启动在预期思维中的作用

IF 2.5 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Magazine Pub Date : 2023-07-10 DOI:10.1002/aaai.12100
Laura M. Hiatt
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引用次数: 0

摘要

预期思维是指识别未来可能出现的问题,并为其做好准备,以减轻发生积极或消极影响的风险(或机会)。在本文中,我们认为预期思维的一个关键的潜在过程是认知启动,即一个人当前的想法在没有意识意图的情况下影响下一个想法。我们从人类认知的两个方面提出这一论点,这两个方面与预期思维有关:语境和创造力。然后,我们利用上下文、创造力和预期思维之间的相似性来支持我们的信念,即认知启动在预期思维的各个方面发挥着关键作用。作为该分析的一部分,我们还讨论了其更广泛的含义,包括如何使用它来改进进行预期思维的计算系统,以及如何利用它来改进人们的预期思维。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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The role of priming in anticipatory thinking

Anticipatory thinking is the act of identifying problems that may arise in the future, and preparing for them in order to mitigate the risk of (or opportunity for) positive or negative impacts occurring. In this paper, we argue that a critical underlying process of anticipatory thinking is cognitive priming, where one's current thoughts influence the next without conscious intention. We make this argument in terms of two aspects of human cognition that are related to anticipatory thinking: context and creativity. We then use the parallels between context, creativity, and anticipatory thinking to support our belief that cognitive priming plays a key role in various aspects of anticipatory thinking. As part of this analysis, we also discuss its broader implications, including how it can be used to improve computational systems that do anticipatory thinking, as well as how it can be leveraged to improve anticipatory thinking in people.

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来源期刊
Ai Magazine
Ai Magazine 工程技术-计算机:人工智能
CiteScore
3.90
自引率
11.10%
发文量
61
审稿时长
>12 weeks
期刊介绍: AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. Technical content should be kept to a minimum. In general, the magazine does not publish articles that have been published elsewhere in whole or in part. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and timely columns on topics of interest to AI scientists.
期刊最新文献
Issue Information AI fairness in practice: Paradigm, challenges, and prospects Toward the confident deployment of real-world reinforcement learning agents Towards robust visual understanding: A paradigm shift in computer vision from recognition to reasoning Efficient and robust sequential decision making algorithms
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