Multivariate EEG activity reflects the Bayesian integration and the integrated Galilean relative velocity of sensory motion during sensorimotor behavior

Abstract

Humans integrate multiple sources of information for action-taking, using the reliability of each source to allocate weight to the data. This reliability-weighted information integration is a crucial property of Bayesian inference. In this study, participants were asked to perform a smooth pursuit eye movement task in which we independently manipulated the reliability of pursuit target motion and the direction-of-motion cue. Through an analysis of pursuit initiation and multivariate electroencephalography activity, we found neural and behavioral evidence of Bayesian information integration: more attraction toward the cue direction was generated when the target motion was weak and unreliable. Furthermore, using mathematical modeling, we found that the neural signature of Bayesian information integration had extra-retinal origins, although most of the multivariate electroencephalography activity patterns during pursuit were best correlated with the retinal velocity errors accumulated over time. Our results demonstrated neural implementation of Bayesian inference in human oculomotor behavior.

A Precise Bare Simulation Approach to the Minimization of Some Distances. I. Foundations

“…Some exemplary (relatively) recent applications of (squared) Mahalanobis distance can be found e.g. in Xu et al [301], Sun et al [302], Timmermann et al [303], Wauchope et al [304], Weinberger [305], Wen et al [306], Yang et al [307], Zhang et al [308], Burssens et al [309], Choi et al [310], Choi et al [311], Dahlin et al [312], Ebrahimi et al [313], Jeong et al [314], Kim et al [315], Nowakowski et al [316], Qu et al [317], Rabby et al [318], Sarno et al [319], Tang et al [320], Tsvieli & Weinberger [321], Zhang et al [322], Zhou et al [323]. Some exemplary (relatively) recent applications of generally non-separable (ordinary/classical) Bregman distances appear e.g.…”