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

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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.