Human Inferences about Sequences: A Minimal Transition Probability Model.
Human Inferences about Sequences: A Minimal Transition Probability Model.
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DOI：
10.1371/journal.pcbi.1005260
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发表时间：
2016-12
影响因子：
4.3
通讯作者：
Dehaene S
中科院分区：
文献类型：
--
作者：
Meyniel F;Maheu M;Dehaene S
大脑不断地推断它所接收到的输入的原因，并使用这些推断来产生对未来观察的统计预期。这些期望及其违反的实验证据包括明确的报告，反应时间的顺序效应，以及电生理学和功能性MRI中记录的不匹配或意外信号。在这里，我们探讨的假设，大脑作为一个接近最佳的推理设备，不断尝试推断它收到的刺激之间的转移概率的时变矩阵，即使这些刺激实际上是完全不可预测的。这个简约的贝叶斯模型，只有一个自由参数，解释了关于惊喜信号，序列效应和随机性感知的广泛发现。值得注意的是，它解释了在这些研究中遇到的重复和交替之间普遍存在的不对称性。我们的分析表明，一个神经机器推断转移概率在于人类序列知识的核心。我们探索的可能性，随时间变化的转移概率的计算可能是一个核心的构建块的序列知识在人类。然后，人类可以使用这些估计来预测未来的观测结果。从这样一个模型得出的期望值应该符合几个属性。我们列出六个这样的属性，我们成功地测试他们对各种实验结果报告在不同领域的文献在过去的世纪。我们重点介绍了其他小组的五项代表性研究。这些发现包括在许多行为任务中证明的“顺序效应”，即由最近的观察历史引起的表现的普遍波动。我们还考虑了在电生理学甚至功能性MRI中记录的“类脑”信号，这些信号是由随机的观察流引起的。据报道，这些信号以定量的方式被观测的局部和全局统计所调制。最后，我们考虑了众所周知的主观偏见的随机性，即人类是否认为一个给定的观察序列是随机产生的。因此，我们的模型统一了许多以前的研究结果，并表明，一个神经机器推断转移概率必须位于人类序列知识的核心。
The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge. We explore the possibility that the computation of time-varying transition probabilities may be a core building block of sequence knowledge in humans. Humans may then use these estimates to predict future observations. Expectations derived from such a model should conform to several properties. We list six such properties and we test them successfully against various experimental findings reported in distinct fields of the literature over the past century. We focus on five representative studies by other groups. Such findings include the “sequential effects” evidenced in many behavioral tasks, i.e. the pervasive fluctuations in performance induced by the recent history of observations. We also consider the “surprise-like” signals recorded in electrophysiology and even functional MRI, that are elicited by a random stream of observations. These signals are reportedly modulated in a quantitative manner by both the local and global statistics of observations. Last, we consider the notoriously biased subjective perception of randomness, i.e. whether humans think that a given sequence of observations has been generated randomly or not. Our model therefore unifies many previous findings and suggests that a neural machinery for inferring transition probabilities must lie at the core of human sequence knowledge.
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影响因子：
4.3
作者：
Bornstein AM;Daw ND
通讯作者：
Daw ND
DOI：
10.1073/pnas.0809667106
发表时间：
2009-02-03
影响因子：
11.1
作者：
Bekinschtein, Tristan A.;Dehaene, Stanislas;Naccache, Lionel
通讯作者：
Naccache, Lionel
影响因子：
1.7
作者：
BERTELSON, P
通讯作者：
BERTELSON, P
影响因子：
19.9
作者：
Fiser, Jozsef;Berkes, Pietro;Orban, Gergo;Lengyel, Mate
通讯作者：
Lengyel, Mate
影响因子：
16.2
作者：
Beck JM;Ma WJ;Kiani R;Hanks T;Churchland AK;Roitman J;Shadlen MN;Latham PE;Pouget A
通讯作者：
Pouget A