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随机性的感知:论条纹的时间。

Perception of randomness: On the time of streaks.

机构信息

University of Texas Health Science Center at Houston, Houston, TX, USA.

出版信息

Cogn Psychol. 2010 Dec;61(4):333-42. doi: 10.1016/j.cogpsych.2010.07.001. Epub 2010 Aug 21.

DOI:10.1016/j.cogpsych.2010.07.001
PMID:20728080
Abstract

People tend to think that streaks in random sequential events are rare and remarkable. When they actually encounter streaks, they tend to consider the underlying process as non-random. The present paper examines the time of pattern occurrences in sequences of Bernoulli trials, and shows that among all patterns of the same length, a streak is the most delayed pattern for its first occurrence. It is argued that when time is of essence, how often a pattern is to occur (mean time, or, frequency) and when a pattern is to first occur (waiting time) are different questions and bear different psychological relevance. The waiting time statistics may provide a quantitative measure to the psychological distance when people are expecting a probabilistic event, and such measure is consistent with both of the representativeness and availability heuristics in people's perception of randomness. We discuss some of the recent empirical findings and suggest that people's judgment and generation of random sequences may be guided by their actual experiences of the waiting time statistics.

摘要

人们倾向于认为随机顺序事件中的连胜是罕见而引人注目的。当他们实际遇到连胜时,他们往往会认为潜在过程是非随机的。本文研究了伯努利试验序列中模式出现的时间,并表明在相同长度的所有模式中,连胜是其首次出现的延迟模式。本文认为,当时间至关重要时,模式出现的频率(平均时间,或频率)和模式首次出现的时间(等待时间)是不同的问题,具有不同的心理相关性。等待时间统计数据可以为人们期望概率事件时的心理距离提供定量衡量标准,这种衡量标准与人们对随机性的代表性和可用性启发式一致。我们讨论了一些最近的实证发现,并认为人们对随机序列的判断和生成可能受到他们对等待时间统计数据的实际经验的指导。

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引用本文的文献

1
Regular and random judgements are not two sides of the same coin: Both representativeness and encoding play a role in randomness perception.有规律的判断和随机判断并非同一枚硬币的两面:代表性和编码在随机感知中都发挥了作用。
Psychon Bull Rev. 2021 Oct;28(5):1707-1714. doi: 10.3758/s13423-021-01934-9. Epub 2021 May 6.
2
Human Inferences about Sequences: A Minimal Transition Probability Model.人类对序列的推理:一种最小转移概率模型。
PLoS Comput Biol. 2016 Dec 28;12(12):e1005260. doi: 10.1371/journal.pcbi.1005260. eCollection 2016 Dec.
3
Reply to Aksentijevic: It is a matter of what is countable and how neurons learn.
回复阿克森蒂耶维奇:这涉及到什么是可计数的以及神经元如何学习的问题。
Proc Natl Acad Sci U S A. 2015 Jun 23;112(25):E3160. doi: 10.1073/pnas.1508265112. Epub 2015 Jun 1.
4
Latent structure in random sequences drives neural learning toward a rational bias.随机序列中的潜在结构驱动神经学习向合理偏差发展。
Proc Natl Acad Sci U S A. 2015 Mar 24;112(12):3788-92. doi: 10.1073/pnas.1422036112. Epub 2015 Mar 9.