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

机译:避免小行星的突发性好奇:选择结果的每次试验信息获取推动信息搜索

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摘要

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.
机译:人们似乎经常出于自身的需要而渴望获得信息,但是目前尚不清楚是什么驱动了这种渴望。解决不确定性在刺激信息搜寻中的重要作用表明,收集信息的内在动机与绩效提升之间存在紧密的联系,这被认为是长期学习的动力。通过使用一个避免小行星运动的游戏,我们可以在实验时间范围内研究学习和寻求信息,我们证明了寻求信息的动机可以与长期学习成果分开,而寻求信息的最佳方式是通过预测-审判结果不确定性。具体来说,参与者更愿意花时间进行惩罚,以期收到有关选择结果不确定性不断增加的试验反馈。我们发现小组和个人对反馈请求率和信息增益之间线性关系的强烈支持,这取决于每次试验的结果不确定性。与长期技能获取或评估相比,该信息更好地反映了选择结果的情景记录中的空白。我们的结果表明,这种易于计算的数量可以推动信息搜索,潜在地允许简单的生物体智能地收集信息以用于环境的各种情景记录,而不必预期对未来性能的影响。

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