainty, whereas algorithms based on sampling predict a change in response slope. Two experiments provide evidence for both bias and slope changes, and computational modeling confirms that a hybrid model is the best quantitative account of the data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Cognition - Volume 173, April 2018, Pages 34-42
Journal: Cognition - Volume 173, April 2018, Pages 34-42
نویسندگان
Samuel J. Gershman,