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  • 标题:Adaptable history biases in human perceptual decisions
  • 作者:Arman Abrahamyan ; Laura Luz Silva ; Steven C. Dakin
  • 期刊名称:Proceedings of the National Academy of Sciences
  • 印刷版ISSN:0027-8424
  • 电子版ISSN:1091-6490
  • 出版年度:2016
  • 卷号:113
  • 期号:25
  • 页码:E3548-E3557
  • DOI:10.1073/pnas.1518786113
  • 语种:English
  • 出版社:The National Academy of Sciences of the United States of America
  • 摘要:When making choices under conditions of perceptual uncertainty, past experience can play a vital role. However, it can also lead to biases that worsen decisions. Consistent with previous observations, we found that human choices are influenced by the success or failure of past choices even in a standard two-alternative detection task, where choice history is irrelevant. The typical bias was one that made the subject switch choices after a failure. These choice history biases led to poorer performance and were similar for observers in different countries. They were well captured by a simple logistic regression model that had been previously applied to describe psychophysical performance in mice. Such irrational biases seem at odds with the principles of reinforcement learning, which would predict exquisite adaptability to choice history. We therefore asked whether subjects could adapt their irrational biases following changes in trial order statistics. Adaptability was strong in the direction that confirmed a subject’s default biases, but weaker in the opposite direction, so that existing biases could not be eradicated. We conclude that humans can adapt choice history biases, but cannot easily overcome existing biases even if irrational in the current context: adaptation is more sensitive to confirmatory than contradictory statistics.
  • 关键词:decision making ; choice bias ; bias adaptation ; choice history ; computational modeling
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