P300 amplitude variations, prior probabilities, and likelihoods: A Bayesian ERP study

Bruno Kopp, Caroline Seer, Florian Lange, Anouck Kluytmans, Antonio Kolossa, Tim Fingscheidt, Herbert Hoijtink

The capability of the human brain for Bayesian inference was assessed by manipulating probabilistic contingencies in an urn-ball task. Event-related potentials (ERPs) were recorded in response to stimuli that differed in their relative frequency of occurrence (.18 to .82). A veraged ERPs with sufficient signal-to-noise ratio (relative frequency of occurrence > .5) were used for further analysis. Research hypotheses about relationships between probabilistic contingencies and ERP amplitude variations were formalized as (in-)equality constrained hypotheses. Conducting Bayesian model comparisons, we found that manipulations of prior probabilities and likelihoods were associated with separately modifiable and distinct ERP responses. P3a amplitudes were sensitive to the degree of prior certainty such that higher prior probabilities were related to larger frontally distributed P3a waves. P3b amplitudes were sensitive to the degree of likelihood certainty such that lower likelihoods were associated with larger parietally distributed P3b waves. These ERP data suggest that these antecedents of Bayesian inference (prior probabilities and likelihoods) are coded by the human brain.