Meyniel Florent, Maheu Maxime, Dehaene Stanislas
Cognitive Neuroimaging Unit, CEA DRF/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, France.
Université Paris Descartes, Sorbonne Paris Cité, Paris, France.
PLoS Comput Biol. 2016 Dec 28;12(12):e1005260. doi: 10.1371/journal.pcbi.1005260. eCollection 2016 Dec.
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.
大脑不断推断其接收到的输入的原因，并利用这些推断生成关于未来观察的统计预期。这些预期及其违背的实验证据包括明确的报告、对反应时间的顺序效应，以及在电生理学和功能磁共振成像中记录的失配或惊奇信号。在这里，我们探讨这样一个假设：大脑就像一个近乎最优的推理装置，即使这些刺激实际上完全不可预测，它也会不断尝试推断其接收到的刺激之间随时间变化的转移概率矩阵。这个简约的贝叶斯模型只有一个自由参数，却能解释关于惊奇信号、顺序效应和随机性感知的广泛研究结果。值得注意的是，它解释了那些研究中重复和交替之间普遍存在的不对称性。我们的分析表明，用于推断转移概率的神经机制是人类序列知识的核心。
