Title Tracking the Time Course of Bayesian Inference With Event-Related Potentials: A Study Using the Central Cue Posner Paradigm
Authors MARTÍN GÓMEZ, CARMEN, Arjona, Antonio, Donnarumma, Francesco, Maisto, Domenico, RODRÍGUEZ MARTÍNEZ, ELENA ISABEL, Pezzulo, Giovanni, MARTÍN GÓMEZ, CARMEN, RODRÍGUEZ MARTÍNEZ, ELENA ISABEL
External publication No
Means Front. Psychol.
Scope Article
Nature Científica
JCR Quartile 2
SJR Quartile 1
JCR Impact 2.06700
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068665523&doi=10.3389%2ffpsyg.2019.01424&partnerID=40&md5=55df58fea4cd0f0fd997580c63dba6bb
Publication date 19/06/2019
ISI 000472148500001
Scopus Id 2-s2.0-85068665523
DOI 10.3389/fpsyg.2019.01424
Abstract 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 trialby-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.
Keywords predictive coding; Bayesian processing; ERPs; P300; Contingent Negative Variation; Central Cue Posner Paradigm
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