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为避免小行星而产生的间歇性好奇心:对选择结果的每试信息增益驱动信息寻求。

Episodic curiosity for avoiding asteroids: Per-trial information gain for choice outcomes drive information seeking.

机构信息

Department of Psychology, Umeå University, S-901 87, Umeå, Sweden.

Department of Psychology, University of Minnesota, Minneapolis, MN, 55455, USA.

出版信息

Sci Rep. 2019 Aug 2;9(1):11265. doi: 10.1038/s41598-019-47671-x.

Abstract

Humans often appear to desire information for its own sake, but it is presently unclear what drives this desire. The important role that resolving uncertainty plays in stimulating information seeking has suggested a tight coupling between the intrinsic motivation to gather information and performance gains, construed as a drive for long-term learning. Using an asteroid-avoidance game that allows us to study learning and information seeking at an experimental time-scale, we show that the incentive for information-seeking can be separated from a long-term learning outcome, with information-seeking best predicted by per-trial outcome uncertainty. Specifically, participants were more willing to take time penalties to receive feedback on trials with increasing uncertainty in the outcome of their choices. We found strong group and individual level support for a linear relationship between feedback request rate and information gain as determined by per-trial outcome uncertainty. This information better reflects filling in the gaps of the episodic record of choice outcomes than long-term skill acquisition or assessment. Our results suggest that this easy to compute quantity can drive information-seeking, potentially allowing simple organisms to intelligently gather information for a diverse episodic record of the environment without having to anticipate the impact on future performance.

摘要

人类似乎常常出于对信息本身的渴望而寻求信息,但目前尚不清楚是什么驱动了这种欲望。解决不确定性在激发信息寻求方面所起的重要作用表明,内在寻求信息的动机与表现增益之间存在紧密的联系,可以理解为对长期学习的一种驱动力。我们使用一种小行星回避游戏,使我们能够在实验时间尺度上研究学习和信息寻求,结果表明,信息寻求的动机可以与长期学习结果分开,通过每次试验结果的不确定性可以最好地预测信息寻求。具体来说,参与者更愿意接受时间惩罚,以在试验中获得反馈,这些试验的结果存在越来越大的不确定性。我们发现,对于反馈请求率与通过每次试验结果不确定性确定的信息增益之间的线性关系,有强烈的群体和个体水平支持。与长期技能获取或评估相比,这种信息更能反映出对选择结果的情景记录的填补空白。我们的研究结果表明,这种易于计算的数量可以驱动信息寻求,这可能使简单的生物体能够在无需预测对未来表现的影响的情况下,为环境的多样化情景记录智能地收集信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/754d/6677824/8db09a7f57d8/41598_2019_47671_Fig1_HTML.jpg

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