適応アルゴリズム理解における認知バイアスの実験的検討

• 寺田 和憲

  岐阜大学工学部電気電子・情報工学科

• 山田 誠二

  国立情報学研究所 総合研究大学院大学 東京工業大学

書誌事項

タイトル別名

• Empirical Investigation of Cognitive Bias for Understanding User-Adaptive Algorithm

抄録

There have been few studies on cognitive bias for algorithm understanding in a human-computer cooperative situation. In the present study, we conducted an experiment with participants to investigate the cognitive process of higher level abstraction (algorithm understanding) performed in a human-computer collaboration task. The most recently used (MRU) algorithm, known to be one of the simplest adaptive algorithms, and probabilistic MRU algorithm were used to test the human capability to understand an algorithm. The experimental results showed that inductive reasoning in which participants observed the history of computer action, and they updated a statistical model while restricting their focus on a certain history with deterministic bias and Markov bias played key role to correctly understand the MRU algorithm. The results also showed that deductive reasoning was used to understand algorithms when participants rely on prior knowledge, and that there was a case in which the algorithm, even known to be the simplest one, was never understood.

収録刊行物

• 人工知能学会論文誌

  人工知能学会論文誌 34 (4), A-I72_1-9, 2019-07-01

  一般社団法人 人工知能学会

キーワード

• algorithm understanding
• inductive reasoning
• deductive reasoning
• adaptive user interface

詳細情報

• CRID: 1390845713080782336
• NII論文ID: 130007672269
• DOI: 10.1527/tjsai.a-i72
• ISSN: 13468030
• ISSN: 13460714
• Web Site: https://www.jstage.jst.go.jp/article/tjsai/34/4/34_A-I72/_pdf
• 本文言語コード: ja
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