Single-trial event-related potential correlates of belief updating

Daniel Bennett, Carsten Murawski, Stefan Bode

Research output: Contribution to journalArticleResearchpeer-review

26 Citations (Scopus)

Abstract

Belief updating—the process by which an agent alters an internal model of its environment—is a core function of the CNS. Recent theory has proposed broad principles by which belief updating might operate, but more precise details of its implementation in the human brain remain unclear. In order to address this question, we studied how two components of the human event-related potential encoded different aspects of belief updating. Participants completed a novel perceptual learning task while electroencephalography was recorded. Participants learned the mapping between the contrast of a dynamic visual stimulus and a monetary reward and updated their beliefs about a target contrast on each trial. A Bayesian computational model was formulated to estimate belief states at each trial and was used to quantify the following two variables: belief update size and belief uncertainty. Robust single-trial regression was used to assess how these model-derived variables were related to the amplitudes of the P3 and the stimulus-preceding negativity (SPN), respectively. Results showed a positive relationship between belief update size and P3 amplitude at one fronto-central electrode, and a negative relationship between SPN amplitude and belief uncertainty at a left central and a right parietal electrode. These results provide evidence that belief update size and belief uncertainty have distinct neural signatures that can be tracked in single trials in specific ERP components. This, in turn, provides evidence that the cognitive mechanisms underlying belief updating in humans can be described well within a Bayesian framework.

Original languageEnglish
Article numbere0076-15.2015
Number of pages14
JournaleNeuro
Volume2
Issue number5
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Keywords

  • Belief updating
  • Computational modeling
  • Learning
  • P3
  • Single-trial
  • SPN

Cite this