Neural variability in the default mode network compresses with increasing belief precision during Bayesian inference.

IF 2.9 2区 医学 Q2 NEUROSCIENCES
Alexander Skowron, Julian Q Kosciessa, Robert C Lorenz, Ralph Hertwig, Wouter van den Bos, Douglas D Garrett
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引用次数: 0

Abstract

To make optimal decisions, intelligent agents must learn latent environmental states from discrete observations. Bayesian frameworks argue that integration of evidence over time allows us to refine our state belief by reducing uncertainty about alternate possibilities. How is this increasing belief precision during learning reflected in the brain? We propose that temporal neural variability should scale with the degree of reduction of uncertainty during learning. In a sample of 47 healthy adults, we found that BOLD signal variability (SDBOLD, as measured across independent learning trials) indeed compressed with successive exposure to decision-related evidence. Crucially, more accurate participants expressed greater SDBOLD compression primarily in default mode network regions, possibly reflecting the increasing precision of their latent state belief during more efficient learning. Further, computational modeling of behavior suggested that more accurate subjects held a more unbiased (flatter) prior belief over possible states that allowed for larger uncertainty reduction during learning, which was directly reflected in SDBOLD changes. Our results provide first evidence that neural variability compresses with increasing belief precision during effective learning, proposing a flexible mechanism for how we come to learn the probabilistic nature of the world around us.

在贝叶斯推理过程中,默认模式网络的神经变异性随着信念精度的提高而压缩。
为了做出最优决策,智能体必须从离散的观察中学习潜在的环境状态。贝叶斯框架认为,随着时间的推移,证据的整合使我们能够通过减少替代可能性的不确定性来完善我们的状态信念。在学习过程中,这种不断增强的信念准确性是如何反映在大脑中的呢?我们提出,时间神经变异性应该与学习过程中不确定性的减少程度成比例。在47名健康成人的样本中,我们发现,随着决策相关证据的不断暴露,BOLD信号变异性(SDBOLD,通过独立学习试验测量)确实被压缩。更重要的是,更准确的参与者主要在默认模式网络区域表达了更大的sdold压缩,这可能反映了他们在更有效的学习过程中潜在状态信念的精确度提高。此外,行为的计算模型表明,更准确的受试者对可能的状态持有更无偏(更平坦)的先验信念,从而在学习过程中允许更大的不确定性减少,这直接反映在SDBOLD的变化中。我们的研究结果首次证明,在有效学习过程中,神经变异性会随着信念精度的提高而压缩,这为我们如何学习周围世界的概率性提供了一种灵活的机制。
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来源期刊
Cerebral cortex
Cerebral cortex 医学-神经科学
CiteScore
6.30
自引率
8.10%
发文量
510
审稿时长
2 months
期刊介绍: Cerebral Cortex publishes papers on the development, organization, plasticity, and function of the cerebral cortex, including the hippocampus. Studies with clear relevance to the cerebral cortex, such as the thalamocortical relationship or cortico-subcortical interactions, are also included. The journal is multidisciplinary and covers the large variety of modern neurobiological and neuropsychological techniques, including anatomy, biochemistry, molecular neurobiology, electrophysiology, behavior, artificial intelligence, and theoretical modeling. In addition to research articles, special features such as brief reviews, book reviews, and commentaries are included.
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