Tracking the Time Course of Bayesian Inference With Event-Related Potentials:A Study Using the Central Cue Posner Paradigm

Frontiers in Psychology

article

Carlos M. Gómez¹  Antonio Arjona¹  Francesco Donnarumma²  Domenico Maisto³  Elena I. Rodríguez-Martínez⁴  Giovanni Pezzulo² 

[1] Human Psychobiology Lab, Department of Experimental Psychology, University of Seville;Institute of Cognitive Sciences and Technologies, National Research Council;Institute for High Performance Computing and Networking, National Research Council;Department of Psychology, Loyola Andalucía University

关键词: predictive coding;    Bayesian processing;    ERPs;    P300;    Contingent Negative Variation;    Central Cue Posner Paradigm;   

DOI  :  10.3389/fpsyg.2019.01424

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

In this study, we asked whether the event-related potentials associated to cue and target stimuli of a Central Cue Posner Paradigm (CCPP) may encode key parameters of Bayesian inference – prior expectation and surprise – on a trial-by-trial basis. Thirty-two EEG channel were recorded in a sample of 19 young adult subjects while performing a CCPP, in which a cue indicated (validly or invalidly) the position of an incoming auditory target. Three different types of blocks with validities of 50%, 64%, and 88%, respectively, were presented. Estimates of prior expectation and surprise were obtained on a trial-by-trial basis from participants’ responses, using a computational model implementing Bayesian learning. These two values were correlated on a trial-by-trial basis with the EEG values in all the electrodes and time bins. Therefore, a Spearman correlation metrics of the relationship between Bayesian parameters and the EEG was obtained. We report that the surprise parameter was able to classify the different validity blocks. Furthermore, the prior expectation parameter showed a significant correlation with the EEG in the cue-target period, in which the Contingent Negative Variation develops. Finally, in the post-target period the surprise parameter showed a significant correlation in the latencies and electrodes in which different event-related potentials are induced. Our results suggest that Bayesian parameters are coded in the EEG signals; and namely, the CNV would be related to prior expectation, while the post-target components P2a, P2, P3a, P3b, and SW would be related to surprise. This study thus provides novel support to the idea that human electrophysiological neural activity may implement a (Bayesian) predictive processing scheme.

【 授权许可】

CC BY