Experience matters: information acquisition optimizes probability gain.

Experience matters: information acquisition optimizes probability gain.
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DOI:
10.1177/0956797610372637
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发表时间:
2010-07
影响因子:
8.2
通讯作者:
Sejnowski TJ
Sejnowski TJ
中科院分区:
心理学1区
文献类型:
--
作者:
Nelson JD;McKenzie CR;Cottrell GW;Sejnowski TJ

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决定获取或关注哪条信息对于感知、分类、医学诊断和科学推理至关重要。信息价值的四种统计理论——信息增益、库尔贝克-利伯勒距离、概率增益(误差最小化)和影响——同样与人类信息获取的现有数据一致。通过计算机优化设计的三个实验能够提供最大的信息量,测试了这些理论中哪一个最能描述人类信息搜索。实验 1 使用自然抽样和基于经验的学习来传达环境概率,发现概率增益比其他统计理论或确定性概率启发法更好地解释了受试者的信息搜索。实验 1 和 2 发现,当使用口头呈现汇总统计的标准方法(而不是基于经验的学习)来传达环境概率时,受试者的行为有所不同。实验 3 发现受试者对概率增益的偏好是稳健的,这表明其他模型对受试者的搜索行为贡献不大。
Deciding which piece of information to acquire or attend to is fundamental to perception, categorization, medical diagnosis, and scientific inference. Four statistical theories of the value of information—information gain, Kullback-Liebler distance, probability gain (error minimization), and impact—are equally consistent with extant data on human information acquisition. Three experiments, designed via computer optimization to be maximally informative, tested which of these theories best describes human information search. Experiment 1, which used natural sampling and experience-based learning to convey environmental probabilities, found that probability gain explained subjects’ information search better than the other statistical theories or the probability-of-certainty heuristic. Experiments 1 and 2 found that subjects behaved differently when the standard method of verbally presented summary statistics (rather than experience-based learning) was used to convey environmental probabilities. Experiment 3 found that subjects’ preference for probability gain is robust, suggesting that the other models contribute little to subjects’ search behavior.
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