Bayesian modelling of hierarchical prior and its updating mechanisms: contextual modulation by cognitive load and sequential dependence

分层先验的贝叶斯建模及其更新机制：认知负荷和顺序依赖性的上下文调制

• 批准号: 277161151
• 负责人: Professor Dr.-Ing. Stefan Glasauer
• 金额: --
• 依托单位: Lehrstuhl für Allgemeine und Experimentelle Psychologie
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2015
• 资助国家: 德国
• 起止时间: 2014-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/277161151?language=en
• 关键词: Bayesian; modelling; hierarchical; prior; its

The aim of project D1 is to uncover the mechanism underlying dynamic contextual calibration in multimodal environments, and to develop a general Bayesian framework describing the prior updating mechanisms in contextual calibration. In particular, we plan to focus on central tendency effects in magnitude estimation. The central tendency effect (also known as the range or regression effect), which has been well documented in the literature (e.g., Helson, 1963; for a review, see Shi, Church, & Meck, 2013), refers to a bias engendered by prior knowledge of the sampled distribution of stimuli presented. Although central tendency effects have been found in various types of sensory estimation, there is at present no consensus on what ranges of statistical information are actually involved in contextual calibration. This issue becomes particularly prominent for multisensory stimuli, given that multiple statistics (priors) are available which are not always consistent with each other. Here, we plan to establish a general computational framework to examine whether the brain uses multiple modality-specific priors or an amodal prior in contextual calibration, and how the brain resolves inconsistencies among priors, as well as how action priors are taken into consideration in magnitude estimation. In addition, we will further develop trial-wise computational Bayesian models (e.g., Kalman filter, particle filters, hierarchical models) to achieve a better prediction of dynamic prior updating and contextual calibration.

项目D1的目的是揭示多模态环境中动态上下文校准的机制，并开发一个通用的贝叶斯框架来描述上下文校准中的先验更新机制。特别是，我们计划集中在震级估计的集中趋势效应。集中趋势效应（也称为范围或回归效应），在文献中已有很好的记载（例如，Helson，1963年;对于综述，参见Shi，Church，& Meck，2013），指的是由所呈现的刺激的采样分布的先验知识产生的偏差。虽然集中趋势效应已被发现在各种类型的感觉估计，目前还没有共识的统计信息的范围实际上涉及的上下文校准。这个问题对于多感官刺激变得特别突出，因为多个统计数据（先验）并不总是相互一致的。在这里，我们计划建立一个通用的计算框架，以检查大脑是否使用多个模态特定的先验或非模态先验的上下文校准，以及大脑如何解决先验之间的不一致，以及如何行动先验被考虑在幅度估计。此外，我们将进一步开发试验式计算贝叶斯模型（例如，卡尔曼滤波器，粒子滤波器，分层模型），以实现更好的预测动态先验更新和上下文校准。